1. 生成式AI赋能个性化学习与智能辅导的技术逻辑与核心路径
1.1 教育场景适配的技术演进脉络
生成式人工智能（Generative AI）作为一项变革性技术，正在深刻影响教育领域，特别是在个性化学习和智能辅导方面展现出巨大潜力12。其核心技术，如大语言模型（LLMs）和多模态生成，通过持续的演进与教育需求的深度融合，为实现高效、自适应的教育体验奠定了基础。
大语言模型（LLMs）的演进与教育适配：
早期的人工智能教育应用主要集中在智能辅导系统（ITS）上，如AutoTutor系统，它通过自然语言对话与学生互动，辅助学生学习科学技术等学科知识，并能对学生的解释进行分析以提供指导性反馈345。而随着Transformer架构的出现和大规模预训练技术的进步，大语言模型（LLMs）如GPT系列取得了显著突破，其在理解、生成和处理自然语言方面的能力大幅提升16。
LLMs在教育场景中的适配主要体现在以下几个方面：
- 个性化内容生成： LLMs能够根据学生的学习进度、理解能力和兴趣偏好，动态生成定制化的学习材料、习题、案例分析甚至虚拟情境17。例如，LLMs可以根据学生对某一知识点的掌握情况，即时生成难度适中、语言风格贴近学生的解释或拓展阅读材料，实现千人千面的学习资源供给。
- 自适应路径规划： 基于LLMs的智能辅导系统能够分析学生的学习行为数据，如答题情况、阅读时长、提问频率等，构建动态的学生画像。结合这些信息，LLMs可以智能调整学习路径，推荐最适合学生的下一步学习内容或练习，确保学习的连续性和有效性18。例如，当学生在某个概念上遇到困难时，系统可以即时识别并提供额外的辅助材料或以不同方式重新解释该概念。
- 智能答疑辅导： LLMs在自然语言理解和生成方面的优势使其成为理想的智能答疑工具。学生可以随时提出问题，LLMs能提供即时、准确且富有解释性的回答，甚至可以进行多轮对话，帮助学生深入理解复杂概念17。这大大减轻了教师的重复性工作负担，并确保学生能及时获得反馈，解决了传统课堂中因教师资源有限而导致的问题。此外，生成式AI还能用于自动生成考试类型的问题，为评估和练习提供支持9。
多模态生成技术的崛起与教育应用：
除了文本生成，多模态生成技术（如文生图、文生视频、图生文等）的快速发展，为教育场景带来了更丰富的可能性。以往的AI教育研究主要侧重于文本到文本的生成，而在利用多模态生成能力解决多媒体数据支持教学方面的研究相对稀缺10。
多模态生成技术在教育中的应用逻辑包括：
- 情境化多媒体教学资源生成： 生成式AI能够根据文本描述（如诗歌、历史事件、科学原理），自动生成与之匹配的图片、动画或视频，帮助学生更直观、生动地理解抽象概念10。例如，针对唐诗学习，系统可以生成与诗歌意境相符的情境视频，降低学生的认知负荷，增强理解效果10。
- 虚拟实验与仿真模拟： 通过多模态生成，可以创建高度逼真的虚拟实验环境或仿真模拟场景，让学生在安全可控的环境中进行实践操作和探索。例如，医学专业的学生可以在虚拟人体模型上练习手术，工程专业的学生可以在模拟环境中调试设备，极大地提升了实践教学的可及性和效率。
- 个性化教学内容的可视化呈现： 对于不同的学习者，AI可以生成不同形式的多媒体内容来辅助学习。例如，对于视觉型学习者，系统可以生成更多图表和动画；对于听觉型学习者，则可以生成讲解音频。这种个性化的多模态呈现，有助于满足不同学生的学习风格偏好。
综上所述，大语言模型和多模态生成技术通过在个性化内容生成、自适应路径规划和智能答疑辅导等核心应用中的深度适配，正在构建一个更加智能、高效、个性化的教育生态系统。这些技术的底层逻辑在于通过算法对海量数据进行学习，以满足教育领域的复杂需求。
1.2 个性化学习服务的技术实现框架
生成式AI赋能的个性化学习服务，其核心在于构建一个能够实时感知、智能决策并动态响应学习者需求的闭环技术体系。这个体系通常围绕学情数据采集、用户画像动态构建、生成式内容响应三大核心环节展开，形成一套完整的技术实现框架。
1. 学情数据采集（Learning Data Acquisition）：
个性化学习服务的基础是对学习者全面而精细的数据获取。这包括但不限于：
- 学习行为数据： 学生的登录时长、学习路径、点击习惯、观看视频进度、练习题尝试次数、答案提交与批改结果等。
- 学习内容互动数据： 在线批注、高亮、笔记、对讲解内容的反馈（如“不理解”、“太简单”）等。
- 测验与评估数据： 章节测试、阶段性评估、模拟考试成绩，以及细化的知识点掌握情况评估。
- 情感与认知状态数据（部分高级系统）： 通过眼动追踪、面部表情识别、语音语调分析等技术，推断学习者的专注度、情绪状态、认知负荷等，但这部分数据的采集和使用需严格遵守隐私伦理规范。
- 元数据： 学生的基本信息（年龄、年级、学科）、学习历史、兴趣偏好等。
这些数据通过学习管理系统（LMS）、在线教育平台、智能硬件设备等多种渠道实时汇聚。
2. 用户画像动态构建（Dynamic User Profile Construction）：
采集到的海量学情数据是原始的，需要经过处理和分析才能转化为有价值的用户画像。这一环节是实现个性化的关键，其目标是建立一个多维度、实时更新的学习者模型。
- 知识图谱映射： 将学生的学习表现与预设的学科知识图谱进行映射，精确定位学生已掌握、待掌握和薄弱的知识点。例如，通过练习题的答题情况，系统能判断学生对某一概念的理解程度及其相关联知识点的掌握情况。
- 学习风格与偏好分析： 基于学习行为数据，分析学生的学习习惯，如偏好视频讲解还是文字阅读，喜欢独立思考还是互动讨论，从而识别其个性化的学习风格（如视觉型、听觉型、阅读/写作型、动觉型）。
- 兴趣与动机识别： 通过学生选择的学习材料、参与的讨论话题、完成的拓展任务等，分析其兴趣点和学习动机，为后续的内容推荐提供依据。
- 认知能力评估： 结合测试结果和学习过程中的表现，评估学生的理解力、记忆力、分析解决问题能力等认知特征。
- 情绪与投入度跟踪： 结合情感识别数据（如可用），系统能够动态感知学生在学习过程中的情绪变化和投入程度，为及时干预和调整学习策略提供依据。
用户画像并非静态，而是随着学习者行为的发生而实时更新和迭代。这通常借助机器学习算法（如聚类、分类、推荐算法）和知识推理引擎来实现。例如，ACE系统就基于领域模型、教学模型和学习者模型（包括偏好、兴趣和知识水平）来生成个性化内容 11。
3. 生成式内容响应（Generative Content Response）：
在动态用户画像的基础上，生成式AI技术被用于实时生成和推送高度个性化的学习内容与辅导策略。这是个性化学习服务的最终呈现。
- 个性化学习路径推荐： 基于学生的知识掌握情况、学习风格和目标，推荐最合适的学习顺序、难度等级和学习资源。例如，如果学生在某个知识点上表现出困难，系统可能会推荐不同形式的补充材料或更基础的预备知识。
- 自适应学习材料生成： 大语言模型（LLMs）能够根据学生的理解水平和兴趣，即时生成定制化的解释、示例、比喻或拓展阅读材料。例如，“AI Study Partner”等工具可以上传内容并进行总结、生成抽认卡和问题，甚至提供会话式聊天机器人辅助学习 12。
- 智能答疑与反馈： 结合LLMs的自然语言理解和生成能力，提供即时、个性化的问答服务。学生可以提出任何学习上的疑问，系统能够提供精准且富有解释性的答案，甚至进行多轮对话，澄清误解。同时，系统还能针对学生的练习题答案提供详细的批改和个性化反馈，指出错误原因并提供改进建议。
- 虚拟教学助手与伴侣： 生成式AI可以模拟教师或学习伙伴，提供鼓励、提示和指导，甚至可以进行角色扮演，模拟面试或口语练习场景，从而提高学习者的参与度和积极性。
- 多模态内容生成： 除了文本，生成式AI还能根据学习需求生成图片、图表、动画或短视频，将抽象概念具象化，提升学习的趣味性和直观性。例如，当讲解一个物理概念时，系统可以生成一个动态模拟动画。
现有成熟系统的设计方案与运行机制：
许多现有的在线学习平台和智能辅导系统已开始融合上述框架。例如，一些自适应学习平台会根据学生的每一次练习结果，实时调整后续的题目难度和知识点讲解侧重。智能写作辅导工具则能根据学生的作文内容，提供语法修正、句式优化、内容组织等方面的个性化建议。这些系统通常采用模块化的设计，包括数据层（用于存储和管理学情数据）、模型层（包含用户画像构建模型和生成模型）和应用层（提供前端交互界面和各项个性化服务）。整个运行机制是一个动态迭代的过程，即“数据采集-画像更新-内容生成-行为反馈-画像再更新”的循环，确保学习服务始终与学习者的最新状态保持同步。这种以学习者为中心的持续优化机制，正是生成式AI个性化学习服务的核心价值所在。
2. 生成式AI对学习效果的影响效应与实证验证
2.1 学习效能提升的实证研究进展
生成式AI在教育领域的应用，为提升学习效果带来了显著的机遇。多项实证研究表明，生成式AI能够从多个维度积极影响学生的学习效能，包括知识掌握效率、学习兴趣激发、自主学习能力培养等方面。
知识掌握效率的提升：
生成式AI通过提供个性化和即时的学习支持，有助于学生更高效地掌握知识。例如，针对大学生的研究发现，学生普遍认为ChatGPT等AI工具在学习环境中具有实用性，并能积极影响他们对工具使用的态度13。这种实用性体现在AI能够根据学生的具体问题提供定制化的解释和示例，帮助学生澄清模糊概念，从而加快知识的理解和吸收。一项针对中学生的准实验研究发现，在教师监督下的生成式AI辅助教学，能够显著提高学生的知识掌握水平，这优于传统计算机辅助教学和无教师监督的AI辅助教学14。此外，生成式AI能生成多样化的练习题和情境，巩固学生对知识点的记忆和应用能力，例如，在高等教育中，生成式AI可以辅助生成教学材料、提升技能发展，并支持学生完成各项任务15。
学习兴趣与动机的激发：
生成式AI的互动性和个性化特点，使其成为激发学生学习兴趣和动机的有效工具。有研究指出，学生对AI在音乐教育中的积极认知，能够显著提升他们的学习动机、参与度和学习成功率。这归因于AI工具能够通过创新型学习工具增加学习兴趣，并通过互动式AI学习环境促进参与16。类似地，其他研究也强调了AI在提升学生学习满意度、愉悦感和动机方面的潜力131718。例如，AI驱动的个性化反馈和交互式工具能够帮助学生更好地与学习材料互动，提高参与度17。生成式AI提供的类人文本生成能力和自动化对话功能，使得学习过程更加引人入胜，有助于保持学生的注意力，减少学习倦怠1920。
自主学习能力（SRL）的培养：
生成式AI在支持学生自我调节学习（Self-Regulated Learning, SRL）方面展现出巨大潜力。一项针对香港中学生的比较研究表明，基于ChatGPT的SRLbot能够有效提升学生的科学知识、行为参与度和学习动机，相较于基于规则的AI聊天机器人效果更佳21。SRLbot能够根据学生的特定学习和SRL情境调整反馈，提供个性化的建议，从而帮助学生养成规律的学习习惯，并有效管理自己的学习过程。生成式AI工具通过提供即时反馈、资源推荐和进度跟踪，赋能学生主动规划、执行和评估自己的学习，从而培养其自主学习能力2122。这种个性化的引导减少了学习焦虑，促进了学习表现和持续学习习惯的形成21。
案例与实践：
在高等教育领域，生成式AI被用于增强学生参与度、个性化学习体验和学业表现预测23。例如，ChatGPT作为一种生成式AI工具，被认为能够显著提升学习体验，提供个性化辅导、高效评估和定制化内容，并对学生的表现进行预测24。这些应用通过模拟真实教学场景、提供虚拟助教服务等方式，为学生创造了一个更具支持性和互动性的学习环境。
总体而言，实证研究结果普遍支持生成式AI在提升学习效能方面的积极作用。它不仅能够帮助学生更有效地掌握知识，还能激发其内在学习动力，并培养其终身学习所必需的自主学习能力。然而，研究也同时强调，这种效能的实现并非一蹴而就，需要结合适当的教学设计和教师的有效引导14。
2.2 效能差异的影响因素与应用边界
尽管生成式AI在提升学习效能方面展现出巨大潜力，但其效果并非普适，而是受到多种因素的调节，并存在特定的应用边界与固有局限性。深入理解这些影响因素和边界，对于指导生成式AI在教育领域的负责任与高效应用至关重要。
1. 技术成熟度与输出质量：
生成式AI模型的不断迭代更新是影响其教育应用效能的关键。早期或非顶级的模型可能存在信息准确性、逻辑连贯性、文化偏见等问题 25。例如，ChatGPT等工具虽然在生成文本方面表现出色，但其输出仍可能包含不准确、过时甚至虚假的信息，特别是在需要精确事实或最新知识的科学教育领域 2627。当AI生成的内容质量不高时，不仅无法有效辅助学习，反而可能误导学生，甚至引发对AI的“元认知惰性”（metacognitive laziness），即学生过于依赖AI而减少自主思考和批判性评估，长此以往可能阻碍深层次学习和知识迁移 28。因此，技术输出的可靠性与准确性是衡量其应用效能的基础。
2. 学习者特征：
学习者的个体差异对生成式AI的学习效果具有显著调节作用。
- 年龄与学习阶段： Meta分析显示，AI对小学生数学成绩的影响效应较小（效应值0.351），且不同年级段的效果有所差异 29。这表明对于认知发展尚未成熟的低龄学生，AI的辅助可能需要更精心设计或更侧重于基础知识的强化。而对于大学生等高等教育阶段的学习者，生成式AI在支持研究、分析和个性化学习方面表现出更大的潜力 273031。
- 自主学习能力与批判性思维： 具有较强自主学习能力和批判性思维的学生，能够更好地利用生成式AI作为工具，进行探索性学习和信息核查。相反，缺乏这些能力的学生可能更容易陷入对AI的过度依赖，导致“元认知惰性”，未能真正提升自身能力 2832。研究强调，培养学生的批判性思维和分析能力，是有效利用AI的关键 33。
- 技术接受度与熟悉度： 学习者对AI工具的接受度、使用便利性和学习自信心，直接影响其使用频率和学习表现 3034。如果学生认为AI工具难以使用或对其效果缺乏信心，即使工具本身功能强大，也难以发挥其应有作用。
3. 使用方式与教学设计：
生成式AI并非万能，其效能发挥与具体的使用场景和教学设计紧密相关。
- 教师引导与监督： 研究表明，在教师监督下使用生成式AI，能显著提升学习效果，优于无教师监督的情况 21。教师的角色从知识传授者转向引导者、设计者和评估者，负责指导学生如何负责任、批判性地使用AI工具，并针对AI的局限性进行补充和纠正 2635。
- 任务类型与目标： 生成式AI在提供个性化反馈、辅助写作、生成练习题、进行初步研究等方面表现突出 2731。但在需要深度创新、复杂问题解决或高阶批判性思维的任务中，AI只能作为辅助工具，不能替代人类的思考过程。例如，研究发现，虽然ChatGPT可以提高论文分数，但对知识获取和迁移没有显著影响 28。
- 伦理与合规性： 学术诚信、隐私保护、算法偏见等伦理问题是生成式AI教育应用不可忽视的边界 25262731323336。学生可能利用AI进行抄袭或学术不端，数据隐私泄露风险也日益增加。教育机构需要制定明确的使用政策和行为规范，并对学生进行AI伦理教育，以确保技术的负责任使用 323738。
4. 固有局限性与应用边界：
- 缺乏情感理解与同理心： 尽管AI可以模拟对话，但它无法真正理解人类情感，也缺乏同理心和人际互动所必需的非语言线索。在需要情感支持、价值引导或复杂人际互动的教育场景中，AI无法替代人类教师的作用。
- 无法替代深度批判性思考： 生成式AI擅长信息整合和模式识别，但其本质是基于预训练数据生成内容，难以进行真正的原创性思考或提供超越其训练范畴的深刻见解。过度依赖AI可能导致学生思考能力退化。
- 数据依赖与偏见： AI模型依赖于其训练数据。如果训练数据存在偏见，AI的输出也可能反映甚至放大这些偏见，从而影响教育公平性和内容的客观性。
- 技术可及性与数字鸿沟： 并非所有学生都拥有使用生成式AI工具所需的设备、网络条件和数字素养。这可能加剧现有的数字鸿沟，导致教育资源分配的不公平。
综上，生成式AI在教育中的效能提升是真实存在的，但其作用受到技术质量、学习者特征、教学设计以及伦理考量等多重因素的制约。未来的研究和实践应聚焦于如何优化人机协作模式，发挥AI优势的同时弥补其局限性，从而在确保教育公平和伦理的前提下，最大限度地释放生成式AI在个性化学习与智能辅导中的潜力。
