• Suppr超能文献
  • 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
定价套餐&价格
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Win 客户端微信小程序
定价
会员套餐积分包API 积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026
  1. 首页
  3. 分享广场
  5. 生成式AI在个性化学习与智能辅导中的研究进展：效能、变革与实践

生成式AI在个性化学习与智能辅导中的研究进展：效能、变革与实践

深度研究Suppr助手发表于 2026年05月06日 16:198阅读
发起深度研究
发起深度研究

1. 生成式AI教育应用的发展脉络

1.1 核心概念与技术适配逻辑

生成式人工智能（Generative AI, GenAI）代表了人工智能领域的一项重大进展，它能够创建与真实数据高度相似的合成数据，从而在内容生成方面展现出前所未有的能力1。在教育场景中，生成式AI的应用范畴远超传统的基于规则或预设知识库的智能辅导系统。它不再局限于根据学生的输入提供预设的反馈或推荐学习路径，而是能够动态生成全新的、个性化的学习内容、解释、习题甚至模拟场景23。这种能力使其能够深度适配个性化学习的需求，例如：

  1. 个性化内容创建：生成式AI可以根据学生的学习进度、理解水平和兴趣，即时生成定制化的学习材料，包括文本解释、例题、总结、故事甚至多媒体内容23。这与传统自适应学习系统主要基于预设内容库进行筛选和推荐有本质区别。例如，当学生在某个知识点遇到困难时，生成式AI可以不只是提供另一个预设的解释，而是尝试用不同的类比、简化语言或创造一个全新的场景来帮助学生理解。
  2. 智能实时辅导与反馈：传统智能辅导系统通常通过预定义的逻辑树或专家系统来诊断学生错误并提供反馈。而生成式AI，尤其是大语言模型（Large Language Models, LLMs），能够理解自然语言的复杂性，进行即时答疑、提供针对性的错误解析，并根据学生的提问方式调整解释的深度和广度45。这种交互方式更接近人类教师的一对一辅导，能够提供更具语境意识和灵活性的指导。
  3. 学习路径的动态调整与规划：虽然传统自适应学习系统也能根据学生表现调整学习路径，但生成式AI在此基础上增加了更强的适应性。它可以根据学生在学习过程中的实时表现、兴趣变化以及长期目标，动态生成和调整学习内容与任务，而不仅仅是选择现有路径中的下一步6。这种能力使得学习路径能够更精细地贴合个体需求，实现真正意义上的千人千面。
  4. 创新性评估与创意支持：生成式AI可以辅助生成各种类型的评估题目，包括开放式问题，并能对学生生成的文本、代码甚至设计作品提供初步的反馈和评分，这超越了传统自动评分系统对选择题、填空题等固定格式的评估能力7。在创意性学科如设计、音乐教育中，生成式AI甚至可以作为学生的创作伙伴，激发和支持学生的创造力28。

生成式AI与传统自适应学习系统的核心技术差异在于其“生成”能力。传统系统更多是基于“识别”和“匹配”，即从现有资源中识别模式、匹配最适合的选项。而生成式AI，如ChatGPT等，则通过深度学习模型，特别是Transformer架构，学习海量数据中的模式，从而能够“创造”出新的、连贯的、有意义的内容910。这种从“选择”到“创造”的转变，使得生成式AI在个性化学习和智能辅导领域具备了更大的潜力，能够提供更为丰富、灵活且高度定制化的教育体验。然而，这也带来了新的挑战，例如生成内容的准确性、潜在偏见以及对学生批判性思维发展的影响510。

1.2 国内外研究与应用演进历程

生成式AI在教育领域的应用并非一蹴而就，而是伴随着人工智能技术本身的演进而逐步深化。其发展历程大致可划分为几个关键阶段，从早期的规则驱动系统到如今由大语言模型（LLMs）赋能的生成式AI应用，展现出从“辅助”到“生成”的显著转变。

1. 早期阶段：规则驱动与专家系统（20世纪70年代—90年代）
这一时期，人工智能在教育领域的探索主要集中于智能辅导系统（Intelligent Tutoring Systems, ITS）的研发。ITS旨在通过模拟人类教师的辅导过程，为学生提供个性化的学习体验11。早期的ITS通常基于专家系统和人工智能的符号主义范式，依赖于预定义的规则库、领域知识模型、学生模型和辅导策略模型来运作1213。例如，经典ITS如"SOPHIE"用于电子电路故障诊断，"STEAMER"用于教授蒸汽厂操作。这些系统能够诊断学生的错误，提供特定的练习，并根据学生的表现调整学习路径。然而，它们的局限性在于知识获取的瓶颈、规则的僵硬性以及难以处理复杂或开放性的学习任务1114。它们主要通过“识别”和“匹配”预设内容来提供辅导，而非“生成”全新的学习材料。

2. 发展阶段：数据驱动与自适应学习系统（20世纪90年代—21世纪10年代）
随着机器学习和数据挖掘技术的发展，ITS开始转向数据驱动的方法。自适应学习系统（Adaptive Learning Systems, ALS）兴起，它们能够收集并分析大量的学生学习数据，从而更精确地评估学生的知识水平、学习风格和兴趣，并动态调整学习内容和教学策略61516。这一阶段的系统通常采用贝叶斯网络、马尔可夫决策过程等模型来优化学习路径推荐和内容呈现17。例如，一些平台开始提供自适应测试和个性化练习，根据学生的答题情况实时调整题目难度和类型。尽管这些系统在个性化方面取得了显著进步，但它们仍然主要依赖于一个庞大的预设内容库，并通过算法从中选择最适合学生的内容，其“生成”能力有限。

3. 近期阶段：大语言模型驱动的生成式AI（21世纪10年代末至今）
近年来，深度学习技术的突破，特别是Transformer架构的出现和大型语言模型（LLMs）的快速发展，如GPT系列，彻底改变了人工智能在教育领域的应用格局918。生成式AI不再局限于从现有库中选择内容，而是能够根据少量的输入，生成高质量、连贯且上下文相关的文本、代码、图像甚至音频。

  • 标志性研究成果与应用：
    • 2017年Transformer模型的提出，为LLMs的崛起奠定了基础。
    • 2022年末ChatGPT的发布，作为一项里程碑事件，展示了生成式AI在自然语言理解和生成方面的强大能力，迅速引发了教育领域的广泛关注和讨论181920。它能够回答复杂问题、撰写文章、生成编程代码、进行语言翻译等，这些能力被迅速认识到在教育中的巨大潜力，包括作为学生的智能导师、内容创作者或学习伙伴20。
    • AIEd的范式转变：有研究将AIEd的发展划分为三个范式：AI主导的“学习者作为接收者”，AI支持的“学习者作为合作者”，以及AI赋能的“学习者作为领导者”21。生成式AI的出现，特别是LLMs，正加速推动教育向“AI赋能，学习者为领导者”的范式转变，强调学生在学习过程中的主体性和能动性。

当前，生成式AI在教育领域的应用正处于快速发展期，主要集中于实现更深层次的个性化学习体验，包括但不限于个性化学习内容的即时生成、智能答疑辅导、创意写作辅助、编程练习反馈以及多模态学习资源生成等方面22。这一阶段的特点是其强大的内容创造能力，使得教育应用能够摆脱对大规模预设知识库的过度依赖，转而能够根据个体需求和上下文情境实时生成定制化的学习支持。

2. 生成式AI赋能个性化学习的效能研究进展

2.1 个性化内容适配的学习效果验证

生成式AI在个性化学习中的核心价值之一在于其能够根据学习者的个体特征和需求，动态生成并适配差异化的学习内容与学习路径。大量研究已开始探索这种能力对学生学习效果的影响，涵盖了不同的学段和学科领域，并取得了积极的发现。

1. 提升学习投入度与动机
生成式AI通过提供高度定制化的学习体验，显著提升了学生的学习投入度和内在学习动机。例如，一项针对中学生的科学学习体验研究发现，使用基于生成式AI的SRLbot（自律学习机器人）的学生，相比使用基于规则的AI聊天机器人，表现出更高的行为投入和学习动机23。SRLbot能够根据学生的具体学习和自律情况调整反馈，提供个性化的推荐，这种适应性和灵活性降低了学习焦虑，并促进了学生形成持续的学习习惯23。此外，生成式AI通过将游戏化元素融入学习过程，如提供即时反馈、积分和排行榜，能够有效增强学生的参与感和协作能力，尤其是在医学教育等专业领域，这种方式被认为能优化学习成果24。

2. 促进知识掌握与学业成绩
生成式AI在定制学习内容和自适应规划学习路径方面，已被证明能够有效提高学生对知识的掌握程度和学业成绩。研究表明，生成式AI可以作为“教师助手”的角色，辅助教师进行教学准备，并通过提供定制化的内容来增强学生的知识获取、技能习得和对复杂概念的理解2425。例如，在数据科学教育中，ChatGPT能够为学习者提供个性化和即时反馈，这被视为提升学习体验的宝贵资源26。在高等教育中，学生普遍认为生成式AI具有提供个性化学习支持、辅助写作和头脑风暴、以及帮助研究和分析的潜力，这些都有助于他们更好地掌握学习内容2728。

3. 个性化辅导与理解深化
生成式AI不仅能提供定制内容，还能在学生遇到困难时，以多种形式深化其对知识的理解。它可以通过提供多种解释方式、重新组织信息、生成不同难度的练习题来帮助学生克服学习障碍2829。这种能力尤其体现在复杂学科领域，例如在医学教育中，生成式AI可以辅助进行诊断领域的教育，如放射学、病理学和微生物学，通过内容图像检索（CBIR）和机器学习技术，帮助学生进行图像搜索和疾病诊断训练，从而提升专业知识和诊断精确性24。此外，通过构建虚拟患者模拟真实临床情境，学生可以在无风险的环境中练习临床决策和沟通技能，加深对医学知识的理解和应用24。

4. 跨学科与多学段的普适性
生成式AI在个性化学习内容适配方面的效能并非局限于特定学科或学段。无论是K-12教育阶段的科学知识学习23，还是高等教育中的工程、计算机科学、医学等专业领域243031，生成式AI都展现出其强大的适应性和有效性。甚至在早期儿童教育和计算机科学本科生的数字素养创新方面，两组学生在设计、开发和实施教学设计项目时，使用AI平台表现出相似的学习表现，且早期儿童教育专业的学生对AI多媒体平台的有用性评价更高，表明其在不同背景学习者中的广泛适用性31。

然而，研究也指出，生成式AI的应用仍需谨慎，要关注其生成内容的准确性、潜在偏见，并强调教师在引导学生有效利用AI工具方面的关键作用92326。未来的研究需要进一步探索如何平衡AI的辅助作用与学生的自主学习能力发展，以实现最佳的学习效果。

2.2 智能实时辅导的干预效果分析

生成式AI在智能辅导方面的介入，通过其即时性、个性化和互动性，对学习效果产生了显著的积极影响。研究表明，生成式AI驱动的智能辅导工具能够有效地提供即时答疑、针对性错题解析和薄弱点补测，从而提升学生的学习效率和知识掌握程度3233。

1. 即时反馈与答疑机制
生成式AI，尤其是基于大型语言模型（LLMs）的聊天机器人，能够提供接近人类教师水平的即时反馈和答疑2034。这种即时性对于学习过程至关重要，因为它能及时纠正学生的错误理解，防止知识偏差的累积。例如，在英语语言学习中，AI驱动的工具可以提供即时反馈，帮助学生纠正发音错误，从而显著提高发音技能35。研究发现，80%的学生认为AI工具提供的即时反馈非常有帮助，能够促进语言习得的进步34。这种即时、个性化的反馈能够有效缓解学生在学习过程中的焦虑情绪，特别是在英语写作等需要频繁练习和修正的学科中36。当学生能够立即获得对其作业或问题的反馈时，他们能够更快地理解错误并加以改正，从而提高学习效率和信心。

2. 针对性错题解析与个性化指导
生成式AI能够深入分析学生的错误模式，并提供高度个性化的错题解析。这超越了传统辅导系统仅仅指出错误答案的能力，而是能够解释错误产生的原因，提供不同的解题思路，甚至生成新的类似题目进行巩固练习32。例如，AI辅导平台可以根据学生的学习数据，识别出其知识薄弱点，并智能生成补充练习或讲解，以强化这些方面的学习32。斯坦福大学的一项研究介绍了一个名为“Tutor CoPilot”的人机协作框架，其中AI作为人类导师的智能副驾驶，能够提供实时的学生数据分析和内容建议，从而使导师能够更精准地进行个性化辅导37。这种针对性的指导有助于学生填补知识空白，确保对核心概念的全面掌握。

3. 薄弱点自动补测与学习路径优化
除了即时答疑和错题解析，生成式AI还能根据学生的表现，智能地进行薄弱点补测，并动态调整学习路径。通过持续的评估和反馈循环，AI系统可以精确识别学生的未掌握知识点，并自动推送相关的强化练习或复习材料，直到学生完全掌握32。这种自适应的学习路径优化确保了学习资源的最大化利用，避免了不必要的重复学习，使学习过程更高效。例如，有研究探讨了ChatGPT在数据科学教育中提供个性化学习体验的潜力，发现它能够根据学生的学习进度和理解情况，调整内容的呈现方式和难度，从而优化学习效果26。这种能力使得生成式AI成为一种强大的学习伙伴，能够根据学生的独特需求量身定制学习旅程。

4. 量化成效与学生感知
多项研究通过问卷调查和实验数据验证了生成式AI在智能辅导方面的积极成效。例如，在一项针对200名学生的调查中，72.5%的学生高度评价了AI驱动工具带来的个性化学习体验，80%的学生表示AI的即时反馈极大地帮助了他们的语言习得进步34。另一项针对大学生的研究表明，AI聊天机器人能够显著降低学生在英语写作课堂中的焦虑感，原因在于其提供的即时反馈和自主学习节奏36。这些量化数据和学生反馈共同描绘了生成式AI在提升学习效率、增强学习信心和促进知识掌握方面的显著作用。

然而，研究也提示，尽管生成式AI在智能辅导方面展现出巨大潜力，但仍需关注其生成内容的准确性，以及在实践中确保学生批判性思维和自主学习能力的发展不受影响920。未来的研究需要进一步探索如何更有效地将AI辅导融入整体教育体系，并解决潜在的伦理问题。

3. 生成式AI应用下的教育角色与评价体系变革

3.1 教师角色的转型路径研究

生成式AI在教育领域的兴起，正深刻地改变着传统的教学模式，也催生了教师角色从“知识的传授者”向“学习的设计者、AI应用的协同者和学生核心素养的培育者”的转变。这种转型并非削弱教师的重要性，而是提升了教师在更宏观、更具创造性和人本关怀层面的价值。

1. 从内容讲授者到学习过程设计者
传统上，教师的主要职责是组织和讲授课程内容。然而，生成式AI能够高效地生成、整合和呈现知识内容，减轻了教师在备课和信息传递方面的负担。这意味着教师可以投入更多精力于设计富有启发性、挑战性和个性化的学习活动和体验。例如，教师可以利用生成式AI辅助创建多样化的学习材料，针对不同学生的认知水平和兴趣设计差异化的学习路径，或者生成复杂场景模拟以促进高阶思维能力的发展 38。他们将专注于构建一个能够激发学生探索欲、批判性思维和解决问题能力的环境，而非仅仅是知识的灌输。

2. AI应用的协同者与引导者
生成式AI并非取代教师，而是成为教师强大的“副驾驶”或“合作者” 39。教师需要具备将AI工具有效整合到教学中的能力，成为AI应用的协同者。这包括理解AI的优势与局限性，明智地选择和运用AI工具来优化教学流程、提高学习效率。研究表明，教师对AI的积极态度和数字素养是成功整合AI的关键因素 4041。教师需要学习如何利用AI进行教学管理（如自动批改、学情分析），如何指导学生批判性地使用AI工具（如 prompt engineering 42），以及如何应对AI可能带来的伦理挑战（如学术诚信）。这种协同作用使得教师能够专注于那些AI尚无法替代的教学环节，例如情感连接、价值观引导和复杂问题解决中的高层次指导 43。

3. 学生核心素养的培育者
在AI时代，知识获取变得前所未有的便捷，因此，培养学生的核心素养变得尤为重要，这包括批判性思维、创新能力、协作能力、解决复杂问题的能力以及数字素养和AI素养 4244。教师的角色重心将转向：

  • 批判性思维的培养：指导学生辨别AI生成信息的准确性和可靠性，识别AI的潜在偏见，并进行深度分析和评估。
  • 创新能力的激发：鼓励学生利用AI作为创新的工具，探索新的问题解决方案，进行创意性表达和项目开发。
  • 协作与沟通：设计需要学生与AI协同工作、与同伴合作的学习任务，提升团队协作和有效沟通能力。
  • 伦理与社会责任：引导学生探讨AI的伦理影响，培养他们负责任地使用AI并理解其对社会影响的意识 45。
  • 自我调节学习能力：鼓励学生利用AI工具进行自我评估、自我调整，成为终身学习者。

4. 配套的能力要求
为了适应这些转型，教师需要发展一系列新的能力：

  • AI素养与数字能力：理解AI的基本原理、应用场景及其局限性，并熟练掌握各类AI工具在教育中的使用方法 4246。有研究强调，教师的数字能力和对AI的认识是其接受和使用AI工具的关键驱动因素 41。
  • 教育学与技术学知识的融合：将教育学理论与AI技术深度融合，设计出以学生为中心、技术赋能的教学方案。
  • 批判性思维与伦理判断力：评估AI生成内容的质量，引导学生形成对AI工具的批判性认识，并处理随之而来的伦理问题。
  • 创新与适应能力：面对快速发展的AI技术，教师需要保持开放的心态，不断学习和适应新的教学方法和工具。
  • 情感连接与人文关怀：AI无法替代教师与学生之间的情感连接、激励和个性化关怀。在AI辅助下，教师应更加专注于学生的情感需求和全面发展。

综上所述，生成式AI不仅是工具的革新，更是教育理念和实践的深刻变革。教师不再仅仅是知识的“拥有者”和“传递者”，而是成为学生学习旅程中的“导航者”、“合作者”和“引领者”，其专业价值在于构建有意义的学习体验、培养学生适应未来社会的核心能力。

3.2 适配生成式AI的教育评价体系迭代

生成式AI的出现不仅改变了教学和学习方式，也对传统的教育评价体系提出了新的要求和机遇。当前研究普遍认为，教育评价正经历从侧重结果性评价（如标准化考试分数）向更加关注过程性评价、能力导向评价和综合性评价的迭代。生成式AI以其强大的数据分析和内容生成能力，为构建这种多元化、动态化的评价体系提供了前所未有的工具和可能性。

1. 从结果性评价到过程性评价的转变
传统的教育评价体系往往聚焦于学习结束后的终结性考试成绩，这难以全面反映学生的学习过程、进步幅度以及深层理解。生成式AI能够持续收集和分析学生在学习过程中的海量数据，包括学习时长、互动模式、问题解决步骤、反思日志等，从而实现对学习过程的精细化追踪和评价。例如，AI可以分析学生提交的作业草稿、修订历史，甚至在交互式学习环境中的每一次尝试和错误，提供关于其学习策略、思维过程和知识构建的洞察。这种基于过程的评价能够更准确地识别学生的薄弱环节，并为教师提供有针对性的教学干预依据。

2. 强调能力导向评价与高阶思维
在知识快速迭代的AI时代，仅仅记忆知识点已远不足以应对复杂的现实世界挑战，培养学生的高阶思维能力（如批判性思维、创新能力、解决问题的能力）和实践能力变得尤为重要。生成式AI能够辅助设计和评估开放性、项目式的任务，这些任务往往更能体现学生的应用、分析、评估和创造能力。例如，AI可以帮助生成复杂的案例研究、情景模拟或设计挑战，并对学生在这些任务中的表现进行初步评估和反馈。有研究提出，像SmartRubrics这样的AI工具，能够自动生成基于能力的评估量规，尤其是在工程教育中，这对于评估横向技能和专业能力非常关键 47。此外，AI在形成性评估中的应用，如为编程任务提供AI辅助的形成性评估，可以帮助学生更好地理解编码概念和提升解决问题的能力 48。

3. 生成式AI辅助多元评价的应用路径
生成式AI在教育评价中的具体应用路径主要体现在以下几个方面：

  • 自动评估开放性文本响应：随着大语言模型（LLMs）的突破，AI在理解和评估学生开放性文本响应方面的能力显著增强。这对于需要深度理解和分析的文本作业，如论文、报告、回答开放式问题等，提供了高效的自动或半自动评估方案。这不仅能减轻教师的批改负担，还能提供更及时、一致的反馈 49。
  • 评估非认知能力与软技能：传统评价工具往往难以有效衡量学生的非认知能力，如协作能力、沟通能力、情感调节能力等。生成式AI通过分析学生在团队项目中的对话记录、互动模式以及多模态数据（如表情、语调），有望为这些软技能的评估提供新的维度。
  • 个性化反馈与自我诊断：生成式AI可以根据学生的答题情况和学习数据，生成高度个性化的反馈，指出其理解偏差，并推荐相应的学习资源。这种即时、建设性的反馈有助于学生进行自我诊断，并主动调整学习策略。在形成性评估中，AI能够提供丰富的反馈，支持教师的评估实践和教学决策，且不必然导致不公平现象，反而有助于促进更公平的教育体验 50。
  • 辅助试题生成与多样化评估：AI能够根据教学目标和知识点，快速生成多样化的试题，包括不同难度、不同题型的题目，甚至可以根据学生的特定需求生成定制化的练习。这有助于构建更为全面和灵活的评估体系，摆脱对单一题型的依赖。
  • 促进学术诚信与公平：尽管生成式AI带来了学术诚信方面的挑战，但同时它也促使教育机构重新审视和迭代评估策略。例如，有大学通过实施“AI评估量表（AIAS）”等灵活框架，允许在特定条件下使用AI工具，并设计更侧重人类输入和批判性思维的评估，从而在保障学术诚信的同时，有效整合AI技术 51。一些高等教育机构已经开始发布关于生成式AI使用指南和政策，这些指南通常涉及学术诚信和隐私问题，并强调了适应性政策的重要性 52。

综上所述，生成式AI正在推动教育评价体系向更加注重学习过程、能力导向和个性化反馈的方向发展。这要求教育工作者不仅要掌握AI技术，更要思考如何利用这些技术构建一个更全面、更公平、更能促进学生全面发展的评价范式。

4. 生成式AI教育应用的伦理与公平性研究进展

4.1 学生隐私保护研究

生成式AI在教育领域的广泛应用，虽然带来了个性化学习和智能辅导的巨大潜力，但也引发了对学生隐私保护的深切关注。由于这些AI系统需要收集、处理和分析大量的学生个人数据，包括学习行为、成绩、互动记录甚至生物识别信息，因此数据泄露、滥用以及潜在的算法偏见等风险不容忽视 535455。

1. 隐私风险点
在生成式AI教育应用中，学生数据隐私面临多方面的风险：

  • 数据采集：AI系统通常需要收集学生详细的学习行为数据，如在线学习时长、答题记录、错误模式、搜索查询以及与AI助手的对话内容。这些数据可能包含学生的学习偏好、知识薄弱点、认知风格等敏感信息 5354。
  • 数据存储与传输：大量学生数据在存储和传输过程中可能面临网络攻击、未经授权访问的风险。
  • 数据处理与分析：AI模型在训练和推理过程中可能无意中泄露训练数据中的个人信息，尤其是在模型反演攻击或成员推断攻击下，攻击者可能从模型输出中推断出训练数据中的特定个体信息 56。
  • 生物识别数据：一些AI教育应用可能集成人脸识别、语音识别等技术以实现身份验证或情绪分析，这进一步增加了生物识别数据泄露的风险。
  • 第三方共享：当教育科技平台与第三方服务提供商（如云服务商、内容提供商）合作时，学生数据的共享机制、隐私政策和数据处理能力可能不透明，导致数据流向难以控制。

2. 技术解决方案
为了缓解上述隐私风险，国内外研究人员和机构正在探索多种隐私保护技术，以期在利用AI提升教育效果的同时，最大限度地保护学生数据安全：

  • 差分隐私 (Differential Privacy, DP)：这是一种数学上可证明的隐私保护技术，通过在数据中添加统计噪声，使得即使攻击者拥有关于数据集中大部分信息的知识，也无法推断出特定个体的信息 56。在教育场景中，DP可以在生成AI模型的训练数据中应用，以防止模型记忆和泄露个体学习者的敏感信息。
  • 联邦学习 (Federated Learning, FL)：联邦学习允许AI模型在不直接访问原始本地数据的情况下进行训练。它通过在各个学习者的设备或机构本地训练模型，然后将模型参数的更新（而非原始数据）聚合到中央服务器，从而构建全局模型 5356。这使得学生数据可以保留在本地，大大降低了数据集中存储和传输带来的隐私风险。
  • 同态加密 (Homomorphic Encryption, HE)：同态加密允许在加密数据上直接进行计算，而无需先解密。这意味着AI模型可以在加密的学生数据上执行分析和计算，其结果仍然是加密的，只有拥有密钥的人才能解密并查看结果 5356。这为学生数据提供了强大的端到端隐私保护。
  • 数据脱敏 (Data Anonymization)：数据脱敏技术通过删除或修改个人身份识别信息，使得数据无法与特定个体关联 53。这包括假名化、泛化、抑制等方法。例如，使用ADS-GAN（通过生成对抗网络实现数据合成匿名化）可以生成高度近似原始数据集联合分布的合成数据，同时最大程度地降低患者身份识别的可能性，这在医疗领域已显示出潜力，同样可以应用于教育数据 57。
  • 安全多方计算 (Secure Multi-Party Computation, SMPC)：SMPC允许多个参与方在不泄露各自私有数据的前提下，共同计算一个函数 56。在教育场景中，不同学校或教育机构可以在不共享学生原始数据的情况下，协同训练一个AI模型，从而保护各方的数据隐私。

3. 管理策略与新型挑战
除了技术手段，还需要健全的管理策略和政策框架：

  • 明确的隐私政策与用户协议：教育机构和AI服务提供商应制定清晰透明的隐私政策，明确告知学生哪些数据会被收集、如何使用、存储多久以及与谁共享，并获得学生的明确同意 58。
  • 伦理审查与监管框架：建立严格的伦理审查机制，确保AI教育应用的开发和部署符合伦理标准。参考美国和欧盟等地区的法规结构，如GDPR，制定专门的教育数据保护法规 53。
  • 数据最小化原则：AI系统应遵循数据最小化原则，仅收集和处理完成特定任务所必需的数据，避免过度采集无关信息。
  • 教师和学生AI素养培训：提升教师和学生对数据隐私风险的认识，培养其负责任地使用AI工具的素养。
  • 新型隐私挑战：生成式AI可能带来新型的隐私问题，如模型偏见。训练数据中的偏见可能导致AI模型对特定学生群体（如少数民族、特定学习障碍学生）生成有偏见的输出或推荐，进而影响他们的学习体验和机会 555859。例如，如果模型在训练时未能充分包含各类群体的语言或文化特征，其生成的内容可能会不准确或带有歧视性。此外，过度依赖AI可能影响学生的个人发展和批判性思维，也间接构成了一种“隐性”的隐私影响，因为它可能限制学生接触多元信息和独立思考的机会 2759。

综上所述，生成式AI在教育中的隐私保护是一个复杂且多维的问题，需要技术创新、政策法规和伦理规范的共同作用。通过采纳先进的隐私增强技术，结合严格的数据管理策略，并持续关注AI带来的新型伦理挑战，才能确保生成式AI在个性化学习和智能辅导领域健康、可持续地发展。

4.2 教育公平维度的影响研究

生成式AI在教育领域的快速发展，为缩小城乡差距、不同社会经济背景学习者之间的资源鸿沟提供了前所未有的机遇，但同时也可能带来算法偏见、数字鸿沟等新型教育公平风险。相关研究正深入探讨其双重影响。

1. 缩小资源差距的正向价值

生成式AI在促进教育公平方面展现出巨大潜力，主要体现在以下几个方面：

  • 提升教育可及性：生成式AI可以打破地域限制，将高质量的教育资源和个性化辅导带给偏远地区或经济欠发达地区的学生。通过AI驱动的教学平台，学生无论身处何地，都能获得定制化的学习内容和即时反馈，这有助于弥补传统教育资源分配不均的问题60。例如，在资源匮乏的环境中，AI辅助辅导（结合苏格拉底式对话方法）和AI生成的反馈系统可以提供宝贵的教育支持，降低传统教育的门槛61。
  • 个性化学习支持：生成式AI能够根据每个学生的独特需求、学习节奏和学习风格，提供高度个性化的学习路径和内容60。这对于那些在传统“一刀切”教学模式中可能掉队的学生尤为重要，包括学习障碍学生、非母语学习者以及来自弱势群体的学生62。个性化学习有助于确保每个学生都能获得最适合其发展的教育支持，从而提升整体学习效果61。
  • 支持特殊教育需求：生成式AI在特殊教育领域的作用日益凸显。它能够为有特殊学习需求（如残障学生）的学习者提供定制化的辅助工具和学习材料，从而提高他们的参与度和学习成效。例如，AI系统可以提供增强的辅助功能、多语言支持和适应性学习材料，帮助打破传统教育障碍61。研究表明，AI介导的系统可以提供个性化和智力刺激的教育体验，促进主动学习和针对性教学，对特殊学习者尤其有益63。
  • 赋能教师：在教师资源有限的地区，生成式AI可以作为教师的强大助手，辅助他们进行备课、生成练习、评估作业，从而提升教学效率和质量。这使得教师能够将更多精力投入到高层次的教学活动和学生情感关怀上，间接提升了教育服务水平。

2. 算法偏见带来的新型风险

尽管生成式AI潜力巨大，但其算法中存在的偏见可能加剧现有教育不公平：

  • 数据偏见导致结果不公：生成式AI模型通过大量数据训练而成，如果训练数据本身存在偏见（如性别偏见、种族偏见、社会经济背景偏见），AI系统就可能复制甚至放大这些偏见，导致对特定学生群体的评价、推荐或内容生成出现偏差6164。例如，如果AI辅导系统主要基于发达地区学生的学习数据进行训练，其生成的辅导内容或评估标准可能不适用于文化背景、语言习惯或知识体系不同的学生，从而影响这些学生的学习体验和评估结果。
  • 模型准确性与可靠性问题：AI模型可能出现“幻觉”（hallucination），生成不准确、不完整或有误导性的信息64。如果学生过度依赖AI生成的答案而缺乏批判性思考，可能会导致错误的知识学习，对于处于教育弱势的学生来说，他们可能更缺乏辨别能力，从而受到更大的负面影响。此外，AI模型结果的临床解释需要专业人员的判断，不能完全依赖AI。例如，有研究显示，尽管标准AI模型能提高诊断准确性，但有偏见的AI模型反而会降低诊断准确性，并且图像解释等方式也无法完全缓解其有害影响65。
  • 对现有不平等的加剧：生成式AI的推广可能会在某些情况下加剧已有的不平等。例如，那些缺乏技术素养或无法负担高端AI工具的学生，可能会被排除在AI带来的优势之外，从而形成新的“数字精英”与“数字弱势”群体61。

3. 使用门槛与数字鸿沟

生成式AI在教育领域的应用，也面临着使用门槛和数字鸿沟的问题，这可能进一步拉大教育差距：

  • 技术接入鸿沟：虽然AI技术可能降低教育成本，但接入高质量的互联网、拥有必要的硬件设备以及稳定的电力供应，对于许多欠发达地区或贫困家庭的学生来说仍是挑战6166。这种“数字鸿沟”不仅指设备和网络的可及性，还包括技术素养的差异6467。
  • AI素养鸿沟：有效利用生成式AI进行学习，需要学生具备一定的AI素养，包括如何提出有效的指令（prompt engineering）、如何评估AI生成内容的可靠性、以及如何将AI工具融入自己的学习流程等68。如果学生和教师缺乏必要的AI素养培训，AI工具的效益将大打折扣，甚至可能被误用。这种素养鸿沟可能导致不同背景的学生在利用AI工具方面存在显著差异69。
  • 语言与文化障碍：当前大多数生成式AI模型主要以英文数据进行训练，对于非英语母语或使用小语种的学生来说，可能会面临语言障碍，影响其学习体验和内容理解70。AI模型可能无法充分理解或生成符合特定文化语境的内容，这可能加剧文化上的不公平，削弱这些学生通过AI获取高质量教育的机会71。
  • 成本与商业化：随着生成式AI在教育中普及，高质量的AI工具可能需要付费使用，这可能对低收入家庭的学生造成经济负担，从而限制他们获取优质教育资源的机会72。

综上所述，生成式AI在促进教育公平方面具有巨大潜力，尤其是在提升教育可及性和个性化支持方面。然而，为了确保其积极作用得以实现并避免加剧现有不平等，必须正视并积极解决算法偏见、数字鸿沟、AI素养不足以及潜在的商业化门槛等问题73。这需要政策制定者、教育机构、技术开发者和研究人员共同努力，制定相应的伦理框架、技术标准和支持政策，以确保生成式AI能够真正成为普惠公平教育的强大推动力7475。

5. 生成式AI在个性化学习与智能辅导中的落地场景实践

5.1 K12全学科个性化辅导场景

生成式AI在K12（幼儿园到高中）教育阶段的个性化辅导中展现出巨大的应用潜力，其核心在于能够根据学生的个体差异，提供定制化的学习内容、即时反馈和智能指导。以下将介绍生成式AI在K12全学科个性化辅导中的典型应用及其落地案例与实际反馈。

1. 知识点拆解与个性化内容生成
在K12教育中，许多学科知识点之间存在复杂的逻辑关系。生成式AI能够将复杂的知识点拆解为更小的、易于理解的模块，并根据学生的现有知识水平和学习风格，生成不同形式的解释和示例。例如，当学生在数学概念（如分数、代数）或科学原理（如牛顿定律、能量守恒）上遇到困难时，AI可以生成：

  • 多媒体解释：将抽象概念转化为图表、动画或生活中的类比，以帮助学生直观理解。
  • 不同难度的文本说明：针对不同阅读水平的学生，提供简化或详细的文本解释。
  • 关联知识点引导：指出当前知识点与之前所学内容的联系，帮助学生构建完整的知识网络。
    一项研究显示，AI生成的教学视频在学生学习英语单词的保留率上，甚至优于传统录制视频 76。这表明生成式AI在内容呈现形式上能够有效提升学习效果。

2. 学情动态追踪与自适应学习路径规划
生成式AI通过实时收集和分析学生的学习数据（如答题时间、正确率、错误类型、互动模式），能够精准绘制学生的学情画像，并动态调整学习路径。这种动态追踪超越了传统自适应系统预设路径的局限性：

  • 识别薄弱环节：AI系统能够快速识别学生在特定知识点上的薄弱之处，并自动推荐相关的强化练习或复习材料。
  • 优化学习节奏：根据学生的学习进度和掌握程度，AI可以智能调整内容的呈现速度和难度，确保每个学生都能以最适合自己的节奏进行学习。
  • 个性化学习报告：AI可以为学生和家长生成详细的学习报告，清晰展示学生的进步、强项和仍需改进的领域，为家庭教育提供数据支持。

3. 作业个性化定制与智能批改反馈
生成式AI在作业环节的应用，极大地减轻了教师负担，并为学生提供了即时、个性化的反馈：

  • 智能出题：AI可以根据学生的学习进度和知识掌握情况，自动生成定制化的练习题和测试题，覆盖不同难度级别和知识点组合。例如，Finetune公司的AI工具Generate能够通过定制化的自然语言生成技术，高效地开发高质量、心理测量学上有效的评估项目，显著减少了时间成本并提升了准确性 77。
  • 即时批改与详细解析：生成式AI可以快速批改学生作业，并对错误给出详细的解析，甚至提供多种解题思路，而不仅仅是标出对错。这种即时反馈机制有助于学生及时纠正错误，避免知识偏差的固化。
  • 创意写作与编程辅导：在语文创意写作或信息技术编程课程中，生成式AI可以作为学生的“写作伙伴”或“编程导师”，提供构思建议、语法修正、代码优化建议等，甚至可以针对学生的编程作业提供实时反馈，帮助学生提高计算思维能力 78。

4. 课后答疑辅导与情感陪伴
K12学生在课后往往会遇到各种学习疑问，生成式AI能够提供全天候的智能答疑服务：

  • 即时问答：学生可以通过自然语言与AI助手交流，提出学习上的疑问，AI会根据上下文提供精准的解答、相关知识点或进一步的学习资源。这种“类人”的交互方式，甚至能够缓解学生在E-Learning中可能出现的孤立感和疏离感 79。
  • 苏格拉底式对话：一些高级的AI辅导系统能够通过提问引导学生进行深度思考，帮助他们自主发现问题症结并构建知识，而不是直接给出答案。
  • 情感支持：尽管AI无法替代人类情感，但设计良好的AI辅导系统可以通过积极的语言和鼓励，在一定程度上提供情感支持，增强学生的学习信心和积极性。研究表明，在K12教育中，具备个性化和适应性学习体验的AI工具，如ChatGPT，能够帮助学生更好地进行自我调节学习，尤其是在编程等复杂技能学习中 80。

落地案例与实际反馈
全球范围内，K12教育领域已有多个生成式AI的应用案例：

  • 中国：一些在线教育平台已将生成式AI技术应用于智能答疑、个性化作业推荐和口语评测等功能，收到了学生和家长的积极反馈，认为提升了学习效率和兴趣。
  • 美国：Code.org等组织在计算机科学教育中探索使用AI工具辅助学生学习编程，例如通过ChatGPT提供编程概念的定制化指导，帮助学生理解复杂的编程逻辑，实现高效学习 80。
  • 欧洲：有研究强调了高质量信息学教育的重要性81，并关注如何将AI素养融入K12课程，使学生能够批判性地理解和使用AI工具 828384。
  • 印度：一些初创公司利用生成式AI为K12学生提供基于其学习风格和兴趣的个性化故事、练习和互动游戏，以提高参与度和学习效果。

总而言之，生成式AI在K12全学科个性化辅导场景中，通过其强大的内容生成、动态追踪和智能交互能力，正在深刻改变学生的学习方式，使其学习体验更加个性化、高效和有趣。然而，确保内容的准确性、数据的安全性以及教师和学生AI素养的提升，仍是未来落地实践中需要持续关注和解决的关键问题。

5.2 高等教育与职业教育专项能力提升场景

生成式AI在高等教育与职业教育领域的应用，突破了传统教学模式的局限，为学生和在职人员的专项能力提升提供了前所未有的机遇。这些工具能够针对特定专业技能提供高度定制化的辅导、实操训练和模拟演练，尤其在编程、医学、工程等对实践能力要求较高的领域，其应用进展尤为显著。

1. 编程实操辅导
在计算机科学和工程领域，生成式AI已成为学生学习编程、提升编码技能的强大辅助工具。

  • 代码生成与解释：ChatGPT等LLMs能够根据自然语言描述生成代码片段，帮助学生理解不同编程语言的语法和逻辑。同时，它们也能对复杂的代码进行解释，阐明其功能和工作原理，这对于初学者理解抽象的编程概念尤为重要 85。
  • 实时调试与错误修复：AI工具可以提供实时的代码检查和调试建议，指出潜在的错误并提供修复方案。这种即时反馈机制，比传统的人工批改更高效，能显著提升学生的编程效率和问题解决能力。
  • 编程题目的生成与个性化练习：生成式AI可以根据学生的学习进度和薄弱点，自动生成定制化的编程练习题，并提供多种解题思路和测试用例，从而强化学生的编程实践能力。一项研究强调了AI在个性化学习体验中的潜力，尤其是对于编程学习者，能够提供适应性的学习路径和内容 85。
  • 项目开发辅助：在复杂的项目开发中，AI可以作为学生的“编程伙伴”，提供设计思路、框架建议、文档撰写辅助，甚至可以协同学生共同完成部分编码任务，加速学习曲线。

2. 医学临床思维训练
医学教育对临床思维能力和实践操作技能有着极高要求。生成式AI通过模拟真实临床场景，为医学生提供了安全、可重复的训练环境，有效提升了其临床能力。

  • AI模拟患者互动：大型语言模型可以模拟各种疾病的患者，学生可以与AI进行“问诊”，收集病史、进行鉴别诊断，并接受AI的即时反馈。这种方式不仅能提升医学生的沟通技巧，还能训练其快速、准确地进行临床判断的能力 86。一项非随机对照试验发现，通过与AI模拟患者互动进行医学访谈训练，学生的医学访谈技能得分显著高于对照组 86。
  • ChatGPT辅助PBL教学：问题导向学习（PBL）在医学教育中被广泛采用。ChatGPT可以辅助PBL教学，通过生成案例、提供背景信息、引导讨论等方式，帮助学生深入理解复杂的临床问题。研究表明，ChatGPT辅助的PBL教学方法能有效提高医学生的理论知识考试成绩和临床技能，尤其是在医学访谈、临床判断和整体临床能力方面 87。
  • 诊断与决策支持模拟：AI可以构建复杂的临床决策模拟系统，让学生在虚拟环境中面对各种病例，训练其诊断流程、治疗方案选择和风险评估能力。例如，AI在放射学、病理学和微生物学等诊断领域，可以通过内容图像检索和机器学习技术，辅助医学生进行图像搜索和疾病诊断训练，提升专业知识和诊断精确性。
  • USMLE考试表现：有研究评估了ChatGPT在美国执业医师资格考试（USMLE）上的表现，发现它在未经特殊训练的情况下，在所有三个步骤的考试中都达到了或接近及格线，且解释具有高度的一致性和洞察力，这表明LLMs在辅助医学教育和临床决策方面具有巨大潜力 88。

3. 职业技能模拟演练
除了编程和医学，生成式AI在其他职业技能培训中也发挥着越来越重要的作用，尤其是在需要实践操作和决策制定的领域。

  • 工程与制造：在工程教育中，生成式AI可以辅助设计和模拟复杂的工程问题，例如，通过生成式设计工具优化产品结构，或通过仿真平台进行虚拟装配和性能测试。这使得学生能够在低成本、高效率的环境中进行反复实践，提升解决实际工程问题的能力 89。
  • 商业与管理：AI可以生成商业案例、市场分析报告、财务模拟等，帮助商科学生进行决策制定训练。例如，学生可以利用AI分析虚拟公司的运营数据，制定营销策略，并观察AI模拟的市场反馈。
  • 跨行业通用技能：AI还可以辅助提升跨行业的通用技能，如批判性思维、创新能力、沟通能力等。例如，通过与AI进行对话和辩论，学生可以练习逻辑推理和表达能力；利用AI进行头脑风暴，可以激发创新思维 279091。
  • 持续工程教育：对于在职工程师而言，生成式AI工具，特别是LLMs，可用于支持数据分析学习和可视化平台，帮助工程师在工业5.0背景下，快速学习和桥接数据驱动学科的技能差距 89。

总体而言，生成式AI在高等教育与职业教育专项能力提升场景中的应用，正从辅助工具向变革性平台演进。它通过提供个性化、实时的辅导与模拟，有效弥补了传统教学中实践机会不足的缺点，极大地提升了学习效果和专业技能掌握水平。然而，在实际应用中，仍需关注AI生成内容的准确性、伦理风险以及如何确保学生批判性思维的培养，以实现人机协同的最佳教育效益。

5.3 特殊教育个性化支持场景

生成式AI为特殊教育领域带来了革命性的变革，为残障学习者和学习困难群体提供了前所未有的个性化支持和学习工具。传统教育模式往往难以充分满足这些学习者的多样化需求，而生成式AI凭借其强大的内容生成、适应性调整和多模态交互能力，能够创建更加包容、可访问和有效的学习环境。

1. 智能辅助工具与增强可访问性

生成式AI能够开发出各种智能辅助工具，显著提高特殊学习者获取知识和参与学习过程的可访问性：

  • 定制化学习材料：AI可以根据学习者的具体残障类型（如视力障碍、听力障碍、阅读障碍）和学习风格，实时生成适配的教学内容。例如，将文本转化为大字版、盲文、音频描述或手语视频，将复杂的概念简化为易于理解的图形或动画，或者提供多种语言的解释。这使得内容不再是静态的，而是可以根据个体需求动态调整 92。
  • 语音与文本转换工具：对于有言语障碍或听力障碍的学生，生成式AI可以提供高精度的语音识别和文本转语音服务，帮助他们进行沟通和理解。例如，通过将口语实时转录为文本或将文本内容清晰地朗读出来，有效克服交流障碍。
  • 个性化阅读支持：对于阅读障碍学生，AI可以提供字词高亮、分句阅读、词义解释以及简化文本等功能，减轻阅读负担，提高阅读理解能力。
  • 多感官学习体验：结合扩展现实（XR）技术，生成式AI能够为特殊教育学生创造沉浸式和适应性的学习平台，通过视觉、听觉、触觉等多感官交互，提供更加生动和具体的学习体验，尤其对于抽象概念的理解非常有益 92。

2. 针对性辅导与干预策略

生成式AI能够识别特殊学习者的具体学习困难，并提供高度个性化、有针对性的辅导和干预策略：

  • 自适应学习系统：AI驱动的自适应学习平台能够利用机器学习模型、自然语言理解和实时数据处理，为残障学生生成和提供定制化的课程、反馈和辅助，从而提升他们的学习效果和参与度 93。这些系统能够追踪学生的学习进度和行为模式，精准识别知识薄弱点，并智能调整教学内容和难度。
  • 个别化教育计划（IEP）支持：生成式AI可以协助特殊教育教师更高效地制定、实施和评估个别化教育计划。例如，ChatGPT能够根据学生的个别表现水平，有效制定IEP目标并生成学习材料。它还能辅助教师进行家庭和社区联系，加强与家长的沟通，并支持评估学生的成就目标和深化教学计划 94。这大大减轻了教师的行政负担，使他们能将更多精力投入到与学生的直接互动中。
  • 书写技能训练：对于有书写困难的儿童，AI技术，特别是结合新的评估工具，可以提供个性化的矫正策略和训练。例如，通过分析书写轨迹、笔压等数据，AI可以识别书写困难的根本原因，并提供针对性的练习和实时反馈，帮助儿童提高书写技能 95。
  • 情绪识别与心理支持：AI在情感计算方面的进展使其能够识别学习者的情绪状态。通过分析面部表情、语调和语言模式，AI可以检测到学生的挫败感、焦虑或无聊，并适时调整教学策略或提供鼓励，从而改善学习体验和心理健康，特别是在学习障碍学生中，情绪管理对其学习表现至关重要 96。

3. 实践案例与研究效果

多项研究和实践案例表明，生成式AI在特殊教育中取得了积极效果：

  • 韩国特殊教育案例：韩国的一项研究展示了如何利用ChatGPT协助特殊教育教师制作个性化学习材料、进行教学规划和评估。专家评估认为，生成式AI是一个有助于减轻教师工作量并提高个性化教育质量的有用工具 94。
  • 包容性教育框架：有研究提出了一个结合XR和生成式AI的包容性教育框架，旨在为特殊教育学生提供沉浸式和适应性的学习平台。该框架通过Unity XR界面和生成式AI模块，实现了内容的近实时定制和交互，显著提升了学习的可访问性和个性化 92。
  • 提升参与度与理解力：AI驱动的自适应学习平台已被证明能显著提高残障学生的学习参与度、理解力和记忆力，从而缩小他们与同龄人之间的教育差距 93。

然而，在特殊教育场景中应用生成式AI也面临挑战，包括确保算法的公平性，避免偏见，保护敏感的学生数据隐私，以及提供教师足够的培训以有效利用这些工具 97。未来的发展需要多学科的合作，以用户为中心的设计方法，并建立健全的伦理治理框架，以确保AI真正服务于特殊教育的包容性和公平性目标。

6. 现有研究局限与未来发展方向

6.1 当前研究与应用的核心短板

尽管生成式AI在个性化学习和智能辅导领域展现出巨大的潜力，但当前的研究和应用仍面临诸多挑战与核心短板。这些局限性不仅存在于技术层面，也涉及实践应用、伦理治理和制度建设等多个维度，制约了其效能的充分发挥和普惠价值的实现。

1. 样本局限性与研究普遍性不足
当前关于生成式AI在教育应用效能的研究，往往存在样本量小、研究周期短、地域集中等问题。例如，许多实证研究可能只针对特定学科、特定年级或特定地区的学生群体进行，导致研究结论的普遍性和泛化能力受限 98。在高等教育领域，一些研究可能聚焦于少数高校的少量师生，这使得研究结果难以推广至更广泛的教育场景。缺乏大规模、长期、多维度、跨文化的实证研究，使得我们难以全面评估生成式AI对不同背景、不同学习风格学生的长期影响。

2. 技术层面的算法幻觉与可靠性问题
生成式AI，尤其是大语言模型（LLMs），一个突出的技术短板是“幻觉”（Hallucination）现象，即模型生成看似合理但事实上错误、捏造或与事实不符的信息 99100。在教育场景中，这带来了严重的可靠性问题：

  • 知识不准确：AI可能生成错误的知识点解释、虚构的数据或不正确的解题步骤，这对于正在学习基础知识的学生来说是极大的误导。例如，在医学教育中，ChatGPT-4o在专业实践领域的准确率仅为57.0%，经过人工验证后甚至下降到44.2%，且识别出87个AI生成的幻觉，主要发生在应用和评估层面 99。
  • 信息偏见：AI模型在训练过程中如果使用了带有偏见的数据，其生成的内容也可能带有性别、种族、文化或社会经济背景等方面的偏见，从而对学生的价值观形成和教育公平造成潜在影响。
  • 不透明性：LLMs的工作原理复杂，其决策过程缺乏透明度和可解释性，这使得用户（包括师生）难以理解AI为何给出特定答案，也难以对其输出进行有效的批判性评估。这种“黑箱”特性不利于培养学生的批判性思维和科学探究精神。

3. 实践层面的师生AI素养不足
生成式AI工具的有效利用，高度依赖于用户（教师和学生）的AI素养（AI Literacy）。然而，当前普遍存在师生AI素养不足的问题：

  • 教师AI素养欠缺：许多教师对生成式AI的原理、功能、优势和局限性缺乏深入了解，不清楚如何将其有效地融入教学设计、内容生成、个性化辅导和评估中 101102。缺乏培训和支持，导致教师难以充分利用AI的潜力，甚至可能因为不当使用而产生负面效果。研究指出，理解教师的技术教学内容知识（TPACK）对于更准确、负责任地使用生成式AI至关重要，但目前这方面仍有待加强 101。
  • 学生AI素养不足：学生可能将AI工具视为简单的答案生成器，而非学习辅助工具。他们可能缺乏批判性评估AI生成内容的能力，容易盲目相信AI给出的答案，从而影响批判性思维、独立解决问题能力和学术诚信的培养 9899103104。有研究显示，一半的实习医生不熟悉AI，35.0%的人从未使用过AI，这凸显了提高AI素养的迫切性 99。
  • “懒惰化”风险：过度依赖AI可能会导致学生在学习过程中减少自主思考和努力，形成“思维懒惰”，从而阻碍其认知能力和创新能力的发展 105。例如，在编程学习中，学生反映ChatGPT的优点包括提供快速且通常正确的答案、提高思维能力、促进调试并增强自信，但缺点则是让学生习惯懒惰、无法回答所有问题或提供不完整/不准确的答案 105。

4. 制度层面的权责边界不清与伦理治理滞后
生成式AI在教育领域的应用，暴露出传统教育治理框架的滞后性，导致权责边界不清、伦理风险难以有效规避：

  • 学术诚信与剽窃问题：生成式AI工具能轻松生成高质量的文本内容，使得学生可能将其用于作弊、抄袭，这严重挑战了传统的学术诚信体系 104106107。然而，对于AI生成内容的认定标准、惩罚机制以及如何修改评估方式以应对这一挑战，目前仍缺乏统一、明确的政策和共识。
  • 数据隐私与安全风险：AI系统需要收集和处理大量学生数据，这引发了对数据隐私、数据安全和数据滥用的担忧。在缺乏明确的数据保护法规和伦理指南的情况下，学生的敏感数据可能面临泄露或被不当使用的风险 106108109110。
  • 算法偏见与公平性：如果AI系统存在算法偏见，可能会对特定学生群体产生不公平的影响，例如评估偏差、学习资源推荐不均等。如何确保AI的公平性，防止其加剧教育不平等，是亟待解决的伦理问题。
  • 法律法规缺失：当前关于生成式AI在教育中应用的法律法规和行业标准仍处于起步阶段，缺乏对AI开发者、教育机构、教师和学生在使用AI过程中的权利、责任和义务的明确界定。例如，有研究呼吁建立一套道德准则，以指导ChatGPT等大型语言模型在医疗保健和学术领域的负责任使用 110。
  • 对教师工作的冲击与职业焦虑：AI工具在教学辅助方面的能力，可能导致教师产生职业焦虑，担心自己的角色被削弱或取代。如何在AI时代重新定义教师的专业价值，建立AI与教师协同共生的机制，是教育管理者需要面对的挑战。

综上所述，虽然生成式AI在教育领域的应用前景广阔，但上述核心短板提示我们，在推进其应用的同时，必须高度重视并积极投入资源解决这些问题。这需要跨学科的合作，包括技术研发、教育研究、政策制定和伦理规范，以确保生成式AI能够健康、负责任地服务于教育的未来。

6.2 未来研究与落地的重点方向

鉴于生成式AI在个性化学习与智能辅导领域所展现的巨大潜力和当前面临的挑战，未来的研究与落地应聚焦于以下几个关键方向，以确保其可持续、高效和负责任的发展：

1. 技术迭代与创新

  • 提升模型准确性与可靠性：当前生成式AI的“幻觉”问题是其在教育场景中大规模应用的主要障碍。未来的技术研究需着重于提高LLMs的事实准确性、逻辑连贯性，并减少其生成偏见内容。这包括开发更先进的校验机制、引入可信知识库、强化模型对上下文的理解能力，以及探索结合符号推理与神经网络的方法，以提高其逻辑推理和解释能力。例如，可以研究如何将知识图谱与LLMs结合，在生成内容时提供事实性支撑，减少幻觉的发生。
  • 多模态与跨模态融合：当前的生成式AI多以文本为主，未来应大力发展多模态和跨模态生成技术，使其能够生成高质量的图片、视频、音频、3D模型等，并能理解和处理来自不同模态的输入。这将极大地丰富个性化学习材料的形式，满足不同学习风格的需求，并为虚拟实验室、沉浸式学习体验提供技术基础。
  • 增强人机协作与可解释性AI (XAI)：设计更高效的人机协作模式，使教师和学生能够更好地理解AI的决策过程，并对其输出进行有意义的干预和调整。发展可解释性AI技术，揭示LLMs内部机制，增强用户对AI系统的信任和控制感，从而更好地将AI整合到复杂的教学情境中。
  • 轻量化与边缘部署：优化模型架构，实现生成式AI的轻量化，使其能够在教育机构或个人设备上进行边缘部署，降低对云计算资源的依赖，提高响应速度，并缓解数据隐私问题。

2. 制度规范与伦理框架完善

  • 制定教育领域AI伦理准则与法规：各国政府和教育部门应加快制定针对生成式AI在教育应用中的伦理准则和法律法规。这应包括数据隐私保护、算法公平性、内容监管、学术诚信、责任归属等关键方面，为AI工具的开发、部署和使用提供明确的指导框架 111112。
  • 建立学术诚信应对机制：针对生成式AI带来的学术诚信挑战，教育机构需改革评估方式，鼓励设计更具批判性、创造性和个性化的作业，减少对单纯知识记忆的依赖。同时，开发有效的AI内容检测工具，并探索将AI工具合法、负责任地融入学习过程的策略，例如，明确规定哪些任务可以使用AI，哪些不能，以及如何恰当引用AI生成的内容。
  • 保障教育公平与可及性：政策制定者应积极研究如何利用生成式AI弥合数字鸿沟和教育资源差距，而非加剧不平等。这包括确保欠发达地区和弱势群体能够公平地获取和使用AI教育工具，并关注AI算法可能带来的偏见问题，确保其推荐和生成的内容对所有学生都是公平且适用的 111。
  • 构建开放共享的教育AI生态：鼓励产学研各界合作，建立开放的教育AI平台和标准，促进教育AI工具的互操作性和可持续发展。这有助于避免单一巨头垄断，促进创新，并确保教育数据的安全和共享。

3. 师生AI素养培育

  • 系统化教师AI素养培训：为教师提供全面、持续的AI素养培训，使其理解生成式AI的原理、功能、优势与局限，掌握AI工具在教学设计、个性化辅导、学情分析和评估中的应用方法。培训内容应涵盖AI伦理、数据隐私、批判性使用AI等重要议题，使教师从“内容讲授者”转型为“学习设计者、AI协同者和学生核心素养培育者” 113114115。
  • 培养学生批判性AI使用能力：教育体系应将AI素养纳入课程，从小培养学生批判性地使用AI工具的能力。这包括教导学生如何提出有效指令（prompt engineering）、如何评估AI生成内容的准确性与可靠性、如何识别AI的潜在偏见、以及如何利用AI进行创造性学习和解决问题 116。
  • 建立AI伦理意识与责任感：通过教育引导学生认识到AI的伦理风险和社会影响，培养其负责任地使用AI工具的意识，并激发他们成为未来AI技术伦理发展的参与者和贡献者。

通过上述多维度的研究与落地，生成式AI有望在个性化学习和智能辅导领域发挥其最大潜力，构建一个更高效、公平、个性化且富有创造力的未来教育生态。

内容由 AI 生成，仅供参考，请仔细甄别

参考文献

1Generative AI: Definition, Concepts, Applications, and Future ProspectsOpenAlex

Mogbojuri Babatunde Oluwagbenro
Generative Artificial Intelligence (AI) represents a significant advancement in AI, enabling the creation of synthetic data that closely mimics real data. This article provides a comprehensive overview of generative AI, including its definition and core concepts such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and autoregressive models. It explores applications across industries, including content creation, data augmentation, healthcare, gaming, fashion, finance, retail, cybersecurity, transportation, energy, education, entertainment, law, agriculture, real estate, and manufacturing. The article discusses future prospects of generative AI, highlighting enhanced creativity, improved human-AI collaboration, and personalized experiences. It also addresses dependencies and challenges, including data quality, computational resources, and ethical considerations. The benefits for businesses adopting generative AI are examined, emphasizing competitive advantage, enhanced customer experience, accelerated innovation, and improved decision-making. The article concludes by outlining the potential return on investment through increased efficiency, revenue growth, market expansion, and risk mitigation.

2Artificial Intelligence-Assisted Music Education: A Critical Synthesis of Challenges and OpportunitiesOpenAlex

Javier Merchán Sánchez-Jara, Sara González Gutiérrez, Javier Cruz Rodríguez, et al.
Artificial intelligence (AI) is a hot topic that presents new challenges and opportunities for the improvement of educational processes. The disruptive and transformative force of this new technological development implies the adaptation of educational ecosystems for its use and integration as a didactic and pedagogical resource. From this perspective, a systematic literature review has been conducted to analyze the didactic potential of generative AI tools in the field of promoting artistic creativity in music education. The research results confirm that the incorporation of AI in music education is paving the way for a more personalized, interactive and efficient learning experience. In addition, the analysis suggests nine fundamental fields of IA implementation in music education: virtual and augmented reality (VR; VA); learning personalization, intelligent tutoring systems; composition assistants; improved historical and contextual learning; assessment systems; interactive ear training and music theory systems; tools for music collaboration and performance; and assistive technologies. Furthermore, the challenges presented by the intersection of AI and digital didactics in the field of music education are discussed.

3Current evaluation and recommendations for the use of artificial intelligence tools in educationOpenAlex

Ferhan Gi̇rgi̇n Sağın, Ali Burak Özkaya, Funda İfakat Tengiz, et al.
Abstract This paper discusses the integration of artificial intelligence (AI) tools in education, delineating their potential to transform pedagogical practices alongside the challenges they present. Generative AI models like ChatGPT, had a disruptive impact on teaching and learning, due to their ability to create text, images, and sound, revolutionizing educational content creation and modification. However, nowadays the educational community is polarized, with some embracing AI for its accessibility and efficiency thus advocating it as an indispensable tool, while others cautioning against risks to academic integrity and intellectual development. This document is designed to raise awareness about AI tools and provide some examples of how they can be used to improve education and learning. From an educator’s perspective, AI is an asset for curriculum development, course material preparation, instructional design and student assessment, while reducing bias and workload. For students, AI tools offer personalized learning experiences, timely feedback, and support in various academic activities. The Turkish Biochemical Society (TBS) Academy recommends educators to embrace and utilize AI tools to enhance educational processes, and engage in peer learning for better adaptation while maintaining a critical perspective on their utility and limitations. The transfer of AI knowledge and methods to the teaching experiences should complement and not replace the educator’s creativity and critical thinking. The paper advocates for an informed embrace of AI, AI fluency among educators and students, ethical application of AI in academic settings, and continuous engagement with the evolving AI technologies, ensuring that AI tools are used to augment critical thinking and contribute positively to education and society.

4New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted RevolutionOpenAlex

Firuz Kamalov, David Santandreu Calonge, Ikhlaas Gurrib
The recent high performance of ChatGPT on several standardized academic tests has thrust the topic of artificial intelligence (AI) into the mainstream conversation about the future of education. As deep learning is poised to shift the teaching paradigm, it is essential to have a clear understanding of its effects on the current education system to ensure sustainable development and deployment of AI-driven technologies at schools and universities. This research aims to investigate the potential impact of AI on education through review and analysis of the existing literature across three major axes: applications, advantages, and challenges. Our review focuses on the use of artificial intelligence in collaborative teacher–student learning, intelligent tutoring systems, automated assessment, and personalized learning. We also report on the potential negative aspects, ethical issues, and possible future routes for AI implementation in education. Ultimately, we find that the only way forward is to embrace the new technology, while implementing guardrails to prevent its abuse.

5The role of ChatGPT in higher education: Benefits, challenges, and future research directionsOpenAlex

Tareq Rasul, Sumesh Nair, Diane Robyn Kalendra, et al.
This paper examines the potential benefits and challenges of using the generative AI model, ChatGPT, in higher education, in the backdrop of the constructivist theory of learning. This perspective-type study presents five benefits of ChatGPT: the potential to facilitate adaptive learning, provide personalised feedback, support research and data analysis, offer automated administrative services, and aid in developing innovative assessments. Additionally, the paper identifies five challenges: academic integrity concerns, reliability issues, inability to evaluate and reinforce graduate skill sets, limitations in assessing learning outcomes, and potential biases and falsified information in information processing. The paper argues that tertiary educators and students must exercise caution when using ChatGPT for academic purposes to ensure its ethical, reliable, and effective use. To achieve this, the paper proposes various propositions, such as prioritising education on the responsible and ethical use of ChatGPT, devising new assessment strategies, addressing bias and falsified information, and including AI literacy as part of graduate skills. By balancing the potential benefits and challenges, ChatGPT can enhance students’ learning experiences in higher education.

6Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature ReviewOpenAlex

Ilie Gligorea, Marius Cioca, Romana Oancea, et al.
The rapid evolution of e-learning platforms, propelled by advancements in artificial intelligence (AI) and machine learning (ML), presents a transformative potential in education. This dynamic landscape necessitates an exploration of AI/ML integration in adaptive learning systems to enhance educational outcomes. This study aims to map the current utilization of AI/ML in e-learning for adaptive learning, elucidating the benefits and challenges of such integration and assessing its impact on student engagement, retention, and performance. A comprehensive literature review was conducted, focusing on articles published from 2010 onwards, to document the integration of AI/ML in e-learning. The review analyzed 63 articles, employing a systematic approach to evaluate the deployment of adaptive learning algorithms and their educational implications. Findings reveal that AI/ML algorithms are instrumental in personalizing learning experiences. These technologies have been shown to optimize learning paths, enhance engagement, and improve academic performance, with some studies reporting increased test scores. The integration of AI/ML in e-learning platforms significantly contributes to the personalization and effectiveness of the educational process. Despite challenges like data privacy and the complexity of AI/ML systems, the results underscore the potential of adaptive learning to revolutionize education by catering to individual learner needs.

7Smart grading: A generative AI-based tool for knowledge-grounded answer evaluation in educational assessmentsOpenAlex

Samuel Tobler
Evaluating text-based answers obtained in educational settings or behavioral studies is time-consuming and resource-intensive. Applying novel artificial intelligence tools such as ChatGPT might support the process. Still, currently available implementations do not allow for automated and case-specific evaluations of large numbers of student answers. To counter this limitation, we developed a flexible software and user-friendly web application that enables researchers and educators to use cutting-edge artificial intelligence technologies by providing an interface that combines large language models with options to specify questions of interest, sample solutions, and evaluation instructions for automated answer scoring. We validated the method in an empirical study and found the software with expert ratings to have high reliability. Hence, the present software constitutes a valuable tool to facilitate and enhance text-based answer evaluation.•Generative AI-enhanced software for customizable, case-specific, and automized grading of large amounts of text-based answers.•Open-source software and web application for direct implementation and adaptation.