3. 生成式AI应用下的教师角色转型与评价体系重构
3.1 教师角色的迭代方向与实践路径
生成式AI在教育领域的快速发展，正在深刻改变传统的教学模式，并促使教师角色发生根本性转变。随着AI工具能够承担越来越多重复性、机械性的教学事务，教师得以从繁琐的工作中解放出来，将更多精力投入到那些AI无法替代，且对学生发展至关重要的职责中。研究普遍认为，未来教师将不再是单纯的知识传授者，而是向学情分析师、学习引导者、情感陪伴者等更高层次的角色迭代 394041。
1. 学情分析师：从经验判断到数据驱动
生成式AI凭借其强大的数据处理和分析能力，能够收集、整合并分析海量的学习数据，为教师提供前所未有的学情洞察。教师可以利用AI工具，快速识别学生的知识薄弱点、学习偏好、认知模式甚至情绪状态。例如，AI驱动的诊断性评估系统可以精准定位学生在某个知识领域内的具体困难，而生成式AI则可以根据这些诊断结果，为教师提供个性化的教学建议或定制化的辅导方案。
- 实践路径： 教师需要提升数据素养和AI工具使用能力，学会解读AI生成的学情报告，并据此调整教学策略。例如，一位小学语文教师可以利用AI系统分析学生在作文中常见的语法错误和表达模式，而非逐字逐句批改，AI能够提供结构化的错误分类和改进建议。教师则根据AI的分析结果，针对性地开展专题辅导，或为不同学生群体推荐个性化的写作练习。这种数据驱动的教学决策，将大大提升教学的精准性和有效性。
2. 学习引导者：从知识灌输到能力培养
当AI承担了知识内容的组织和初步解释工作后，教师的重心将转向引导学生进行深度学习、批判性思考和问题解决。生成式AI可以作为强大的学习助手，为学生提供个性化的学习资源和即时反馈，但如何激发学生的学习动机，培养其高阶思维能力，则需要教师的巧妙引导。教师将设计更具挑战性和开放性的学习任务，鼓励学生利用AI工具进行探索、创造和协作。
- 实践路径： 教师需要掌握引导式教学、探究式学习和项目式学习的方法。例如，在大学课程中，教师可以布置一个开放性的研究课题，要求学生利用生成式AI工具进行资料搜集、论文草稿撰写、数据分析辅助等工作。教师则在过程中充当“教练”角色，引导学生提出好的问题，评估AI生成信息的可靠性，并基于AI的辅助成果进行深入的批判性思考和创新。这种模式下，教师更关注学生学习过程中的思维发展，而非仅仅知识点的掌握。研究表明，教师对AI工具的接受度高，能够更好地利用AI增强教学支持、促进包容性学习和提升数字素养，从而有效推动自身角色的转型 42。
3. 情感陪伴者：从教学主体到人文关怀
生成式AI虽然在模拟对话和提供信息方面表现出色，但它无法提供真正的情感支持、人文关怀和价值观引导。这些是人类教师独有的、不可替代的优势。在AI时代，教师作为学生成长路上的情感支柱和精神导师，其重要性将更加凸显。教师需要关注学生的心理健康，培养他们的社会情感能力，并引导他们建立正确的价值观和人生观。
- 实践路径： 教师需要加强与学生的沟通交流，建立更深层次的师生关系。例如，当学生在学习中遇到挫折或迷茫时，AI可以提供理性的分析和建议，但教师的鼓励、倾听和共情则能给予学生强大的心理支持。此外，教师还需要利用课堂时间，组织更多讨论、辩论和团队合作活动，培养学生的协作精神、同理心和伦理判断能力，这些都是AI难以直接教授的软技能。研究指出，AI能够通过促进个性化和差异化教学来加强师生关系，从而将教师从单纯的知识传递者转变为促进者和指导者 4143。
总结与挑战：
生成式AI带来的教师角色转型，本质上是一场教育范式的深刻变革。教师从知识的“给予者”转变为学习的“设计者、促进者与陪伴者”。然而，这种转型并非没有挑战。教师需要接受系统的专业发展培训，提升AI素养和数字技能 4445；教育机构需要提供充足的技术支持和资源保障；同时，也需要重新审视教师的评价标准，以适应新的角色定位。只有当教师、技术和教育生态系统协同发展时，生成式AI才能真正赋能教师，共同构建更优质、更个性化的未来教育。
3.2 适配AI应用的教育评价体系创新
生成式AI的普及不仅推动了教师角色的转型，也对传统的教育评价体系提出了新的要求，并提供了创新的可能性。传统的终结性评价往往侧重于知识记忆和标准化考试成绩，难以全面反映学生的学习过程、能力发展和个性化成长。在生成式AI赋能的个性化学习环境下，教育评价正逐步向过程性、多维度、以学生为中心的评价模式转变，并深度融合AI学情数据和学习行为轨迹，以构建更全面、更精准的评价体系。
1. 传统评价体系的局限性与转型需求：
传统的教育评价体系主要依赖于期末考试、统一测验等方式，其局限性日益凸显：
- 评价内容片面： 侧重于对学科知识的掌握程度，忽视了学生在高阶思维、创新能力、协作能力、解决问题能力等核心素养方面的培养。
- 评价时效性差： 终结性评价通常在学习周期结束后进行，难以对学习过程进行即时反馈和干预。
- 评价方式单一： 缺乏对学生个性化学习路径、学习习惯、兴趣特长等差异性的考量。
- 难以应对AI辅助学习： 面对学生可能利用生成式AI完成作业或考试的情况，传统的诚信监管和评价方法面临巨大挑战 46。
生成式AI提供的海量、实时学习数据，为克服这些局限性提供了契机，促使评价体系向以下方向发展：
2. 过程性评价的深化与细化：
AI能够实时记录学生的学习行为轨迹，包括但不限于学习时长、交互频率、问题解决路径、知识点掌握进度、错误类型等。这些数据为构建精细化的过程性评价提供了可能。
- 动态学习表现评估： AI系统可以持续追踪学生在学习过程中的表现，如在线课程的参与度、讨论区的贡献、实验操作的步骤准确性、编程练习的效率和正确率等。通过AI算法对这些数据进行分析，能够形成学生动态的学习画像，而非仅仅依赖最终结果。
- 认知负荷与情感投入分析： 结合前述提及的情感与认知状态数据采集技术（如眼动、面部表情识别），AI甚至可以推断学生在学习过程中的认知负荷和情感投入程度。例如，当系统检测到学生长时间停留在某一难点且表现出沮丧情绪时，可以将其视为一个需要关注的评价指标，并触发相应的干预机制。
3. 多维度评价指标的构建：
生成式AI不仅关注学习成果，更关注学习过程中的能力发展和素养养成。
- 知识图谱驱动的知识掌握评估： 结合AI构建的知识图谱，评价系统能够精准评估学生对各个知识点的掌握程度，包括概念理解、应用能力、迁移能力等，并能发现知识盲区或误解。
- 高阶思维能力评估： AI可以设计开放性、探究性的学习任务，并利用自然语言处理技术分析学生利用AI工具进行信息筛选、批判性分析、观点生成和问题解决的能力。例如，评价学生在利用AI辅助撰写报告时，是否能够有效整合AI生成的内容，并加入自己的独立思考和创新见解。
- 协作与沟通能力评估： 在AI支持的协作学习环境中，评价系统可以分析学生在团队项目中的贡献度、沟通效率、解决冲突的能力等。
- 数字素养与AI伦理评估： 随着AI成为学习工具，学生如何负责任、批判性地使用AI，识别AI生成内容的偏见和局限性，遵守学术诚信等，也将成为重要的评价内容 4647。AI甚至可以辅助检测学生使用AI工具的适当性，例如判断学生是否过度依赖AI生成答案，而非进行独立思考。
4. 新型评价指标设计与落地方法：
- 自适应诊断性评估： 传统诊断性评估通常是静态的。结合AI，可以设计自适应的诊断性评估，根据学生的实时表现动态调整题目难度和类型，更精准地诊断学习者在某一知识领域的掌握水平和认知缺陷。
- 行为轨迹分析与预测： AI能够分析学生的学习行为轨迹，构建预测模型，预警学生可能出现的学习困难、辍学风险或特定能力发展瓶颈，从而实现前瞻性的评价与干预。
- 个性化反馈与指导： 生成式AI能够根据学生的学习数据和评价结果，生成个性化的反馈报告和学习建议，帮助学生认识到自身优势与不足，并提供改进方向。这种反馈比传统教师提供的通用性反馈更具针对性和时效性。
- AI辅助的教师评价： AI可以辅助教师完成批改、数据统计等重复性工作，让教师有更多时间投入到对学生高阶能力和非认知能力的评价中。同时，AI也能提供教学有效性的数据分析，帮助教师反思和改进教学方法。
- 区块链与微凭证（Micro-credentialing）结合： 结合AI技术，可以为学生在个性化学习路径中获得的特定技能和能力颁发数字微凭证，这些凭证基于区块链技术进行认证，具有高度的透明度和不可篡改性，从而更灵活、及时地反映学生的学习成果和能力发展 48。
挑战与展望：
尽管生成式AI为教育评价创新提供了广阔前景，但也面临挑战，如数据隐私保护、算法公平性、评价结果的解释性等问题 474950。同时，教育者需要接受培训，以理解和应用这些新的评价工具和方法。未来的教育评价体系将是一个人机协同的复杂系统，AI负责数据分析和自动化任务，教师则专注于高阶评价、人文关怀和价值判断，共同构建一个更全面、公平、有效的学习评价生态。
4. 生成式AI教育应用的伦理风险与教育公平性研究
4.1 学生隐私保护的技术与规范研究
生成式AI在教育领域的广泛应用，特别是其对个性化学习和智能辅导的赋能，高度依赖于对学生数据的收集、存储和分析。然而，这种数据驱动的模式也带来了显著的学生隐私泄露风险和伦理挑战，尤其是在处理敏感的个人学习数据时。因此，学生隐私保护已成为AI教育应用中亟待解决的关键问题，需要技术防护方案和完善的监管规范协同推进 2251525354555657585960616263。
1. 学生隐私泄露的风险：
生成式AI在教育场景中的数据收集、存储和使用全流程都可能伴随着隐私风险 22525456585963：
- 数据过度采集： 为了提供高度个性化的服务，AI系统倾向于收集尽可能多的学生数据，包括学习行为、进度、表现、甚至情绪和认知状态等敏感信息。过度收集的数据增加了泄露的可能性 57。
- 数据存储与传输安全： 集中存储的海量学生数据是网络攻击者觊觎的目标。一旦系统被入侵，学生的个人身份信息、学习记录、学术表现等都可能被窃取或滥用 525357。
- 数据共享与第三方合作： 教育科技平台常常与第三方供应商、研究机构合作，涉及数据的共享和流通。在缺乏严格监管和技术保护的情况下，数据在共享过程中极易失控，导致隐私泄露 52。
- 算法偏见与歧视： 即使数据匿名化，AI模型也可能在处理数据时无意中泄露个体信息，或者其训练数据中存在的偏见可能导致对特定学生群体的歧视性结果，间接损害学生权益 5154555657616364。
- 非预期的数据使用： 学生数据可能被用于除教育目的之外的商业用途，例如定向广告或用户画像分析，这与教育伦理相悖。
- “黑箱”算法问题： 许多AI模型的决策过程不透明，即所谓的“黑箱”问题。这使得追踪和理解AI如何使用学生数据、如何做出个性化推荐变得困难，增加了监管和问责的难度 6165。
2. 技术防护方案研究进展：
为应对上述隐私挑战，研究人员和工程师们正积极探索多种先进的技术手段：
- 差分隐私（Differential Privacy, DP）： 这是一种强大的隐私保护技术，通过向数据中添加经过数学验证的噪声，使得对数据集的任何查询结果都无法推断出单个个体的信息，即使攻击者拥有所有其他信息也无法实现 5257。差分隐私能够在数据分析和模型训练的同时，提供严格的隐私保障。
- 联邦学习（Federated Learning, FL）： 联邦学习是一种分布式机器学习范式，允许在不直接共享原始数据的情况下，在本地设备（如学校服务器或学生个人设备）上训练模型。模型参数在本地更新后，只有聚合后的模型更新信息（而非原始数据）被发送到中央服务器进行汇总，从而有效地保护了原始数据的隐私性 52576667。
- 同态加密（Homomorphic Encryption）： 这项技术允许对加密数据进行计算，而无需先解密。这意味着，即使数据在云端存储或处理，也能保持其加密状态，从而防止在计算过程中数据泄露的风险 52。尽管同态加密计算成本较高，但其在敏感数据处理场景中具有巨大潜力。
- 匿名化和假名化（Anonymization and Pseudonymization）： 这是最基本的数据隐私保护措施。通过移除或替换识别学生身份的直接标识符（如姓名、学号），将原始数据转换为无法直接关联到个体的形式。然而，在教育数据高度丰富和多样的背景下，完全的匿名化非常困难，存在通过数据关联进行“去匿名化”的风险。
- 安全多方计算（Secure Multi-Party Computation, SMPC）： 允许多方在不泄露各自私有输入的情况下，共同计算一个函数。这在需要多家教育机构或平台协作分析数据时，可以有效保护各方数据的隐私。
- 访问控制与权限管理： 严格的基于角色的访问控制机制，确保只有经过授权的人员才能访问特定级别的学生数据。日志审计和监控系统则用于追踪数据访问行为，及时发现异常。
3. 监管规范研究进展：
技术手段需要与完善的法律法规和政策规范相结合，才能形成全面的隐私保护体系。
- 数据保护法规的采纳与执行： 全球范围内，如欧盟的《通用数据保护条例》（GDPR）、美国的《家庭教育权利与隐私法案》（FERPA）和《儿童在线隐私保护法》（COPPA）等，都对教育数据的收集、使用、存储和共享提出了严格要求 525768。研究强调，教育机构在部署AI时必须遵守这些法规，并根据其要求制定相应的政策 515262。中国和西方国家的新闻媒体也普遍关注AI在高等教育中的数据安全问题，尤其是在远程考试监控和学生数据安全方面 53。
- 制定AI伦理指南与政策： 许多国家和国际组织正在积极制定AI伦理准则，其中学生隐私是核心关注点之一。这些指南呼吁教育机构制定清晰的AI使用政策，明确告知学生AI工具如何使用其数据，并提供数据选择和退出机制 516465。
- 建立数据治理框架： 需要建立健全的数据治理框架，明确数据所有权、管理责任、使用权限和问责机制。这包括在机构内部设立数据隐私官，负责监督AI应用的隐私合规性。
- 透明度与可解释性要求： 监管机构和研究者普遍呼吁AI系统提高其决策过程的透明度和可解释性，以便用户和监管者了解AI如何使用数据、如何做出决策，从而更容易发现和纠正潜在的隐私侵犯或偏见问题 5861。
- 伦理审查委员会的作用： 在教育领域部署AI系统前，应通过独立的伦理审查委员会进行评估，确保其设计和实施符合伦理标准，特别是对未成年学生的保护 52。
挑战与未来方向：
尽管技术和规范都在不断进步，但学生隐私保护仍面临诸多挑战。例如，技术方案的部署成本高昂，对计算资源要求高 57；不同国家和地区的数据保护法规存在差异，增加了跨国教育合作的复杂性 525368；公众和学生对AI隐私风险的认知不足，也可能阻碍隐私保护措施的有效实施 59。未来的研究需要进一步探索如何平衡个性化教育的效益与隐私保护的需求，开发更经济高效的隐私增强技术，并推动全球范围内AI教育应用隐私保护标准的协同与统一。同时，教育者、AI开发者、政策制定者和学生之间的多方对话和协作，是构建安全、可信赖的AI教育生态的关键 5158。
4.2 教育公平的双重影响与优化路径
生成式AI在教育领域的应用，对于教育公平性具有复杂的双重影响：一方面，它展现出缩小教育资源差距、促进教育普惠的巨大潜力；另一方面，也可能因算法偏见、数字鸿沟等因素，加剧现有的不公平现象。深入理解其积极作用与潜在风险，并探索相应的优化路径，是确保生成式AI能够真正促进教育公平的关键 6970。
1. 促进教育公平的积极作用：缩小资源差距与增强可及性
生成式AI在推动教育公平方面具有显著的正向潜力，主要体现在以下几个方面：
- 增强教育可及性，打破地域和经济壁垒： 生成式AI能够提供定制化的学习内容和智能辅导服务，特别是在教育资源匮乏的地区，这弥补了优质师资和教育资源的不足 69。例如，AI驱动的个性化学习平台可以为偏远地区的学生提供与城市学生同等质量的教学资源和辅导，降低了获取优质教育的成本。文献指出，AI辅助教学，特别是AI生成的反馈系统，在资源受限的环境中提供了宝贵的教育支持 69。