8Integrating Generative AI and ChatGPT in Design Education: Impacts on Critical Thinking DevelopmentOpenAlex

Brian Otieno Ouma, Eliud Kamau Mwangi, Adia Auma Okoth, et al.
The integration of generative artificial intelligence (AI), and more particularly ChatGPT, into design education has brought about opportunities and challenges in developing critical thinking among students. As the design discipline increasingly intersects with emerging technologies, educators must take into account the implications of AI-powered tools on cultivating essential cognitive skills. The aim of this study is to investigate the implications of generative AI and ChatGPT on the critical thinking of design students in educational contexts. A mixed-methods approach was employed, combining quantitative data from pre- and post-intervention surveys with qualitative data from reflective diaries and focus group discussions. The subjects of the study were students majoring in visual communication design who utilized ChatGPT and generative AI tools throughout the semester. The findings attest to the improvement in critical thinking capability in problem definition, conceptualization, and self-reflection. In addition, the study affirmatively confirms that use of AI guided by others—additive to formal learning structures—may enable students' autonomy and creative thinking without reducing their creativity. These results suggest that generative AI, as an adjunct incorporated curriculally in strategic ways, could act as a pedagogical catalyst for advancing students' ability to think higher-order. This present study is part of the new debate concerning AI education in two essential ways: first, by means of a model of efficient application of generative AI by morals-based models. Research in the future will have to consider long-term effects and cross-disciplinary usage to further pedagogical strategies.

9Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policyOpenAlex

Yogesh K. Dwivedi, Nir Kshetri, Laurie Hughes, et al.
Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT’s capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT’s use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts.

10Ethical use of ChatGPT in education—Best practices to combat AI-induced plagiarismOpenAlex

Attila Kővári
The emergence of ChatGPT, a high-performance artificial intelligence language model developed by OpenAI, has generated both excitement and concern in academia (Li, 2024). Equipped with advanced natural language processing techniques, ChatGPT is able to generate human-like text that provides coherent and contextually relevant responses to a wide range of queries. This unprecedented capability has raised optimism and concern as it could fundamentally change traditional practices in academia, industry and everyday life (Cambra-Fierro et al., 2024).The basic function of "ask me anything" and "I might have a good answer" is no longer just a concern in many fields. The scientific knowledge disseminated in journals is already struggling with the role that such technology will play. Questions arise about whether it will be, and can be, coauthored (Tang, 2024). Professors who create knowledge immediately face the challenge of assessing students in the presence of such technology. These are practical and legitimate questions.While ChatGPT has many benefits in terms of increased student engagement, collaboration and accessibility outcomes, it also has very serious academic integrity implications: at its core is plagiarism. This paper offers comprehensive strategies on how educators can help mitigate these risks by promoting ethical use and fairness within the academic use of AI tools.ChatGPT was truly disruptive, which should have surprised no one. It can be seen that these technologies are being adopted very quickly from university labs; ChatGPT reached one million users in its first five days and now has over 180 million (Duarte, 2024). This kind of rapid adoption demonstrates a remarkable property of generative AI: that it persists with coherent and contextually relevant text.One of the main problems with AI models like ChatGPT is the range of threats they pose, including black box algorithms, including black box algorithms, discrimination, biases, vulgarity, copyright infringement -plagiarism -and many others, such as the generation of fake text content or fake media (Sloan, Powis & Tan, 2024). Therefore, organisations need disciplined risk management approaches to effectively address these threats. Considering the continuous evolution of artificial intelligence algorithms due to the rapidity of data sources, the review of heterogeneity and variability bias in periodic risk assessments should also be weighed against ethical considerations (Schwartz et al., 2022).The experience was that the resulting text lacked an obvious logical structure, contained speculative information, did not elaborate on critical data, and did not provide original contributions (Giuggioli & Pellegrini 2023). Any article on the topic would be conventional, lack logic and facts, and would not be critically engaging. In addition, ChatGPT references are generally incorrect; titles and authors, as well as other publication details, are misstated. Such inaccuracies require careful double-checking, especially in professional contexts such as journalism and software development.Inaccuracy, poor logical flow, factual inaccuracies, lack of critical analysis and lack of originality of AI-generated content can result from the current state of technology (Yang, 2024). This is based on deep learning models that are trained using very extensive datasets of prior information that may be outdated or of low quality. Although improvements in training models and data quality may improve the performance of AIs, it is not clear that improvements based on technical level necessarily lead to significant gains in innovation (Dwivedi et al., 2023).The recent applications of generative AI in text, film and music production all indicate that these platforms will at best be partners in the innovation process, complementing rather than replacing human intelligence. In the case of complex activities requiring creativity and emotional intelligence, a well-formulated request alone is not sufficient for AI to produce markedly different and original outputs. Human oversight and collaboration remain essential (Liu, 2024). Research, practice, and urgent policy decisions in an era of rapidly evolving AI technologies require researchers, practitioners, and policymakers to critically engage with these changes. Building on the strengths of AI, while being aware of its limitations and making serious efforts to improve them, will foster an environment in which generative AI tools such as ChatGPT are used responsibly and effectively.Integrating ChatGPT into the scientific environment is not without its challenges. The primary concern is the possibility of plagiarism. Students may get used to using ChatGPT to create essays and assignments, which they then submit as their own work. This undermines the educational process and devalues academic credentials. Another challenge is the potential for inequality. Students who have access to ChatGPT can complete assignments in much less time and possibly better, giving them an unfair advantage over students who do not have ChatGPT. This may further increase existing inequalities in educational outcomes. On the other hand, it is difficult to distinguish content created by students from content created by AI. Because ChatGPT generates human-like, coherent text, the difficulty of distinguishing it from the "original" student content makes it difficult for educators to detect AI-assisted plagiarism.While this work focuses on addressing the risks of plagiarism, ChatGPT and other AI tools hold great promise for improving learning outcomes and stimulating creativity. Through adaptive tutoring systems, these tools can improve personalized learning, provide immediate feedback and facilitate deeper interaction with course material. Furthermore, AI-driven creative applications allow students to experiment with problem-solving and critical thinking in new ways, ultimately resulting in a more dynamic and engaging learning environment.The rise of large language models, such as ChatGPT, in education has led many educators and institutions to develop ways to prevent misuse. These approaches aim to protect academic integrity while adapting to the new environment of AI-enhanced learning environments. Different strategies have been introduced in different educational settings with varying degrees of success.This is probably the reason why many educational facilities have started to establish clear policies on how and when to employ AI tools such as ChatGPT. Many of these often tend to explain the emphasis on proper citation or attribution in the case of using generated AI content in a student's work. For example, some universities require students to mention what AI tool they used throughout the assignment, similar to citing sources from academic literature.A number of universities have now implemented high-tech, AI-detecting tools that work within plagiarism-checking programs. Indeed, services such as Turnitin have just this year introduced algorithms which detect AI-generated text by flagging submissions that are out of character for a student and/or contain unnatural patterns of speech. In addition, new software designed to detect AIassisted content is being developed and implemented, further complicating student efforts to misrepresent AI-generated text as their own.Another effective strategy is the design of assessments that increasingly require a high level of originality and creativity on the part of the student, for which AI tools are less effective. For example, assignments of a personal reflective nature, or those which require original research questions or specific local contexts, make it harder for students to fall back on AI-generated content only. This strategy minimizes not only the chances of misuse of AI but also fosters deeper learning and critical thinking skills among students.Some educators have been adopting oral examinations wherein students are made to present and defend ideas, assignments, and research projects. These face-to-face or virtual exchanges permit the instructor to engage directly with the student to determine the depth of understanding of course material. In these oral exams, it will be almost impossible for the students to use AI tools because it involves real-time response and justification.In contexts where group work is fostered, students often have to work in teams on elaborate projects, which already raises noticeable obstacles for AI-generated content to fit smoothly inside the final product. Group-based assignments by their very nature require communication, coordination, and collaboration among team members, aspects that no AI could imitate. Moreover, the mechanisms of peer review make students evaluate the work of their colleagues, thus automatically increasing the chances of identification of inconsistencies or any potential misuse of AI tools.Empirical evidence supports the importance of using adaptive and reflective evaluation to reduce AIrelated plagiarism. Successful pilot programs at highly regarded colleges that incorporate reflective and personalized tasks are highlighted by Moorhouse et al (2023). These programs limit the misuse of AI by requiring individualized responses tailored to students. Furthermore, Dempere et al (2023) provide evidence in favor of technology-based and ethics-based interventions, showing that ethical AI use campaigns in combination with AI recognition technologies greatly improve academic integrity compliance. Taken together, these studies show that integrating educational awareness campaigns and adaptive assessment provides a strong foundation for successful prevention of AIenabled plagiarism.To address the challenges of using generative AI in education, educators can use a number of strategies to prevent ChatGPT plagiarism. Cotton et al (2024) highlight the dual nature of ChatGPT in academia, highlighting the problems associated with scientific integrity and the prospect of increased engagement. They call for proactive institutional measures such as the integration of AIrecognition technologies, education of students on the ethical use of AI, and the creation of explicit policies on the use of AI tools. By implementing these tactics, universities can protect academic integrity and encourage ethical use of AI. Zeb et al (2024) highlight the dual nature of ChatGPT in higher education, pointing to both its potential benefits for student engagement and its risks related to academic integrity. They recommend that institutions implement clear policies, create assessment tasks that require critical thinking, and provide training to guide ethical AI use. By integrating these measures, educators can harness the benefits of AI tools like ChatGPT while minimizing risks of misuse.Strategies for the prevention of plagiarism, taking into account the opinions and suggestions:Technological Solutions• There are various plagiarism detectors that can find copied content. If there is a possibility to search for texts in student submissions that match existing sources, a possible case of plagiarism is flagged. Educators can also invest in advanced technologies to detect artificial intelligence-generated content through language patterns and stylistic anomalies. • Use learning analytics to track learner progress and detect unexplained patterns in learner performance. This could include sudden, unexplained improvement or different writing style, which is often a sign of AI-enabled plagiarism. • Use adaptive testing methods where questions are modified or reformulated based on previous student responses. This will make the AI tools more difficult to work with, as it will be very difficult to generate or predict correct answers when incorporating dynamic approaches.• Educating students about plagiarism is one of the most effective ways to combat plagiarism through education. Students need to be made aware of what exactly plagiarism is and the damage it does to learning and to the academic integrity built in the name of educational institutions. This can be achieved through teaching materials, classroom discussions, and clear communication of the consequences of plagiarism. • Include reflective writing exercises in which learners should discuss the learning process, the challenges encountered, and the insights gained. This can help teachers to assess the credibility of students' work and understand their thinking processes. • Peer assessment should be incorporated, where students are asked to evaluate each other's work. This both raises the quality of the work submitted and allows inconsistencies and possible plagiarism to be detected. • Encourage projects in which pupils produce individual, creative outputs. Such products could include multimedia presentations that engage users through their senses. This could include podcasts or other digital communication tools that are unlikely to be replicable by AI.• Design assessments that allow linking to personal experiences, local contexts, or specific curricula. These types of personalized tasks are less effective for general AI tools. • In addition to the written essay, encourage students to communicate what they have learned through a variety of media, such as slide shows, audio recordings, films, and portfolios. AI has difficulty replicating these alternative assessment methods, which encourage learners to develop more versatile skills.• Setting clear guidelines for the use of artificial intelligence tools such as ChatGPT is essential. Students need to know how and in what context to use such tools, i.e., proper citation and attribution of AI-generated texts. • Requiring students to submit an outline of their work can help instructors identify potential AI-generated content early in the process. This approach allows for timely feedback and guidance, reducing the likelihood of students resorting to plagiarism. • Regularly checking student submissions and work. This could include thorough reading of assignments, oral presentations to check understanding, and the use of detection devices to flag suspicious content.• Large tasks are broken down into smaller tasks structured by key points, with appropriate deadlines. This approach ensures that students build up their work gradually, making it more difficult to complete a whole project with AI. • Oral examinations can be a sure test of originality; students have to justify their arguments and even defend their work with oral answers, which in a sense makes it impossible to include AI-generated content in this assessment scenario.To further minimize the risk of AI-assisted plagiarism, educators can design assessments that are less prone to misuse. Some extended ways to minimize AI misuse:Critical Thinking and Problem-Solving Tasks• Tasks that require highly critical thinking or problem solving are unlikely to be performed satisfactorily by AI. This may include group discussions, project presentations, and interactive activities that require the individual to use their knowledge and skills. • Designing open-ended tasks that encourage originality and creativity can create conditions in which AI tools are less useful. For example, having students formulate their own research questions or arguments fosters independent thinking. • Refine tasks to focus on areas where AI tools fall short, such as in-depth critical analysis and personalized responses.• Demonstrate practical applications: create assessments in which students apply theoretical knowledge to practical, real-world problems. Case studies, simulations, and project-based learning activities are contexts in which AI's ability to generate relevant content is limited. • Design assessments that replicate real-life tasks and situations in authentic contexts, such as service-learning projects, internships, or community-based research. Such tasks require personal engagement and cannot be easily outsourced to AI. • Develop role-playing exercises and simulations in which students take on designated roles or characters. This is a great way to increase creativity and critical thinking, elements that are difficult for AI to simulate.• Create personalized tasks for each student or cohort that include dynamic elements such as current events, specific local problems, or personal reflections. Individualizing tasks minimizes the applicability of general AI responses. • Providing more personalized feedback and requiring follow-up actions based on that feedback, which fosters deeper engagement with material and reduces reliance on AI. • In a portfolio-based assessment, the student collects work done over time. Portfolios show progress or improvement in learning, which is challenging for AI to simulate.Collaborative and Peer-Based Learning• Group projects are those in which learners have to work together to create a final product, ensuring authentic input as collaboration requires communication and coordination that AI cannot replicate. • Peer-assisted learning activities, where learners tutor or mentor their classmates. This reinforces knowledge and requires explanation and justification, which AI cannot provide.• Real-time or proctored exams prevent students from using AI in assessments. This approach greatly reduces plagiarism and ensures the work represents each student's abilities. • Conduct timed assessments, such as in-class essays or timed online tests, to limit students' use of AI tools. This format emphasizes students' ability to think and respond quickly based on their own knowledge.• Use mixed forms of assessment: written work, presentations, and practical demonstrations.Multimodal assessments require diverse skills, making it difficult for AI alone to handle all elements. • Interactive and adaptive learning systems, which vary the difficulty and nature of questions based on student performance, provide personalization that challenges AI.• Frequent, low-level assessments to monitor students' progress on an ongoing basis. This allows for early detection of irregularities and reduces the likelihood of last-minute reliance on AI.Although the above-mentioned tactic offers a sound method for curbing AI-assisted plagiarism, its application may present a number of ethical and practical difficulties.Some universities, especially those with limited resources, may find the high of using plagiarism detectors and learning analytics Furthermore, the of these technologies on to with rapidly evolving AI further increasing technology including learning analytics and adaptive require the of large of student This raises questions about data and especially when data is The of information that can be and may be limited by the that and other with data student work can be for AI created using detection methods, especially when students use language patterns or have a writing This can lead to that student and require by a a of time and to implementing and measures, such as teaching plagiarism, oral exams, and large tasks into smaller It can be difficult for institutions to provide teachers with the tools and they need to these into their reliance on technology detection can from students' ethical such as plagiarism detectors are a thorough awareness of academic integrity through education key to the AI is strategies need to be modified and their strategies as generative artificial intelligence technology potential as well as ongoing and may be further by this ongoing strategies in this paper with a number of approaches that educators have already to The will out the these methods and make based on their success.This makes education the most effective of plagiarism than awareness of the tools and the consequences of their that are in awareness among students about the ethical use of AI tools and consequences of plagiarism tend to show compliance. In ensuring a of there is a need to have students how their learning and will be by For example, some universities introduced or online that how to use AI tools with in about originality and proper assessments of or personalized in the misuse of AI. the questions based on previous which makes it difficult for AI models to know the correct assessment continuous track the progress of of AI misuse. these approaches are implemented into practice, educators are then in a to students' learning through and less to last-minute AI-generated assessments are the against AI-assisted academic in that written work, oral presentations, and practical together the that students will a range of skills. Moreover, portfolio-based students and present a of work over a a more of the student's and thus have made it more easily to in quality or many institutions have adopted or are detection software for this of AI. data these tools can often flag AI-generated content while the academic integrity as AI improve in learning and The ethical and successful integration of AI into education on addressing these and policy need to the face of ChatGPT and other generative AI This paper in this by strategies for integrating AI tools into the university

11A Critical Review of Development of Intelligent Tutoring Systems: Retrospect, Present and ProspectOpenAlex

Neelu Jyothi Ahuja, Roohi Sille
This paper introduces, Intelligent Tutoring Systems along with their typical architecture, developmental history, past and present systems and concludes with a broad discussion on wide-spanning focus areas for future developmental research. A critical analysis of the developmental history highlighting the theme behind the developed systems, their purpose and the key ITS concept, have been presented. A closer look revealed that, development of a certain concept proved to become a turning point for all future developments of that era. All such key concepts and subsequent developments have been examined. The paper provides recommendations and pointers, to the areas that need to be probed further and drilled down to establish ITS success for generations to come.

12Intelligent tutoring systems: a history and an example of an ITS for scienceOpenAlex

Janice D. Gobert, Michael A. Sao Pedro, Haiying Li, et al.

13Research on intelligent tutoring systemOpenAlex

XU Gao-pan
This paper introduced the history and development of intelligent tutoring system briefly,discussed the subsistent problem of the intelligent tutoring system.And brought forward the direction of intelligent tutoring system.

14Evolution and Revolution in Artificial Intelligence in EducationOpenAlex

Ido Roll, Ruth Wylie

15Personalization of learning through adaptive technologies in the context of sustainable development of teachers’ educationOpenAlex

Maiia Marienko, Nosenko, Yuliya, Аліса Сухіх, et al.
The article highlights the issues of personalized learning as the global trend of the modern ICTbased educational systems development. The notion, the main stages of evolution, the main features and principles of adaptive learning systems application for teachers’ training are outlined. It is emphasized that the use and elaboration of the adaptive cloud-based learning systems are essential to provide sustainable development of teachers’ education. The current trends and peculiarities of the cloud-based adaptive learning systems development and approach of their implementation for teachers’ training are considered. The general model of the adaptive cloud-based learning system structure is proposed. The main components of the model are described; the issues of tools and services selection are outlined. The methods of the cloudbased learning components introduction within the adaptive systems of teacher training are considered. The current research developments of modeling and implementation of the adaptive cloud-based systems are outlined.

16The Evolution of E-Learning Platforms: From U-Learning to AI-Driven Adaptive Learning SystemsOpenAlex

Ahmad Zain Sarnato, Windy Dian Sari, Sri Tuti Rahmawati, et al.
Background. Information and communication technology development has brought significant changes in how learning is carried out, primarily through e-learning platforms. From the introduction of u-learning (ubiquitous learning) that allows access to learning anywhere and anytime to the emergence of adaptive learning systems driven by artificial intelligence (AI), this evolution continues to change the educational landscape. Purpose. This study examines the evolution of e-learning platforms from u-learning to AI-based adaptive learning systems. The main focus is understanding how each development phase has improved learning effectiveness and met individual learning needs. Method. This research uses a qualitative approach with literature study methods. Data was gathered from various academic sources, including journals, books, and conference reports discussing the evolution of e-learning. Thematic analysis is used to identify critical patterns and trends in developing e-learning platforms. Results. The results show that the evolution of e-learning has brought significant improvements in accessibility, interactivity, and personalization of learning. U-learning allows for more flexible access to education. At the same time, AI-based adaptive learning systems offer a more personalized learning experience by tailoring teaching materials and methods according to student’s needs and abilities. These findings emphasize the importance of technology in improving learning effectiveness and efficiency. Conclusion. The study concludes that e-learning platforms have evolved significantly from u-learning to AI-based adaptive learning systems, improving learning quality and effectiveness. Integrating AI in e-learning offers excellent potential for creating more personalized and compelling learning experiences. Recommendations for follow-up research include further exploring the long-term impact of adaptive learning systems and developing more advanced technologies to support more inclusive and efficient learning.

17Intelligent and Adaptive Tutoring Systems: How to Integrate LearnersOpenAlex

Mehri Mohammad Bagheri
This article is a non-expert overview of intelligent technology-based instructional machines, from the first stages of their emergence up to this date. It is attempted to demonstrate their trend of evolution, and how the advances in other related fields such as education, psychology, and computer science affected them and contributed to their progress. It starts by an account of the concept of intelligent technology-based instruction and continues by defining the notion of learner integration in such systems. Furthermore, Intelligent Tutoring Systems (ITSs) are pictured: how they evolved, their architecture, and how they contributed to the field of Adaptive Learning Systems. Finally, a number of adaptive learning platforms which are the state of the art and currently used by large numbers of actual users are introduced and described.

18ChatGPT for good? On opportunities and challenges of large language models for educationOpenAlex

Enkelejda Kasneci, Kathrin Seßler, Stefan Küchemann, et al.

19UTILIZING ARTIFICIAL INTELLIGENCE IN EDUCATION TO ENHANCE TEACHING EFFECTIVENESSOpenAlex

M. Nasir, Muhammad Hasan, Adlim Adlim, et al.
Artificial Intelligence in Education (AIEd) has been evolving for some time, and the advent of GPT chat at the end of December 2022 has opened up new opportunities, potentials, and challenges in educational practice. Advances in computational technology and information processing have led to widespread applications of Artificial Intelligence (AI) in the field of education. Over the last 20 years, the number of papers on AIED has been steadily increasing, with a dramatic rise since 2015 until the present. In its brief history, AIEd has undergone several paradigm shifts. This research aims to explore the use of AI in education by examining the publication trends sourced from metadata from Google Scholar, PubMed, CrossRef, OpenAlex, and Scopus. The development and application of Artificial Intelligence (AI) technology, particularly in education, significantly supports educational reform and profoundly influences the learning styles of learners. Artificial Intelligence in Education (AIED) can assist teachers in preparing teaching materials, presentation media, and accurate evaluations. Furthermore, AIED can help students adapt their traditional learning styles according to their differences, thus realizing intelligent teaching that meets students' learning needs. Teachers' positive perceptions of educational technology (ET) are beneficial for using AI technology to aid teaching positively, which in turn can enhance teaching effectiveness. Overall, the trend of AIEd development has successfully empowered learner personalization, enabling learners to think critically and innovatively, and fostering personalized learning.

20What Is the Impact of ChatGPT on Education? A Rapid Review of the LiteratureOpenAlex

Chung Kwan Lo
An artificial intelligence-based chatbot, ChatGPT, was launched in November 2022 and is capable of generating cohesive and informative human-like responses to user input. This rapid review of the literature aims to enrich our understanding of ChatGPT’s capabilities across subject domains, how it can be used in education, and potential issues raised by researchers during the first three months of its release (i.e., December 2022 to February 2023). A search of the relevant databases and Google Scholar yielded 50 articles for content analysis (i.e., open coding, axial coding, and selective coding). The findings of this review suggest that ChatGPT’s performance varied across subject domains, ranging from outstanding (e.g., economics) and satisfactory (e.g., programming) to unsatisfactory (e.g., mathematics). Although ChatGPT has the potential to serve as an assistant for instructors (e.g., to generate course materials and provide suggestions) and a virtual tutor for students (e.g., to answer questions and facilitate collaboration), there were challenges associated with its use (e.g., generating incorrect or fake information and bypassing plagiarism detectors). Immediate action should be taken to update the assessment methods and institutional policies in schools and universities. Instructor training and student education are also essential to respond to the impact of ChatGPT on the educational environment.

21Artificial intelligence in education: The three paradigmsOpenAlex

Fan Ouyang, Pengcheng Jiao
With the development of computing and information processing techniques, artificial intelligence (AI) has been extensively applied in education. Artificial intelligence in education (AIEd) opens new opportunities, potentials, and challenges in educational practices. In its short history, AIEd has been undergoing several paradigmatic shifts, which are characterized into three paradigms in this position paper: AI-directed, learner-as-recipient, AI-supported, learner-as-collaborator, and AI-empowered, learner-as-leader. In three paradigms, AI techniques are used to address educational and learning issues in varied ways. AI is used to represent knowledge models and direct cognitive learning while learners are recipients of AI service in Paradigm One; AI is used to support learning while learners work as collaborators with AI in Paradigm Two; AI is used to empower learning while learners take agency to learn in Paradigm Three. Overall, the development trend of AIEd has been developing to empower learner agency and personalization, enable learners to reflect on learning and inform AI systems to adapt accordingly, and lead to an iterative development of the learner-centered, data-driven, personalized learning.

22Evaluating the role of Artificial Intelligence in sustainable development goals with an emphasis on “quality education”OpenAlex

Hatoon S. AlSagri, Shahab Saquib Sohail
The integration of Artificial Intelligence (AI), particularly advancements in Generative AI technologies such as Large Language Models (LLMs)—of which ChatGPT is a notable example—marks a pivotal step in addressing global challenges within the framework of Sustainable Development Goals (SDGs). Specifically, within SDG4 (Quality Education), these open AI technologies have the potential to revolutionize education by providing scalable, personalized learning experiences, improving access to quality education, and optimizing resource allocation. By reducing barriers to educational equity and supporting lifelong learning, AI contributes not only to enhancing educational outcomes but also to the broader pillars of sustainability—social, economic, and environmental. Despite these promising applications, the existing literature reveals significant gaps in understanding the full scope of AI’s role in achieving SDGs. This study employs a bibliometric analysis to quantify the current research on AI’s contribution to sustainable development, with a particular focus on educational quality and sustainability. Our analysis highlights emerging trends, key contributors, and prevalent themes in the academic discourse, providing a robust foundation for future research. We argue that a deeper understanding of AI’s capabilities can inform more effective policies and strategic initiatives, fostering equitable and sustainable educational systems. Future research should further explore ethical considerations and long-term societal impacts of AI integration into education and other sectors. This study offers a comprehensive overview of the current research landscape, identifies critical areas for future investigation, and sets the stage for continued academic inquiry into AI and sustainability.

23Empowering student self‐regulated learning and science education through <scp>ChatGPT</scp> : A pioneering pilot studyOpenAlex

Davy Tsz Kit Ng, Chee Wei Tan, Jac Ka Lok Leung
In recent years, AI technologies have been developed to promote students' self‐regulated learning (SRL) and proactive learning in digital learning environments. This paper discusses a comparative study between generative AI‐based (SRLbot) and rule‐based AI chatbots (Nemobot) in a 3‐week science learning experience with 74 Secondary 4 students in Hong Kong. The experimental group used SRLbot to maintain a regular study habit and facilitate their SRL, while the control group utilized rule‐based AI chatbots. Results showed that SRLbot effectively enhanced students' science knowledge, behavioural engagement and motivation. Quantile regression analysis indicated that the number of interactions significantly predicted variations in SRL. Students appreciated the personalized recommendations and flexibility of SRLbot, which adjusted responses based on their specific learning and SRL scenarios. The ChatGPT‐enhanced instructional design reduced learning anxiety and promoted learning performance, motivation and sustained learning habits. Students' feedback on learning challenges, psychological support and self‐regulation behaviours provided insights into their progress and experience with this technology. SRLbot's adaptability and personalized approach distinguished it from rule‐based chatbots. The findings offer valuable evidence for AI developers and educators to consider generative AI settings and chatbot design, facilitating greater success in online science learning. Practitioner notes What is already known about this topic AI technologies have been used to support student self‐regulated learning (SRL) across subjects. SRL has been identified as an important aspect of student learning that can be developed through technological support. Generative AI technologies like ChatGPT have shown potential for enhancing student learning by providing personalized guidance and feedback. What this paper adds This paper reports on a case study that specifically examines the effectiveness of ChatGPT in promoting SRL among secondary students. The study provides evidence that ChatGPT can enhance students' science knowledge, motivation and SRL compared to a rule‐based AI chatbot. The study offers insights into how ChatGPT can be used as a tool to facilitate SRL and promote sustained learning habits. Implications for practice and/or policy The findings of this study suggest that educators should consider the potential of ChatGPT and other generative AI technologies to support student learning and SRL. Educators and students should be aware of the limitations of AI technologies and ensure that they are used appropriately to generate desired responses. It is also important to equip teachers and students with AI competencies to enable them to use AI for learning and teaching.

24AI in Medical Education Curriculum: The Future of Healthcare LearningOpenAlex

Waqar M. Naqvi, Habiba Sundus, Gaurav Mishra, et al.
To address the evolving, quantitative nature of healthcare in the twenty-first century, it is imperative to integrate artificial intelligence (AI) with healthcare education. To bridge this educational gap, it is imperative to impart practical skills for the utilisation and interpretation of AI in healthcare settings, integrate technology into clinical operations, develop AI technologies, and enhance human competencies [1]. The swift rise of AI in contemporary society can be ascribed to the progress of intricate algorithms, cost-effective graphic processors, and huge annotated databases. AI has been a crucial component of healthcare education in recent years and has been implemented by numerous medical institutions globally. AI is widely prevalent in medical education in Western countries, in contrast to developing countries. The disparity could be mitigated through more infrastructural assistance from medical institutions in underdeveloped nations. It is crucial to raise awareness among medical educators and students regarding AI tools to facilitate the development and integration of AI-based technologies in medical education [2]. AI can impact the student learning process through three methods: direct instruction (transferring knowledge to the student in a teacher-like role), instructional support (assisting students as they learn), and learner empowerment (facilitating collaboration among multiple students to solve complex problems based on teacher feedback). Incorporating artificial intelligence (AI) tools into education can augment students' knowledge, foster skill acquisition, and deepen comprehension of intricate medical topics [2,3]. Virtual reality (VR) can enhance the immersion of learning sessions with virtual patients. Virtual Reality (VR) is a software-driven technology that generates a virtual environment with three-dimensional characteristics. Virtual Reality (VR) uses a head-mounted display or glasses to build a computer-simulated environment that provides a convincing and lifelike experience for the user. Conversely, augmented reality (AR) enhances the real-world environment by superimposing virtual elements onto a user's perspective of the actual world through a smartphone or similar device. By integrating these technologies, learners are able to investigate and actively participate in intricate clinical situations, resulting in a more pleasurable and efficient learning experience [4,5]. AI-powered games utilise data mining methodologies to examine the data gathered during gameplay and enhance the player's knowledge and abilities. In addition, they provide a personalised and engaging encounter that adapts the speed and level of challenge according to the player's achievements. Incorporating game components such as points, badges, and leaderboards enhances the enjoyment and engagement of the learning process. The implementation of gamification in the learning process boosts student engagement, fosters collaborative efforts, and optimises learning results. Additionally, they offer chances for clinical decision-making without any potential risks and provide instant feedback to the students, thereby becoming an essential component of undergraduate medical education [6]. By incorporating artificial intelligence (AI) techniques into learning management systems (LMS), learners are equipped with the necessary resources to achieve mastery at their own individualised pace. These computer algorithms assess the learner's level of understanding and deliver personalised educational material to help them achieve mastery of the content. The AI-powered platforms guide learners by effectively organising and arranging learning experiences, and then implementing targeted remedial actions. These customised and adaptable teaching techniques enhance the effectiveness and efficiency of learning. Virtual patients are computer-based simulations that replicate real-life clinical events and are used for training and education in health professions. Virtual patients are built to simulate authentic symptoms, react to students' treatments, and create dynamic therapeutic encounters. The student assumes the position of a healthcare provider and engages in activities such as gathering information, proposing potential diagnoses, implementing medical treatment, and monitoring the patient's progress. These simulations can accurately reproduce a range of medical settings and expose trainees to the problems they might encounter in real-world situations. Medical students can enhance their communication and clinical reasoning skills by engaging with virtual patients in a simulated environment that closely resembles real-life situations [6,7]. Furthermore, AI-driven solutions can be advantageous for educational purposes in diagnostic fields such as radiology, pathology, and microbiology. Content-based image retrieval (CBIR) is a highly promising method utilised in the field of radiology for educational and research purposes. CBIR facilitates the search for photos that have similar content with a reference image, utilising information extracted from the images [8]. Moreover, artificial intelligence (AI) integrated with machine learning techniques is currently being employed to accurately diagnose microbial illnesses. This application of AI has significant potential in training and educating specialists in the field of microbiology. Conversely, the current progress in AI-driven deep learning technologies that specifically target cellular imaging has the potential to revolutionise education in diagnostic pathology [9]. Ultimately, incorporating AI training into the medical education curriculum is a transformative step that will shape the future of healthcare practitioners. This sequence provides enhanced diagnostic precision, personalised learning prospects, and heightened ethical awareness. These potential benefits surpass the obstacles, initiating a new era in medical education where human beings and technology collaborate to deliver optimal patient care. The purposeful and calculated integration of AI into medical education will have a pivotal impact on shaping the future of healthcare as we navigate this unexplored territory.

25AI-powered EFL pedagogy: Integrating generative AI into university teaching preparation through UTAUT and activity theoryOpenAlex

M. Zaim, Safnil Arsyad, Budi Waluyo, et al.
This study explores the integration of generative AI into English as a Foreign Language (EFL) teaching preparation within Indonesian higher education, addressing the growing need to understand how emerging technologies can enhance pedagogical practices in a rapidly evolving educational landscape. By employing the Unified Theory of Acceptance and Use of Technology (UTAUT) and Activity Theory, the research provides a robust analytical framework to examine the factors influencing lecturers' adoption of generative AI. The study is particularly relevant as generative AI offers significant potential to improve teaching efficiency and content personalization, yet its adoption presents challenges in aligning outputs with educational standards and maintaining meaningful teacher-student interaction. Using a mixed-methods approach, the research combined quantitative data from structured questionnaires with qualitative insights from reflective compositions, where lecturers critically evaluated their experiences with generative AI. Structural Equation Modeling (SEM) revealed that performance expectancy and social influence significantly and positively influenced behavioral intention, while effort expectancy had no significant effect. Facilitating conditions, unexpectedly, negatively impacted behavioral intention, likely due to satisfaction with existing resources reducing the perceived necessity for new tools. A strong positive correlation between behavioral intention and actual use behavior demonstrated the critical role of intention in driving adoption. Thematic analysis provided further depth by emphasizing both the benefits and challenges of generative AI, accentuating the importance of balancing its use with human instruction to ensure quality teaching and interaction. The study stresses the need for the strategic integration of generative AI, offering practical and theoretical insights into its adoption and implications for advancing EFL teaching in higher education.

26Investigating the Effectiveness of ChatGPT for Providing Personalized Learning Experience: A Case StudyOpenAlex

Raneem N. Albdrani, Amal A. Al-Shargabi
The demand for personalized learning experiences that cater to the unique needs of individual learners has increased with the emergence of data science. This paper investigates the potential use of ChatGPT, a generative AI tool, in providing personalized learning experiences for data science education, specifically focusing on Deep Learning. The paper presents a case study that applies the 5Es model to test personalized learning for students using ChatGPT. The study aims to answer the question of how educators can leverage ChatGPT in their pedagogy to enhance student learning, and whether ChatGPT can provide a better learning experience than traditional teaching methods. The paper also discusses the limitations faced during the study and the findings. The results suggest that ChatGPT can be a valuable resource for data science education, providing personalized and instant feedback to learners. However, ethical considerations such as the potential for biased or inaccurate responses and the need for transparency in AI-generated content should be carefully ad-dressed by educators. The study highlights ChatGPT’s potential as a research tool for data science educators to investigate the effectiveness of AI in personalized learning experiences. Overall, this paper contributes to the ongoing dialogue on the role of AI in data science education and provides insights into how educators can utilize ChatGPT to enhance student learning and engagement.

27Students’ voices on generative AI: perceptions, benefits, and challenges in higher educationOpenAlex

Cecilia Ka Yuk Chan, Wenjie Hu
Abstract This study explores university students’ perceptions of generative AI (GenAI) technologies, such as ChatGPT, in higher education, focusing on familiarity, their willingness to engage, potential benefits and challenges, and effective integration. A survey of 399 undergraduate and postgraduate students from various disciplines in Hong Kong revealed a generally positive attitude towards GenAI in teaching and learning. Students recognized the potential for personalized learning support, writing and brainstorming assistance, and research and analysis capabilities. However, concerns about accuracy, privacy, ethical issues, and the impact on personal development, career prospects, and societal values were also expressed. According to John Biggs’ 3P model, student perceptions significantly influence learning approaches and outcomes. By understanding students’ perceptions, educators and policymakers can tailor GenAI technologies to address needs and concerns while promoting effective learning outcomes. Insights from this study can inform policy development around the integration of GenAI technologies into higher education. By understanding students’ perceptions and addressing their concerns, policymakers can create well-informed guidelines and strategies for the responsible and effective implementation of GenAI tools, ultimately enhancing teaching and learning experiences in higher education.

28Generative Artificial Intelligence in Higher Education: Exploring Ways of Harnessing Pedagogical Practices with the Assistance of ChatGPTOpenAlex

Κλεοπάτρα Νικολοπούλου
There is a growing interest in using generative artificial intelligence (AI) for educational purposes within the higher education environments, while AI applications (such as ChatGPT) can transform traditional teaching and learning methods. ChatGPT is an advanced AI tool that generates new content and human-like responses. The purpose of this paper is to use ChatGPT as a research assistant in order to explore ways AI can be harnessed to enhance pedagogical practices in higher education. This is a qualitative study, in which the output-responses generated by ChatGPT provided a starting point for the investigation. AI can be harnessed to enhance pedagogical practices in higher education in various ways including personalized learning, automated assessment and feedback generation, virtual assistants and chatbots, content creation, resource recommendation, time management, language translation and support, research assistance, simulations and virtual labs. Other educational affordances that can strengthen the teaching and learning experience regard collaboration and communication, accessibility and inclusivity, as well as AI literacy. When implementing AI tools such as ChatGPT in higher education, ethical considerations (e.g., data privacy, transparency, accessibility, cultural sensitivity), potential misuses and concerns need to also be addressed. Although ChatGPT can aid the generation of content-ideas for further exploration, it is a complementary-supportive tool, and its output necessitates human evaluation and review. The integration of ChatGPT and other AI tools in the higher educational process/practices has implications for educators, students, design of curricula, and university policy makers. Received: 17 January 2024 | Revised: 27 February 2024 | Accepted: 19 March 2024 Conflicts of Interest The author declares that she has no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study.

29The implementation of the cognitive theory of multimedia learning in the design and evaluation of an AI educational video assistant utilizing large language modelsOpenAlex

Rana AlShaikh, Norah Al-Malki, Maida Almasre
The integration of Artificial Intelligence (AI) holds immense potential for revolutionizing education; especially, in contexts where multimodal learning experiences are designed. This paper investigated the potential benefits of Generative Artificial Intelligence (AI) in education, concentrating on the design and evaluation of an AI Educational Video Assistant tailored for multimodal learning experiences. The tool, utilizing the principles of the Cognitive Theory of Multimedia Learning (CTML), comprises three modules: Transcription, Engagement, and Reinforcement, each focusing on distinct aspects of the learning process. It Integraties Automatic Speech Recognition (ASR) using OpenAI's Whisper and Google's Large Language Model (LLM) Bard. Our twofold objective includes both the development of this AI assistant tool and the assessment of its effect on improving the learning experiences. For the evaluation, a mixed methods approach was adopted, combining human evaluation by nine educational experts with automatic metrics. Participants provided their perceptions on the tool's effectiveness in terms of engagement, content organization, clarity, and usability. Additionally, automatic metrics including Content Distinctiveness and Readability scores were computed. The results from the human evaluation suggest positive impacts across all assessed domains. The automatic metrics further proved the tool's ability in content generation and readability. Collectively, these preliminary results highlight the tool's potential to revolutionize educational design and provide personalized and engaging learning experiences.

30Generative AI in Engineering and Computing Education: A Scoping Review of Empirical Studies and Educational PracticesOpenAlex

Jonathan Álvarez Ariza, Milena Benítez Restrepo, Carola Hernández Hernández
Since the release of diverse generative AI (GenAI) tools such as ChatGPT, Google Gemini, DALL<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\cdot $ </tex-math></inline-formula>E, and GitHub Copilot, there has been much debate around the impacts and implications of these tools on education. Currently, extant literature remarks on the affordances, challenges, and opportunities of GenAI, but few studies report and analyze empirical studies and educational practices coming up by GenAI usage in learning settings. Then, in this Scoping Review (ScR) based on 146 studies retrieved from the databases SCOPUS, Web of Science (WoS), and ERIC, we analyzed the implications of integrating GenAI in engineering and computing education from K-12 to tertiary levels. We adopted an approach starting from the bibliometric features of the studies in terms of authors, cites, years, or cluster topics, and navigating to the identification of methodologies, strategies, AI literacy instruments and guidelines, learning outcomes, and students’ and teachers’ perceptions, among other features. We advocate that current educational practices in engineering and computing with GenAI can indicate to us a roadmap of its potentialities, uses, and risks from the standpoint of both teachers and students, and this could help us to create more reflexive methodologies that enhance the teaching-learning process based on the evidence. Our purpose with the outcomes and conclusions of this scoping review is to support educators, faculty members, and other stakeholders in engineering and computing education to co-create educational methodologies that articulate GenAI with curricula, AI literacy, and prompt engineering encompassing students’ learning domains such as cognitive, affective, or behavioral.

31Harnessing Generative Artificial Intelligence for Digital Literacy Innovation: A Comparative Study between Early Childhood Education and Computer Science UndergraduatesOpenAlex

Ioannis Kazanidis, Νικόλαος Πέλλας
The recent surge of generative artificial intelligence (AI) in higher education presents a fascinating landscape of opportunities and challenges. AI has the potential to personalize education and create more engaging learning experiences. However, the effectiveness of AI interventions relies on well-considered implementation strategies. The impact of AI platforms in education is largely determined by the particular learning environment and the distinct needs of each student. Consequently, investigating the attitudes of future educators towards this technology is becoming a critical area of research. This study explores the impact of generative AI platforms on students’ learning performance, experience, and satisfaction within higher education. It specifically focuses on students’ experiences with varying levels of technological proficiency. A comparative study was conducted with two groups from different academic contexts undergoing the same experimental condition to design, develop, and implement instructional design projects using various AI platforms to produce multimedia content tailored to their respective subjects. Undergraduates from two disciplines—Early Childhood Education (n = 32) and Computer Science (n = 34)—participated in this study, which examined the integration of generative AI platforms into educational content implementation. Results indicate that both groups demonstrated similar learning performance in designing, developing, and implementing instructional design projects. Regarding user experience, the general outcomes were similar across both groups; however, Early Childhood Education students rated the usefulness of AI multimedia platforms significantly higher. Conversely, Computer Science students reported a slightly higher comfort level with these tools. In terms of overall satisfaction, Early Childhood Education students expressed greater satisfaction with AI software than their counterparts, acknowledging its importance for their future careers. This study contributes to the understanding of how AI platforms affect students from diverse backgrounds, bridging a gap in the knowledge of user experience and learning outcomes. Furthermore, by exploring best practices for integrating AI into educational contexts, it provides valuable insights for educators and scholars seeking to optimize the potential of AI to enhance educational outcomes.

32Adoption of Artificial Intelligence (AI) For Development of Smart Education as the Future of a Sustainable Education SystemOpenAlex

Deepshikha Aggarwal, Deepti Sharma, Archana B. Saxena
Adoption of artificial intelligence (AI) for development of Smart education as the future of a sustainable education system is gaining momentum worldwide. AI can transform the way we teach and learn, making education more personalized and efficient. With AI, adaptive learning platforms can analyse students' strengths and weaknesses, tailoring lessons to their individual needs. Virtual tutors powered by AI can provide instant feedback and personalized guidance. AI can also assist in content creation and assessment, automating tasks like grading and feedback. By integrating AI into education, we can create a more inclusive and accessible learning environment for all students, empowering them to thrive in the digital age. AI has the potential to revolutionize education by personalizing learning experiences and making them more efficient. Adaptive learning platforms that use AI can analyse students' strengths and weaknesses, and tailor lessons to their individual needs. Virtual tutors powered by AI can provide instant feedback and personalized guidance, enhancing the learning process. AI can also automate tasks like content creation, assessment, grading, and feedback. By integrating AI into education, we can create a more inclusive and accessible learning environment for students, empowering them to excel in the digital age. This transformative technology is set to shape the future of education worldwide. With AI, the possibilities are endless.

33Empowering Education through Generative AI: Innovative Instructional Strategies for Tomorrow's LearnersOpenAlex

Kadaruddin Kadaruddin
As the educational landscape endures continuous change, artificial intelligence (AI) has presented unprecedented opportunities to revolutionize instructional methods. Among these cutting-edge AI technologies, Generative AI has emerged as a promising instrument with the potential to empower educators and students through innovative instructional strategies. This article aims to investigate the various applications of Generative AI in education and cast light on its role in shaping the future of education. The objectives of this study are twofold: first, to investigate the various instructional strategies that can be enhanced by employing Generative AI, and second, to assess the potential impact of these strategies on student learning outcomes. To accomplish these goals, a comprehensive literature review was conducted analyzing existing studies and applications of Generative AI in educational settings. The results and discussions emphasize the numerous educational benefits of Generative AI. Educators can personalize learning experiences, create interactive content, and facilitate adaptive assessments by leveraging the capabilities of Generative AI. This individualized strategy has the potential to boost learner engagement and knowledge retention. However, despite the numerous advantages, ethical concerns and difficulties arise. The responsible incorporation of Generative AI in education requires addressing issues such as data privacy, algorithmic bias, and the educator's role in directing AI-driven learning experiences. The research concludes by emphasizing that Generative AI holds enormous promise for empowering education and transforming instructional practices. The findings highlight the importance of ongoing collaboration between educators, policymakers, and AI developers to ensure the ethical and equitable integration of Generative AI into educational environments. By embracing the potential of Generative AI while remaining vigilant regarding its challenges, the field of education can unlock novel opportunities to nurture an inclusive, adaptive, and learner-centric pedagogical landscape for tomorrow's learners.

34Artificial Intelligence and English Language Learning: Exploring the Roles of AI-Driven Tools in Personalizing Learning and Providing Instant FeedbackOpenAlex

Olusegun Oladele Jegede
This study investigated the impact of AI-driven tools on English language learning, motivated by the increasing integration of artificial intelligence in education and the need for empirical evidence on its effectiveness. The purpose of the study was to explore how AI-driven tools personalize learning, provide instant feedback, and affect learner perceptions. Using a quantitative research design, data were collected from 200 students across four international schools via questionnaires. Three major findings emerged: AI-driven tools significantly enhanced personalized learning experiences, with 72.5% of students rating personalization highly; instant feedback from AI tools was found to be very helpful by 80% of students, leading to improved language acquisition progress; and 80% of students recommended AI-driven tools, citing increased enjoyment and engagement. The study concluded that AI-driven tools effectively support personalized learning and provide beneficial instant feedback, though challenges such as technical issues and the need for human interaction remain. It was recommended that educators integrate AI tools thoughtfully, ensuring a balance with traditional methods and addressing technical and accessibility concerns. Further research should investigate long-term impacts and optimal implementation strategies.

35Elsa Speak App: Automatic Speech Recognition (ASR) for Supplementing English Pronunciation SkillsOpenAlex

Adhan Kholis
Nowadays, artificial intelligence (AI) became a special concern in language teaching for the reason that it can assist and enhance language learning for all levels of education. Again, it had beneficial roles for supplementing language teaching like ELSA Speak App one of Automatic Speech Recognition (ASR) used for teaching pronunciation. It studied how students heard, voiced, uttered, vocalized, and asserted the English words in the oral language, but the students often pronounced incorrect words with the result that the uttered words had faulty meaning. This study aimed to carry out English Language Speech Assistant (ELSA) Speak App to improve English language pronunciation skills to higher education learners that were the English Department Students of Nahdlatul Ulama University of Yogyakarta (UNU). The data were collected using a test of pronunciation and interview. The researcher also taught in the classroom. The results showed that ELSA Speak can increase the students’ pronunciation skills. It can be seen from the average scores obtained from the teaching cycles from two to four in grade. Clearly, ELSA Speak helped the students pronounce diverse words more easily and comprehensively. Also, the available features offered by this app like instant feedback enabled the students to pronounce precisely. In conclusion, ELSA Speak can improve the students’ pronunciation skills well and effectively. Indeed, it can motivate the students to engage in learning to pronounce.

36AI chatbot-based learning: alleviating students' anxiety in english writing classroomOpenAlex

Santhy Hawanti, Khudoiberdieva Munisa Zubaydulloevna
In the ever-evolving landscape of education, integrating innovative technologies can enhance the learning experience for students. ChatGPT, a cutting-edge language processing tool developed by OpenAI, offers exciting possibilities for teaching writing. This advanced AI model can be a powerful asset in the classroom, providing students with valuable resources and support as they develop their writing skills. Seventy-three college students participated in the quasi-experiment. The findings demonstrate that AI chatbot-based instruction reduces students' anxiety about learning English writing. AI chatbots offer instant feedback, allowing students to correct errors immediately. This quick feedback loop can prevent students from ruminating over their mistakes, thus reducing anxiety. With AI chatbot, students can learn at their own pace. They can take time to understand concepts, practice writing, and receive feedback without feeling rushed. This flexibility can alleviate the pressure of strict deadlines in traditional classroom settings. The findings imply teachers to implement chatbot-based learning in the classroom.

37Tutor CoPilot: A Human-AI Approach for Scaling Real-Time ExpertiseOpenAlex

Rose Wang, Ana M. Ribeiro, Carly D. Robinson, et al.

38Integration of MATH41 and Generative AI in Pre-Service Mathematics Teacher Education: An Empirical Study on Lesson Design CompetencyOpenAlex

Sejun Oh
Generative artificial intelligence (AI) tools are rapidly transforming mathematics education by enabling automated problem generation, dynamic visualizations, and adaptive learning experiences. This study presents an empirical study on incorporating MATH41 into a 15-week pre-service teacher preparation course for mathematics majors. Twenty-four participants learned to create parameterized math problems, generate vector graphics. Following guided training, each participant designed and micro-taught a 20-minute lesson incorporating MATH41-generated resources. Reflection journals and final lesson plans were analyzed thematically, while 20 participants completed a self-assessment survey on lesson design competency. Results reveal that the automated problem generation capabilities motivated pre-service teachers to explore a broader range of instructional strategies, including personalized tasks and diverse problem variants. Reflection data indicate that while integrating AI tools can significantly boost confidence and creativity in lesson planning, careful pedagogical alignment remains essential to avoid superficial learning. Participants underscored the importance of maintaining teacher oversight—especially when adapting AI-generated problems for particular learner needs. Additionally, their post-course self-assessments showed high confidence in digital tool integration, yet they acknowledged that anticipating student misconceptions requires further field experience. Overall, this study contributes to understanding how teacher education programs can enhance lesson design competencies via structured AI tool integration. It also highlights the critical role of reflective practice in ensuring that automated content creation fosters deeper instructional effectiveness rather than uncritical AI dependency.

39A systematic review of conversational AI in language education: focusing on the collaboration with human teachersOpenAlex

Hyangeun Ji, Insook Han, Yujung Ko
Despite the increasing use of conversational artificial intelligence (AI) in language learning, few studies explored how to develop collaborative partnership between AIs and humans. This systematic review examines empirical evidence of human-computer collaboration from 24 studies conducted in an AI-integrated language learning environment and published between 2015 and 2021. The roles of conversational AIs and teachers in each language learning phase with challenges of and suggestions for conversational AI-integrated language learning were identified. Although limited evidence for collaboration between conversational AIs and human teachers was found, future language education should integrate conversational AIs to promote intelligence amplification and decrease human teachers’ workload through classroom orchestration. The study concludes with guidelines and recommendations for teachers and AI researchers.

40Pre-service teachers’ attitudes towards artificial intelligence and its integration into EFL teaching and learningOpenAlex

Silvia Pokrivčáková
Abstract Even though artificial intelligence (AI) is no new occurrence, with its beginnings dating back to the 1950s, its use has gained popularity worldwide, especially in recent years, and its presence and importance has grown in many areas of human lives, including education. Surveys conducted internationally have found generally positive attitudes of university students towards artificial intelligence (AI) and its inclusion into various fields of research and study. However, only few research probes have been conducted among students of philology and future language teachers. No such research has been conducted among university students or pre-service EFL teachers in Central Europe. This paper aims to fill this gap in educational research knowledge, as knowing teachers’ and teacher students’ attitudes towards AI can be a key factor in the success or failure of applying AI in education. Therefore, the aim of the study is to determine the level of knowledge and dominant attitudes towards AI in general, AI in learning/teaching EFL and the inclusion of AI in the teacher training curriculum among pre-service EFL teachers in Slovakia. To collect data from the respondents, a cross-sectional survey in the form of a KAP questionnaire was conducted in November-December 2022. 137 pre-service English language teachers responded to a pre-tested online questionnaire consisting of 19 closed-ended (5-point Likert scale) items and one open-ended question. Slovak EFL pre-service teachers were equally interested (38.67%) and uninterested (39.42%) in the ongoing discussion about AI in education. Overall, they self-reported having no (61.31%) or unsatisfactory (21.17%) understanding of the basic computational principles of AI. On the other hand, they were significantly more satisfied with their knowledge of AI-based applications for EFL teaching, which they considered adequate (35.04%). Nevertheless, almost half of the students (45.25%) rated their knowledge as inadequate. It was therefore encouraging to learn that 64.24% of the respondents agreed that AI education should be included in their university curriculum and had predominantly positive expectations of AI and its application in education. 63.50% of them agreed with the statement that AI will improve education in general (compared to only 18.98% who disagreed). They shared a predominantly positive attitude towards the incorporation of AI into EFL and showed their optimistic expectations regarding the impact of AI on teaching and learning English as a foreign language. Slovak EFL pre-service teachers did not express any concerns about the future of their profession. However, a majority of them (53.28%) agreed that EFL teachers might lose some of their skills when using AI in their practice and a significant number (42.33%) feared that AI would make EFL teaching less personal. These findings are consistent with previous research conducted internationally.

41Integrating AI in Education: An Analysis of Factors Influencing the Acceptance, Concerns, Attitudes, Competencies and Use of Generative Artificial Intelligence Among Polish TeachersOpenAlex

Łukasz Tomczyk, Aleksandra Majkut
This paper presents the results of a survey on the use of artificial intelligence (AI) among teachers in Poland ( N = 289). The survey, conducted in December 2024 and January 2025, used the extended educational technology acceptance model (EETAM) to capture factors supporting and blocking the integration of AI in education (primary and secondary schools). The aim of the study is to identify factors influencing the acceptance and use of AI tools by teachers in their professional work. Based on the EETAM, the relationships between perceived usefulness, ease of use, digital competence, AI‐related concerns, attitudes, intention to use and actual use of AI are analysed. This study is unique in that it fills a local empirical gap (based on EETAM assumptions) on AI usage patterns among teachers. Cluster analysis revealed three groups of teachers: (1) intensive users of AI (13.5%), (2) occasional users (30%) and (3) nonusers (43.5%). The results indicate that the most frequently used functions of AI are information retrieval and text translation, while more advanced applications remain marginally used in educational contexts. In turn, structural modelling confirmed the key role of perceived usefulness and digital competence in shaping intentions to use AI, which in turn have a strong influence on actual use. Perceived ease of use had a moderate impact on usability, while anxiety relating to AI appeared to be of minimal importance. The overall model explained 72% of the variance in intention to use AI and 30% of actual use, highlighting the high effectiveness of the research approach adopted. The results highlight the need to further improve teacher education programmes in the context of AI use, especially with regard to the development of teachers’ digital competences, as well as to strengthen positive attitudes towards AI, also by emphasising the praxeological nature of the solutions analysed.

42What are artificial intelligence literacy and competency? A comprehensive framework to support themOpenAlex

Thomas K. F. Chiu, Zubair Ahmad, Murod Ismailov, et al.
Artificial intelligence (AI) education in K–12 schools is a global initiative, yet planning and executing AI education is challenging. The major frameworks are focused on identifying content and technical knowledge (AI literacy). Most of the current definitions of AI literacy for a non-technical audience are developed from an engineering perspective and may not be appropriate for K–12 education. Teacher perspectives are essential to making sense of this initiative. Literacy is about knowing (knowledge, what skills); competency is about applying the knowledge in a beneficial way (confidence, how well). They are strongly related. This study goes beyond knowledge (AI literacy), and its two main goals are to (i) define AI literacy and competency by adding the aspects of confidence and self-reflective mindsets, and (ii) propose a more comprehensive framework for K–12 AI education. These definitions are needed for this emerging and disruptive technology (e.g., ChatGPT and Sora, generative AI). We used the definitions and the basic curriculum design approaches as the analytical framework and teacher perspectives. Participants included 30 experienced AI teachers from 15 middle schools. We employed an iterative co-design cycle to discuss and revise the framework throughout four cycles. The definition of AI competency has five abilities that take confidence into account, and the proposed framework comprises five key components: technology, impact, ethics, collaboration, and self-reflection. We also identify five effective learning experiences to foster abilities and confidences, and suggest five future research directions: prompt engineering, data literacy, algorithmic literacy, self-reflective mindset, and empirical research.

43Through tensions to identity-based motivations: Exploring teacher professional identity in Artificial Intelligence-enhanced teacher trainingOpenAlex

Yang-Zheng Lan
This mixed-methods case study explores teacher professional identity (TPI) tensions and motivations for Artificial Intelligence (AI) integration in teaching within a Chinese university-level AI-enhanced teacher training programme. Surveys were completed by 216 attendees, followed by in-depth qualitative analysis of 15 selected teachers. The study reveals TPI groupness-individuality, humanity-technology, and continuity-openness tensions. Three conceptual models are introduced: Human Intelligence and AI as Navigator, Collaborator, and Inventor. It underscores the critical role of tailored AI-enhanced teacher training in harmonising educators’ diverse identities and motivations with technological advancements, signalling a strategic approach for effective AI integration in global teacher education. • Explores teacher professional identity (TPI) tensions in Artificial Intelligence (AI)-enhanced training. • Examines TPI tension-based motivations to engage with AI. • Introduces models: Human Intelligence and AI as Navigator, Collaborator, Innovator. • Promotes worldwide navigation of TPI with AI. • Provides practical and global implications tailored to three models.

44Potential Benefits and Risks of Artificial Intelligence in EducationOpenAlex

Mahmut Özer
Artificial Intelligence (AI) technologies are rapidly advancing and causing profound transformations in all aspects of life. In particular, the widespread adoption of generative AI systems like ChatGPT is taking this transformation to even more dramatic dimensions. In this context, the most comprehensive impact is observed in educational systems. Educational systems, on one hand, are faced with the urgent need to rapidly restructure education in response to skill changes in professions caused by the proliferation of such systems in the labor market. On the other hand, challenging questions arise about whether and to what extent these systems should be integrated into education, how they should be integrated if at all, and how ethical issues arising from AI systems can be addressed. This study evaluates the potential benefits and possible risks of using AI systems in educational systems from the perspectives of students, teachers, and education administrators. Therefore, the study discusses the potential uses of AI systems in education, as well as the risks they may pose. Policy recommendations are developed to maximize the benefits of AI systems while mitigating the ethical and other issues they may cause. Additionally, the study emphasizes the importance of increasing AI literacy for all education stakeholders. It suggests that raising awareness of both the benefits and ethical issues caused by AI systems can contribute to enhancing the benefits of these systems in education while minimizing their potential harms.

45What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in educationOpenAlex

Ahmed Tlili, Boulus Shehata, Michael Agyemang Adarkwah, et al.
Abstract Artificial Intelligence (AI) technologies have been progressing constantly and being more visible in different aspects of our lives. One recent phenomenon is ChatGPT, a chatbot with a conversational artificial intelligence interface that was developed by OpenAI. As one of the most advanced artificial intelligence applications, ChatGPT has drawn much public attention across the globe. In this regard, this study examines ChatGPT in education, among early adopters, through a qualitative instrumental case study. Conducted in three stages, the first stage of the study reveals that the public discourse in social media is generally positive and there is enthusiasm regarding its use in educational settings. However, there are also voices who are approaching cautiously using ChatGPT in educational settings. The second stage of the study examines the case of ChatGPT through lenses of educational transformation, response quality, usefulness, personality and emotion, and ethics. In the third and final stage of the study, the investigation of user experiences through ten educational scenarios revealed various issues, including cheating, honesty and truthfulness of ChatGPT, privacy misleading, and manipulation. The findings of this study provide several research directions that should be considered to ensure a safe and responsible adoption of chatbots, specifically ChatGPT, in education.

46Opportunities, challenges and school strategies for integrating generative AI in educationOpenAlex

Davy Tsz Kit Ng, Eagle Kai Chi Chan, Chung Kwan Lo
The increasing accessibility of Generative Artificial Intelligence (GenAI) tools has led to their exploration and adoption in education. This qualitative study investigates the opportunities and challenges associated with integrating GenAI in education, and the strategies that encourage teachers and students to embrace GenAI in school settings. We recruited 76 educators in Canada to participate in a professional training seminar about GenAI and expressed their views through online surveys. Through written reflections, an optimistic outlook on GenAI's role in education was identified among the teachers, and some discipline-specific ideas were proposed. Thematic analysis reveals three key practices of AI implementation: teaching/learning, administration and assessments. However, three major challenges are also identified: school's readiness, teachers' AI competencies, and students' AI literacy and ethics. Teachers suggest several strategies to motivate GenAI integration, including professional development, clear guidelines, and access to AI software and technical support. Finally, Singh's Teach AI Global Initiative Guidance and Socio-ecological Model are adapted and proposed to support schools in becoming AI-ready by addressing teachers' and students' needs, facilitating organizational learning, and promoting improvement and transformation to foster their literacy development. Recommendations were provided for developing effective strategies to embrace GenAI in education.

47From Mapping to Action: SmartRubrics, an AI Tool for Competency-Based Assessment in Engineering EducationOpenAlex

Jorge Hochstetter, Marlene Negrier, Mauricio Diéguez, et al.
Competency-based assessment in engineering education is becoming increasingly critical as the profession faces rapid technological advances and the growing need for cross-cutting competencies. This paper introduces SmartRubrics, an AI-based tool designed to support the automated generation of competency-based assessment rubrics. The development of this tool is based on a systematic literature mapping study conducted between 2019 and 2024, which identified key gaps, such as the limited integration of digital tools and the under-representation of transversal skills in current assessment practices. By addressing these gaps, SmartRubrics aims to support the standardisation, accessibility, and potential enhancement of competency-based assessment practices, aligned with UNESCO’s Sustainable Development Goal 4 (SDG4). Preliminary testing of the prototype with computer science educators has provided valuable information on the effectiveness of the tool and areas for improvement. Future work includes further experimental validation in real educational settings to assess the impact of the tool on teaching and learning practices.

48AI-AFACT: Designing AI-Assisted Formative Assessment of Coding Tasks in Web Development EducationOpenAlex

Franz Knipp, Werner Winiwarter

49Automatic assessment of text-based responses in post-secondary education: A systematic reviewOpenAlex

Rujun Gao, Hillary Merzdorf, Saira Anwar, et al.
Text-based open-ended questions in academic formative and summative assessments help students become deep learners and prepare them to understand concepts for a subsequent conceptual assessment. However, grading text-based questions, especially in large (>50 enrolled students) courses, is tedious and time-consuming for instructors. Text processing models continue progressing with the rapid development of Artificial Intelligence (AI) tools and Natural Language Processing (NLP) algorithms. Especially after breakthroughs in Large Language Models (LLM), there is immense potential to automate rapid assessment and feedback of text-based responses in education. This systematic review adopts a scientific and reproducible literature search strategy based on the PRISMA process using explicit inclusion and exclusion criteria to study text-based automatic assessment systems in post-secondary education, screening 838 papers and synthesizing 93 studies. To understand how text-based automatic assessment systems have been developed and applied in education in recent years, three research questions are considered: 1) What types of automated assessment systems can be identified using input, output, and processing framework? 2) What are the educational focus and research motivations of studies with automated assessment systems? 3) What are the reported research outcomes in automated assessment systems and the next steps for educational applications? All included studies are summarized and categorized according to a proposed comprehensive framework, including the input and output of the system, research motivation, and research outcomes, aiming to answer the research questions accordingly. Additionally, the typical studies of automated assessment systems, research methods, and application domains in these studies are investigated and summarized. This systematic review provides an overview of recent educational applications of text-based assessment systems for understanding the latest AI/NLP developments assisting in text-based assessments in higher education. Findings will particularly benefit researchers and educators incorporating LLMs such as ChatGPT into their educational activities.