- 支持特殊教育需求，实现包容性学习： 生成式AI可以生成多模态的教学材料，如将文字内容转换为语音、图片或视频，为有听觉、视觉障碍的学生提供更易于理解的学习方式 6971。AI驱动的语言支持工具也能帮助非母语学习者克服语言障碍，使其能够平等地参与学习 6971。通过个性化学习系统，AI技术正在通过高级语言支持、个性化学习系统和全面的心理健康服务，彻底改变教育的可及性 71。
- 个性化学习，满足多样化需求： 传统教育模式往往难以顾及每个学生的独特学习速度、风格和兴趣。生成式AI可以根据学生的个体差异，动态调整学习内容、路径和难度，提供真正意义上的个性化学习体验 6972。这种“千人千面”的教学方式，有助于提升每个学生的学习效率和参与度，从而提升整体教育质量，缩小因学习能力差异造成的成就差距。研究表明，生成式AI能够增强学生参与度和学习成果，通过定制化学习实现教育公平 73。
- 开放教育资源（OER）的生成与普及： 生成式AI能够辅助教师快速创建和更新高质量的开放教育资源，降低了教育内容的生产成本，并促进了知识的自由传播 69。这使得更多学生，无论其经济状况如何，都能够获取丰富的学习材料。
2. 带来新的不公平性风险：算法偏见与数字鸿沟
尽管生成式AI具有促进教育公平的潜力，但也必须警惕其可能带来的新的不公平性风险：
- 算法偏见（Algorithmic Bias）： 生成式AI模型在训练过程中使用了大量的历史数据，如果这些数据本身存在社会、文化或历史偏见，AI模型在生成内容或进行决策时，就可能复制甚至放大这些偏见 466974。例如，如果AI训练数据在特定族裔、性别或社会经济背景上存在偏差，AI生成的学习内容或评估结果可能对这些群体产生歧视，或者未能充分体现其文化背景，从而加剧教育不平等 5170。这种偏见可能导致对某些学生群体的刻板印象强化，或不公平的资源分配。
- 数字鸿沟（Digital Divide）： 生成式AI工具的有效使用依赖于高质量的数字基础设施（如高速互联网、性能良好的设备）和较高的数字素养。然而，在贫困地区或低收入家庭，学生可能缺乏这些基本的数字条件，无法平等地接触和使用AI教育工具 364669。这种“数字贫困”可能导致现有教育不平等的进一步加剧，形成新的“数字精英”与“数字弱势”群体。研究表明，教育水平、就业状况和经济状况是影响老年人数字素养的关键因素，这提示了数字鸿沟对不同社会经济群体的影响 75。
- 过度依赖与批判性思维的削弱： 如果学生过度依赖AI生成答案或完成任务，可能会削弱其独立思考、批判性分析和问题解决的能力。这种“AI依赖”可能导致学生在没有AI辅助的情况下，难以应对复杂问题，从而影响其长期的学习和职业发展，这对于未能有效利用AI或被AI“剥夺”学习机会的弱势学生更为不利。
- 数据隐私与安全问题： 低收入家庭或欠发达地区的学生可能更容易在不知情的情况下，因使用免费或低成本的AI工具而泄露个人数据，或者其数据未能得到充分的保护，从而引发隐私泄露风险，进一步损害其权益 767778。
- 技术供应商的垄断与商业化： 随着AI教育市场的发展，少数大型技术公司可能形成垄断，导致教育资源的商业化和高昂成本，这可能使贫困学生更难获得高质量的AI教育服务 79。
3. 优化路径与干预策略研究
为最大限度发挥生成式AI在促进教育公平方面的积极作用，并有效规避其潜在风险，需要多方面协同的优化路径和干预策略：
- 加强AI模型的公平性审查与偏见缓解：
- 多样化训练数据： 确保AI模型的训练数据具有广泛的代表性，涵盖不同文化、社会经济背景和族裔群体，以减少潜在的偏见。
- 算法审计与去偏技术： 对AI算法进行定期的公平性审计，开发并应用去偏技术，以识别和纠正算法中的歧视性倾向 70。
- 透明度与可解释性： 提高AI决策过程的透明度，让教育者和学生能够理解AI推荐或评估的依据，从而更容易发现和纠正不公平现象。
- 弥合数字鸿沟，保障技术可及性：
- 建立健全的伦理规范与监管框架：
- 教师的关键作用：人机协同的公平实践：
- 专业发展与培训： 教师需要接受培训，理解AI的工作原理、潜在偏见和伦理风险，学会如何批判性地评估AI生成的内容，并将其整合到公平的教学实践中。
- 促进批判性思维： 教师应设计鼓励学生深度思考和批判性评估AI生成内容的教学活动，培养学生成为AI的有效使用者和批判性思考者。
- 情感与价值观引导： 教师在AI时代的角色更加侧重于情感支持、价值观引导和人际互动，确保学生在享受技术便利的同时，获得全面而均衡的发展。
综上所述，生成式AI在促进教育公平方面具有巨大的潜力，但同时也伴随着显著的风险。只有通过技术、政策、教育和伦理等多方面的协同努力，才能确保生成式AI成为缩小而非扩大教育差距的强大工具，最终实现普惠、公平和高质量的教育。
5. 生成式AI个性化学习与智能辅导的典型落地场景
5.1 通用规模化教学场景实践
生成式AI凭借其强大的内容生成、理解和交互能力，在规模化教学场景中展现出显著的效率提升和个性化赋能潜力。从大规模开放在线课程（MOOC）的个性化反馈，到K12教育的学科同步辅导，再到职业技能的仿真训练，生成式AI的应用正在重塑这些通用教学场景的实践模式，并已积累了可复制的推广经验。
1. MOOC个性化反馈：提升学习体验和完成率
大规模开放在线课程（MOOC）虽然极大地拓宽了教育的可及性，但普遍面临学习者参与度低、完成率不高的挑战。生成式AI在此场景下的应用核心是提供大规模、实时的个性化反馈，以弥补传统MOOC因师生比过高而难以提供个体化指导的不足。
落地案例与运营成效：
- 智能讨论区助手： 生成式AI可以实时监控MOOC讨论区，识别学习者的问题、困惑点和常见错误，并自动生成相关的解释、链接或启发性问题，引导学习者深入思考。这不仅能提供即时支持，还能促进学习者之间的 Peer-to-Peer 互动。
- 作业与评估的个性化反馈： 对于编程作业、写作任务或开放性问题，生成式AI可以根据预设的评分标准和学生提交的内容，提供细致的个性化反馈，指出优缺点，并给出改进建议。例如，AI可以分析学生的代码逻辑、文本结构或论证严谨性，生成比传统自动化评分更具建设性的反馈，显著提升学生对反馈的满意度和学习效果。
- 学习路径与资源推荐： 基于学习者在MOOC平台上的行为数据（观看时长、习题得分、讨论参与等），生成式AI能够动态调整学习者的推荐内容，如推荐更适合其当前水平的辅助材料、挑战性习题或相关拓展课程。
- 实证研究： 有研究指出，将生成式AI工具与教学设计矩阵相结合，能够有效地支持MOOC虚拟课堂的开发，并通过提供个性化和丰富的教育体验，提高大学生的学习参与度和满意度 80。
可复制的推广经验： MOOC平台应积极整合生成式AI模块，构建智能反馈系统。关键在于确保AI反馈的准确性、及时性和建设性。同时，需引导学习者正确看待AI反馈，培养其批判性地利用AI辅助学习的能力。运营方可通过A/B测试等方式，持续优化AI反馈机制，提升MOOC的完成率和学习质量。
2. K12学科同步辅导：实现普惠与精准教学
K12教育是生成式AI个性化学习应用最具潜力的领域之一。它能够为学生提供与课堂教学同步的个性化辅导，尤其在数学、语文、英语等学科，有效缓解家长辅导压力，并弥补教育资源不均衡的现状。
落地案例与运营成效：
- 智能答疑机器人： 学生在完成作业或预习时遇到问题，可以通过拍照、语音或文字向AI提问。生成式AI能立即识别问题内容，并给出详细的解题步骤、知识点解释或相关例题，甚至进行多轮对话以确保学生真正理解。这种即时、无限次答疑服务，极大地支持了学生的自主学习。
- 个性化练习册与试卷生成： 基于学生的学情数据（错题记录、知识点掌握情况），生成式AI能够动态生成定制化的练习题或模拟试卷，题目难度、类型和知识点分布都与学生的当前水平和薄弱环节相匹配。这比传统的“题海战术”更为高效和精准。
- 学情分析报告： AI系统可以定期为家长和教师生成详细的学情分析报告，清晰呈现学生的知识点掌握程度、学习进度、优势劣势等，为家校共育和教师精准教学提供数据支撑。
- 实证研究： 现有研究表明，AI聊天机器人能够提升学生的学习动机和语言技能，同时降低教育成本和教师工作量，但在交互限制、误导性答案和原创性方面仍存在挑战 81。一项针对数学教育的系统综述也强调了AI在解决方程、几何可视化以及提供自适应学习系统和生成式AI平台等方面的应用 82。
可复制的推广经验： 关键在于AI辅导内容要紧密贴合国家课程标准和教材体系，确保内容的权威性和准确性。同时，要注重人机协作，强调教师和家长的引导作用，防止学生过度依赖AI而丧失独立思考能力。此外，需关注不同年龄段学生的认知特点，设计符合其发展规律的交互界面和辅导方式。
3. 职业技能仿真训练：降低成本与提高效率
在职业教育和企业培训领域，生成式AI结合多模态生成技术，能够构建高逼真度的仿真训练环境，为技能学习者提供安全、可重复的实践机会，尤其适用于高风险、高成本或难以现场操作的职业技能。
落地案例与运营成效：
- 虚拟操作技能训练： 例如，在药学教育中，大型语言模型可以作为临床药师学生的训练策略，辅助他们在药物剂量调整方面进行学习 83。在工业领域，AI可以模拟生产线故障、设备维修等场景，工人可在虚拟环境中进行故障诊断和排除训练。
- 情境化沟通与决策训练： 生成式AI可以模拟客户、患者、同事等不同角色，构建复杂的沟通场景，让学习者练习谈判技巧、应急处理、团队协作等软技能。例如，销售人员可在AI模拟的客户面前练习产品推销，获得即时反馈。
- 个性化课程内容生成： 针对不同职业技能培训需求，生成式AI可快速生成定制化的课程模块、案例分析、实训手册等，大幅缩短课程开发周期，降低培训成本。
- 实证研究： 机器人技术作为智能机器人，已被提出作为一种长期的课堂技术，通过提供基于AI的实践经验来支持学生的AI素养和认知发展 84。这表明仿真训练等实践性应用在培养AI技能方面具有重要价值。
可复制的推广经验： 仿真训练的成功关键在于模拟环境的真实感和反馈机制的准确性。需要结合行业专家知识，持续优化AI模型，确保仿真训练与实际工作场景的高度一致性。同时，要建立完善的评估体系，将仿真训练结果与实际工作表现挂钩，以验证训练效果。推广时，可采取模块化、可定制的解决方案，满足不同行业和企业的特定需求。
这些通用场景的落地实践表明，生成式AI正从根本上提升教育的效率、公平性和个性化水平，其可复制的经验为未来更广泛的应用奠定了基础。
5.2 细分特色教育场景创新
除了通用规模化教学场景，生成式AI也在一些小众和细分的教育领域展现出独特的创新价值，为特定学习群体提供前所未有的个性化支持。这些场景的探索不仅弥补了传统教育模式的不足，也突显了生成式AI在差异化应用中的潜力。
1. 障碍人群特殊教育：赋能无障碍学习与全面发展
生成式AI在特殊教育领域具有革命性的意义，它能够为残障学生提供高度定制化、无障碍的学习体验，帮助他们克服生理或认知障碍，融入主流教育，并促进其全面发展。
- 创新探索：
- 个性化学习路径与自适应内容： 对于有学习障碍或注意力缺陷的学生，生成式AI可以根据其认知特点和学习进度，生成结构更简单、语言更清晰、步骤更细化的学习材料。例如，可以将复杂的文本信息简化为易于理解的摘要、图示或语音。对于自闭症学生，AI可以创建可预测、一致的学习环境，减少外界刺激，帮助他们更好地专注于学习内容。
- 智能辅助工具与无障碍交互： 生成式AI驱动的语音识别、文本转语音（TTS）和手语翻译工具，能够显著提升听障和视障学生的学习效率和参与度。例如，AI可以将课堂讲授实时转换为文字或手语动画；对于视障学生，AI可以朗读屏幕内容或描述图片信息。这使得他们能够无障碍地获取知识，并与教学内容进行交互 85。
- 社交技能与情绪管理辅导： 生成式AI可以创建虚拟社交场景，让自闭症或社交焦虑的学生在安全、可控的环境中练习社交对话和情境应对，获得即时反馈，从而提升社交能力。AI聊天机器人也能提供情绪识别和初步的情绪疏导，帮助学生管理压力和焦虑。
- 个性化写作支持： 对于有读写障碍（如失读症）的学生，生成式AI可以辅助其进行写作，提供拼写检查、语法修正、句子重构和内容建议，帮助他们克服书面表达的困难 8687。
- 差异化应用价值：
2. 拔尖人才定制化培养：激发潜力与突破创新边界
在拔尖人才培养领域，生成式AI并非简单地提供知识，而是作为一种高级认知工具，帮助天赋学生拓展思维边界，深化探究能力，并加速创新进程。
- 创新探索：
- 超个性化学习挑战与项目： 生成式AI可以根据拔尖学生的兴趣特长、知识广度和深度，定制极具挑战性的研究课题、案例分析或跨学科项目。例如，AI可以生成复杂的科学问题，提供多角度的参考资料，并辅助学生构建创新的解决方案。
- 虚拟导师与思想伴侣： 对于高层次人才，生成式AI可以扮演虚拟导师的角色，与其进行高水平的对话，启发深层思考，提供不同领域的视角，甚至模拟反方辩论，帮助学生进行批判性思维训练。例如，ChatGPT等工具可以用于支持独立研究，提供虚拟指导，从而满足资优学生的独特认知和情感需求 89。
- 创新内容生成与原型开发： 在艺术、设计、工程等领域，生成式AI可以作为学生创新过程中的辅助工具，快速生成创意原型、设计草图、代码片段或理论模型，帮助学生将抽象构想具象化，加速创新迭代。例如，AI可以根据学生的描述生成一段旋律、一个建筑设计概念图，或一段实现特定功能的代码。
- 跨学科知识融合与拓展： 生成式AI能够快速检索、整合和分析海量的跨学科信息，为拔尖人才提供更广阔的知识视野和问题解决思路，帮助他们发现不同学科间的内在联系，培养系统性思维。
- 差异化应用价值：
总而言之，生成式AI在细分特色教育场景中的创新应用，展现了其“因材施教”的强大能力。它不仅能够帮助弱势群体克服学习障碍，实现教育公平，也能助力拔尖人才突破传统教育模式的限制，攀登更高的学术和创新高峰。这些差异化应用凸显了生成式AI作为个性化教育赋能者的独特价值。
6. 未来研究方向与实践推进建议
生成式AI在个性化学习与智能辅导领域的应用展现出巨大的潜力和广阔的前景，然而，现有研究和实践仍存在诸多不足和待解决的问题。为了更好地发挥生成式AI的赋能作用，并规避其潜在风险，未来的研究和实践推进应聚焦于以下几个关键维度：技术适配迭代、规则体系完善、场景差异化创新。
6.1 技术适配迭代
当前生成式AI在教育领域的应用仍处于早期阶段，技术的进一步成熟与教育场景的深度适配是其效能最大化的基础。
- 多模态融合与认知增强模型的研发： 现有生成式AI在教育应用中多以文本处理为主，而人类学习是一个多感官、多通道协同的复杂过程。未来的研究应聚焦于多模态生成式AI的深度融合，例如，如何将文本、图像、语音、视频、3D模型甚至触觉反馈无缝集成，以创造更具沉浸感和个性化的学习体验。此外，应探索开发能够模拟人类认知过程（如推理、问题解决、情感理解）的认知增强模型，使其能够更准确地理解学生的学习状态和思维模式，提供更智能、更具启发性的辅导。这将超越简单的信息生成，实现真正的“智能辅导”。
- 小样本学习与领域自适应能力的提升： 教育数据往往具有稀疏性、异质性强等特点，尤其是对于个性化学习和特殊教育场景，难以获取大规模高质量的标注数据。未来的研究应致力于提升生成式AI在小样本学习（Few-shot Learning）和领域自适应（Domain Adaptation）方面的能力，使其能够仅凭少量特定领域的教育数据，就能快速学习并生成高质量、高准确性的内容，降低AI模型在不同教育场景下的部署成本和时间。
- 可信赖AI与可解释性AI（XAI）的研究： 生成式AI的“黑箱”特性限制了其在教育关键决策中的应用，例如学业评估、学习路径规划等。未来的研究必须强化可信赖AI（Trustworthy AI）的构建，包括提高模型的透明度、可解释性、鲁棒性和公平性。开发能够解释其生成内容和决策过程的XAI模型，将有助于教师和学生理解AI的建议，建立信任，并能及时发现和纠正潜在的偏见或错误，这对于确保教育决策的公正性和科学性至关重要。例如，模型应能解释为何推荐某个学习资源或为何对某个答案给出特定反馈。
- 人机协同优化模型： 生成式AI不应是替代人类教师的工具，而应是增强人类教学能力的伙伴。未来的研究应深入探索高效的人机协同模式，开发能够智能分配任务、优化协作流程的AI系统。这包括AI如何理解教师的意图、如何将任务拆解并与教师进行有效配合，以及如何通过AI反馈循环不断提升教师的教学效能。例如，AI可以在备课时提供多样的教学素材，在课堂中协助实时学情分析，在课后辅助个性化作业批改，而教师则专注于情感互动、价值引领和高阶思维的培养。