50<scp>AI</scp> and formative assessment: The train has left the stationOpenAlex

Xiaoming Zhaı, Ross H. Nehm
Abstract In response to Li, Reigh, He, and Miller's commentary, Can we and should we use artificial intelligence for formative assessment in science , we argue that artificial intelligence (AI) is already being widely employed in formative assessment across various educational contexts. While agreeing with Li et al.'s call for further studies on equity issues related to AI, we emphasize the need for science educators to adapt to the AI revolution that has outpaced the research community. We challenge the somewhat restrictive view of formative assessment presented by Li et al., highlighting the significant contributions of AI in providing formative feedback to students, assisting teachers in assessment practices, and aiding in instructional decisions. We contend that AI‐generated scores should not be equated with the entirety of formative assessment practice; no single assessment tool can capture all aspects of student thinking and backgrounds. We address concerns raised by Li et al. regarding AI bias and emphasize the importance of empirical testing and evidence‐based arguments in referring to bias. We assert that AI‐based formative assessment does not necessarily lead to inequity and can, in fact, contribute to more equitable educational experiences. Furthermore, we discuss how AI can facilitate the diversification of representational modalities in assessment practices and highlight the potential benefits of AI in saving teachers’ time and providing them with valuable assessment information. We call for a shift in perspective, from viewing AI as a problem to be solved to recognizing its potential as a collaborative tool in education. We emphasize the need for future research to focus on the effective integration of AI in classrooms, teacher education, and the development of AI systems that can adapt to diverse teaching and learning contexts. We conclude by underlining the importance of addressing AI bias, understanding its implications, and developing guidelines for best practices in AI‐based formative assessment.

51The AI Assessment Scale (AIAS) in action: A pilot implementation of GenAI-supported assessmentOpenAlex

Leon Furze, Mike Perkins, Jasper Roe, et al.
The rapid adoption of generative artificial intelligence (GenAI) technologies in higher education has raised concerns about academic integrity, assessment practices and student learning. Banning or blocking GenAI tools has proven ineffective, and punitive approaches ignore the potential benefits of these technologies. As a result, assessment reform has become a pressing topic in the GenAI era. This paper presents the findings of a pilot study conducted at British University Vietnam exploring the implementation of the Artificial Intelligence Assessment Scale (AIAS), a flexible framework for incorporating GenAI into educational assessments. The AIAS consists of five levels, ranging from “no AI” to “full AI,” enabling educators to design assessments that focus on areas requiring human input and critical thinking. The pilot study results indicate a significant reduction in academic misconduct cases related to GenAI and enhanced student engagement with GenAI technology. The AIAS facilitated a shift in pedagogical practices, with faculty members incorporating GenAI tools into their modules and students producing innovative multimodal submissions. The findings suggest that the AIAS can support the effective integration of GenAI in higher education, promoting academic integrity while leveraging technology’s potential to enhance learning experiences. Implications for practice or policy: Higher education institutions should adopt flexible frameworks like the AIAS to guide ethical integration of GenAI into assessment practices. Educators should design assessments that leverage GenAI capabilities, while supporting critical thinking and human input. Institutional policies related to GenAI should be developed in consultation with stakeholders and regularly updated to keep pace with technological advancements. Policymakers should prioritise research funding into the impacts of GenAI on higher education to inform evidence-based practices.

52Investigating the higher education institutions’ guidelines and policies regarding the use of generative AI in teaching, learning, research, and administrationOpenAlex

Yunjo An, Ji Hyun Yu, Shadarra James
Abstract This study examined the guidelines issued by the top 50 U.S. universities regarding the use of Generative AI (GenAI) in academic and administrative activities. Employing a mixed methods approach, the research combined topic modeling, sentiment analysis, and qualitative thematic analysis to provide a comprehensive understanding of institutional responses to GenAI. Topic modeling identified four core topics: Integration of GenAI in Learning and Assessment, GenAI in Visual and Multimodal Media, Security and Ethical Considerations in GenAI, and GenAI in Academic Integrity. These themes were further explored through sentiment analysis, which revealed highly positive attitudes towards GenAI across all institution types, with significant differences between faculty and student-targeted guidelines. Qualitative thematic analysis corroborated these findings and provided deeper insights, revealing that 94% of universities had faculty guidelines emphasizing the importance of establishing and communicating course-specific GenAI policies. This analysis also highlighted recurring themes such as academic integrity and privacy concerns, which aligned with the security and ethical considerations identified in the topic modeling. The study highlights the rapid evolution of GenAI guidelines in higher education and the need for flexible, stakeholder-specific policies that address both the opportunities and challenges presented by this technology.

53Building Privacy and Preserving AI Models for Secure Student Data Management in Educational Technology PlatformsOpenAlex

Edwin Ohiorenuan Imohimi
Artificial Intelligence (AI) integrated with educational technology (EdTech) platforms revolutionizes personalized learning through adaptive assessments as well as provides real-time feedback. These innovative educational systems heavily depend on the collection of huge amounts of personal student information which creates acute data protection challenges and algorithmic mainframe problems together with ethical boundaries issues. The widespread application of AI models in digital education necessitates data protection systems which defend students especially underaged students against surveillance programs that could harm their privacy rights. The research investigates how AI development programs intersect with protected data operations in educational software systems through the technologies of differential privacy along with federated learning along with homomorphic encryption. This paper reviews regulatory structures from the US and EU together with worldwide Southern regions while using case studies to illustrate successful and unsuccessful applications. The paper develops an interdisciplinary framework which combines innovative practices with data protection mechanisms according to policy standards and design principles for achieving sustainable AI deployment in worldwide educational structures.

54Ethics of Artificial Intelligence in Education: Student Privacy and Data ProtectionOpenAlex

Lan Huang
Rapid advances in artificial intelligence (AI) technology are profoundly altering human societies and lifestyles. Individuals face a variety of information security threats while enjoying the conveniences and customized services made possible by AI. The widespread use of AI in education has prompted widespread public concern regarding AI ethics in this field. The protection of pupil data privacy is an urgent matter that must be addressed. On the basis of a review of extant interpretations of AI ethics and student data privacy, this article examines the ethical risks posed by AI technology to student personal information and provides recommendations for addressing concerns regarding student data security.

55The ethical implications of using generative chatbots in higher educationOpenAlex

Ryan Williams
Incorporating artificial intelligence (AI) into education, specifically through generative chatbots, can transform teaching and learning for education professionals in both administrative and pedagogical ways. However, the ethical implications of using generative chatbots in education must be carefully considered. Ethical concerns about advanced chatbots have yet to be explored in the education sector. This short article introduces the ethical concerns associated with introducing platforms such as ChatGPT in education. The article outlines how handling sensitive student data by chatbots presents significant privacy challenges, thus requiring adherence to data protection regulations, which may not always be possible. It highlights the risk of algorithmic bias in chatbots, which could perpetuate societal biases, which can be problematic. The article also examines the balance between fostering student autonomy in learning and the potential impact on academic self-efficacy, noting the risk of over-reliance on AI for educational purposes. Plagiarism continues to emerge as a critical ethical concern, with AI-generated content threatening academic integrity. The article advocates for comprehensive measures to address these ethical issues, including clear policies, advanced plagiarism detection techniques, and innovative assessment methods. By addressing these ethical challenges, the article argues that educators, AI developers, policymakers, and students can fully harness the potential of chatbots in education, creating a more inclusive, empowering, and ethically sound educational future.

56Privacy-Preserving Techniques in Generative AI and Large Language Models: A Narrative ReviewOpenAlex

Georgios Feretzakis, Konstantinos Papaspyridis, Aris Gkoulalas-Divanis, et al.
Generative AI, including large language models (LLMs), has transformed the paradigm of data generation and creative content, but this progress raises critical privacy concerns, especially when models are trained on sensitive data. This review provides a comprehensive overview of privacy-preserving techniques aimed at safeguarding data privacy in generative AI, such as differential privacy (DP), federated learning (FL), homomorphic encryption (HE), and secure multi-party computation (SMPC). These techniques mitigate risks like model inversion, data leakage, and membership inference attacks, which are particularly relevant to LLMs. Additionally, the review explores emerging solutions, including privacy-enhancing technologies and post-quantum cryptography, as future directions for enhancing privacy in generative AI systems. Recognizing that achieving absolute privacy is mathematically impossible, the review emphasizes the necessity of aligning technical safeguards with legal and regulatory frameworks to ensure compliance with data protection laws. By discussing the ethical and legal implications of privacy risks in generative AI, the review underscores the need for a balanced approach that considers performance, scalability, and privacy preservation. The findings highlight the need for ongoing research and innovation to develop privacy-preserving techniques that keep pace with the scaling of generative AI, especially in large language models, while adhering to regulatory and ethical standards.

57Anonymization Through Data Synthesis Using Generative Adversarial Networks (ADS-GAN)OpenAlex

Jinsung Yoon, Lydia N. Drumright, Mihaela van der Schaar
The medical and machine learning communities are relying on the promise of artificial intelligence (AI) to transform medicine through enabling more accurate decisions and personalized treatment. However, progress is slow. Legal and ethical issues around unconsented patient data and privacy is one of the limiting factors in data sharing, resulting in a significant barrier in accessing routinely collected electronic health records (EHR) by the machine learning community. We propose a novel framework for generating synthetic data that closely approximates the joint distribution of variables in an original EHR dataset, providing a readily accessible, legally and ethically appropriate solution to support more open data sharing, enabling the development of AI solutions. In order to address issues around lack of clarity in defining sufficient anonymization, we created a quantifiable, mathematical definition for "identifiability". We used a conditional generative adversarial networks (GAN) framework to generate synthetic data while minimize patient identifiability that is defined based on the probability of re-identification given the combination of all data on any individual patient. We compared models fitted to our synthetically generated data to those fitted to the real data across four independent datasets to evaluate similarity in model performance, while assessing the extent to which original observations can be identified from the synthetic data. Our model, ADS-GAN, consistently outperformed state-of-the-art methods, and demonstrated reliability in the joint distributions. We propose that this method could be used to develop datasets that can be made publicly available while considerably lowering the risk of breaching patient confidentiality.

58Exploring ethical dimensions of AI-enhanced language education: A literature perspectiveOpenAlex

Mohamed Sitheeque Peer Mohamed
Advances in artificial intelligence (AI), particularly in generative AI, continue to affect language education paradigms. The integration of AI in language education raises deep-seated ethical concerns such as privacy and data security, potential biases and hidden ideologies in the output, transparency and accountability, dependency and autonomy, digital divide, and job displacement and professional development. The article analyzes these ethical concerns and introduces the multifaceted dimensions of ethics associated with AI in language education. This article comprehensively examines the potential biases of AI in language education. These biases can be algorithmic, demographic, cultural, linguistic, temporal, confirmation, ideological and political. The analysis includes factors contributing to biases, such as training data , labelling and annotation, product design decisions, policy decisions, and algorithms. This paper analyzes algorithmic transparency and advocates for more transparent AI systems to address bias in algorithms. Violations of student privacy emerge as one of the profound ethical issues in the discourse on AI-enhanced language education. The article also examines the challenges and risks associated with the protection of student data privacy, emphasizing the need for robust privacy frameworks to alleviate concerns regarding privacy, human agency and the lack of transparency in the collection of an excessive amount of personal information. By synthesizing the key findings, the paper will conclude with a potential framework of ethical guidelines for the responsible and ethical integration of AI in language education.

59Unveiling the shadows: Beyond the hype of AI in educationOpenAlex

Abdulrahman M. Al-Zahrani
Despite the wave of enthusiasm for the role of Artificial Intelligence (AI) in reshaping education, critical voices urge a more tempered approach. This study investigates the less-discussed 'shadows' of AI implementation in educational settings, focusing on potential negatives that may accompany its integration. Through a multi-phased exploration consisting of content analysis and survey research, the study develops and validates a theoretical model that pinpoints several areas of concern. The initial phase, a systematic literature review, yielded 56 relevant studies from which the model was crafted. The subsequent survey with 260 participants from a Saudi Arabian university aimed to validate the model. Findings confirm concerns about human connection, data privacy and security, algorithmic bias, transparency, critical thinking, access equity, ethical issues, teacher development, reliability, and the consequences of AI-generated content. They also highlight correlations between various AI-associated concerns, suggesting intertwined consequences rather than isolated issues. For instance, enhancements in AI transparency could simultaneously support teacher professional development and foster better student outcomes. Furthermore, the study acknowledges the transformative potential of AI but cautions against its unexamined adoption in education. It advocates for comprehensive strategies to maintain human connections, ensure data privacy and security, mitigate biases, enhance system transparency, foster creativity, reduce access disparities, emphasize ethics, prepare teachers, ensure system reliability, and regulate AI-generated content. Such strategies underscore the need for holistic policymaking to leverage AI's benefits while safeguarding against its disadvantages.

60An Introduction to Generative AI Tools for Education 2030OpenAlex

Ramandeep Sandhu, Harpreet Kaur Channi, Deepika Ghai, et al.
The year 2030 marks a significant juncture in the evolution of education, where Generative Artificial Intelligence (AI) tools are poised to revolutionize the learning experience. In education society, the importance of generative AI is to improve the accessibility of learning at the global level so that personalized learning experiences can be provided to every learner as per their needs. This chapter explores the multifaceted role of generative AI tools in reshaping educational practices, envisioning a future where these tools foster personalized, adaptive, and engaging learning environments. Generative AI tools, characterized by their ability to create and adapt content autonomously, are instrumental in tailoring educational materials to individual learner needs. This chapter surveys the landscape of generative AI applications in education, including content generation, interactive simulations, intelligent tutoring systems, and dynamic learning pathways. These tools aim to provide adaptive, context-aware learning experiences that cater to diverse learning styles and preferences. The adaptability of generative AI tools extends to the creation of personalized learning pathways. By leveraging data analytics and machine learning algorithms, these tools dynamically adjust content delivery, pacing, and complexity, ensuring that each learner's educational journey is optimized for their unique requirements. The discussion encompasses the potential of generative AI tools to support both formal and informal learning settings. Generative AI tools also play a crucial role in promoting inclusivity in education. By generating diverse and culturally relevant content, these tools contribute to breaking down barriers and addressing disparities in access to quality education. This chapter explores how generative AI can be leveraged to create content that resonates with learners from different backgrounds, fostering a more inclusive educational landscape.

61Generative AI and Educational (In)EquityOpenAlex

Sonja Gabriel
This paper examines the complex relationship between generative artificial intelligence (AI) and educational equity, analysing both the opportunities and challenges presented by these emerging technologies in educational contexts. The paper begins by establishing fundamental distinctions between educational equality and equity, emphasizing how various socioeconomic, cultural, and systemic factors contribute to persistent educational disparities. It then provides a comprehensive overview of generative AI technologies, particularly focusing on Large Language Models (LLMs) and their applications in educational settings. The analysis reveals several promising applications of generative AI for promoting educational equity, including enhanced accessibility features for students with disabilities, personalized learning experiences, and the creation of Open Educational Resources (OER). The paper highlights how AI-assisted tutoring, incorporating Socratic dialogue methods, and AI-generated feedback systems can provide valuable educational support, especially in resource-constrained environments. These technologies demonstrate potential in breaking down traditional barriers to education by offering multilingual support, adaptive learning materials, and immediate feedback mechanisms. However, the paper also addresses significant challenges and risks associated with implementing generative AI in education. These include concerns about digital divides, both in terms of access to technology and digital literacy skills, as well as the potential for AI systems to perpetuate existing biases. The research emphasizes the importance of thoughtful integration of AI technologies in educational settings, suggesting that the most effective approach may be a balanced combination of human instruction and AI-supported learning. By examining these various aspects, the paper contributes to ongoing discussions about how to harness generative AI's potential while ensuring its implementation promotes, rather than hinders, educational equity. The findings have significant implications for educators, policymakers, and educational institutions working to create more equitable learning environments in an increasingly technology-driven world.

62ADVANCING EDUCATIONAL EQUITY: THE ROLE OF GENERATIVE AI IN CREATING ACCESSIBLE AND INCLUSIVE HIGHER EDUCATIONOpenAlex

Gaurav Krishan Ohri
This article explores the transformative role of generative AI in creating more accessible and inclusive higher education environments.It examines how AI technologies revolutionize educational accessibility through advanced language support, personalized learning systems, and comprehensive mental health services.The article investigates the implementation of AI-powered solutions across various domains, including communication tools, assessment methodologies, inclusive design practices, and student support systems.The article demonstrates how these technologies are breaking down traditional educational barriers, particularly for students with disabilities, non-native speakers, and those from underserved populations.Through

63Enhancing inclusive education in the UAE: Integrating AI for diverse learning needs.PubMed

Alia El Naggar, Eman Gaad, Shannaiah Aubrey Mae Inocencio
Res Dev Disabil. 2024 Apr;147:104685. doi: 10.1016/j.ridd.2024.104685. Epub 2024 Feb 8.
INTRODUCTION: Artificial Intelligence (AI) mediated systems have become important in educational set-ups, and it is still debatable whether or not they can be useful in special needs education. This qualitative research scrutinizes the experiences and perceptions of exceptional learners, as an example of special needs education, engaged in AI-mediated discussions versus traditional classroom dialogues. The study aims to reveal how these learners process and construct knowledge differently when AI is incorporated into their discussions and how it compares to conventional learning environments. METHODS: The methodology entailed a detailed qualitative analysis, drawing upon cognitive psychology to assess how exceptional learners process information and engage in higher-order thinking. Data were collected through interviews, observation, and content analysis of AI-mediated discussions. RESULTS: FINDINGS: from the study highlighted the capacity of AI technologies to offer personalized and intellectually stimulating educational experiences that resonate with constructivist approaches, promoting active learning and tailored instruction for exceptional learners. However, the research also brought to light certain challenges, including the tendency for confirmation bias and the risk of information overload within AI-mediated environments, which can complicate the learning trajectory within the zone of proximal development. DISCUSSIONS: The study underscores the dynamic interplay between AI technologies and educational processes for exceptional learners. It suggests that while AI can enhance personalized learning, it also introduces unique challenges that must be navigated carefully. Ultimately, this research lays a theoretical and empirical groundwork for the thoughtful integration of AI in supporting inclusive education, emphasizing the importance of continuous evaluation and adaptation.

64Critical analysis of the technological affordances, challenges and future directions of Generative AI in education: a systematic reviewOpenAlex

Nan Wang, Xiao Wang, Yu-Sheng Su
Generative artificial intelligence has been regarded as a transformative tool. While responsible and ethical applications could bring opportunities to education, their misuse could pose demanding challenges. It is necessary to clarify the technological affordances and challenges in a normative way to lay the foundation for future development. This study addressed the dearth of literature by performing a systematic review, aiming to (i) explore the utility and availability from the technological affordances perspective; (ii) summarize the current challenges in risks prevention; and (iii) propose possible directions for future research and practice. A total of 27 academic articles published in core journals between 2020 and 2023 were analyzed, and the inductive grounded approach was used to categorize the coding schemes. The findings revealed four technological affordances: accessibility, personalization, automation, and interactivity; and five challenges: academic integrity risk, response errors and bias, over-dependence risk, the widening digital divide, and privacy and security. We propose future directions, encourage educational organizations to formulate guidelines for the ethical use of AI in education, call on educators to embrace future trends in AI education instead of shunning its use, and guide students to treat it as a thought aid and reference, rather than relying on it entirely.

65Measuring the Impact of AI in the Diagnosis of Hospitalized Patients: A Randomized Clinical Vignette Survey Study.PubMed

Sarah Jabbour, David Fouhey, Stephanie Shepard, et al.
JAMA. 2023 Dec 19;330(23):2275-2284. doi: 10.1001/jama.2023.22295.
IMPORTANCE: Artificial intelligence (AI) could support clinicians when diagnosing hospitalized patients; however, systematic bias in AI models could worsen clinician diagnostic accuracy. Recent regulatory guidance has called for AI models to include explanations to mitigate errors made by models, but the effectiveness of this strategy has not been established. OBJECTIVES: To evaluate the impact of systematically biased AI on clinician diagnostic accuracy and to determine if image-based AI model explanations can mitigate model errors. DESIGN, SETTING, AND PARTICIPANTS: Randomized clinical vignette survey study administered between April 2022 and January 2023 across 13 US states involving hospitalist physicians, nurse practitioners, and physician assistants. INTERVENTIONS: Clinicians were shown 9 clinical vignettes of patients hospitalized with acute respiratory failure, including their presenting symptoms, physical examination, laboratory results, and chest radiographs. Clinicians were then asked to determine the likelihood of pneumonia, heart failure, or chronic obstructive pulmonary disease as the underlying cause(s) of each patient's acute respiratory failure. To establish baseline diagnostic accuracy, clinicians were shown 2 vignettes without AI model input. Clinicians were then randomized to see 6 vignettes with AI model input with or without AI model explanations. Among these 6 vignettes, 3 vignettes included standard-model predictions, and 3 vignettes included systematically biased model predictions. MAIN OUTCOMES AND MEASURES: Clinician diagnostic accuracy for pneumonia, heart failure, and chronic obstructive pulmonary disease. RESULTS: Median participant age was 34 years (IQR, 31-39) and 241 (57.7%) were female. Four hundred fifty-seven clinicians were randomized and completed at least 1 vignette, with 231 randomized to AI model predictions without explanations, and 226 randomized to AI model predictions with explanations. Clinicians' baseline diagnostic accuracy was 73.0% (95% CI, 68.3% to 77.8%) for the 3 diagnoses. When shown a standard AI model without explanations, clinician accuracy increased over baseline by 2.9 percentage points (95% CI, 0.5 to 5.2) and by 4.4 percentage points (95% CI, 2.0 to 6.9) when clinicians were also shown AI model explanations. Systematically biased AI model predictions decreased clinician accuracy by 11.3 percentage points (95% CI, 7.2 to 15.5) compared with baseline and providing biased AI model predictions with explanations decreased clinician accuracy by 9.1 percentage points (95% CI, 4.9 to 13.2) compared with baseline, representing a nonsignificant improvement of 2.3 percentage points (95% CI, -2.7 to 7.2) compared with the systematically biased AI model. CONCLUSIONS AND RELEVANCE: Although standard AI models improve diagnostic accuracy, systematically biased AI models reduced diagnostic accuracy, and commonly used image-based AI model explanations did not mitigate this harmful effect. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT06098950.

66Integrating Generative AI Into K-12 Curriculums and Pedagogies in IndiaOpenAlex

Durgesh M. Sharma, K. Venkata Ramana, R. Jothilakshmi, et al.
Generative artificial intelligence (AI) can revolutionize K-12 education in India by enhancing curriculums and pedagogies. This chapter explores the principles of generative AI, including generative adversarial networks (GANs), variational autoencoders (VAEs), and natural language generation (NLG), and its potential to create content indistinguishable from human-generated materials. Generative AI addresses challenges like resource scarcity, linguistic diversity, and personalized learning experiences, while emphasizing ethical considerations, data privacy, and bridging the digital divide. The future of K-12 education in India will see personalized learning journeys, inclusivity, and empowered educators. A comprehensive policy framework, infrastructure support, and teacher training are needed to realize the full potential of generative AI in shaping the educational landscape.

67Experiences of a Community-Based Digital Intervention Among Older People Living in a Low-Income Neighborhood: Qualitative Study.PubMed

Si Yinn Lu, Sungwon Yoon, Wan Qi Yee, et al.
JMIR Aging. 2024 Apr 25;7:e52292. doi: 10.2196/52292.
BACKGROUND: Older adults worldwide experienced heightened risks of depression, anxiety, loneliness, and poor mental well-being during the COVID-19 pandemic. During this period, digital technology emerged as a means to mitigate social isolation and enhance social connectedness among older adults. However, older adults' behaviors and attitudes toward the adoption and use of digital technology are heterogeneous and shaped by factors such as age, income, and education. Few empirical studies have examined how older adults experiencing social and economic disadvantages perceive the learning of digital tools. OBJECTIVE: This study aims to examine the motivations, experiences, and perceptions toward a community-based digital intervention among older adults residing in public rental flats in a low-income neighborhood. Specifically, we explored how their attitudes and behaviors toward learning the use of smartphones are shaped by their experiences related to age and socioeconomic challenges. METHODS: This study adopted a qualitative methodology. Between December 2020 and March 2021, we conducted semistructured in-depth interviews with 19 participants aged ≥60 years who had completed the community-based digital intervention. We asked participants questions about the challenges encountered amid the pandemic, their perceived benefits of and difficulties with smartphone use, and their experiences with participating in the intervention. All interviews were audio recorded and analyzed using a reflexive thematic approach. RESULTS: Although older learners stated varying levels of motivation to learn, most expressed ambivalence about the perceived utility and relevance of the smartphone to their current needs and priorities. While participants valued the social interaction with volunteers and the personalized learning model of the digital intervention, they also articulated barriers such as age-related cognitive and physical limitations and language and illiteracy that hindered their sustained use of these digital devices. Most importantly, the internalization of ageist stereotypes of being less worthy learners and the perception of smartphone use as being in the realm of the privileged other further reduced self-efficacy and interest in learning. CONCLUSIONS: To improve learning and sustained use of smartphones for older adults with low income, it is essential to explore avenues that render digital tools pertinent to their daily lives, such as creating opportunities for social connections and relationship building. Future studies should investigate the relationships between older adults' social, economic, and health marginality and their ability to access digital technologies. We recommend that the design and implementation of digital interventions should prioritize catering to the needs and preferences of various segments of older adults, while working to bridge rather than perpetuate the digital divide.

68Strategies for Integrating Generative AI into Higher Education: Navigating Challenges and Leveraging OpportunitiesOpenAlex

Gila Kurtz, Meital Amzalag, Nava Shaked, et al.
The recent emergence of generative AI (GenAI) tools such as ChatGPT, Midjourney, and Gemini have introduced revolutionary capabilities that are predicted to transform numerous facets of society fundamentally. In higher education (HE), the advent of GenAI presents a pivotal moment that may profoundly alter learning and teaching practices in aspects such as inaccuracy, bias, overreliance on technology and algorithms, and limited access to educational AI resources that require in-depth investigation. To evaluate the implications of adopting GenAI in HE, a team of academics and field experts have co-authored this paper, which analyzes the potential for the responsible integration of GenAI into HE and provides recommendations about this integration. This paper recommends strategies for integrating GenAI into HE to create the following positive outcomes: raise awareness about disruptive change, train faculty, change teaching and assessment practices, partner with students, impart AI learning literacies, bridge the digital divide, and conduct applied research. Finally, we propose four preliminary scale levels of a GenAI adoption for faculty. At each level, we suggest courses of action to facilitate progress to the next stage in the adoption of GenAI. This study offers a valuable set of recommendations to decision-makers and faculty, enabling them to prepare for the responsible and judicious integration of GenAI into HE.

69Higher education crisis: Academic misconduct with generative AIOpenAlex

NaYoung Song
Abstract Higher educational institutions (HEIs) are facing a significant challenge in maintaining academic integrity due to the technological integration of generative artificial intelligence (AI). The widespread use of AI tools by college students has resulted in an increase in plagiarism and cheating, highlighting the need for effective implementation of this technology. However, there is a lack of research on the best practices for using AI in academic settings. HEIs must take responsibility for addressing these issues, as the majority of institutions do not have formal guidelines for AI use, leading to confusion among students and instructors. To combat academic misconduct, HEIs should establish clear objectives and policies for the equitable, inclusive, and ethical use of AI. Improving AI literacy among students and faculty is crucial, as it ensures that everyone has equal access to technology, preventing a digital divide. Moreover, proactive education on the ethical use of AI is vital for HEIs to prepare students for the AI‐driven future of education and maintain academic integrity.

70Generative-AI, a Learning Assistant? Factors Influencing Higher-Ed Students' Technology AcceptanceOpenAlex

Kraisila Kanont, Pawarit Pingmuang, Thewawuth Simasathien, et al.
This study investigates the factors influencing the adoption of Generative-AI tools amongst Thai university students, employing the Technology Acceptance Model (TAM) as a theoretical framework. Data from 911 higher education students from 10 different Thai Universities Health Sciences, Sciences and Technology, Social Sciences and Humanities, and Vocational Fields were analysed via Structural Equation Modelling (SEM). The instrument used in collecting the data was a questionnaire. Results indicated that Expected Benefits, Perceived Usefulness, Attitude Toward Technology, and Behavioural Intention all significantly impacted student adoption of Generative AI. Intriguingly, Perceived Ease of Use was negatively correlated with Perceived Usefulness, challenging conventional TAM assumptions. This study underscores the need to address language barriers, foster a culture of innovation, and establish ethical guidelines to promote responsible AI use within education. Despite inherent limitations, this research contributes to our understanding of AI adoption in educational settings and helps inform strategies for equitable access and responsible innovation. The result demonstrated that the easier a tool was to use, the less value leaners seemed to see in it for their learning process. It can be implied that as Generative-AI get more intuitive, learners think they're less helpful. These finding challenges a few of those assumptions we usually make within the TAM model. It also points out the characteristic of learners which affects their learning preferences and expectation. Another finding showed the impact of language barrier on non-native English speaker that obstruct the user experience in AI services. Moreover, the role of universities in fostering both AI integration for learning for and the ethical implementation of Generative AI. By providing a supportive environment that encourages AI experimentation, redesign learning, empowering learners and faculty instructors to investigate how Generative AI can be applied across disciplines, and developing guidelines for ethical use, universities play a critical role in shaping the effective and responsible integration of AI into the next educational landscape.

71The Manifesto for Teaching and Learning in a Time of Generative AI: A Critical Collective Stance to Better Navigate the FutureOpenAlex

Aras Bozkurt, Junhong Xiao, Robert Farrow, et al.
This manifesto critically examines the unfolding integration of Generative AI (GenAI), chatbots, and algorithms into higher education, using a collective and thoughtful approach to navigate the future of teaching and learning. GenAI, while celebrated for its potential to personalize learning, enhance efficiency, and expand educational accessibility, is far from a neutral tool. Algorithms now shape human interaction, communication, and content creation, raising profound questions about human agency and biases and values embedded in their designs. As GenAI continues to evolve, we face critical challenges in maintaining human oversight, safeguarding equity, and facilitating meaningful, authentic learning experiences. This manifesto emphasizes that GenAI is not ideologically and culturally neutral. Instead, it reflects worldviews that can reinforce existing biases and marginalize diverse voices. Furthermore, as the use of GenAI reshapes education, it risks eroding essential human elements&mdash;creativity, critical thinking, and empathy&mdash;and could displace meaningful human interactions with algorithmic solutions. This manifesto calls for robust, evidence-based research and conscious decision-making to ensure that GenAI enhances, rather than diminishes, human agency and ethical responsibility in education.

72Higher Education’s Generative Artificial Intelligence Paradox: The Meaning of Chatbot ManiaOpenAlex

Juergen Rudolph, Fadhil Mohamed Mohamed Ismail, Ştefan Popenici
Higher education is currently under a significant transformation due to the emergence of generative artificial intelligence (GenAI) technologies, the hype surrounding GenAI and the increasing influence of educational technology business groups over tertiary education. This commentary, prepared for the Special Issue of the Journal of University Teaching &amp; Learning Practice (JUTLP) on “Enhancing student engagement using Artificial Intelligence (AI) and chatbots,” delves into the complex landscape of opportunities and threats that AI chatbots, including ChatGPT, introduce to the realm of higher education. We argue that while GenAI offers promise in enhancing pedagogy, research, administration, and student support, concerns around academic integrity, labour displacement, embedded biases, environmental sustainability, increased commercialisation, and regulatory gaps necessitate a critical approach. Our commentary advocates for the development of critical AI literacy among educators and students, emphasising the necessity to foster an environment of responsible innovation and informed use of AI. We posit that the successful integration of AI in higher education must be grounded in the principles of ethics, equity, and the prioritisation of educational aims and human values. By offering a critical and nuanced exploration of these issues, our commentary aims to contribute to the ongoing discourse on how higher education institutions can navigate the rise of GenAI, ensuring that technological advancements benefit all stakeholders while upholding core academic values.