6.2 规则体系完善
生成式AI在教育领域的负责任应用离不开健全的规则体系和政策框架。
- 学生数据隐私与安全保护标准的制定与实施： 随着AI对学生数据的深度依赖，隐私泄露风险日益突出。未来需要细化和完善学生数据收集、存储、使用、共享和销毁的全生命周期管理规范。这包括但不限于：明确最小化数据收集原则、强化数据加密技术、推行联邦学习等隐私计算范式、建立数据匿名化与去标识化的行业标准。此外，应建立独立的数据安全审计机制，定期评估AI教育产品的隐私合规性，并对违规行为设立明确的惩罚措施。
- 学术诚信与伦理规范的建设： 生成式AI可能引发新的学术不端行为，如AI辅助代写、内容剽窃等。各级教育机构应尽快制定并发布清晰、可操作的生成式AI使用指南和学术诚信政策，明确学生在何种情况下可以使用AI，如何正确引用AI生成内容，以及违反规定的后果。同时，鼓励开发能够识别AI生成内容的辅助工具，并强调过程性评价、口头答辩和个性化任务在防范学术不端中的作用。伦理规范还需涵盖AI内容的潜在偏见、歧视以及对学生批判性思维和原创性的影响。
- AI教育产品准入与评估机制的建立： 鉴于市场上AI教育产品鱼龙混杂，质量参差不齐，未来应建立科学、公正的AI教育产品准入与评估机制。这包括对AI产品的技术成熟度、教育有效性、伦理合规性、数据安全性和用户体验进行多维度审查。评估标准应由教育专家、AI技术专家和伦理学者共同制定，并定期更新，确保引入教育领域的AI产品是高质量、负责任和有益的。
- 教师AI素养与专业发展标准： 教师是AI教育应用落地的关键。未来需要建立针对教师的AI素养培训体系和专业发展标准，使其掌握生成式AI的基本原理、使用方法、潜在优势与风险，并能够将AI工具有效融入教学设计、学情分析和个性化辅导中。这包括提供持续的在职培训、在线课程和实践工作坊，帮助教师适应新的教学范式，成为AI时代的学习设计师和引导者。
6.3 场景差异化创新
生成式AI的教育应用不应局限于通用模式，而应在不同教育场景中进行差异化探索，以最大化其独特价值。
- 特殊教育与包容性学习的深度应用创新： 针对障碍人群，未来研究应进一步探索生成式AI在多感官交互、情绪识别与干预、社交技能训练等方面的应用。例如，开发能够根据自闭症儿童的情绪状态实时调整交互策略的AI伴侣，或为读写障碍学生提供更精准、更个性化的读写辅助工具。研究还应关注如何利用AI技术，通过生成多模态的、适应性强的学习材料，帮助边缘化学习者克服语言、认知或身体障碍，从而真正实现教育的公平性和包容性。
- 高层次人才培养与拔尖创新能力的激发： 在拔尖创新人才培养方面，生成式AI应从知识传授转向能力激发。未来的创新应侧重于利用AI模拟真实科研环境、提供复杂的开放性问题、生成跨学科的知识融合路径，以及扮演高级思维训练的“虚拟陪练”。例如，开发能够与学生进行高阶学术对话、辅助学生进行假设验证、提供多角度批判性分析的AI导师，帮助学生突破思维定势，培养原创性、批判性和解决复杂问题的能力。
- 职业教育与终身学习的动态适应性平台： 职业教育和终身学习对实践性和时效性要求高。生成式AI可创新应用于：根据产业发展趋势和个人职业路径，动态生成定制化的职业技能培训课程；提供高逼真度的虚拟实训场景，降低实训成本和风险；通过AI驱动的技能评估和就业市场分析，为学习者提供个性化的职业发展建议。这将使职业教育更具前瞻性和适应性。
- 国际教育与跨文化交流的桥梁： 生成式AI在语言翻译、文化背景生成方面的优势，使其能够成为国际教育和跨文化交流的有力工具。未来的创新可以包括：生成多语言的学习材料和教学内容，帮助学生克服语言障碍；模拟不同文化背景下的对话场景，提升学生的跨文化沟通能力；通过AI生成的世界各地文化习俗、历史事件介绍，促进学生的全球视野和文化理解。
实践推进建议：
- 教育主管部门： 制定国家层面的AI教育发展战略和伦理规范；加大对AI教育基础设施建设的投入，特别是缩小数字鸿沟；设立专项研究基金，鼓励产学研合作，推动核心技术攻关和创新应用试点。
- 学校： 积极拥抱生成式AI，将其纳入学校信息化发展规划；组织教师进行常态化的AI素养和技能培训；鼓励教师探索AI与教学的深度融合，并建立校本的AI应用案例库和最佳实践指南；修订学生行为准则和学术诚信政策，引导学生负责任地使用AI。
- 技术服务商： 秉持“以学习者为中心”的理念，开发更符合教育规律、更具教育价值的生成式AI产品；加强与教育机构和研究者的合作，确保技术研发能够解决教育实践中的真问题；重视产品的伦理合规性设计，特别是数据隐私保护和算法公平性，并提供清晰的产品使用说明和技术支持。
通过上述多维度的研究和实践推进，生成式AI有望在未来成为推动教育现代化、实现个性化学习和智能辅导的关键引擎，最终构建一个更智能、更公平、更高效的教育生态系统。
内容由 AI 生成，仅供参考，请仔细甄别
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Subhankar Maity, Aniket Deroy
Generative Artificial Intelligence (AI) is revolutionizing educational technology by enabling highly personalized and adaptive learning environments within Intelligent Tutoring Systems (ITS). This report delves into the integration of Generative AI, particularly large language models (LLMs) like GPT-4, into ITS to enhance personalized education through dynamic content generation, real-time feedback, and adaptive learning pathways. We explore key applications such as automated question generation, customized feedback mechanisms, and interactive dialogue systems that respond to individual learner needs. The report also addresses significant challenges, including ensuring pedagogical accuracy, mitigating inherent biases in AI models, and maintaining learner engagement. Future directions highlight the potential advancements in multimodal AI integration, emotional intelligence in tutoring systems, and the ethical implications of AI-driven education. By synthesizing current research and practical implementations, this report underscores the transformative potential of Generative AI in creating more effective, equitable, and engaging educational experiences.
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The rapid evolution of technology has significantly transformed the educational landscape, with the advent of Large Language Models (LLMs) introducing new possibilities for personalized learning. This systematic review examines the educational impact of LLM-based learning systems compared to traditional educational approaches, focusing on six critical research questions. These questions explore the effectiveness of LLMs in enhancing student engagement, emotional and social development, real-time progress monitoring, and their role in creating fair and rigorous examination environments. Furthermore, the review addresses challenges such as ethical considerations, privacy concerns, and the extent to which LLMs can simulate real-world teaching experiences. A total of 55 studies, published between 2020 and 2024, were systematically analyzed to explore the impact of Large Language Models (LLMs) on educational outcomes, including emotional, social, and academic development. These studies included a combination of peer-reviewed articles, conference papers, and journal publications, which were selected through a set of predetermined inclusion and exclusion criteria. Quality assessment criteria ensured the inclusion of high-quality research focusing on the application of LLM-based AI technology in education. The review also highlights key challenges and limitations, including issues of accessibility, ethical dilemmas, and the integration of AI into traditional education systems. Findings underscore the potential of LLMs to revolutionize education through personalized learning, while also addressing the critical need for rigorous evaluation and ethical deployment to ensure equitable and effective outcomes.
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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.
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Jayaron Jose, Blessy Jayaron Jose
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O. P. Rishi, Rekha Govil, Madhavi Sinha
Online learning with Intelligent Tutoring System (ITS) is becoming very popular where the system models the student-s learning behavior and presents to the student the learning material (content, questions-answers, assignments) accordingly. In today-s distributed computing environment, the tutoring system can take advantage of networking to utilize the model for a student for students from other similar groups. In the present paper we present a methodology where using Case Based Reasoning (CBR), ITS provides student modeling for online learning in a distributed environment with the help of agents. The paper describes the approach, the architecture, and the agent characteristics for such system. This concept can be deployed to develop ITS where the tutor can author and the students can learn locally whereas the ITS can model the students- learning globally in a distributed environment. The advantage of such an approach is that both the learning material (domain knowledge) and student model can be globally distributed thus enhancing the efficiency of ITS with reducing the bandwidth requirement and complexity of the system.
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Xin Jia, Wenjie Zhou, Xu Sun, et al.