73Generative AI in Higher Education: Balancing Innovation and IntegrityOpenAlex

Nigel Francis, Sue Jones, David P. Smith
Generative Artificial Intelligence (GenAI) is rapidly transforming the landscape of higher education, offering novel opportunities for personalised learning and innovative assessment methods. This paper explores the dual-edged nature of GenAI's integration into educational practices, focusing on both its potential to enhance student engagement and learning outcomes and the significant challenges it poses to academic integrity and equity. Through a comprehensive review of current literature, we examine the implications of GenAI on assessment practices, highlighting the need for robust ethical frameworks to guide its use. Our analysis is framed within pedagogical theories, including social constructivism and competency-based learning, highlighting the importance of balancing human expertise and AI capabilities. We also address broader ethical concerns associated with GenAI, such as the risks of bias, the digital divide, and the environmental impact of AI technologies. This paper argues that while GenAI can provide substantial benefits in terms of automation and efficiency, its integration must be managed with care to avoid undermining the authenticity of student work and exacerbating existing inequalities. Finally, we propose a set of recommendations for educational institutions, including developing GenAI literacy programmes, revising assessment designs to incorporate critical thinking and creativity, and establishing transparent policies that ensure fairness and accountability in GenAI use. By fostering a responsible approach to GenAI, higher education can harness its potential while safeguarding the core values of academic integrity and inclusive education.

74Advancing SDG 4: Harnessing Generative AI to Transform Learning, Teaching, and Educational Equity in Higher EducationOpenAlex

Vengalarao Pachava, Olusiji Adebola Lasekan, Claudia Myrna Méndez-Alarcón, et al.
Objective: The objective of this study is to investigate the transformative potential of generative AI in advancing Sustainable Development Goal 4 (SDG 4), with the aim of enhancing equity, accessibility, and quality in higher education through the integration of AI-driven systems and practices. Theoretical Framework: This research is underpinned by the AI Academic Convergence (AIAC) Framework, which aligns with theories such as constructivism, Vygotsky’s cultural-historical theory, and Bloom’s Taxonomy. These frameworks provide a solid basis for understanding the interplay between personalized learning, cognitive engagement, stakeholder collaboration, and ethical governance in educational ecosystems. Method: The methodology adopted for this research comprises a Literature-Driven Conceptual Framework Development approach, synthesizing peer-reviewed studies across key themes: personalized learning, operational efficiency, collaborative learning, and ethical AI governance. Data collection involved systematic literature reviews of scholarly articles, books, and conference proceedings within the past decade. Results and Discussion: The results reveal that the AIAC Framework promotes tailored, adaptive learning pathways, enhances faculty roles as AI-enabled mentors, and optimizes administrative workflows through predictive analytics. The discussion contextualizes these findings within existing theories, emphasizing the framework's ability to mitigate challenges such as algorithmic bias, equity gaps, and data privacy concerns. Limitations include the need for empirical validation and addressing resource disparities in underprivileged contexts. Research Implications: The practical and theoretical implications of this research are significant for higher education institutions, policymakers, and AI practitioners. These include fostering innovative teaching practices, advancing equitable access to AI-enhanced tools, and aligning educational strategies with labor market demands through predictive analytics and collaborative governance. Originality/Value: This study contributes to the literature by introducing the AIAC Framework, an innovative and scalable model for integrating generative AI into education. Its value lies in bridging the digital divide, fostering lifelong learning, and positioning higher education institutions as leaders in ethical and sustainable AI integration, ultimately advancing the mission of SDG 4.

75The Role of AI in Nursing Education and Practice: Umbrella Review.PubMed

Rabie Adel El Arab, Omayma Abdulaziz Al Moosa, Fuad H Abuadas, et al.
J Med Internet Res. 2025 Apr 4;27:e69881. doi: 10.2196/69881.
BACKGROUND: Artificial intelligence (AI) is rapidly transforming health care, offering substantial advancements in patient care, clinical workflows, and nursing education. OBJECTIVE: This umbrella review aims to evaluate the integration of AI into nursing practice and education, with a focus on ethical and social implications, and to propose evidence-based recommendations to support the responsible and effective adoption of AI technologies in nursing. METHODS: We included systematic reviews, scoping reviews, rapid reviews, narrative reviews, literature reviews, and meta-analyses focusing on AI integration in nursing, published up to October 2024. A new search was conducted in January 2025 to identify any potentially eligible reviews published thereafter. However, no new reviews were found. Eligibility was guided by the Sample, Phenomenon of Interest, Design, Evaluation, Research type framework; databases (PubMed or MEDLINE, CINAHL, Web of Science, Embase, and IEEE Xplore) were searched using comprehensive keywords. Two reviewers independently screened records and extracted data. Risk of bias was assessed with Risk of Bias in Systematic Reviews (ROBIS) and A Measurement Tool to Assess Systematic Reviews, version 2 (AMSTAR 2), which we adapted for systematic and nonsystematic review types. A thematic synthesis approach, conducted independently by 2 reviewers, identified recurring patterns across the included reviews. RESULTS: The search strategy yielded 18 eligible studies after screening 274 records. These studies encompassed diverse methodologies and focused on nursing professionals, students, educators, and researchers. First, ethical and social implications were consistently highlighted, with studies emphasizing concerns about data privacy, algorithmic bias, transparency, accountability, and the necessity for equitable access to AI technologies. Second, the transformation of nursing education emerged as a critical area, with an urgent need to update curricula by integrating AI-driven educational tools and fostering both technical competencies and ethical decision-making skills among nursing students and professionals. Third, strategies for integration were identified as essential for effective implementation, calling for scalable models, robust ethical frameworks, and interdisciplinary collaboration, while also addressing key barriers such as resistance to AI adoption, lack of standardized AI education, and disparities in technology access. CONCLUSIONS: AI holds substantial promises for revolutionizing nursing practice and education. However, realizing this potential necessitates a strategic approach that addresses ethical concerns, integrates AI literacy into nursing curricula, and ensures equitable access to AI technologies. Limitations of this review include the heterogeneity of included studies and potential publication bias. Our findings underscore the need for comprehensive ethical frameworks and regulatory guidelines tailored to nursing applications, updated nursing curricula to include AI literacy and ethical training, and investments in infrastructure to promote equitable AI access. Future research should focus on developing standardized implementation strategies and evaluating the long-term impacts of AI integration on nursing practice and patient outcomes.

76From recorded to <scp>AI</scp> ‐generated instructional videos: A comparison of learning performance and experienceOpenAlex

Tao Xu, Yuan Liu, Yaru Jin, et al.
Abstract Generative AI (GAI) and AI‐generated content (AIGC) have been increasingly involved in our work and daily life, providing a new learning experience for students. This study examines whether AI‐generated instructional videos (AIIV) can facilitate learning as effectively as traditional recorded videos (RV). We propose an instructional video generation pipeline that includes customized GPT (Generative Pre‐trained Transformer), text‐to‐speech and lip synthesis techniques to generate videos from slides and a clip or a photo of a human instructor. Seventy‐six students were randomly assigned to learn English words using either AIIV or RV, with performance assessed by retention, transfer and subjective measures from cognitive, emotional, motivational and social perspectives. The findings indicate that the AIIV group performed as well as the RV group in facilitating learning, with AIIV showing higher retention but no significant differences in transfer. RV was found to offer a stronger sense of social presence. Although other subjective measures were similar between the two groups, AIIV was perceived as slightly less favourable. However, the AIIV was still found to be moderately to highly attractive, addressing concerns related to the uncanny valley effect. This research demonstrates that AIGC can be an effective tool for learning, offering valuable implications for the use of GAI in educational settings. Practitioner notes What is already known about this topic Instructional videos, especially those featuring a teacher's presence, have been widely used in second language learning to facilitate learning. Producing instructional videos is costly and burdensome. Generative AI has great potential for generating educational content. What this paper adds An AI‐generated instructional video (including generated lecture text, voice and appearance) demonstrated greater improvement in students' retention performance in English word learning than a traditional recorded video. Students perceived no significant differences between the AI‐generated instructional video and recorded video in satisfaction, motivation, trust, cognitive load, emotions and parasocial interaction dimensions, although the AI‐generated instructional video group reported slightly lower values. Despite AI‐generated instructional video eliciting a significantly lower value of social presence than recorded video, it led to a reduction in cognitive load and better performance. Implications for practice and/or policy We recommend using the AI‐generated instructional video in both physical and online classes for its positive effects on both learning achievement and learning experience. The findings indicate the equivalence principle in AI‐generated content, highlighting that the appearance, voice and lecture text generated by current AI technology have reached a certain level of quality.

77Generative AI in K12: Analytics From Early AdoptionOpenAlex

Brad Bolender, Sara Vispoel, Geoffrey Converse, et al.
The integration of generative AI in K12 education and assessment development holds the potential to revolutionize instructional practices, assessment development, and content alignment. This article presents analytical insights and findings from early adoption studies utilizing AI-powered tools developed by Finetune—Generate and Catalog. Generate enhances the efficiency of assessment item development through customized natural language generation, producing high-quality, psychometrically valid items. Catalog intelligently tags and aligns educational content to various standards and frameworks, improving precision and reducing subjectivity. Through three comprehensive case studies, we explore the practical applications, benefits, and lessons learned from employing these AI systems in real-world educational settings. The purpose of this series of studies was to investigate the ways generative AI is currently being used in practical applications in test development to improve processes and products. The studies demonstrate significant reductions in time and costs, enhanced accuracy, and consistency in content alignment, and improved quality of educational and assessment materials. The findings underscore the substantial benefits and critical importance of customized AI systems, rigorous training for both AI models and users, and adopting appropriate evaluation metrics. With the use of off-the-shelf generative AI models expanding rapidly, it is vital that the effectiveness of AI systems that are highly customized through collaborations with measurement experts be presented, in order to maximize benefits and uphold the fundamental principles and best practices of test development.

78Effect of Artificial Intelligence Convergence Education Using ChatGPT on Computational Thinking of High School Students in KoreaOpenAlex

Seung-Ju Hong, Youngjun Lee, Seong-Won Kim
Artificial intelligence (AI) has been integrated into various fields, accelerating innovation in existing ones. Consequently, AI has been employed in education to drive changes such as learning analytics and personalized learning. Recently, the development of generative AI has further transformed the educational landscape. Given the increasing use of generative AI in education, this study was conducted to explore its educational applications. We developed an educational program incorporating generative AI based on design thinking principles for high school students. To verify its effectiveness, high school students in Korea were selected as research subjects and divided into an experimental group (n=53) and a control group (n=42). A test tool aligned with the 2022 revised curriculum in Korea measured computational thinking skills. The study results showed that the group receiving AI convergence education using generative AI significantly improved their computational thinking. The improvement in computational thinking was also significant compared to the control group, providing strong evidence of the benefits of AI in education. This study confirmed that AI convergence education, utilizing generative AI grounded in design thinking, effectively develops computational thinking skills in high school students. The findings of this research highlight the potential educational value of integrating generative AI into both AI design thinking and convergence education. The study provides reassurance that generative AI can be a powerful tool in enhancing student learning experience and outcomes, paving the way for future educational innovations.

79Advantages and Constraints of a Hybrid Model K-12 E-Learning Assistant ChatbotOpenAlex

Eric Hsiao‐Kuang Wu, Chun-Han Lin, Yu‐Yen Ou, et al.
E-Learning has become more and more popular in recent years with the advance of new technologies. Using their mobile devices, people can expand their knowledge anytime and anywhere. E-Learning also makes it possible for people to manage their learning progression freely and follow their own learning style. However, studies show that E-Learning can cause the user to experience feelings of isolation and detachment due to the lack of human-like interactions in most E-Learning platforms. These feelings could reduce the user's motivation to learn. In this paper, we explore and evaluate how well current chatbot technologies assist users' learning on E-Learning platforms and how these technologies could possibly reduce problems such as feelings of isolation and detachment. For evaluation, we specifically designed a chatbot to be an E-Learning assistant. The NLP core of our chatbot is based on two different models: a retrieval-based model and a QANet model. We designed this two-model hybrid chatbot to be used alongside an E-Learning platform. The core response context of our chatbot is not only designed with course materials in mind but also everyday conversation and chitchat, which make it feel more like a human companion. Experiment and questionnaire evaluation results show that chatbots could be helpful in learning and could potentially reduce E-Learning users' feelings of isolation and detachment. Our chatbot also performed better than the teacher counselling service in the E-Learning platform on which the chatbot is based.

80Artificial Intelligence Supporting Independent Student Learning: An Evaluative Case Study of ChatGPT and Learning to CodeOpenAlex

Kendall Hartley, ‪Merav Hayak‬‏, Un Hyeok Ko
Artificial intelligence (AI) tools like ChatGPT demonstrate the potential to support personalized and adaptive learning experiences. This study explores how ChatGPT can facilitate self-regulated learning processes and learning computer programming. An evaluative case study design guided the investigation of ChatGPT’s capabilities to aid independent learning. Prompts mapped to self-regulated learning processes elicited ChatGPT’s support across learning tools: instructional materials, content tools, assessments, and planning. Overall, ChatGPT provided comprehensive, tailored guidance on programming concepts and practices. It consolidated multimodal information sources into integrated explanations with examples. ChatGPT also effectively assisted planning by generating detailed schedules. However, its interactivity and assessment functionality demonstrated shortcomings. ChatGPT’s effectiveness relies on learners’ metacognitive skills to seek help and assess its limitations. The implications include ChatGPT’s potential to provide Bloom’s two-sigma tutoring benefit at scale.

81Towards high-quality informatics K-12 education in Europe: key insights from the literatureOpenAlex

Demetrios G. Sampson, Panagiotis Kampylis, Jesús Moreno-León, et al.
Abstract This paper explores the evolving landscape of informatics education in European primary and secondary schools, analysing academic and grey literature to define the state of play and open questions related to ‘high-quality informatics education’. It underlines the strategic importance of promoting high-quality informatics education to prepare students for life and work in the digital era, contributing to European societies and economies’ social and economic resilience. Drawing on a review of over 180 recent academic publications, policy documents, and grey literature, it provides an overview of how informatics education is being implemented across Europe and beyond, highlighting recent curricular developments, pedagogical practices, and policy initiatives. The paper also identifies and analyses key open issues related to high-quality informatics education, organised into four clusters: student-related (e.g., equity and inclusion), teacher-related (e.g., professional development, shortage of qualified teachers), school-related (e.g., the need for whole-school approach) and curriculum- and resource-related (e.g., competing curriculum priorities, quality of teaching and learning materials). Finally, the paper offers recommendations for policymakers, researchers, and practitioners (school leaders and educators) related to the key open issues of high-quality K-12 informatics education. Overall, the paper contributes to the discussion on high-quality informatics K-12 education in Europe towards identifying and addressing major challenges for equitable access to quality informatics education for all European K-12 students.

82AI literacy in K-12: a systematic literature reviewOpenAlex

Lorena Casal Otero, Alejandro Catalá, Carmen Fernández-Morante, et al.
Abstract The successful irruption of AI-based technology in our daily lives has led to a growing educational, social, and political interest in training citizens in AI. Education systems now need to train students at the K-12 level to live in a society where they must interact with AI. Thus, AI literacy is a pedagogical and cognitive challenge at the K-12 level. This study aimed to understand how AI is being integrated into K-12 education worldwide. We conducted a search process following the systematic literature review method using Scopus. 179 documents were reviewed, and two broad groups of AI literacy approaches were identified, namely learning experience and theoretical perspective. The first group covered experiences in learning technical, conceptual and applied skills in a particular domain of interest. The second group revealed that significant efforts are being made to design models that frame AI literacy proposals. There were hardly any experiences that assessed whether students understood AI concepts after the learning experience. Little attention has been paid to the undesirable consequences of an indiscriminate and insufficiently thought-out application of AI. A competency framework is required to guide the didactic proposals designed by educational institutions and define a curriculum reflecting the sequence and academic continuity, which should be modular, personalized and adjusted to the conditions of the schools. Finally, AI literacy can be leveraged to enhance the learning of disciplinary core subjects by integrating AI into the teaching process of those subjects, provided the curriculum is co-designed with teachers.

83A Differentiated Discussion About AI Education K-12OpenAlex

Gerald Steinbauer, Martin Kandlhofer, Tara Chklovski, et al.
AI Education for K-12 and in particular AI literacy gained huge interest recently due to the significantly influence in daily life, society, and economy. In this paper we discuss this topic of early AI education along four dimensions: (1) formal versus informal education, (2) cooperation of researchers in AI and education, (3) the level of education, and (4) concepts and tools.

84Exploring Generative Models with Middle School StudentsOpenAlex

Safinah Ali, Daniella DiPaola, Irene Lee, et al.
Applications of generative models such as Generative Adversarial Networks (GANs) have made their way to social media platforms that children frequently interact with. While GANs are associated with ethical implications pertaining to children, such as the generation of Deepfakes, there are negligible efforts to educate middle school children about generative AI. In this work, we present a generative models learning trajectory (LT), educational materials, and interactive activities for young learners with a focus on GANs, creation and application of machine-generated media, and its ethical implications. The activities were deployed in four online workshops with 72 students (grades 5-9). We found that these materials enabled children to gain an understanding of what generative models are, their technical components and potential applications, and benefits and harms, while reflecting on their ethical implications. Learning from our findings, we propose an improved learning trajectory for complex socio-technical systems.

85ARTIFICIAL INTELLIGENCE AS A SUPPORT TOOL IN TEACHING PROGRAMMING TO FUTURE BACHELOR'S STUDENTS OF VOCATIONAL EDUCATIONOpenAlex

Bohdan Rozputnia, Л. С. Шевченко, Volodymyr Umanets, et al.
The article presents a thorough examination of the potential applications of artificial intelligence (AI) in supporting the instruction of programming to future bachelor's degree students in vocational education. It explores the pivotal domains of AI integration into the educational process, encompassing the utilization of adaptive learning systems, intelligent tutoring systems, automated code evaluation systems, and generative models that enhance both the theoretical and practical training of students. It demonstrates the ways in which AI enhances the personalization of educational content, facilitates rapid feedback loops, and optimizes the verification process of software solutions. It has been determined that the integration of AI facilitates the creation of adaptive learning environments. In such environments, automated algorithms analyze test results, the history of students' interaction with educational materials, and the personal pace of information assimilation. Consequently, this facilitates the development of customized educational pathways that are tailored to the distinct characteristics of each student. The implementation of intelligent tutoring systems, such as ChatGPT, GitHub Copilot, or Google AI Studio based on Gemini, facilitates the elucidation of complex programming concepts, including the principles of recursion, sorting algorithms, and other fundamental principles. This, in turn, contributes to the cultivation of critical thinking and self-study skills. In the article, the authors analyze the challenges associated with the introduction of AI in the educational process. The primary challenges identified pertain to issues of academic integrity, particularly when future bachelors of vocational education employ AI capabilities to automatically generate solutions without a comprehensive grasp of the subject matter. Additionally, the article addresses technical limitations concerning the substantial computing resources required and the integration of contemporary algorithms into existing educational platforms. The article further underscores the necessity for specialized professional development programs to equip educators with the skills to effectively utilize AI in vocational education. Additionally, it emphasizes the establishment of ethical frameworks to guide the implementation of AI technologies in this context, ensuring that the principles of academic integrity are preserved and the integrity of the educational process is maintained. The authors of the article propose a number of recommendations and approaches to optimize the process of AI integration, create integrated learning environments, and improve existing assessment methods with regard to automated code verification. The findings of the study can be utilized to enhance pedagogical approaches in programming, to improve the quality of training in the field of information technology, and to promote the development of competitive graduates.

86Enhancing Medical Interview Skills Through AI-Simulated Patient Interactions: Nonrandomized Controlled Trial.PubMed

Akira Yamamoto, Masahide Koda, Hiroko Ogawa, et al.
JMIR Med Educ. 2024 Sep 23;10:e58753. doi: 10.2196/58753.
BACKGROUND: Medical interviewing is a critical skill in clinical practice, yet opportunities for practical training are limited in Japanese medical schools, necessitating urgent measures. Given advancements in artificial intelligence (AI) technology, its application in the medical field is expanding. However, reports on its application in medical interviews in medical education are scarce. OBJECTIVE: This study aimed to investigate whether medical students' interview skills could be improved by engaging with AI-simulated patients using large language models, including the provision of feedback. METHODS: This nonrandomized controlled trial was conducted with fourth-year medical students in Japan. A simulation program using large language models was provided to 35 students in the intervention group in 2023, while 110 students from 2022 who did not participate in the intervention were selected as the control group. The primary outcome was the score on the Pre-Clinical Clerkship Objective Structured Clinical Examination (pre-CC OSCE), a national standardized clinical skills examination, in medical interviewing. Secondary outcomes included surveys such as the Simulation-Based Training Quality Assurance Tool (SBT-QA10), administered at the start and end of the study. RESULTS: The AI intervention group showed significantly higher scores on medical interviews than the control group (AI group vs control group: mean 28.1, SD 1.6 vs 27.1, SD 2.2; P=.01). There was a trend of inverse correlation between the SBT-QA10 and pre-CC OSCE scores (regression coefficient -2.0 to -2.1). No significant safety concerns were observed. CONCLUSIONS: Education through medical interviews using AI-simulated patients has demonstrated safety and a certain level of educational effectiveness. However, at present, the educational effects of this platform on nonverbal communication skills are limited, suggesting that it should be used as a supplementary tool to traditional simulation education.

87Application of ChatGPT-assisted problem-based learning teaching method in clinical medical education.PubMed

Zeng Hui, Zhu Zewu, Hu Jiao, et al.
BMC Med Educ. 2025 Jan 11;25(1):50. doi: 10.1186/s12909-024-06321-1.
INTRODUCTION: Artificial intelligence technology has a wide range of application prospects in the field of medical education. The aim of the study was to measure the effectiveness of ChatGPT-assisted problem-based learning (PBL) teaching for urology medical interns in comparison with traditional teaching. METHODS: A cohort of urology interns was randomly assigned to two groups; one underwent ChatGPT-assisted PBL teaching, while the other received traditional teaching over a period of two weeks. Performance was assessed using theoretical knowledge exams and Mini-Clinical Evaluation Exercises. Students' acceptance and satisfaction with the AI-assisted method were evaluated through a survey. RESULTS: The scores of the two groups of students who took exams three days after the course ended were significantly higher than their scores before the course. The scores of the PBL-ChatGPT assisted group were significantly higher than those of the traditional teaching group three days after the course ended. The PBL-ChatGPT group showed statistically significant improvements in medical interviewing skills, clinical judgment and overall clinical competence compared to the traditional teaching group. The students gave highly positive feedback on the PBL-ChatGPT teaching method. CONCLUSION: The study suggests that ChatGPT-assisted PBL teaching method can improve the results of theoretical knowledge assessment, and play an important role in improving clinical skills. However, further research is needed to examine the validity and reliability of the information provided by different chat AI systems, and its impact on a larger sample size.

88Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language modelsOpenAlex

Tiffany H. Kung, Morgan Cheatham, Arielle Medenilla, et al.
We evaluated the performance of a large language model called ChatGPT on the United States Medical Licensing Exam (USMLE), which consists of three exams: Step 1, Step 2CK, and Step 3. ChatGPT performed at or near the passing threshold for all three exams without any specialized training or reinforcement. Additionally, ChatGPT demonstrated a high level of concordance and insight in its explanations. These results suggest that large language models may have the potential to assist with medical education, and potentially, clinical decision-making.

89Editorial: Continuing engineering education for a sustainable futureOpenAlex

Patricia Caratozzolo, Guillermo M. Chans, Ángeles Domínguez
Valverde-Rebaza, Rodrigues, et al. presented a new hierarchical taxonomy for IT job classifications to address the lack of granularity in global standards, such as ISCO-08. The Bee-inspired Employment and Expertise Taxonomy (BEET) was built through clustering analysis of job postings and expert collaboration. This framework supports improved workforce forecasting and informs curriculum design for Industry 5.0-aligned education. By aligning labor market demands with skill-based education, BEET offers a practical tool for institutions to prepare learners for an evolving digital economy.The original research study by Azofeifa et al. explored how integrating Industry 4.0 technologies and Education 4.0 principles can foster future skills through continuing engineering education. The authors analyze a redesigned course for practicing engineering, incorporating collaborative problem-solving and digital tools. Results show enhanced learner engagement and the development of transversal competencies such as adaptability, systems thinking, and innovation. The findings emphasize the value of immersive learning environments and lifelong upskilling strategies to prepare professionals for the evolving demands of Industry 5.0.Caratozzolo et al. presented a novel taxonomy for Continuing Engineering Education that aligns with UNESCO's ISCED classification, aiming to clarify terminology and facilitate international comparisons. This structure categorizes CEE initiatives based on target audience, program purpose, and delivery mode. By promoting consistency in how programs are described and analyzed, the new taxonomy provides a valuable tool for researchers, policymakers, and institutions seeking to benchmark and improve lifelong learning offerings for engineers in a global context.Smith et al.'s study investigated the role of Scotland's SCQF framework in supporting flexible lifelong learning pathways for engineers. By recognizing informal and non-formal learning, the SCQF enables learners to upskill and reskill more fluidly in response to evolving workforce demands. The authors illustrate how national qualification systems can empower individuals and institutions to adapt to economic and technological changes, contributing to more inclusive and responsive models of continuing engineering education.Drawing on the Academic Women in STEM Mentoring Program (A-WSTEM), García-Silva et al. examine how structured mentoring supports women's professional growth and retention in academia. Based on surveys and interviews, the findings highlight the importance of relational support, role modeling, and access to informal networks in fostering confidence and leadership. The research contributes to broader gender equity efforts by positioning mentoring as a strategic intervention in upskilling and promoting the career development of women in engineering education.Focusing on digital design education, Cal Y. Mayor-Peña et al. proposed a gamified learning framework aligned with Education 4.0 principles. The authors design and evaluate a virtual platform that uses game elements to boost motivation, interaction, and knowledge retention among engineering students. The results indicate improved engagement and skill acquisition, suggesting that gamification can be a powerful pedagogical tool for developing digital and creative competencies essential for Industry 5.0. The research also underscores the importance of learner-centered innovation in continuing education.Escobar-Castillejos et al. evaluated a custom-built digital platform to support methods engineering education for industrial engineering students. Using usability testing and educational impact assessments, the authors demonstrate that the platform enhances student interaction, task analysis, and decision-making skills. The research suggests that digital tools can significantly improve learners' comprehension of complex workflows and their readiness for smart manufacturing environments. It underscores the role of adaptive learning environments in preparing engineering professionals for Industry 5.0's collaborative and data-rich settings.The study by Ramírez-Cedillo et al. presented "Student 5.0," an immersive, interdisciplinary course on automation and manufacturing systems designed for the Industry 5.0 era. The course fosters cross-functional collaboration and digital fluency through project-based learning and technology integration. Learners navigate realistic industrial challenges using simulation tools, IoT systems, and agile methodologies. The experience cultivates systems thinking, problem-solving, and innovation-key future skills for sustainable industry. The study highlights the need for flexible, hands-on learning models in continuing engineering education.Valverde-Rebaza, González et al. investigated the use of generative AI tools, particularly large language models like ChatGPT, and visualization platforms to support data analytics learning in engineering. Through an instructional redesign and testing phase, the study demonstrates how these tools can scaffold conceptual understanding, automate data interpretation, and foster independent learning. The findings emphasize the potential of AI to personalize continuing education and bridge skills gaps in data-driven disciplines, a critical need for engineers operating in Industry 5.0 contexts.The case study conducted by DelaTorre-Diaz et al. examined the effects of curriculum standardization in a data analysis course for undergraduate engineering students. By implementing a unified structure across multiple campuses, the authors evaluate gains in tool proficiency, conceptual consistency, and academic outcomes. The findings suggest that standardization enhances both instructional efficiency and student learning. The article highlights how cohesive curricular frameworks can support quality assurance in CEE by ensuring that foundational competencies are uniformly delivered in rapidly evolving technical domains.Elizondo-García et al. investigated how ChatGPT influences learning in mathematics and biology courses that use a challenge-based learning (CBL) model. Through classroom observations and student feedback, the study examines whether AI tools enhance or obscure individual problem-solving processes. Results indicate nuanced outcomes: while ChatGPT can scaffold learning and support engagement, its overuse may hinder authentic understanding. The research raises critical questions about integrating generative AI into quality-focused instructional design, highlighting the need for ethical guidance in CEE.Mirón-Mérida and García-García analyzed how ChatGPT impacts the development of Spanish-language writing skills among engineering students. The authors assess improvements in argumentation, structure, and linguistic accuracy through controlled experimentation. The results suggest that while AI can support basic writing processes, it may also limit deeper reflection and critical thinking if over-relied upon. The study offers valuable insights into the appropriate role of AI in enhancing quality communication competencies, a crucial yet often overlooked component of engineering education.The original research by Nava-Manzo et al. focused on the relationship between continuing education engagement and the psychological well-being of engineering faculty. Using survey data, the authors examine how professional development activities affect emotional exhaustion, self-efficacy, and institutional commitment indicators. The findings show that structured learning opportunities can serve as protective factors for faculty mental health. By linking professionalization with wellness, the study expands the scope of quality assurance in CEE to include support for the human dimension of teaching.The article by Camacho-Zuñiga et al. examines a case study from a Mexican private university that has restructured its educational model around lifelong and continuous learning principles. Through curricular integration, industry collaboration, and flexible credentialing, the model supports students in developing transversal competencies needed for ongoing professional growth. The study highlights how institutional design can promote a mindset of learning beyond graduation-an increasingly critical aspect of continuing engineering education in dynamic, innovation-driven environments.Chans et al. explored how international mobility programs influence the development of transversal competencies in engineering students. Based on qualitative interviews, the study shows that experiences abroad enhance students' adaptability, cross-cultural communication, and global collaboration skills. These competencies are essential for engineering professionals operating in a globally connected and interdisciplinary workforce. The article supports the integration of international experience into competency-based education frameworks for lifelong learning and Industry 5.0 readiness. Pelaez-Sanchez et al. worked on designing and validating instruments to assess digital competencies in higher education. Drawing on Industry 5.0 frameworks, the authors construct a multidimensional toolset to measure skills, such as digital literacy, data fluency, and digital ethics. The validated instruments offer actionable insights for educators seeking to align instructional design with evolving technological needs. The study contributes to quality assurance in CEE by providing robust evaluation methods for one of the most critical competencies of the digital era. This article by Valdes-Ramirez et al. presented a large-scale, data-driven analysis of sustainability competencies among STEM students at a leading Mexican university. The authors employ quantitative survey techniques to evaluate how curricular and extracurricular activities influence systems thinking, ethical awareness, and environmental responsibility. The findings suggest that intentional integration of sustainability themes enhances key graduate attributes aligned with Industry 5.0 goals. The study presents a model for assessing and strengthening sustainability education within engineering curricula and continuing education programs.Vasquez-Lopez et al. explored the implementation of challenge-based learning (CBL) in engineering education through structured academic-industry collaboration. Based on a multi-semester case study, the authors presented a framework for company selection, challenge formulation, team formation, and evaluation. Findings emphasize the importance of aligning educational goals with industry needs while ensuring student ownership of problem-solving. The study offers practical insights into how structured engagement with industry partners can enhance experiential learning and long-term workforce relevance in continuing engineering education.Focusing on gender equity in the automotive sector, Zavala-Parrales et al. reviewed and analyzed educational strategies to increase women's participation and leadership in engineering roles. The authors examined case studies of vocational training, mentorship programs, and leadership development initiatives, highlighting their impact on recruitment, retention, and advancement. The article positions gender-inclusive education as essential to sustainable innovation and industry competitiveness. It underscores how strategic partnerships between academia and industry can advance both equity and skills development in continuing education.Together, these 19 contributions reveal a rich tapestry of innovation in engineering education. They show that CEE must evolve beyond technical upskilling to address broader imperatives-including sustainability, equity, inclusion, and mental well-being. The articles reflect a shared commitment to reforming educational models, embedding sustainability, ensuring equity, and embracing new technologies-not as end goals, but as tools to empower learners and reshape professional futures. As we look ahead, the insights from this Research Topic provide a roadmap for CEE programs worldwide. Institutions must build adaptable, inclusive, and high-impact learning ecosystems, which means forming deeper industry alliances, adopting flexible credentials, promoting diversity, equity, and inclusion, incorporating ethical considerations, and supporting the mental health of both learners and educators. Above all, it means reaffirming the value of engineering education not just as an economic lever, but as a cornerstone of sustainable global development.