Question Generation (QG) is an essential component of the automatic intelligent tutoring systems, which aims to generate high-quality questions for facilitating the reading practice and assessments. However, existing QG technologies encounter several key issues concerning the biased and unnatural language sources of datasets which are mainly obtained from the Web (e.g. SQuAD). In this paper, we propose an innovative Examination-type Question Generation approach (EQG-RACE) to generate exam-like questions based on a dataset extracted from RACE. Two main strategies are employed in EQG-RACE for dealing with discrete answer information and reasoning among long contexts. A Rough Answer and Key Sentence Tagging scheme is utilized to enhance the representations of input. An Answer-guided Graph Convolutional Network (AG-GCN) is designed to capture structure information in revealing the inter-sentences and intra-sentence relations. Experimental results show a state-of-the-art performance of EQG-RACE, which is apparently superior to the baselines. In addition, our work has established a new QG prototype with a reshaped dataset and QG method, which provides an important benchmark for related research in future work. We will make our data and code publicly available for further research.
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Xu Chen, Di Wu
Generative AI is widely recognized as one of the most influential technologies for the future, having sparked a paradigm shift in scientific research. The field of education has also been greatly impacted by this transformative technology, with researchers exploring the applications of generative AI, particularly ChatGPT, in education. However, existing research primarily focuses on generating text from text, and there remains a relative scarcity of studies on leveraging multimodal generation capabilities to address key challenges in multimodal data supported instruction. In this paper, we present a technical framework for generating Tang poetry situational videos, emphasizing the utilization of generative AI to address the need for multimedia teaching resources. Our framework comprises three main modules: textual situational comprehension, image creation, and video generation. Moreover, we have developed a situational video generation system that incorporates various technologies, including text-to-text generation models, text-to-image generation models, image interpolation, text-to-speech synthesis, and video synthesis. To ascertain the efficacy of the modules within the Tang poetry situational video generation system, we undertook a comparative analysis utilizing the prevalent text-to-image and text-to-video generation models. The empirical findings indicate that our approach is capable of generating images that exhibit greater semantic similarity with the poems, thereby enabling a better comprehension of the poem's connotations and its key components. Concurrently, the Tang poetry videos generated can significantly contribute to the reduction of cognitive load and the enhancement of understanding during the learning process. Our research showcases the potential of generative AI in the education field, specifically in the domain of multimodal teaching resources.
11ACE - adaptive courseware environmentOpenAlex
Marcus Specht, Reinhard Oppermann
Abstract The Adaptive Courseware Environment (ACE) is a WWW-based tutoring framework which combines methods of knowledge representation, instructional planning, and adaptive media generation to deliver individualized courseware via the WWW. ACE is based on a domain model of the subject matter, a pedagogical model on how to teach a curriculum, and learner modeling on different levels, e.g., preferences, interests, and knowledge. Based on these three components individualized web-content (HTML, Java, pictures) is generated and presented to the learners. Taking into account the interests and the knowledge of a learner ACE can adapt different aspects of the instructional process, e.g., adapting the curriculum by selection of content, adapting the presentation of contents by choosing appropriate media and combining them, adapting the teaching strategies for specific contents, annotating hyperlinks, and by recommending appropriate hyperlinks and contents. Currently three systems have been realized on the basis of ACE and adaptive components of each system have been evaluated in empirical studies. Additionally experimental studies of the applied adaptive methods have shown improvements in efficiency and effectiveness of learning compared to classical static hypermedia.
12AI Study Partner : Development of an LLM and Gen AI-Enhanced Study Assistant ToolOpenAlex
P Jayavardhini
In recent years, Generative AI has started to play a pivotal role in transforming the educational landscape, making learning more personalized, engaging, and accessible. Unlike traditional educational tools, Gen AI can adapt to each student's unique learning style and pace, offering customized support that can significantly enhance understanding and retention of information. It enables the creation of intelligent tutoring systems, interactive study aids, and personalized learning experiences that can assess and respond to individual needs in real time This paper introduces the AI Study Partner, an innovative tool designed to revolutionize the educational landscape by integrating Generative Artificial Intelligence (Gen AI) and Large Language Models (LLMs). The AI Study Partner is engineered to cater to the diverse needs of learners by offering a suite of six key features: the ability to upload and interact with any type of content, summarization of extensive lessons, generation of flashcards for effective study, automated question creation with auto-evaluation capabilities, a conversational chatbot assistant, and an advanced smart search function. These features collectively aim to create a more personalized, engaging, and efficient learning experience. The development of the AI Study Partner responds to the pressing need for educational tools that accommodate the varying paces and styles of learning, making education more accessible and effective. By leveraging the latest advancements in AI, the tool not only facilitates a deeper understanding of complex subjects but also encourages independent study habits and critical thinking skills. This research outlines the conceptual framework, design methodology, and technical implementation of the AI Study Partner, highlighting its potential to positively impact education by providing a versatile and interactive learning platform.The AI Study Partner represents a significant step forward in the pursuit of creating adaptive, responsive, and personalized educational experiences for learners worldwide. Key Words : Artificial Intelligence in Education, Personalized Learning , Large Language Models (LLMs), Generative AI (Gen AI), User Engagement in Learning Data-Driven Education, Vector database, AI based Feedback Conversational Chatbots, Smart Search.
13What drives students toward ChatGPT? An investigation of the factors influencing adoption and usage of ChatGPTOpenAlex
Chandan Kumar Tiwari, Mohd Abass Bhat, Shagufta Tariq Khan, et al.
Purpose The purpose of this paper is to identify the factors determining students’ attitude toward using newly emerged artificial intelligence (AI) tool, Chat Generative Pre-Trained Transformer (ChatGPT), for educational and learning purpose based on technology acceptance model. Design/methodology/approach The recommended model was empirically tested with partial least squares structural equation modeling using 375 student survey responses. Findings The study revealed that students have a favorable view of the instructional use of ChatGPT. Usefulness, social presence and legitimacy of the tool, as well as enjoyment and motivation, contribute to a favorable attitude toward using this tool in a learning environment. However, perceived ease of use was not found to be a significant determinant in the adoption and utilization of ChatGPT by the students. Practical implications This research is intended to benefit enterprises, academic institutions and the global community by offering light on how students perceive the ChatGPT service in an educational setting. Furthermore, the application enhances confidence and interest among learners, leading to improved literacy and general awareness. Eventually, the outcome of this research will help AI developers to improve their product and service delivery, as well as benefit regulators in regulating the usage of AI-based bots. Originality/value Due to its novelty, the current research on AI-based ChatGPT usage in the education sector is rather restricted. This study provides the adoption aspects of ChatGPT, a new AI-based technology for students, thereby contributing significantly to the existing research on the adoption of advanced education technologies. In addition, the literature lacks research on the adoption of ChatGPT by students for educational purposes; this study addresses this gap by identifying adoption determinants of ChatGPT in education.
14Can Generative Artificial Intelligence be a Good Teaching Assistant?—An Empirical Analysis Based on Generative <scp>AI</scp> ‐Assisted TeachingOpenAlex
Qianwen Tang, Wenbo Deng, Yidan Huang, et al.
ABSTRACT Background Generative Artificial Intelligence (AI) shows promise in enhancing personalised learning and improving educational efficiency. However, its integration into education raises concerns about misinformation and over‐reliance, particularly among adolescents. Teacher supervision plays a critical role in mitigating these risks and ensuring the effective use of Generative AI in classrooms. Despite the growing interest in Generative AI, there is limited empirical research on its actual impact and the role of teacher oversight. Objective The purpose of this study is to systematically assess the role of Generative AI in classroom teaching, with a specific focus on how teacher supervision shapes its effectiveness. Method This study employed a quasi‐experimental design to examine differences in learning outcomes among students under three instructional methods: traditional computer‐assisted teaching, Generative AI‐assisted teaching without teacher supervision and Generative AI‐assisted teaching with teacher supervision. The study was implemented in the context of a two‐week Information Science and Technology course in a middle school, involving three classes with 45, 41 and 45 students, respectively. To ensure consistency in teaching styles, all classes were taught by the same experienced teacher. Data collection included a knowledge test to assess knowledge mastery, as well as questionnaires to measure learning satisfaction and engagement. The collected data were analysed using one‐way ANOVA to compare the effectiveness of the three teaching methods. Results and Conclusion Compared with traditional computer‐assisted teaching, Generative AI‐assisted teaching can significantly enhance students' learning satisfaction, but can not improve their learning engagement and knowledge mastery level. Furthermore, in the process of Generative AI‐assisted teaching, teacher supervision can significantly increase students' learning engagement and knowledge mastery compared with situations without teacher supervision. This study indicated Generative AI's potential as an educational tool and underscored the essential role of teacher supervision. Implications This study fills a critical gap by providing empirical evidence on how Generative AI and teacher supervision interact to improve classroom learning outcomes. It shows that Generative AI's potential to enhance learning outcomes is significantly amplified with teacher oversight.
15Generative AI Solutions for Faculty and Students: A Review of Literature and Roadmap for Future ResearchOpenAlex
Giulio Marchena Sekli, Amy Godo, Jose Carlos Veliz
Aim/Purpose: This paper aims to address the gap in comprehensive, real-world applications of Generative Artificial Intelligence (GenAI) in education, particularly in higher education settings. Despite the evident potential of GenAI in transforming educational practices, there is a lack of consolidated knowledge about its practical effectiveness and real-world impact. Background: This study addresses this gap by conducting a systematic literature review to collate and analyze real-life instances of GenAI applications in higher education, thus providing a nuanced understanding of its practical implementations and measurable outcomes. Methodology: The paper utilizes a systematic literature review methodology, adopting the PRISMA approach complemented by a thematic analysis procedure to ensure a comprehensive and in-depth evaluation of the literature. It synthesizes information from relevant articles from 2022 to 2024, focusing on the applications of GenAI in higher education. This analysis covers various aspects, including research settings, analysis scales, data types, collection tools, and analytical methods. Contribution: The paper contributes to the academic community by offering a comprehensive review of GenAI applications in education, highlighting the current precision level of these tools, and providing strategic recommendations for their effective use in academia. Furthermore, the research defines seven specific cases where Gen AI can be utilized as a reference for educational institutions in their adoption strategies. Findings: Key findings include the versatility of GenAI in generating teaching materials, enhancing skill development, supporting student tasks, academic performance evaluation, feedback delivery, and its role as a virtual assistant and in research support. Recommendations for Practitioners: Practitioners are advised to explore the integration of GenAI for diverse educational purposes, from content creation to student assessment, while being cognizant of its limitations and ethical considerations. Recommendation for Researchers: Future research should focus on addressing the gaps identified, such as the implications of GenAI in research roles, its application in various disciplines, and the exploration of newly developed AI tools tailored to specific educational needs. Impact on Society: The findings of this paper highlight the potential of GenAI in revolutionizing the educational sector, offering personalized learning experiences, and significantly influencing teaching methodologies and student engagement, but it also reveals significant deficiencies of Generative AI, known as hallucinations, which can impact the expected results. Future Research: Subsequent research should explore the evolving capabilities of GenAI models, their impact on various academic disciplines, and the development of pedagogical strategies to optimize their use in education.
16Unlocking the Beat: How <scp>AI</scp> Tools Drive Music Students' Motivation, Engagement, Creativity and Learning SuccessOpenAlex
Lixia Chen
ABSTRACT This study explores the relationships among music students' artificial intelligence (AI) perceptions, motivation, engagement, creativity and learning success. Through a random sampling method, 521 Chinese music students participated in the research, which employed a range of questionnaires to assess AI perceptions, motivation, engagement, learning outcomes and creativity. The study utilised SPSS (version 27) and AMOS (version 24) for comprehensive statistical analysis. Findings reveal a significant relationship between students' perceptions of AI in music education and their motivation, engagement and learning success. Positive AI perceptions were found to enhance motivation by increasing interest in innovative learning tools and fostering engagement through interactive AI‐based learning environments. Moreover, these perceptions were predictive of higher motivation, engagement and learning success. The study suggests that AI can play a crucial role in enhancing educational outcomes by making learning more interactive, personalised and engaging, thus improving overall student performance and creativity in music education.
17The Impact of Adaptive Learning Technologies, Personalized Feedback, and Interactive AI Tools on Student Engagement: The Moderating Role of Digital LiteracyOpenAlex
Husam Yaseen, Abdelaziz Saleh Mohammad, Najwa Ashal, et al.
Using adaptive learning technologies, personalized feedback, and interactive AI tools, this study investigates how these tools affect student engagement and what the mediating role of individuals’ digital literacy is at the same time. The study will target 500 students from different faculties such as science, engineering, humanities, and social sciences. With the changing trends in educational technology, it is important to know if these tools allow students to interact with learning materials. Through this study, we explore how adaptive learning technologies, which adapt content to students’ progress, are influenced by student motivation and participation during the learning process using AI tools that provide real-time feedback and interaction. Also, digital literacy is presented as a moderating factor that may either accelerate or impede the effectiveness of these tools. These findings demonstrate that more adaptive learning technologies, which have organized feedback, and interactive AI tools help improve student engagement. Additionally, students with higher levels of digital literacy are more involved with digital tools. This research recognizes that teachers should incorporate these technologies into their courses in such a manner as it synergizes with student’s digital capabilities to reap the benefits of technology on students’ engagement and learning outcomes.
19Shaping the Future of Education: Exploring the Potential and Consequences of AI and ChatGPT in Educational SettingsOpenAlex
Simone Grassini
Over the last decade, technological advancements, especially artificial intelligence (AI), have significantly transformed educational practices. Recently, the development and adoption of Generative Pre-trained Transformers (GPT), particularly OpenAI’s ChatGPT, has sparked considerable interest. The unprecedented capabilities of these models, such as generating humanlike text and facilitating automated conversations, have broad implications in various sectors, including education and health. Despite their immense potential, concerns regarding their widespread use and opacity have been raised within the scientific community. ChatGPT, the latest version of the GPT series, has displayed remarkable proficiency, passed the US bar law exam, and amassed over a million subscribers shortly after its launch. However, its impact on the education sector has elicited mixed reactions, with some educators heralding it as a progressive step and others raising alarms over its potential to reduce analytical skills and promote misconduct. This paper aims to delve into these discussions, exploring the potential and problems associated with applying advanced AI models in education. It builds on extant literature and contributes to understanding how these technologies reshape educational norms in the “new AI gold rush” era.
20Enhancing Peer Engagement and Student Motivation Through AI-Gamified Interactive Learning ToolsOpenAlex
John Marvin D. Renacido, Ersyl T. Biray
Artificial Intelligence (AI) has become widely accepted in diverse fields and applications in the fast-paced development of technology. It has even penetrated the threshold of education. This chapter will tackle the potential benefits of utilizing AI-powered gamification and interactive learning tools and lay down approaches to attaining a responsible approach to AI Integration. It will also explore interactive learning tools teachers can employ in the classroom and determine the impact of AI-powered gamification on peer engagement. By this, some drawbacks of using AI in education will be revealed to avoid frustration in coping with its mechanisms. Further, it will also provide virtual and augmented reality applications for a clearer view of how these devices work. This chapter will uncover the psychology of gamification to realize the underlying mental implications of this pedagogy for students.
21Empowering 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.
22The Generative AI Landscape in Education: Mapping the Terrain of Opportunities, Challenges, and Student PerceptionOpenAlex
Zishan Ahmed, Shakib Sadat Shanto, Most. Humayra Khanom Rime, et al.
Generative AI (GAI) technologies like ChatGPT are permanently changing academic education. Their integration opens up vast opportunities for bespoke learning and better student interaction but also brings about academic honesty issues and the application of real-life educators. This study aims to fill the literature gap regarding the use of multiple GAI tools and their effect on academic outcomes via a comprehensive review. A systematic literature review was performed following PRISMA guidelines to synthesize results on the potential and drawbacks of GAI in educational domains. We included theoretical and empirical papers that used qualitative, quantitative, or mixed-methods study designs. We have also explored conceptual frameworks and the most creative AI applications with a special emphasis on uniqueness and practicability. Experiences, and Perceptions Concerning To compile the information needed we gathered insights into what students were going through by conducting the survey which contains 200 respondents of undergraduate university students gathering insights into the college students’ experiences and perceptions related to GAI used for educational purposes. At the basic level, GAI comprises areas like personalization, task automation, teacher assistance, and efficiency among others, and respective solutions for the immersion of a learner in learning processes to reform directions. However, it generates plenty of challenges such as the question of assessment integrity, the risk that too much automated grading could overwhelm educational value, and relevantly the veracity of AI-generated content as well as the potential disruption to skills like critical thinking, in addition to data privacy and ethical issues. Student Perception Survey the text also indicates that most students, as per the student perception survey found AI systems useful in academic support. However, they also know the other side of the coin and are very familiar with the technology constraints and challenges.
23Shaping the Future of Higher Education: A Technology Usage Study on Generative AI InnovationsOpenAlex
Weina Pang, Zhe Wei
Generative Artificial Intelligence (GAI) is rapidly reshaping the landscape of higher education, offering innovative solutions to enhance student engagement, personalize learning experiences, and improve academic performance prediction. This study provides an in-depth exploration of GAI applications in educational contexts, drawing insights from 67 case studies meticulously selected from over 300 papers presented at the AIED 2024 conference. The research focuses on eight key themes from student engagement and behavior analysis to the integration of generative models into educational tools. These case studies illustrate the potential of GAI to optimize teaching practices, enhance student support systems, and provide tailored interventions that address individual learning needs. However, this study also highlights challenges such as scalability, the need for balanced and diverse datasets, and ethical concerns regarding data privacy and bias. Further, it emphasizes the importance of improving model accuracy, transparency, and real-world applicability in educational settings. The findings underscore the need for continued research to refine GAI technologies, ensuring they are scalable, adaptable, and equitable, ultimately enhancing the effectiveness and inclusivity of AI-driven educational tools across diverse higher education environments. It should be noted that this study primarily draws from papers presented at the AIED 2024 conference, which may limit global representativeness and introduce thematic biases. Future studies are encouraged to include broader datasets from diverse conferences and journals to ensure a more comprehensive understanding of GAI applications in higher education.