90Creative Use of OpenAI in Education: Case Studies from Game DevelopmentOpenAlex

Fiona French, David Levi, Csaba Maczo, et al.
Educators and students have shown significant interest in the potential for generative artificial intelligence (AI) technologies to support student learning outcomes, for example, by offering personalized experiences, 24 h conversational assistance, text editing and help with problem-solving. We review contemporary perspectives on the value of AI as a tool in an educational context and describe our recent research with undergraduate students, discussing why and how we integrated OpenAI tools ChatGPT and Dall-E into the curriculum during the 2022–2023 academic year. A small cohort of games programming students in the School of Computing and Digital Media at London Metropolitan University was given a research and development assignment that explicitly required them to engage with OpenAI. They were tasked with evaluating OpenAI tools in the context of game development, demonstrating a working solution and reporting on their findings. We present five case studies that showcase some of the outputs from the students and we discuss their work. This mode of assessment was both productive and popular, mapping to students’ interests and helping to refine their skills in programming, problem-solving, critical reflection and exploratory design.

91Integrating Artificial Intelligence in Higher Education: Enhancing Interactive Learning Experiences and Student Engagement Through ChatGPTOpenAlex

Marius Schönberger
Abstract This research delves into the transformative potential of Generative Artificial Intelligence (AI), particularly ChatGPT, in enhancing higher education. It aims to explore how these advanced AI tools can be integrated into different educational settings to improve interactive learning experiences and student engagement, addressing the current challenges and opportunities in academic and administrative applications. Adopting a qualitative approach, the research utilizes the case vignette method to simulate realistic scenarios in various academic disciplines. It examines the potential applications and outcomes of AI in higher education, structured around key areas like intelligent tutoring systems, assessment, personalization and student profiling. This study employs the 4Cs framework (Critical Thinking, Creativity, Collaboration and Communication) to evaluate the effectiveness of AI integration in improving educational outcomes. The study reveals that ChatGPT can significantly enhance learning experiences by providing personalized tutoring, efficient assessment, tailored content and predictive insights into student performance. However, challenges such as ensuring content accuracy, ethical concerns and balancing AI with human interaction are also identified. Best practices for effectively integrating ChatGPT in higher education are proposed, emphasizing the complementarity of AI and human elements in education. This research contributes to the growing body of knowledge on AI in education by providing a nuanced understanding of generative AI's potential and challenges in higher education. It offers valuable insights and practical recommendations for educators and institutions, guiding the effective integration of AI technologies to enhance teaching and learning.

92Framework Design for Reinforcing the Potential of XR Technologies in Transforming Inclusive EducationOpenAlex

Monica Barbu, Dragoș Daniel Iordache, Ionuţ Petre, et al.
This study presents a novel approach to inclusive education by integrating augmented reality (XR) and generative artificial intelligence (AI) technologies into an immersive and adaptive learning platform designed for students with special educational needs. Building upon existing solutions, the approach uniquely combines XR and generative AI to facilitate personalized, accessible, and interactive learning experiences tailored to individual requirements. The framework incorporates an intuitive Unity XR-based interface alongside a generative AI module to enable near real-time customization of content and interactions. Additionally, the study examines related generative AI initiatives that promote inclusion through enhanced communication tools, educational support, and customizable assistive technologies. The motivation for this study arises from the pressing need to address the limitations of traditional educational methods, which often fail to meet the diverse needs of learners with special educational requirements. The integration of XR and generative AI offers transformative potential by creating adaptive, immersive, and inclusive learning environments. This approach ensures real-time adaptability to individual progress and accessibility, addressing critical barriers such as static content and lack of inclusivity in existing systems. The research outlines a pathway toward more inclusive and equitable education, significantly enhancing opportunities for learners with diverse needs and contributing to broader social integration and equity in education.

93AI-driven adaptive learning platforms: Enhancing educational outcomes for students with special needs through user-centric, tailored digital toolsOpenAlex

Ayobami O. Ayeni, Rodney E. Ovbiye, Ayomide S. Onayemi, et al.
The inclusion of AI powered adaptive learning systems in education systems can transform the learning experience of every learner. Especially, students with disabilities can benefit immensely from this technology. These platforms employ machine learning models, natural language understanding, and real time data processing to generate and deliver lessons, provide feedback, and assist learners in the most effective way. This paper addresses the consequences of using AI powered adaptive learning tools on educational outcomes and engagement for learners with disabilities. It aims to show how personalized interventions that enhance accessibility, cognitive development, and learning outcomes are achieved. The study uses data from user's interaction pattern, provided workload during the training, and the user's mental processes to define the processes that increase the efficiency of these systems. The results AI powered adaptive learning systems help achieve higher engagement, understanding, and memory in all students with learning disabilities, thereby lessening the educational gap between them and their peers. This study supports the necessity of and puts forth some elements of an inclusive AI design. Further work can incorporate ethical AI governance, multi-disciplinary approaches and design driven by users, to address the needs of students with disabilities in relation to adaptive learning systems. AI-powered adaptive learning platforms hold immense potential to create more inclusive, personalized, and effective learning environments for students with disabilities.

94Application of Generative AI Technology for Individualized Education: A Case Study of Special Education in Elementary SchoolsOpenAlex

Hye-Jin Kam, EunKyung Kim
연구목적: 일반학교 특수교사는 개별화교육계획(IEP) 수립, 운영, 통합교육 지원, 일반교사와의 협력, 학생별 개별화된 교육자료 제작 등 많은 역할을 동시에 수행해야 하는 어려움을 겪고 있다. 본 연구에서는 생성형 AI를 활용하여, 개별 학생의 학습 수준과 요구에 맞춘 맞춤형 교육 콘텐츠를 효과적이고 효율적으로 제작하고 수업 계획 및 운영, 평가 등 특수교사의 업무를 지원하는 AI 기술 활용사례를 제시하고자 하였다. 연구방법: IEP 수립 및 학습자료 제작, 가정 및 지역사회 연계, 성취목표 평가 및 심화지도안 등 4개 주제 15개 소주제 영역에서, 생성형 AI 중 하나인 ChatGPT의 활용 가능성을 탐색하였다. 3명의 가상의 학생을 설정하고, ChatGPT를 통해 도출된 결과의 내용 타당성 및 현장 활용 가능성 등에 대한 현장 전문가들의 검토를 반영하였다. 연구결과: ChatGPT는 학생 개별 수행 수준에 맞춘 IEP 목표 수립과 학습자료 작성을 효율적으로 수행할 수 있었으며, 특수교사는 자료를 생성하고 수정하는 과정에서 기술을 쉽게 활용할 수 있었다. 또한, 가정 및 지역사회 연계 활동에서 학부모와의 소통을 강화하고, 학생의 일상생활 적용을 지원하고, 수행 수준을 반영한 개별화된 평가 및 심화학습안 개발이 가능하였다. 결론: 생성형 AI 기술은 특수교육 현장에서 개별 학생의 요구를 반영한 수업계획 수립 및 교육자료 제작에 있어 효과적인 도구로 활용될 수 있다. ChatGPT를 활용한 교육자료 제작은 빠르고 효율적이며, 교사들이 수작업으로 교육 자료 준비에 들이는 시간을 줄이고 학생들의 개별적인 교육적 요구를 반영할 수 있다. 전문가 평가 결과, 생성형 AI는 특수교사의 업무를 경감하고 개별화 교육의 질을 높이는 유용한 도구로 활용될 수 있음을 시사하였다.

95Teaching and Rehabilitation of Handwriting for Children in the Digital Age: Issues and Challenges.PubMed

Nathalie Bonneton-Botté, Ludovic Miramand, Rodolphe Bailly, et al.
Children (Basel). 2023 Jun 22;10(7):1096. doi: 10.3390/children10071096.
Handwriting is a determining factor for academic success and autonomy for all children. Making knowledge accessible to all is a challenge in the context of inclusive education. Given the neurodevelopmental diversity within a classroom of children, ensuring that the handwriting of all pupils progresses is very demanding for education professionals. The development of tools that can take into account the variability of the profiles and learning abilities of children with handwriting difficulties offers a new potential for the development of specific and adapted remediation strategies. This narrative review aims to present and discuss the challenges of handwriting learning and the opportunities offered by new technologies involving AI for school and health professionals to successfully improve the handwriting skills of all children.

96Integrating artificial intelligence to assess emotions in learning environments: a systematic literature reviewOpenAlex

Angel Olider Rojas Vistorte, Ángel Deroncele-Acosta, Juan Luis Martín Ayala, et al.
Introduction: Artificial Intelligence (AI) is transforming multiple sectors within our society, including education. In this context, emotions play a fundamental role in the teaching-learning process given that they influence academic performance, motivation, information retention, and student well-being. Thus, the integration of AI in emotional assessment within educational environments offers several advantages that can transform how we understand and address the socio-emotional development of students. However, there remains a lack of comprehensive approach that systematizes advancements, challenges, and opportunities in this field. Aim: This systematic literature review aims to explore how artificial intelligence (AI) is used to evaluate emotions within educational settings. We provide a comprehensive overview of the current state of research, focusing on advancements, challenges, and opportunities in the domain of AI-driven emotional assessment within educational settings. Method: The review involved a search across the following academic databases: Pubmed, Web of Science, PsycINFO and Scopus. Forty-one articles were selected that meet the established inclusion criteria. These articles were analyzed to extract key insights related to the integration of AI and emotional assessment within educational environments. Results: The findings reveal a variety of AI-driven approaches that were developed to capture and analyze students' emotional states during learning activities. The findings are summarized in four fundamental topics: (1) emotion recognition in education, (2) technology integration and learning outcomes, (3) special education and assistive technology, (4) affective computing. Among the key AI techniques employed are machine learning and facial recognition, which are used to assess emotions. These approaches demonstrate promising potential in enhancing pedagogical strategies and creating adaptive learning environments that cater to individual emotional needs. The review identified emerging factors that, while important, require further investigation to understand their relationships and implications fully. These elements could significantly enhance the use of AI in assessing emotions within educational settings. Specifically, we are referring to: (1) federated learning, (2) convolutional neural network (CNN), (3) recurrent neural network (RNN), (4) facial expression databases, and (5) ethics in the development of intelligent systems. Conclusion: This systematic literature review showcases the significance of AI in revolutionizing educational practices through emotion assessment. While advancements are evident, challenges related to accuracy, privacy, and cross-cultural validity were also identified. The synthesis of existing research highlights the need for further research into refining AI models for emotion recognition and emphasizes the importance of ethical considerations in implementing AI technologies within educational contexts.

97Inclusion of Children With Special Needs in the Educational System, Artificial Intelligence (AI)OpenAlex

Pradnya Mehta, Geetha Chillarge, Sarita D. Sapkal, et al.
Special education for students is essential to gain/get/obtain equity and accessibility. AI enables new tools, customized learning, and improved accessibility for students with different learning needs. This chapter examines AI in inclusive education and its benefits for special needs pupils. AI-based adaptive learning, assistive intelligent tutoring, and early intervention data analytics boost inclusive education. AI algorithms customize learning in adaptive learning systems. AI-powered voice recognition and accessibility technologies assist impaired students' study. AI-powered tutoring methods improve student performance. AI-powered data analytics can identify struggling students and give assistance. AI in inclusive education has downsides. The chapter addresses AI bias, fairness, data privacy and security, transparency, explainability, and stakeholder participation. Ethics should govern inclusive education's usage of AI technology. AI should support human judgement and interaction. Responsible AI can improve education for all children, regardless of learning needs.

98Evaluating the impact of students' generative AI use in educational contextsOpenAlex

Dwayne Wood, Scott H. Moss
Purpose The purpose of the study was to evaluate the impact of generative artificial intelligence (GenAI) on students' learning experiences and perceptions through a master’s-level course. The study specifically focused on student engagement, comfort with GenAI and ethical considerations. Design/methodology/approach The study used an action research methodology employing qualitative data collection methods, including pre- and post-course surveys, reflective assignments, class discussions and a questionnaire. The AI-Ideas, Connections, Extensions (ICE) Framework, combining the ICE Model and AI paradigms, is used to assess students' cognitive engagement with GenAI. Findings The study revealed that incorporating GenAI in a master’s-level instructional design course increased students' comfort with GenAI and their understanding of its ethical implications. The AI-ICE Framework demonstrated most students were at the initial engagement level, with growing awareness of GenAI’s limitations and ethical issues. Course reflections highlighted themes of improved teaching strategies, personal growth and the practical challenges of integrating GenAI responsibly. Research limitations/implications The small sample size poses challenges to the analytical power of the findings, potentially limiting the breadth and applicability of conclusions. This constraint may affect the generalizability of the results, as the participants may not fully represent the broader population of interest. The researchers are mindful of these limitations and suggest caution in interpreting the findings, acknowledging that they may offer more exploratory insights than definitive conclusions. Future research endeavors should aim to recruit a larger cohort to validate and expand upon the initial observations, ensuring a more robust understanding. Originality/value The study is original in its integration of GenAI into a master's-level instructional design course, assessing both the practical and ethical implications of its use in education. By utilizing the AI-ICE Framework to evaluate students' cognitive engagement and employing action research methodology, the study provides insights into how GenAI influences learning experiences and perceptions. This approach bridges the gap between theoretical understanding and the real-world application of GenAI, offering actionable strategies for its responsible use in educational settings.

99Integrating AI into clinical education: evaluating general practice trainees’ proficiency in distinguishing AI-generated hallucinations and impacting factorsOpenAlex

Jiacheng Zhou, Jintao Zhang, Rongrong Wan, et al.
OBJECTIVE: To assess the ability of General Practice (GP) Trainees to detect AI-generated hallucinations in simulated clinical practice, ChatGPT-4o was utilized. The hallucinations were categorized into three types based on the accuracy of the answers and explanations: (1) correct answers with incorrect or flawed explanations, (2) incorrect answers with explanations that contradict factual evidence, and (3) incorrect answers with correct explanations. METHODS: This multi-center, cross-sectional survey study involved 142 GP Trainees, all of whom were undergoing General Practice Specialist Training and volunteered to participate. The study evaluated the accuracy and consistency of ChatGPT-4o, as well as the Trainees' response time, accuracy, sensitivity (d'), and response tendencies (β). Binary regression analysis was used to explore factors affecting the Trainees' ability to identify errors generated by ChatGPT-4o. RESULTS: A total of 137 participants were included, with a mean age of 25.93 years. Half of the participants were unfamiliar with AI, and 35.0% had never used it. ChatGPT-4o's overall accuracy was 80.8%, which slightly decreased to 80.1% after human verification. However, the accuracy for professional practice (Subject 4) was only 57.0%, and after human verification, it dropped further to 44.2%. A total of 87 AI-generated hallucinations were identified, primarily occurring at the application and evaluation levels. The mean accuracy of detecting these hallucinations was 55.0%, and the mean sensitivity (d') was 0.39. Regression analysis revealed that shorter response times (OR = 0.92, P = 0.02), higher self-assessed AI understanding (OR = 0.16, P = 0.04), and more frequent AI use (OR = 10.43, P = 0.01) were associated with stricter error detection criteria. CONCLUSIONS: The study concluded that GP trainees faced challenges in identifying ChatGPT-4o's errors, particularly in clinical scenarios. This highlights the importance of improving AI literacy and critical thinking skills to ensure effective integration of AI into medical education.

100Hallucinations in ChatGPT: A Cautionary Tale for Biomedical ResearchersOpenAlex

Jerome Goddard

101Perspectives of Generative AI in Chemistry Education Within the TPACK FrameworkOpenAlex

Yael Feldman-Maggor, Ron Blonder, Giora Alexandron
Abstract Artificial intelligence (AI) has made remarkable strides in recent years, finding applications in various fields, including chemistry research and industry. Its integration into chemistry education has gained attention more recently, particularly with the advent of generative AI (GAI) tools. However, there is a need to understand how teachers’ knowledge can impact their ability to integrate these tools into their practice. This position paper emphasizes two central points. First, teachers technological pedagogical content knowledge (TPACK) is essential for more accurate and responsible use of GAI. Second, prompt engineering—the practice of delivering instructions to GAI tools—requires knowledge that falls partially under the technological dimension of TPACK but also includes AI-related competencies that do not fit into any aspect of the framework, for example, the awareness of GAI-related issues such as bias, discrimination, and hallucinations. These points are demonstrated using ChatGPT on three examples drawn from chemistry education. This position paper extends the discussion about the types of knowledge teachers need to apply GAI effectively, highlights the need to further develop theoretical frameworks for teachers’ knowledge in the age of GAI, and, to address that, suggests ways to extend existing frameworks such as TPACK with AI-related dimensions.

102<scp>AI</scp> in teacher education: Unlocking new dimensions in teaching support, inclusive learning, and digital literacyOpenAlex

Jia Zhang, Zhuo Zhang
Abstract Background AI can positively influence teaching by offering support for classroom management, creating inclusive learning environments, enhancing digital skills, personalizing teaching methods, and strengthening teacher‐student relationships. Objectives This quantitative research study investigates the opportunities, difficulties, and consequences of incorporating AI into teacher education. Methods Data were collected through structured questionnaires from 202 college students and 68 staff members. The analysis was conducted using SPSS software. Results The study provides a novel contribution by its thorough investigation of the diverse effects of AI on teacher education. It offers beneficial perspectives on the possible benefits and challenges, illuminating the far‐reaching changes that AI could bring to the terrain of learning and instruction and teaching methods in the time yet to come. The research sought to assess the effect of AI adoption in teacher education across five main dimensions: (i) its influence on teaching support and classroom management, (ii) its role in creating inclusive and accessible learning environments, (iii) its contribution to improving teachers' digital literacy and computer skills, and enhancing access to digital teaching resources, (iv) its positive influence on identifying students' learning styles and facilitating the adoption of diverse teaching methods, and (v) its role in strengthening teacher‐student relationships through improved interactions. Conclusion The findings elucidate the promising opportunities that AI presents in the field of teacher education, along with the obstacles that require resolution for the effective fusion of AI educational settings.

103ChatGPT for Good? On Opportunities and Challenges of Large Language Models for EducationOpenAlex

Enkelejda Kasneci, Kathrin Seßler, Stefan Küchemann, et al.
Large language models represent a significant advancement in the field of AI. The underlying technology is key to further innovations and, despite critical views and even bans within communities and regions, large language models are here to stay. This position paper presents the potential benefits and challenges of educational applications of large language models, from student and teacher perspectives. We briefly discuss the current state of large language models and their applications. We then highlight how these models can be used to create educational content, improve student engagement and interaction, and personalize learning experiences. With regard to challenges, we argue that large language models in education require teachers and learners to develop sets of competencies and literacies necessary to both understand the technology as well as their limitations and unexpected brittleness of such systems. In addition, a clear strategy within educational systems and a clear pedagogical approach with a strong focus on critical thinking and strategies for fact checking are required to integrate and take full advantage of large language models in learning settings and teaching curricula. Other challenges such as the potential bias in the output, the need for continuous human oversight, and the potential for misuse are not unique to the application of AI in education. But we believe that, if handled sensibly, these challenges can offer insights and opportunities in education scenarios to acquaint students early on with potential societal biases, criticalities, and risks of AI applications. We conclude with recommendations for how to address these challenges and ensure that such models are used in a responsible and ethical manner in education.

104Shaping integrity: why generative artificial intelligence does not have to undermine educationOpenAlex

Myles Joshua Toledo Tan, Nicholle Mae Amor Tan Maravilla
The integration of generative artificial intelligence (GAI) in education has been met with both excitement and concern. According to a 2023 survey by the World Economic Forum, over 60% of educators in advanced economies are now using some form of artificial intelligence (AI) in their classrooms, a significant increase from just 20% five years ago (World Economic Forum, 2023). The rapid adoption of AI technologies in education highlights their potential to revolutionize the learning experience. AI tools, such as intelligent tutoring systems and adaptive learning platforms, offer personalized educational experiences that can meet the unique needs of each student. However, with this potential comes significant ethical concerns, particularly regarding academic integrity.The International Center for Academic Integrity reported that 58% of students admitted to using AI tools to complete assignments dishonestly, highlighting the urgency of addressing these ethical concerns (International Center for Academic Integrity, 2023). This statistic underscores a critical issue: while AI has the potential to enhance education, its misuse can undermine the very foundations of academic integrity. The rise of AI technology has raised concerns about academic integrity. With tools that can generate text, solve problems, and even assist with research, students may find it easier to engage in plagiarism or other forms of cheating. This shift challenges traditional educational values, as it blurs the lines between original work and AI-generated content (Mohammadkarimi, 2023). Curriculum designers are thus faced with the challenge of integrating AI in ways that uphold ethical standards and promote genuine learning. This requires balancing the innovative potential of AI tools with a commitment to academic integrity, ensuring that technology enhances rather than undermines the educational experience.To navigate this landscape responsibly, it is essential to revisit established ethical frameworks and educational theories. The ethical principles guiding our use of technology in education have remained consistent, even as the tools themselves have evolved. By referencing seminal works and foundational theories, we can demonstrate that the core values of honesty, fairness, and responsibility are timeless. For example, deontological ethics, as articulated by Immanuel Kant, emphasizes the importance of adhering to moral principles such as honesty and integrity, rather than the consequences of actions (Kant, 1785). In the context of AI in education, deontological ethics would require that the use of AI respects fundamental moral principles. For example, it would be crucial to ensure that AI systems are designed and implemented in ways that uphold students&#39; rights to privacy, ensure fairness, and avoid deception. Adhering to these principles would be seen as morally obligatory, regardless of the potential benefits or drawbacks of AI in educational settings. Similarly, consequentialism, as articulated by John Stuart Mill, evaluates actions based on their outcomes. Mill&#39;s version of consequentialism, known as utilitarianism, argues that the best actions are those that promote happiness or better well-being. In the context of AI in education, applying Mill&#39;s consequentialist principles would involve assessing how the use of AI impacts educational outcomes. If AI can be used to enhance learning, provide personalized educational experiences, or address inequalities and inequities in education, then its use would be considered morally justified according to Mill&#39;s framework, as it promotes overall well-being and positive outcomes for students.These ethical frameworks provide a robust foundation for the responsible use of GAI in modern educational settings. Moreover, educational theories such as constructivist learning and Self-Determination Theory (SDT) offer valuable insights into how AI can be used to enhance learning. Constructivist learning theory posits that students construct knowledge through active engagement with content, a process that can be greatly facilitated by AI tools. This approach emphasizes the importance of students&#39; engagement in hands-on activities and interactions, which help them construct meaningful connections with new information (Hein, 1991). AI tools can significantly enhance this constructivist approach by providing personalized and interactive learning experiences. SDT, on the other hand, emphasizes the importance of autonomy, competence, and relatedness in fostering intrinsic motivation among students (Deci &amp; Ryan, 2000). Integrating AI tools that align with the principles of SDT can help create a more engaging and supportive learning environment among students. This discussion will explore how GAI can be integrated into education in ways that support rather than erode academic integrity. By examining the ethical frameworks of deontological ethics and consequentialism, and educational theories like constructivist learning and SDT, we will argue that AI, when used responsibly, can enhance digital literacy, foster intrinsic motivation, and support genuine knowledge construction. The principles discussed in older foundational papers remain relevant, proving that ethical guidelines established decades ago still hold value in today&#39;s technologically advanced classrooms (Floridi &amp; Taddeo, 2016;Ryan &amp; Deci, 2017).The goal is to illustrate that the ethical use of GAI in education not only preserves but can also enhance academic integrity. Through responsible integration and ethical education, AI can empower students to become motivated, ethical, and engaged learners, well-prepared for the complexities of the modern world. By grounding our arguments in established ethical and educational theories, we can provide a comprehensive framework for understanding the potential benefits and challenges of AI in education.The integration of GAI in education raises significant concerns about its potential to disrupt traditional assessment methods. The ability of GAI to generate essays, problem solutions, and even creative works has sparked fears of plagiarism and academic dishonesty, challenging conventional forms of evaluation such as take-home exams, essays, or homework assignments. These concerns are valid, as the ease with which students can use AI-generated content without truly engaging in the learning process threatens to undermine academic integrity (Popenici and Kerr, 2017) .However, the disruptive nature of GAI also presents an opportunity to reimagine assessment practices in ways that prioritize authentic learning and deeper understanding. The rise of AI necessitates a shift away from traditional assessments focused on rote memorization and information recall, toward more authentic assessment methods that require students to demonstrate higher-order thinking skills. For example, project-based tasks, real-world problem-solving activities, oral presentations, and open-ended assignments that demand personal reflection and original insights can reduce the likelihood of misuse and encourage students to engage meaningfully with course material (Borenstein and Howard, 2020). Furthermore, GAI can play a constructive role in formative assessment by providing personalized feedback throughout the learning process. AI-driven tools can help students revise drafts, practice skills, and receive immediate guidance on areas needing improvement, fostering a deeper connection to the material. This approach transforms GAI from a potential threat to a valuable asset that supports continuous learning and skill development. Additionally, incorporating self-assessment and metacognitive practices, where students reflect on their progress and learning strategies, can ensure that AI augments rather than diminishes students&#39; active participation in their education.It is also essential to address the ethical considerations involved in using AI for assessment. Concerns such as data privacy, algorithmic bias, and the fairness of AI-generated evaluations must be taken seriously (Borenstein and Howard, 2020) . Developing clear institutional policies that set boundaries on acceptable AI use in assessments can help maintain fairness and transparency. These policies should include guidelines for combining AI insights with human judgment to ensure that assessments reflect not only the outputs o AI but also the educator&#39;s understanding of the student&#39;s abilities and efforts.By embracing these strategies, educators and institutions can harness the potential of GAI to enhance assessments while maintaining academic integrity. This balanced approach allows for the responsible integration of AI in education, ensuring that it supports meaningful learning experiences and prepares students to navigate an AI-driven world with integrity.Constructivist learning theory posits that learners construct knowledge through experiences and reflections, actively engaging with content to build understanding. GAI, with its advanced capabilities, aligns well with this theory, offering tools that promote exploration, interaction, and personalized learning to the that AI academic integrity, some argue that AI, when used has the potential to enhance educational experiences by providing personalized learning and students&#39; learning needs 2020). not that AI critical thinking or deeper discussion highlights the potential of AI in educational that it traditional methods to learning tools, such as intelligent tutoring systems and adaptive learning platforms, provide students with educational experiences. These systems learning and content to meet ensuring that students engage with material an of For an can a student&#39;s in and offer to address these This personalized approach not only supports knowledge but also students to of their learning &amp; a a the The a GAI that interactive and based on can these to the on and development. Through this exploration, construct a deeper understanding of the of students actively engage with content, on the consequences of actions and is in where a GAI students in creative By a student&#39;s and providing feedback on and the AI students their &amp; 2023). Additionally, it can or students&#39; and them to about their This interactive process supports constructivist principles by students to and build their &amp; argue that AI tools may encourage academic by it easier for students to work with However, this the potential for AI to promote genuine learning when used than AI can enhance the learning process by offering personalized immediate and adaptive content, which deeper engagement and learning outcomes For in a can students through providing and them to and encourage active learning and promote a deeper understanding of and rather than &amp; Additionally, as AI tools designed with from educators help align the technology with ethical considerations to reduce the of academic Furthermore, it is to that AI is education and and the ethical of these tools must reflect this shift to support genuine learning experiences while academic integrity GAI can learning, of constructivist In a project-based learning students can use AI tools to or AI can assist by and providing feedback on the and of their work This process students to engage in and build knowledge a where students are with a AI can generate and based on from the students. with the AI and with each to and their This interactive process is the of constructivist learning, fostering not only knowledge but also critical thinking and problem-solving are AI tools that are used by students that have significant potential to enhance a constructivist learning experience. is the According to supports the constructivist that learners construct their understanding of knowledge by students to explore and with and receive immediate This interactive engagement students to with the content, their and their learning in meaningful their educational according to a constructivist approach is a for AI it emphasizes on knowledge and active learning. an innovative approach to artificial intelligence (AI) through a constructivist on it in the underscores the of the course in learning and providing a more educational experience. The increase in and the in the of the approach in both and overall learning GAI aligns with constructivist learning theory by providing tools that exploration, interaction, and personalized learning. than dishonesty, AI can enhance academic integrity by genuine learning experiences. Through personalized interactive and AI students to an active role in their education, knowledge in meaningful and engaging By embracing these educators can create learning that students for the complexities of the modern world &amp; rise of GAI in education has sparked on its ethical and the importance of fostering digital By examining ethical frameworks such as deontological ethics and consequentialism, we can argue that responsible use of GAI in the can enhance students&#39; digital and them to navigate the digital world and (Floridi &amp; Taddeo, ethics, which on to moral or a foundation for integrating AI in This framework emphasizes the importance of principles such as honesty, fairness, and for (Kant, 1785). In the context of GAI, this ensuring that AI tools are used to support and enhance learning rather than students&#39; or in a the an AI can generate interactive and based on can the importance of using these tools students to engage with the material and By adhering to principles of honesty and integrity, students to use AI as a that enhances their understanding rather than as a to assignments 2020). as articulated by John Stuart in evaluates the of actions based on their outcomes. not AI, the principles of this framework can still be to about its use in By to positive as learning, critical and digital and designers can for the responsible integration of these benefits underscores how AI tools can to better educational and foster more digital a for example, a GAI can assist students in creative by providing feedback on and can students to use this feedback to their skills, fostering a deeper understanding of and The positive outcomes of abilities and critical engagement with AI tools illustrate the ethical benefits of responsible AI use (Borenstein &amp; Howard, 2020). promote digital literacy, it is crucial to students and educators on the ethical use of AI tools. This them to how AI the potential and of AI and the importance of using AI By fostering a of digital literacy, educators empower students to navigate the digital world with a critical and ethical a where an students through can use this opportunity to the ethical considerations of AI in research, such as data privacy, bias, and the importance of data By engaging in these students a understanding of the role of AI in and the ethical of using AI in can enhance digital and ethical In a project-based learning students can use AI tools to or can the importance of ethical such as to and ensuring that This approach not only enhances students&#39; digital but also ethical values that are essential in the digital in a where students are with a an AI can generate and can students to the AI-generated the ethical of using AI in and ensure and in their This process students the ethical of AI and to use AI in their ethical frameworks of deontological ethics and provide valuable insights into the responsible use of GAI in By the importance of principles such as honesty, fairness, and positive educators can foster digital and ethical among students. students to and navigate the ethical of AI tools prepares them to to the digital ensuring that use AI to enhance learning and uphold ethical Through responsible AI integration and ethical education, we can create a of and to in a technologically advanced integration of AI in education for learning experiences but raises ethical The for ethical reflection is in The of in and which argues that and must engage in to navigate the complexities of AI in educational and that for AI in education to be it must to principles such as fairness, and transparency. These principles are in and AI from or educational Furthermore, the of AI, as discussed by is crucial in ensuring that AI systems foster and not Similarly, concerns about the of AI on and the of educators through of to the for ethical frameworks that avoid or on this by highlighting the context of AI in education, the that are emphasizes that AI must be as a that educational inequities not The potential for AI to and necessitates that educators and reflect on its how AI systems or challenge educational offer a in addressing these concerns through their which is designed to help educators AI into their By using the to their presents that help educators and reflect on the of AI in This approach that the benefits of AI are balanced with ethical considerations to privacy, bias, and the impacts of AI on education, a and of AI this ethical the for AI for AI, human privacy, and These guidelines align with the to ensure that AI systems in education promote fairness and rather than inequities in educational and outcomes. The guidelines also the importance of continuous and to ensure AI systems remain with these ethical principles. By the importance of and these guidelines the frameworks by which for an ethical approach to AI integration in education these ethical in their framework the importance of a approach to AI that ethical foundations like autonomy, and These principles align with the for AI in education to promote well-being and while autonomy, ensuring to AI and fostering transparency. The framework also highlights that the potential of AI can include the of human and privacy, it essential for educational AI systems to be designed in ways that support rather than undermine and By these principles into the and of AI, educators and can more navigate the ethical challenges by AI in educational fostering a AI that supports human posits that are when their needs for autonomy, competence, and relatedness are GAI, with its to provide personalized feedback and learning can significantly support SDT by fostering intrinsic motivation among students. By students to of their learning, AI can enhance engagement and academic integrity (Deci &amp; Ryan, &amp; Deci, can enhance students&#39; of by offering them more over their learning process. In a the an AI can create interactive and can explore these tools their which of the to into more This students to of their learning, fostering a of example, a in the use the AI to policies and their This personalized students a deeper understanding of by their and &amp; Ryan, tools can also support the for by providing personalized feedback that students their and In a an AI-driven can a student&#39;s work and provide feedback on and This feedback students their and areas for improvement, fostering a of &amp; Deci, a a The AI can in and interactions, guiding the to their students their through this in their which enhances their intrinsic motivation to engage with the can also relatedness by learning and providing for meaningful In a project-based learning AI tools can help students work on or For in a an can of students through and discussion &amp; a of students using AI to a The AI each with and them to their and This process a of as students work to and from each other fostering intrinsic motivation through autonomy, competence, and GAI can also promote academic integrity. students are and engaged in their learning, are to to learning experiences education more and the to (Deci for example, students using AI to explore the are to a genuine in the This intrinsic motivation them to original work and engage with the material. Similarly, in the students by the to their are more to in their work and avoid plagiarism &amp; Ryan, a where students using AI-generated and educators can the importance of ethical and transparency. The AI personalized students to explore and their This hands-on learning approach intrinsic motivation by the and engaging &amp; Deci, a in a can use AI to the of This personalized the a deeper understanding of in by their and values (Deci &amp; Ryan, GAI, by the principles of SDT, can foster intrinsic motivation among students. Through personalized feedback and learning AI students to of their learning, their of autonomy, competence, and This intrinsic motivation not only engagement but also promotes academic integrity. By integrating AI tools in educational educators can create learning that students for the complexities of the modern ensuring that are motivated, ethical, and engaged learners &amp; Deci, integration of GAI in education has sparked significant regarding its on academic integrity. argue that AI tools by providing for students to complete assignments. However, a of established educational theories and ethical frameworks a used responsibly, GAI can foster intrinsic motivation, enhance digital literacy, and support constructivist learning academic integrity rather than integration of GAI in educational education, and is and learning. The integration of AI technologies in education, particularly through tools like significant benefits in fostering learning and advanced In education, GAI advanced and to enhance learning and problem-solving frameworks and significantly enhance education by providing interactive learning and personalized Moreover, GAI has the potential to revolutionize education by personalized and GAI hold potential to enhance education and By content data creative and GAI tools can provide valuable learning experiences and 2023). GAI potential to education by learning, and educational However, to these it is essential to address of responsible and ethical potential and academic integrity. By comprehensive bias, and fostering critical thinking skills, educators and institutions can ensure that AI technologies to a technologically and educational landscape posits that students are when their needs for autonomy, competence, and relatedness are GAI can significantly enhance these fostering intrinsic motivation among students. students are motivated, are more to engage with the material and maintain academic integrity. AI tools enhance by students to their learning process. In a for students can use AI-generated interactive and to explore of the their This students to of their learning which promotes a genuine in the the likelihood of as students are by and a to AI tools support by providing personalized feedback that students their skills. In a an AI-driven can a student&#39;s work and offer for This feedback not only enhances the student&#39;s but also their students in their their intrinsic motivation to engage with the This motivation academic integrity, as students in their work and are to or GAI also relatedness by learning. In project-based learning AI tools can help students work more For example, in a an can through discussion and This process a of and among which supports their intrinsic motivation to and students to their and their learning are more to to ethical standards and maintain academic is essential in today&#39;s and GAI can play a crucial role in fostering this frameworks such as deontological ethics and provide valuable insights into the responsible use of AI in education, the importance of honesty, fairness, and positive ethics, which on to moral underscores the for using AI tools can students to use AI by principles such as honesty and integrity. For when using AI-generated in a educators can students to engage with the ensuring that their use of AI supports genuine learning rather than By these ethical values, educators help students the importance of maintaining academic which evaluates the of actions based on their supports the responsible use of AI in The ethical use of AI should to positive educational such as learning, critical and digital In a an AI can provide constructive feedback that students their skills. This positive not only their but also a of responsibility in using AI tools students the benefits of using AI to enhance their skills, are more to use these tools responsibly, maintaining academic students on the ethical use of AI tools is crucial for fostering digital In a an can students through on ethical considerations such as data and By engaging in these students a understanding of the role of AI in and the ethical that with This students to navigate the digital world and the likelihood of learning theory emphasizes that students construct knowledge through experiences and GAI aligns well with this theory, offering tools that promote exploration, interaction, and personalized learning By constructivist AI enhances academic integrity by deeper understanding and critical a the an AI that interactive and allows students to and outcomes. This hands-on students construct a deeper understanding of than students actively engage with the content, on the consequences of This active engagement a genuine in learning, the to in a a GAI that feedback on students their skills. By with and students engage in a creative process that aligns with constructivist principles. This interactive learning students to about their fostering a deeper understanding of and students are in their learning are to engage in learning, of constructivist theory, is also by In project-based learning AI tools can by and providing feedback on the of students&#39; For example, in a an AI can help students a by and This process students to engage in and build knowledge students work to are more to to ethical standards and maintain academic when integrated in education, not erode academic integrity. it intrinsic motivation, enhances digital literacy, and supports constructivist learning principles. By autonomy, competence, and AI tools help students a genuine in their the likelihood of education and personalized feedback empower students to navigate the digital world responsibly, ensuring that use AI tools to enhance their learning rather than as Through interactive and learning experiences, GAI deeper understanding and critical academic integrity in today&#39;s educational provide guidance for using AI in education, we on integrating AI in ways that support established educational while adhering to ethical is crucial in this as educators must actively involve students in understanding how AI tools are their and ethical use is This the to and actively AI an of course and learning ensuring that its integration aligns with educational like digital and critical thinking skills. By potential data concerns, and of AI-generated content, educators foster a of critical engagement where students to use AI and rather than on This approach students with the and integrity to navigate an AI-driven for educators is crucial for the integration of AI in and must the and to this a and to in and provide support and to ensure that this is and should educators with for using AI tools, while also ethical considerations like data privacy, algorithmic bias, and the of AI-generated By and and can ensure that educators are well-prepared to navigate the potential and benefits of This comprehensive support educators to students in using AI tools responsibly, fostering genuine learning and academic integrity, rather than misuse or on technology to an approach to integrating AI is and this must be the as should the impacts of AI on learning outcomes and be to their This feedback from the of AI tools, and to ensure AI to meaningful educational experiences. can support this process by guidelines and providing that promote evaluation and of AI integration practices in By these both the and educators can AI in ways that not only enhance learning but also foster ethical engagement with

105Augmented intelligence in programming learning: Examining student views on the use of ChatGPT for programming learningOpenAlex

Ramazan Yılmaz, Fatma Gizem Karaoğlan Yılmaz
With the diversification of generative artificial intelligence (AI) applications, the interest in their use in every segment and field of society in recent years has been increasing rapidly. One of these areas is programming learning and program writing processes. One of the generative AI tools used for this purpose is ChatGPT. The use of ChatGPT in program writing processes has become widespread, and this tool has a certain potential in the programming process. However, when the literature is examined, research results related to using ChatGPT for this purpose have yet to be found. The existing literature has a gap that requires exploration. This study aims to analyze the students' perspectives on using ChatGPT in the field of programming and programming learning. The study encompassed a cohort of 41 undergraduate students enrolled in a public university's Computer Technology and Information Systems department. The research was carried out within the scope of the Object-Oriented Programming II course for eight weeks. Throughout the research process, students were given project assignments related to the course every week, and they were asked to use ChatGPT while solving them. The research data was collected using a form consisting of open-ended questions and analyzed through content analysis. The research findings revealed both the advantages and disadvantages of ChatGPT usage, as perceived by the students. The students stated that the main benefits of using ChatGPT in programming learning are providing fast and mostly correct answers to questions, improving thinking skills, facilitating debugging, and increasing self-confidence. On the other hand, the main limitations of using ChatGPT in programming education were getting students used to laziness, being unable to answer some questions, or giving incomplete/incorrect answers, causing professional anxiety in students. Based on the results of the research, it can be said that it would be useful to integrate generative AI tools into programming courses considering the advantages they provide in programming teaching. However, appropriate measures should be taken regarding the limitations it brings. Based on the research findings, several recommendations were proposed regarding the integration of ChatGPT into lessons.

106Will ChatGPT undermine ethical values in nursing education, research, and practice?OpenAlex

Abdul‐Fatawu Abdulai, Lillian Hung
ChatGPT is an artificial intelligence-based chatbot that uses deep learning techniques to generate natural language text (ChatGPT, 2022). While ChatGPT can enhance efficiency, there are growing concerns about its safety and future implications for humanity. On March 29, 2023, industry leaders in artificial intelligence (AI) signed a petition to pause research into AI that is more powerful than the ChatGPT4. According to the petitioners, this action was in response to the uncontrolled “race to develop and deploy ever more powerful digital minds that no one—not even their creators—can understand, predict, or reliably control” (Future of Life Institute, 2023, para. 1). Indeed, AI leaders are concerned about the “significant risk to humanity” that could result from an uncontrolled AI tools that lacks the necessary safety regulations. As nursing researchers in digital health technology and AI, we are concerned not only about the safety implications of ChatGPT, but also about its ability to capture the ethical values, principles, and core tenets that underpin the unique discipline of nursing. Recent editorials and commentaries in some nursing journals have discussed the potential benefits, limitations, and risks of using ChatGPT in nursing education and practice (Archibald & Clark, 2023; Odom-Forren, 2023; Scerri & Morin, 2023). In this commentary, we extend upon these discussions by reflecting on how the use of ChatGPT may undermine the values, principles, and core assumptions that underpin nursing research, education, and practice. We aim to stimulate a dialogue around current and emerging trends in using ChatGPT in nursing. We also want to engage nursing scholars, educators, and practitioners across international communities to discuss nursing assumptions in the context of technology use. Open AI acknowledged that ChatGPT cannot ensure the confidentiality of information entrusted onto it. Based on this, open AI advises against the inclusion of potentially sensitive, confidential, and identifiable information. The limitations of ChatGPT in processing confidential information may undermine the privacy and confidentiality inherent in nursing practice. Nurses are entrusted with clients' personal and health information and they have a legal and ethical responsibility to protect such information at all times. Compromising such information using Open AI tools like ChatGPT might endanger the nurse–client relationship, thus creating distrust in healthcare systems (Scerri & Morin, 2023). Aside from the potential for creating distrust in nurse–patient relationships, we are also concerned that ChatGPT might not capture human emotions, such as empathy and compassion, that are central to the core discipline of nursing. It is important to note that nursing is value-based, emphasizing compassion, empathy, care, respect, and dignity for the patient. On the other hand, Open AI prioritizes the practical usefulness of computing over offering explanatory or theoretical analysis. Without attuning to nursing disciplinary perspectives, ChatGPT may not be able to integrate the unique perspectives of nursing into the ChatGPT algorithms. With ChatGPT becoming common in the healthcare field (Sallam, 2023), it is important to critically reflect upon the potential and problematics of mixing AI data with a humanistic discipline like nursing, especially when they do not fit well. Another point of reflection is the use of ChatGPT in light of the decision-making process in Nursing. ChatGPT uses deep learning algorithms to process sequences of data, learn patterns in the data and generate text based on a query (ChatGPT, 2022). In effect, the output of ChatGPT is based on data available at any point in time. On the other hand, decision-making and clinical judgment in nursing go beyond data and include intuition, tacit knowledge, wisdom, and personal experiences. Even though ChatGPT may be designed to learn, reason, and solve problems that mimic human cognition, it may not be possible to identify or solve nursing problems that demand wisdom, critical thinking, intuition, and personal judgment. To illustrate, the American Nurses Association has included wisdom in the Data-Information-Knowledge framework of computing systems—changing the framework to Data-Information-Knowledge-Wisdom (Nelson, 2020). While AI tools like ChatGPT may be able to represent and generate information relevant to the first three dimensions, wisdom has been a more elusive concept that has not been adequately represented in computerized systems (Matney et al., 2011). This suggests that an overreliance on ChatGPT means a gradual disengagement in critical thinking, wisdom, intuition, tacit knowledge, and ethical judgment, which are considered attributes that define professional nursing practice, education, and research. The complexity and particularity inherent in nursing also constitute ethical values that may not sit well with the use of AI tools like ChatGPT. A recent blog post by a nursing student indicated how ChatGPT can aid nurses' understanding of complex concepts by breaking down complex ideas into simpler pieces (Tran, 2023). It is important to note that nursing adopts a holistic approach to care and clients cannot simply be understood by breaking the whole into parts as this blog post seems to suggest. Nursing considers each individual client as unique and complex. The reductionist approaches of ChatGPT may be overly simplistic and may not be capable of analyzing or understanding clients that present with complex health challenges or tailoring responses to individual cases. Therefore, ChatGPT-generated responses may not fully capture the holistic knowledge needed to address the complex contexts and nuances of individual health challenges. The reductionist approaches of ChatGPT may also conflate the disciplinary boundaries between nursing and medicine. Current AI applications specific to medicine are increasingly applied in disease diagnosis, personalized disease treatment, medical imaging, and genomic sequencing (Amisha et al., 2019). AI may be common in Medicine because AI applications are programmed to function in ways that are similar to the biomedical model of the human body (Kumar et al., 2022). Rather than focusing on individual ailments, nursing disciplinary boundaries transcend the biomedical model of care to focus on the broader picture of the patient, including the psychological, emotional, and social factors that come together to determine one's health. Therefore, Adopting AI tools like ChatGPT in nursing education and practice may be akin to adopting biomedical approaches in situations that demand psycho-sociological approaches to care. Another major concern of ChatGPT in nursing research and education relates to using the tool as an author and how that practice can facilitate academic dishonesty by enabling students to generate written assignments, discussion posts, or research papers without engaging in original thought or research (Choi et al., 2023). ChatGPT was recently listed as a coauthor in published peer-reviewed nursing papers (ChatGPT Generative Pre-Trained Transformer & Zhavoronkov, 2022; O'Connor & ChatGPT, 2023). Such practice raised serious questions about human and AI authors as the latter do not have accountability as a human author (Stokel-Walker, 2023). This practice of generating content using ChatGPT undermines the moral value of integrity in nursing, which demands nurses to be honest with themselves, their patients, and other healthcare teams. Furthermore, nursing decisions involve moral and value judgments and reliance on ChatGPT in nursing education may produce nurses with less adherence to moral values, little or no sense of trustworthiness, and overconfidence in handling complex cases. While industry experts have called for a pause to AI research, the reality is that the development of AI models will continue to advance and these may well find their way into nursing practice, education, and research. Until robust regulatory mechanisms on AI are developed, nursing must adopt some actionable strategies on ChatGPT that will ensure that the use of the AI tool does not distort the ethical values of nursing practice, education, and research. For instance, nursing educators may consider working with students/learners/trainees to establish clear guidelines and ethical standards that govern the use of AI tools like ChatGPT in nursing education, research, and practice. Additionally, promoting a culture of academic integrity and raising awareness of the ethical implications of AI use can help mitigate against academic dishonesty while preserving integrity and honest nursing practice. To counteract disengaged critical thinking and ensure holistic nursing practice that responds to complex situations, educators should prioritize the development of critical thinking and problem-solving skills in nursing curricula. This can be achieved by incorporating activities that encourage students to critically analyze and evaluate information, synthesize knowledge from various sources, and apply their understanding. Nursing academics should also emphasize the importance of contextualized and individualized decision-making in nursing by incorporating case studies, simulations, and real-life scenarios that highlight the importance of patients' unique needs and complex circumstances. Developing clear guidelines and promoting academic integrity, fostering critical thinking skills and independent problem solving, and encouraging contextualized and individualized decision-making can help ensure that AI tools like ChatGPT are used responsibly and ethically, ultimately enhancing the quality of nursing care practice, research, and education. Using ChatGPT in nursing poses ethical challenges that could undermine nursing's core values at the same time that integrating AI in nursing research, education, and practice offers potential advancement. As AI tools continue to grow, it is vital for nursing scholars and educators to engage in critically reflective dialogues and philosophical analyses of the implications, ethics, and potential drawbacks associated with using AI in nursing contexts. By critically questioning the underlying assumptions of AI-driven innovations, we can ensure that the evolving role of AI in nursing aligns with the values, principles, and goals traditionally shaping the profession, promoting holistic, person-centered, and ethical healthcare outcomes. Failing to do so, we risk eroding the foundations that make nursing a unique and essential discipline. No new data were generated for this commentary.

107ChatGPT—A double‐edged sword for healthcare education? Implications for assessments of dental studentsOpenAlex

Kamran Ali, Noha Barhom, Faleh Tamimi, et al.
INTRODUCTION: Open-source generative artificial intelligence (AI) applications are fast-transforming access to information and allow students to prepare assignments and offer quite accurate responses to a wide range of exam questions which are routinely used in assessments of students across the board including undergraduate dental students. This study aims to evaluate the performance of Chat Generative Pre-trained Transformer (ChatGPT), a generative AI-based application, on a wide range of assessments used in contemporary healthcare education and discusses the implications for undergraduate dental education. MATERIALS AND METHODS: This was an exploratory study investigating the accuracy of ChatGPT to attempt a range of recognised assessments in healthcare education curricula. A total of 50 independent items encompassing 50 different learning outcomes (n = 10 per item) were developed by the research team. These included 10 separate items based on each of the five commonly used question formats including multiple-choice questions (MCQs); short-answer questions (SAQs); short essay questions (SEQs); single true/false questions; and fill in the blanks items. Chat GPT was used to attempt each of these 50 questions. In addition, ChatGPT was used to generate reflective reports based on multisource feedback; research methodology; and critical appraisal of the literature. RESULTS: ChatGPT application provided accurate responses to majority of knowledge-based assessments based on MCQs, SAQs, SEQs, true/false and fill in the blanks items. However, it was only able to answer text-based questions and did not allow processing of questions based on images. Responses generated to written assignments were also satisfactory apart from those for critical appraisal of literature. Word count was the key limitation observed in outputs generated by the free version of ChatGPT. CONCLUSION: Notwithstanding their current limitations, generative AI-based applications have the potential to revolutionise virtual learning. Instead of treating it as a threat, healthcare educators need to adapt teaching and assessments in medical and dental education to the benefits of the learners while mitigating against dishonest use of AI-based technology.

108Artificial Intelligence and Healthcare Simulation: The Shifting Landscape of Medical EducationOpenAlex

Allan J. Hamilton
The impact of artificial intelligence (AI) will be felt not only in the arena of patient care and deliverable therapies but will also be uniquely disruptive in medical education and healthcare simulation (HCS), in particular. As HCS is intertwined with computer technology, it offers opportunities for rapid scalability with AI and, therefore, will be the most practical place to test new AI applications. This will ensure the acquisition of AI literacy for graduates from the country's various healthcare professional schools. Artificial intelligence has proven to be a useful adjunct in developing interprofessional education and team and leadership skills assessments. Outcome-driven medical simulation has been extensively used to train students in image-centric disciplines such as radiology, ultrasound, echocardiography, and pathology. Allowing students and trainees in healthcare to first apply diagnostic decision support systems (DDSS) under simulated conditions leads to improved diagnostic accuracy, enhanced communication with patients, safer triage decisions, and improved outcomes from rapid response teams. However, the issue of bias, hallucinations, and the uncertainty of emergent properties may undermine the faith of healthcare professionals as they see AI systems deployed in the clinical setting and participating in diagnostic judgments. Also, the demands of ensuring AI literacy in our healthcare professional curricula will place burdens on simulation assets and faculty to adapt to a rapidly changing technological landscape. Nevertheless, the introduction of AI will place increased emphasis on virtual reality platforms, thereby improving the availability of self-directed learning and making it available 24/7, along with uniquely personalized evaluations and customized coaching. Yet, caution must be exercised concerning AI, especially as society's earlier, delayed, and muted responses to the inherent dangers of social media raise serious questions about whether the American government and its citizenry can anticipate the security and privacy guardrails that need to be in place to protect our healthcare practitioners, medical students, and patients.

109A comprehensive AI policy education framework for university teaching and learningOpenAlex

Cecilia Ka Yuk Chan
Abstract This study aims to develop an AI education policy for higher education by examining the perceptions and implications of text generative AI technologies. Data was collected from 457 students and 180 teachers and staff across various disciplines in Hong Kong universities, using both quantitative and qualitative research methods. Based on the findings, the study proposes an AI Ecological Education Policy Framework to address the multifaceted implications of AI integration in university teaching and learning. This framework is organized into three dimensions: Pedagogical, Governance, and Operational. The Pedagogical dimension concentrates on using AI to improve teaching and learning outcomes, while the Governance dimension tackles issues related to privacy, security, and accountability. The Operational dimension addresses matters concerning infrastructure and training. The framework fosters a nuanced understanding of the implications of AI integration in academic settings, ensuring that stakeholders are aware of their responsibilities and can take appropriate actions accordingly.

110ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid ConcernsOpenAlex

Malik Sallam
ChatGPT is an artificial intelligence (AI)-based conversational large language model (LLM). The potential applications of LLMs in health care education, research, and practice could be promising if the associated valid concerns are proactively examined and addressed. The current systematic review aimed to investigate the utility of ChatGPT in health care education, research, and practice and to highlight its potential limitations. Using the PRIMSA guidelines, a systematic search was conducted to retrieve English records in PubMed/MEDLINE and Google Scholar (published research or preprints) that examined ChatGPT in the context of health care education, research, or practice. A total of 60 records were eligible for inclusion. Benefits of ChatGPT were cited in 51/60 (85.0%) records and included: (1) improved scientific writing and enhancing research equity and versatility; (2) utility in health care research (efficient analysis of datasets, code generation, literature reviews, saving time to focus on experimental design, and drug discovery and development); (3) benefits in health care practice (streamlining the workflow, cost saving, documentation, personalized medicine, and improved health literacy); and (4) benefits in health care education including improved personalized learning and the focus on critical thinking and problem-based learning. Concerns regarding ChatGPT use were stated in 58/60 (96.7%) records including ethical, copyright, transparency, and legal issues, the risk of bias, plagiarism, lack of originality, inaccurate content with risk of hallucination, limited knowledge, incorrect citations, cybersecurity issues, and risk of infodemics. The promising applications of ChatGPT can induce paradigm shifts in health care education, research, and practice. However, the embrace of this AI chatbot should be conducted with extreme caution considering its potential limitations. As it currently stands, ChatGPT does not qualify to be listed as an author in scientific articles unless the ICMJE/COPE guidelines are revised or amended. An initiative involving all stakeholders in health care education, research, and practice is urgently needed. This will help to set a code of ethics to guide the responsible use of ChatGPT among other LLMs in health care and academia.

111Artificial intelligence in education : challenges and opportunities for sustainable developmentOpenAlex

Francesc Pedró, Miguel Subosa, Axel Rivas, et al.
Artificial Intelligence is a booming technological domain capable of altering every aspect of our social interactions. In education, AI has begun producing new teaching and learning solutions that are now undergoing testing in different contexts. AI requires advanced infrastructures and an ecosystem of thriving innovators, but what about the urgencies of developing countries? Will they have to wait for the “luxury” of AI? Or should AI be a priority to tackle as soon as possible to reduce the digital and social divide?These are some of the questions guiding this document. In this regard, this urgent discussion should be taken up with a clear picture of what is happening and what can be done. This document gathers examples of how AI has been introduced in education worldwide, particularly in developing countries. It also sows the seeds of debates and discussions in the context of the 2019 Mobile Learning Week and beyond, as part of the multiple ways to accomplish Sustainable Development Goal 4, which targets education. The first section of this document analyses how AI can be used to improve learning outcomes. It presents examples of how AI technology can help education systems use data to improve educational equity and quality in the developing world. The section is divided into two topics that address pedagogical and system-wide solutions:i) AI to promote personalisation and better learning outcomes, exploring how AI can favour access to education, collaborative environments and intelligent tutoring systems to support teachers. We briefly introduce cases from countries such as China, Uruguay, Brazil, South Africa and Kenya as examples experimental solutions conceived from public policies, philanthropic and private organisations. ii) Data analytics in Education Management Information Systems (EMIS). Here we present opportunities for improving a state’s capacity to manage large-scale educational systems by increasing data from schools and learning, presenting cases from United Arab Emirates, Kenya, Bhutan, Kyrgyzstan and Chile.The second section “Preparing learners to thrive in an AI-saturated future” explores the different means by which governments and educational institutions are rethinking and reworking educational programmes to prepare learners for the increasing presence of AI in all aspects of human activity. Based on examples from different contexts, the section is also divided into two main parts: i) “A new curriculum for a digital and AI powered world” elaborates further on the importance of advancing in digital competency frameworks for teachers and students. Some current initiatives are presented such as the “Global Framework to Measure Digital Literacy” and “ICT Competencies and Standards from the Pedagogical Dimension”. The discussion of the curricular dimension is broadened to include new experiences for developing computational thinking in schools with examples from the European Union, United Kingdom, Estonia, Argentina, Singapore and Malaysia.ii) The second part is more focused on strengthening AI capacities through post-basic education and training. How can each country prepare the conditions for an AI-powered world? Here we present some of the most advanced cases from developed countries who are generating comprehensive plans to tackle this question, namely France, South Korea and China. We also present some cases from the technical and vocational education and training sector and some opportunities from non-formal and informal learning scenarios.The last section addresses the challenges and policy implications that should be part of the global and local conversations regarding the possibilities and risks of introducing AI in education and preparing students for an AI-powered context. Six challenges are presented: The first challenge lies in developing a comprehensive view of public policy on AI for sustainable development. The complexity of the technological conditions needed to advance in this field require the alignment of multiple factors and institutions. Public policies have to work in partnership at international and national levels to create an ecosystem of AI that serves sustainable development. The second challenge is to ensure inclusion and equity for AI in education. The least developed countries are at risk of suffering new technological, economic and social divides with the development of AI. Some main obstacles such as basic technological infrastructure must be faced to establish the basic conditions for implementing new strategies that take advantage of AI to improve learning.The third challenge is to prepare teachers for an AI-powered education while preparing AI to understand education, though this must nevertheless be a two-way road: teachers must learn new digital skills to use AI in a pedagogical and meaningful way and AI developers must learn how teachers work and create solutions that are sustainable in real-life environments. The fourth challenge is to develop quality and inclusive data systems. If we are headed towards the datafication of education, the quality of data should be our chief concern. It ́s essential to develop state capabilities to improve data collection and systematisation. AI developments should be an opportunity to increase the importance of data in educational system management.The fifth challenge is to make research on AI in education significant. While it can be reasonably expected that research on AI in education will increase in the coming years, it is nevertheless worth recalling the difficulties that the education sector has had in taking stock of educational research in a significant way both for practice and policy-making.The sixth challenge deals with ethics and transparency in data collection, use and dissemination. AI opens many ethical concerns regarding access to education system, recommendations to individual students, personal data concentration, liability, impact on work, data privacy and ownership of data feeding algorithms. AI regulation will thus require public discussion on ethics, accountability, transparency and security.The document ends with an open invitation to create new discussions around the uses, possibilities and risks of AI in education for sustainable development.

112From policy to practice: the regulation and implementation of generative AI in Swedish higher education institutesOpenAlex

Charlotte Erhardt, Helena Kullenberg, Anastasios Grigoriadis, et al.
Abstract Background The rapid development of generative artificial intelligence (GenAI) is reshaping higher education by offering innovative solutions in course design, assessment, and learning experiences. Despite its potential, GenAI integration poses ethical, pedagogical, and practical challenges, but also a risk of academic misconduct. This study explores how Swedish higher education institutions (HEIs) are addressing GenAI through guidelines, policy documents, and public website information. Methods A qualitative manifest content analysis for objectivity and consistency was conducted on GenAI-related documents and website information from Swedish HEIs. Forty-nine institutions were contacted, with 36 providing relevant data. Data collection involved email correspondence and systematic searches on public websites. Results Few formal GenAI guidelines exist across Swedish HEIs. Independent institutions were more likely to have established guidelines for both staff and students, whereas universities or university colleges often provided more GenAI-related information on their websites. Five categories were identified: Good academic practice; GenAI use and governance in education; Information governance; Ethical and social impact; and GenAI essentials, the latter unique to websites. Good academic practice was the most emphasized, focusing on transparency, responsibility, and the challenges of GenAI-related misconduct. Conclusions Taken together, GenAI integration in higher education remains early and uneven, with some institutions implementing formal guidelines while others are still developing policies. This inconsistency calls for national directives to balance GenAI´s benefits with ethical concerns, promote GenAI literacy, and ensure equitable access. Rapid technological change challenges HEIs to update policies that ensure academic integrity and fairness. Future research should foster collaborative policy development among HEIs, policymakers, and technology providers.

113Enhancing teacher AI literacy and integration through different types of cases in teacher professional developmentOpenAlex

Ai-Chu Elisha Ding, Lehong Shi, Haotian Yang, et al.
Integrating artificial intelligence (AI) into teaching practices is increasingly vital for preparing students for a technology-centric future. This study examined the influence of a case-based AI professional development (PD) program on AI integration strategies and AI literacy among seven middle school science teachers. Employing three distinct case problems, from well-structured to ill-structured, the AI PD program aimed to stimulate teachers’ reflection on AI literacy development and encourage the construction of problem-solving and AI integration strategies within various pedagogical contexts. Analysis of video-recorded case discussions revealed that teachers primarily drew on personal experiences for collaborative problem-solving across the three cases. However, the complexity of the case problems influenced their approach to knowledge co-construction, and dealing with ill-structured problems promoted the application of new knowledge. Through analyzing the survey data, we found a marked increase in teachers’ AI literacy, particularly in the domain of knowing and understanding AI, suggesting a pivotal role for direct instruction that supports AI literacy growth. However, their application of this AI knowledge was limited during the case discussions, while other domains of teacher AI literacy were more frequently employed. The findings highlight the importance of combining direct instruction with case-based discussions in AI-related PD programs to bolster teacher AI literacy effectively. The research has implications for using a case-based learning approach during short-term PD initiatives and advocates the ongoing need for comprehensive AI literacy development to facilitate teachers’ AI integration in subject-specific teaching.

114K-12 Education in the Age of AI: A Call to Action for K-12 AI LiteracyOpenAlex

Ning Wang, James C. Lester
Abstract The emergence of increasingly powerful AI technologies calls for the design and development of K-12 AI literacy curricula that can support students who will be entering a profoundly changed labor market. However, developing, implementing, and scaling AI literacy curricula poses significant challenges. It will be essential to develop a robust, evidence-based AI education research foundation that can inform AI literacy curriculum development. Unlike K-12 science and mathematics education, there is not currently a research foundation for K-12 AI education. In this article we provide a component-based definition of AI literacy, present the need for implementing AI literacy education across all grade bands, and argue for the creation of research programs across four areas of AI education: (1) K-12 AI Learning &amp; Technology; (2) K-12 AI Education Integration into STEM, Language Arts, and Social Science Education; (3) K-12 AI Professional Development for Teachers and Administrators; and (4) K-12 AI Assessment.

115The effects of generative AI on initial language teacher education: The perceptions of teacher educatorsOpenAlex

Benjamin Luke Moorhouse, Lucas Kohnke
Since the public release of ChatGPT in November 2022, generative AI tools—capable of creating human-like content such as audio, code, images, text, simulations, 3D objects, and videos—have gained significant attention. While the impact of these tools on language teaching and learning has been widely speculated, the perspective of language teacher educators concerning their influence on initial language teacher education (ILTE) remains unexplored. This study investigates how teacher educators, who play a crucial role in adapting ILTE to technological advancements, perceive the effects of generative AI tools on ILTE. Data were collected through in-depth interviews with thirteen English language teacher educators from all four Hong Kong government-funded universities offering ILTE. Findings reveal that participants believe generative AI tools will substantially affect the ILTE curriculum, instruction, and assessment. However, most participants believed they lacked the confidence and competence to address the implications of generative AI tools effectively. This study highlights the need for further research and training to support teacher educators in adapting ILTE to the emerging influence of generative AI.

116Generative artificial intelligence (AI) powered conversational educational agents: The inevitable paradigm shiftOpenAlex

Aras Bozkurt
Generative AI, specifically ChatGPT, represents a significant technological advancement in natural language processing (NLP) large language models (LLM) with far-reaching implications in many dimensions of our lives, including education. This paper discusses the prospects of generative AI in utilizing language and its potential role as a conversational agent within the educational realm. Emulating the most advanced human technology, language, generative AI’s success relies on understanding and generating human-like text. However, its comprehension is solely based on patterns and structures it learns from its training data. With the advent of AI-driven conversational agents, prompt engineering emerges as a vital form of digital literacy. The convergence of general and educational technologies necessitates preparedness for a future dominated by AI. This paper highlights the importance of vigilance and prudence in harnessing the potential of generative AI technologies, emphasizing the responsibility of humans, as creators, in mitigating any potential mishaps. In conclusion, this paper suggests that preparedness for a future dominated by AI is essential, as generative AI technologies have the potential to profoundly impact teaching and learning methods, and necessitate new ways of thinking.