24Integrating 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.
25Unlocking the Power of ChatGPT: A Framework for Applying Generative AI in EducationOpenAlex
Jiahong Su, Weipeng Yang
Purpose Artificial intelligence (AI) chatbots, such as ChatGPT and GPT-4, developed by OpenAI, have the potential to revolutionize education. This study explores the potential benefits and challenges of using ChatGPT in education (or “educative AI”). Design/Approach/Methods This paper proposes a theoretical framework called “IDEE” for educative AI such as using ChatGPT and other generative AI in education, which includes identifying the desired outcomes, determining the appropriate level of automation, ensuring ethical considerations, and evaluating effectiveness. Findings The benefits of using ChatGPT in education or more generally, educative AI, include a more personalized and efficient learning experience for students as well as easier and faster feedback for teachers. However, challenges such as the untested effectiveness of the technology, limitations in the quality of data, and ethical and safety concerns must also be considered. Originality/Value This study explored the opportunities and challenges of using ChatGPT in education within the proposed theoretical framework.
26Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial IntelligenceOpenAlex
Grant Cooper
Abstract The advent of generative artificial intelligence (AI) offers transformative potential in the field of education. The study explores three main areas: (1) How did ChatGPT answer questions related to science education? (2) What are some ways educators could utilise ChatGPT in their science pedagogy? and (3) How has ChatGPT been utilised in this study, and what are my reflections about its use as a research tool? This exploratory research applies a self-study methodology to investigate the technology. Impressively, ChatGPT’s output often aligned with key themes in the research. However, as it currently stands, ChatGPT runs the risk of positioning itself as the ultimate epistemic authority, where a single truth is assumed without a proper grounding in evidence or presented with sufficient qualifications. Key ethical concerns associated with AI include its potential environmental impact, issues related to content moderation, and the risk of copyright infringement. It is important for educators to model responsible use of ChatGPT, prioritise critical thinking, and be clear about expectations. ChatGPT is likely to be a useful tool for educators designing science units, rubrics, and quizzes. Educators should critically evaluate any AI-generated resource and adapt it to their specific teaching contexts. ChatGPT was used as a research tool for assistance with editing and to experiment with making the research narrative clearer. The intention of the paper is to act as a catalyst for a broader conversation about the use of generative AI in science education.
27The 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.
28Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performanceOpenAlex
Yizhou Fan, Luzhen Tang, Huixiao Le, et al.
Abstract With the continuous development of technological and educational innovation, learners nowadays can obtain a variety of supports from agents such as teachers, peers, education technologies, and recently, generative artificial intelligence such as ChatGPT. In particular, there has been a surge of academic interest in human‐AI collaboration and hybrid intelligence in learning. The concept of hybrid intelligence is still at a nascent stage, and how learners can benefit from a symbiotic relationship with various agents such as AI, human experts and intelligent learning systems is still unknown. The emerging concept of hybrid intelligence also lacks deep insights and understanding of the mechanisms and consequences of hybrid human‐AI learning based on strong empirical research. In order to address this gap, we conducted a randomised experimental study and compared learners' motivations, self‐regulated learning processes and learning performances on a writing task among different groups who had support from different agents, that is, ChatGPT (also referred to as the AI group), chat with a human expert, writing analytics tools, and no extra tool. A total of 117 university students were recruited, and their multi‐channel learning, performance and motivation data were collected and analysed. The results revealed that: (1) learners who received different learning support showed no difference in post‐task intrinsic motivation; (2) there were significant differences in the frequency and sequences of the self‐regulated learning processes among groups; (3) ChatGPT group outperformed in the essay score improvement but their knowledge gain and transfer were not significantly different. Our research found that in the absence of differences in motivation, learners with different supports still exhibited different self‐regulated learning processes, ultimately leading to differentiated performance. What is particularly noteworthy is that AI technologies such as ChatGPT may promote learners' dependence on technology and potentially trigger “metacognitive laziness”. In conclusion, understanding and leveraging the respective strengths and weaknesses of different agents in learning is critical in the field of future hybrid intelligence. Practitioner notes What is already known about this topic Hybrid intelligence, combining human and machine intelligence, aims to augment human capabilities rather than replace them, creating opportunities for more effective lifelong learning and collaboration. Generative AI, such as ChatGPT, has shown potential in enhancing learning by providing immediate feedback, overcoming language barriers and facilitating personalised educational experiences. The effectiveness of AI in educational contexts varies, with some studies highlighting its benefits in improving academic performance and motivation, while others note limitations in its ability to replace human teachers entirely. What this paper adds We conducted a randomised experimental study in the lab setting and compared learners' motivations, self‐regulated learning processes and learning performances among different agent groups (AI, human expert and checklist tools). We found that AI technologies such as ChatGPT may promote learners' dependence on technology and potentially trigger metacognitive "laziness", which can potentially hinder their ability to self‐regulate and engage deeply in learning. We also found that ChatGPT can significantly improve short‐term task performance, but it may not boost intrinsic motivation and knowledge gain and transfer. Implications for practice and/or policy When using AI in learning, learners should focus on deepening their understanding of knowledge and actively engage in metacognitive processes such as evaluation, monitoring, and orientation, rather than blindly following ChatGPT's feedback solely to complete tasks efficiently. When using AI in teaching, teachers should think about which tasks are suitable for learners to complete with the assistance of AI, pay attention to stimulating learners' intrinsic motivations, and develop scaffolding to assist learners in active learning. Researcher should design multi‐task and cross‐context studies in the future to deepen our understanding of how learners could ethically and effectively learn, regulate, collaborate and evolve with AI.
29Examining the Effects of Artificial Intelligence on Elementary Students’ Mathematics Achievement: A Meta-AnalysisOpenAlex
Sunghwan Hwang
With the increasing attention to artificial intelligence (AI) in education, this study aims to examine the overall effectiveness of AI on elementary students’ mathematics achievement using a meta-analysis method. A total of 21 empirical studies with 30 independent samples published between January 2000 and June 2022 were used in the study. The study findings revealed that AI had a small effect size on elementary students’ mathematics achievement. The overall effect of AI was 0.351 under the random-effects model. The effect sizes of eight moderating variables, including three research characteristic variables (research type, research design, and sample size) and five opportunity-to-learn variables (mathematics learning topic, intervention duration, AI type, grade level, and organization), were examined. The findings of the study revealed that mathematics learning topic and grade level variables significantly moderate the effect of AI on mathematics achievement. However, the effects of other moderator variables were found to be not significant. This study also suggested practical and research implications based on the results.
30Unlocking Potential: Key Factors Shaping Undergraduate Self-Directed Learning in AI-Enhanced Educational EnvironmentsOpenAlex
Di Wu, Shuling Zhang, Zhiyuan Ma, et al.
This study investigates the factors influencing undergraduate students’ self-directed learning (SDL) abilities in generative Artificial Intelligence (AI)-driven interactive learning environments. The advent of generative AI has revolutionized interactive learning environments, offering unprecedented opportunities for personalized and adaptive education. Generative AI supports teachers in delivering smart education, enhancing students’ acceptance of technology, and providing personalized, adaptive learning experiences. Nevertheless, the application of generative AI in higher education is underexplored. This study explores how these AI-driven platforms impact undergraduate students’ self-directed learning (SDL) abilities, focusing on the key factors of teacher support, learning strategies, and technology acceptance. Through a quantitative approach involving surveys of 306 undergraduates, we identified the key factors of motivation, technological familiarity, and the quality of AI interaction. The findings reveal the mediating roles of self-efficacy and learning motivation. Also, the findings confirmed that improvements in teacher support and learning strategies within generative AI-enhanced learning environments contribute to increasing students’ self-efficacy, technology acceptance, and learning motivation. This study contributes to uncovering the influencing factors that can inform the design of more effective educational technologies and strategies to enhance student autonomy and learning outcomes. Our theoretical model and research findings deepen the understanding of applying generative AI in higher education while offering important research contributions and managerial implications.
31Students’ 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.
32Student Perceptions of AI-Assisted Writing and Academic Integrity: Ethical Concerns, Academic Misconduct, and Use of Generative AI in Higher EducationOpenAlex
Brady Lund, Nishith Reddy Mannuru, Zoë Abbie Teel, et al.
The rise of generative AI in higher education has disrupted our traditional understandings of academic integrity, moving our focus from clear-cut infractions to evolving ethical judgment. In this study, a survey of 401 students from major U.S. universities provides insight into how beliefs, behaviors, and policy awareness intersect in shaping how students interact with AI-assisted writing. The findings indicate that students’ ethical beliefs—not institutional policies—are the strongest predictors of perceived misconduct and actual AI use in writing. Policy awareness was found to have no significant effect on ethical judgments or behavior. Instead, students who believe AI writing is cheating were found to be substantially less likely to view it as ethical or engage with it. These findings suggest that many students do not treat AI use in learning activities as an extension of conventional cheating (e.g., plagiarism), but rather as a distinct category of academic conduct/misconduct. Rather than using punitive models to attempt to punish students for using AI, this study suggests that education about AI ethics and the risk of AI overreliance may prove more successful for curbing unethical AI use in higher education.
33Why and how to embrace AI such as ChatGPT in your academic lifeOpenAlex
Zhicheng Lin
Generative artificial intelligence (AI), including large language models (LLMs), is poised to transform scientific research, enabling researchers to elevate their research productivity. This article presents a how-to guide for employing LLMs in academic settings, focusing on their unique strengths, constraints and implications through the lens of philosophy of science and epistemology. Using ChatGPT as a case study, I identify and elaborate on three attributes contributing to its effectiveness-intelligence, versatility and collaboration-accompanied by tips on crafting effective prompts, practical use cases and a living resource online (https://osf.io/8vpwu/). Next, I evaluate the limitations of generative AI and its implications for ethical use, equality and education. Regarding ethical and responsible use, I argue from technical and epistemic standpoints that there is no need to restrict the scope or nature of AI assistance, provided that its use is transparently disclosed. A pressing challenge, however, lies in detecting fake research, which can be mitigated by embracing open science practices, such as transparent peer review and sharing data, code and materials. Addressing equality, I contend that while generative AI may promote equality for some, it may simultaneously exacerbate disparities for others-an issue with potentially significant yet unclear ramifications as it unfolds. Lastly, I consider the implications for education, advocating for active engagement with LLMs and cultivating students' critical thinking and analytical skills. The how-to guide seeks to empower researchers with the knowledge and resources necessary to effectively harness generative AI while navigating the complex ethical dilemmas intrinsic to its application.
34AI-Powered E-Learning for Lifelong Learners: Impact on Performance and Knowledge ApplicationOpenAlex
Hyun Yong Ahn
The widespread integration of artificial intelligence (AI) technologies, such as generative AI tools like ChatGPT, in education and workplaces requires a clear understanding of the factors that influence their adoption and effectiveness. This study explores how the ease of using AI tools, the ability to apply knowledge gained from them, and users’ confidence in learning with AI impact individuals’ performance and frequency of use. We also examine how these factors affect academic success and job performance among adults engaged in lifelong learning. Using data from 300 participants analyzed with Partial Least Squares Structural Equation Modeling (PLS-SEM), we found that, when AI tools are easy to use, individuals experience greater benefits and are more likely to use them regularly. Applying knowledge from AI tools enhances both personal performance and usage frequency. Additionally, having confidence in one’s ability to learn with AI leads to significant improvements in personal outcomes and an increased use of AI tools. These findings highlight the importance of designing user-friendly AI technologies, promoting the practical application of AI-generated knowledge, and building users’ confidence to maximize the benefits of AI. Educators, policymakers, and AI developers can use these insights to develop strategies that enhance academic and job performance through effective AI integration. Future research should consider other influencing factors and employ longitudinal studies to further validate these findings.
35Transforming education: exploring the influence of generative AI on teaching performanceOpenAlex
Heni Mulyani, Mohammad I. Azim, Elvia R. Shauki, et al.
The emergence of Generative Artificial Intelligence (AI) marks a revolutionary advancement in education. This study explores the profound impact of implementing Generative AI on teachers’ teaching performance, with a focus on enhancing teaching effectiveness and pedagogical practices. This research uses a survey methodology, employing a proportionated stratified random sampling technique. A total of 466 participants, consisting of teachers, were involved in this study, with questionnaires serving as the primary tool for data collection. The primary data analysis method used in this study was the Structural Equation Model (SEM). Research indicates that Generative AI significantly enhances teaching performance by improving ease of use, usefulness, and learning. Teacher perceptions of AI’s usability influence its integration into student-focused learning, learning material development, and teaching practice enhancement. Additionally, the ease of learning is crucial for its adoption. Alongside these promising opportunities, the study also highlights challenges that need to be addressed for successful AI integration in education, such as technical limitations and the necessity for teacher training. By exploring the application of Generative AI in depth, this research offers valuable insights into leveraging technology to foster more inclusive, personalized, and practical education in the digital age.
36Generative AI in Education and Research: Opportunities, Concerns, and SolutionsOpenAlex
Eman A. Alasadi, Carlos R. Baiz
In this article, we discuss the role of generative artificial intelligence (AI) in education. The integration of AI in education has sparked a paradigm shift in teaching and learning, presenting both unparalleled opportunities and complex challenges. This paper explores critical aspects of implementing AI in education to advance educational goals, ethical considerations in scientific publications, and the attribution of credit for AI-driven discoveries. We also examine the implications of using AI-generated content in professional activities and describe equity and accessibility concerns. By weaving these key questions into a comprehensive discussion, this article aims to provide a balanced perspective on the responsible and effective use of these technologies in education, highlighting the need for a thoughtful, ethical, and inclusive approach to their integration.
37Educational Design Principles of Using AI Chatbot That Supports Self-Regulated Learning in Education: Goal Setting, Feedback, and PersonalizationOpenAlex
Daniel Chang, Michael Pin-Chuan Lin, Shiva Hajian, et al.
The invention of ChatGPT and generative AI technologies presents educators with significant challenges, as concerns arise regarding students potentially exploiting these tools unethically, misrepresenting their work, or gaining academic merits without active participation in the learning process. To effectively navigate this shift, it is crucial to embrace AI as a contemporary educational trend and establish pedagogical principles for properly utilizing emerging technologies like ChatGPT to promote self-regulation. Rather than suppressing AI-driven tools, educators should foster collaborations among stakeholders, including educators, instructional designers, AI researchers, and developers. This paper proposes three key pedagogical principles for integrating AI chatbots in classrooms, informed by Zimmerman’s Self-Regulated Learning (SRL) framework and Judgment of Learning (JOL). We argue that the current conceptualization of AI chatbots in education is inadequate, so we advocate for the incorporation of goal setting (prompting), self-assessment and feedback, and personalization as three essential educational principles. First, we propose that teaching prompting is important for developing students’ SRL. Second, configuring reverse prompting in the AI chatbot’s capability will help to guide students’ SRL and monitoring for understanding. Third, developing a data-driven mechanism that enables an AI chatbot to provide learning analytics helps learners to reflect on learning and develop SRL strategies. By bringing in Zimmerman’s SRL framework with JOL, we aim to provide educators with guidelines for implementing AI in teaching and learning contexts, with a focus on promoting students’ self-regulation in higher education through AI-assisted pedagogy and instructional design.
38ChatGPT and Generative Artificial Intelligence for Medical Education: Potential Impact and OpportunityOpenAlex
Christy Boscardin, Brian C. Gin, Polo Black Golde, et al.
ABSTRACT: ChatGPT has ushered in a new era of artificial intelligence (AI) that already has significant consequences for many industries, including health care and education. Generative AI tools, such as ChatGPT, refer to AI that is designed to create or generate new content, such as text, images, or music, from their trained parameters. With free access online and an easy-to-use conversational interface, ChatGPT quickly accumulated more than 100 million users within the first few months of its launch. Recent headlines in the popular press have ignited concerns relevant to medical education over the possible implications of cheating and plagiarism in assessments as well as excitement over new opportunities for learning, assessment, and research. In this Scholarly Perspective, the authors offer insights and recommendations about generative AI for medical educators based on literature review, including the AI literacy framework. The authors provide a definition of generative AI, introduce an AI literacy framework and competencies, and offer considerations for potential impacts and opportunities to optimize integration of generative AI for admissions, learning, assessment, and medical education research to help medical educators navigate and start planning for this new environment. As generative AI tools continue to expand, educators need to increase their AI literacy through education and vigilance around new advances in the technology and serve as stewards of AI literacy to foster social responsibility and ethical awareness around the use of AI.
39Towards hybrid <scp>human‐AI</scp> learning technologiesOpenAlex
Inge Molenaar
Abstract Education is a unique area for application of artificial intelligence (AI). In this article, the augmentation perspective and the concept of hybrid intelligence are introduced to frame our thinking about AI in education. The involvement of quadruple helix stakeholders (i.e., researchers, education professionals, entrepreneurs, and policymakers) is necessary to understand the opportunities and challenges of different educational use cases from an integrated point of view. To facilitate a meaningful dialogue, a common language about AI in education is needed. This article outlines elements of such a common language. The detect‐diagnose‐act framework is used to describe the core functions of AI in education. The six levels of automation model is introduced to develop our thinking about the roles of AI, learners, and teachers in educational arrangements. In this model, the transition of control between teacher and technology is articulated at different levels and related to the augmentation perspective. Finally, the future of AI in education is discussed using self‐regulated learning as an example. The proposed common language will help to support a coordinated development of an interdisciplinary dialogue between quadruple helix stakeholders to strengthen meaningful application of AI for learning and teaching.
40Exploring the impact of AI on teacher leadership: regressing or expanding?OpenAlex
Norma Ghamrawi, Tarek Shal, Najah A. R. Ghamrawi
Abstract This study aimed to investigate the impact of Artificial Intelligence (AI) on teacher leadership, specifically examining whether AI is expanding or regressing teacher leadership, as perceived by teachers who were using AI in their teaching practices. Using a qualitative research design, the study employed semi-structured interviews to collect data from 13 teachers from five countries. The data were then analyzed using thematic analysis. The findings of the study indicated that the use of AI has the potential to both expand and regress teacher leadership. AI can expand teacher leadership by providing tools for personalization, curriculum development, automating administrative tasks, and supporting professional development. However, AI was also viewed to be regressing teacher leadership, by narrowing the role because technology was taking over some of its aspects. Five sets of competencies were suggested by teachers for teacher leaders to sustain their roles in an AI era. The study concludes that the impact of AI on teacher leadership depends on how it is implemented and integrated into the education system. It highlights the importance of continued research and training in this area to inform future education policies and practices.
41Exploring the impact of ChatGPT: conversational AI in educationOpenAlex
Anissa M. Bettayeb, Manar Abu Talib, Al Zahraa Sobhe Altayasinah, et al.
Artificial intelligence integration, specifically ChatGPT, is becoming increasingly popular in educational contexts. This research paper provides a systematic literature review that examines the effects of incorporating ChatGPT into education. The study examines four primary research questions: the benefits and challenges of ChatGPT, its impact on student engagement and learning outcomes, ethical considerations and safeguards, and the effects on educators and teachers, based on an analysis of numerous scientific research articles published between 2022 and 2023. The results emphasize the numerous benefits of ChatGPT, such as the opportunity for students to investigate AI technology, personalized assistance, and improved learning experiences. Furthermore, advantages such as enhanced learning and enhanced information accessibility are identified. Nevertheless, ethical considerations and biases in AI models are also highlighted. ChatGPT enhances student engagement by offering personalized responses, prompt feedback, and rapid access to information, resulting in enhanced learning outcomes and the growth of critical thinking abilities. Ethical considerations and safeguards, including user education, privacy protection, human supervision, and stated guidelines, are essential for responsible use. The integration of ChatGPT transforms the role of educators from content delivery to assistance and guidance, thereby fostering personalized and differentiated learning. Educators have to consider ethical considerations while monitoring student usage in order to facilitate this transformation. Educational institutions can increase student engagement, learning outcomes, and the responsible use of AI in education by addressing challenges, establishing ethical guidelines, and leveraging the strengths of ChatGPT. This will prepare students for future challenges.
42<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.
43A Human-Centered Learning and Teaching Framework Using Generative Artificial Intelligence for Self-Regulated Learning Development Through Domain Knowledge Learning in K–12 SettingsOpenAlex
Siu Cheung Kong, Yin Yang
The advent of generative artificial intelligence (AI) has ignited an increase in discussions about generative AI tools in education. In this study, a human-centred learning and teaching framework (HCLTF) that uses generative AI tools for self-regulated learning development through domain knowledge learning was proposed to catalyse changes in educational practices. The framework illustrates how generative AI tools can revolutionise educational practices and transform the processes of teaching and learning to become human-centred. It emphasises the evolving roles of teachers, who increasingly become skilful facilitators and humanistic storytellers who craft differentiated instructions and attempt to develop students' individualised learning. Drawing upon insights from neuroscience, the framework guides students to employ generative AI tools to augment their attentiveness, stimulate active engagement in learning, receive immediate feedback, and encourage self-reflection. The pedagogical approach is also reimagined; teachers equipped with generative AI tools and AI literacy can refine their teaching strategies to better equip students to meet future challenges. The practical application of the framework is demonstrated in a case study involving the development of Chinese language writing ability among primary students within a K–12 educational context. This paper also reports the results of a 60-hour development programme for teachers. Specifically, providing in-service teachers with cases involving uses of the proposed framework helped them to better understand the generative AI concepts and integrate them into their teaching and learning and increased their perceived ability to design AI-integrated courses that would enhance students' attention, engagement, confidence, and satisfaction.
44Opportunities, 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.
46Generative AI in Higher Education: Balancing Innovation and Integrity.PubMed
Nigel J Francis, Sue Jones, David P Smith
Br J Biomed Sci. 2025 Jan 9;81:14048. doi: 10.3389/bjbs.2024.14048. eCollection 2024.
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.
47Ethical and regulatory challenges of Generative AI in education: a systematic reviewOpenAlex
Iván Miguel García-López, Laura Trujillo Liñán
Introduction Generative Artificial Intelligence (GenAI) is transforming education by enabling personalized learning and more efficient teaching practices. However, it raises critical ethical concerns, including data privacy, algorithmic bias, and educational inequality, requiring comprehensive regulatory frameworks and pedagogical strategies. Methods A Systematic Literature Review (SLR) was conducted, analyzing 53 peer-reviewed articles published between 2020 and 2024. The search was performed in Scopus and Web of Science using defined inclusion criteria focused on GenAI applications in education. Data were synthesized thematically and supported by theoretical frameworks from ethics, regulation, and learning sciences. Results The findings reveal that while GenAI enhances personalized feedback, instructional automation, and learning accessibility, it simultaneously introduces risks such as loss of cognitive autonomy, institutional misuse of student data, and lack of regulatory oversight. Case studies from Stanford and the University of Toronto illustrate both opportunities and limitations of GenAI adoption in higher education. Discussion GenAI can benefit education if implemented within ethical, legal, and pedagogical boundaries. The study highlights the urgency of designing inclusive regulatory frameworks, strengthening digital literacy, and integrating GenAI tools with constructivist and self-determined learning models. This review offers practical recommendations for educators, policymakers, and technologists aiming to use GenAI responsibly in educational environments.
48The AI revolution in micro-credentialing: personalized learning pathsOpenAlex
Pooja Shanmughan, Jeena Joseph, Bala Subramanian S, et al.
In the modern educational landscape, the pace of technological change has ushered in a transformation where artificial intelligence (AI) is integrated into learning and development in nearly all areas. A lot of that transformation has been through the rise of micro-credentialing, whereby the need for flexible, skill-specific education becomes very close to the needs of the changing global workforce (Kiiskilä et al., 2022). Often referred to as mini-qualifications for demonstrating a certain level of competency in a certain area, micro-credentials are increasingly being recognized for the potential role they play in filling in the gaps left by the traditional configurations of education (Keoy et al., 2024). They provide a channel through which the learner is able to both obtain and demonstrate the skill in a way that is both effective and relevant to their career. McGreal and Olcott (2022) critically examine the role of micro-credentials in higher education, highlighting their potential as strategic tools for institutional adaptation while cautioning that they are not a universal solution to institutional challenges. They emphasize the importance of a strategic assessment by university leaders to determine whether microcredentials align with their institutional goals and resources, rather than automatically adopting them as a revenue-generating initiative (McGreal and Olcott, 2022).Artificial Intelligence (AI) refers to the simulation of human intelligence in machines programmed to think and learn like humans. About micro-credentialing, AI covers most technologies ranging from machine learning to natural language processing and down to expert systems. Figure 1 visually summarizes the relationship between AI technologies-Neural Networks, Natural Language Processing, and Expert Systems-and their specific applications in micro-credentialing. Recent advances in AI are largely driven by neural networks, particularly deep learning models. These networks, inspired by the brain's architecture, excel at pattern recognition and prediction from large datasets. In micro-credentialing, neural networks enhance personalized learning by analyzing extensive learner data, enabling more effective educational technologies. Machine learning algorithms use overflowing data sets to identify patterns in data and infer incomplete information about learners to propose a personalized learning pathway. NLP helps develop intelligent tutoring systems so the students themselves can turn in for the facility to obtain real-time feedback and facilitate interactive learning experiences. Expert systems, mimicking human expert decisions, help devise curricula and assessments and make them adaptive by a learner's needs. For example, IBM uses AI to develop tailored reskilling programs, and platforms like Coursera use AI to suggest courses to learners based on their learner profiles. Of course, all of these AI technologies will help enhance micro-credentialing by increasing the personalization, efficiency, and responsiveness of education to learners' and industry demands. Artificial Intelligence in micro-credentialing will prove to be a game-changer, adding a crucial aspect to any learning experience: personalization (McGreal, 2024). Artificial Intelligence technologies analyze extensive datasets of behavior, preferences, and performance of the learners to develop an educational trajectory that is not only in sync with the learning style of the individual but is also adaptive to the changing requirements of the industry (Blaj-Ward et al., 2023). Personalization like this, scaling learner engagement and success through AI, will make learning a dynamic and frictionless process that will be responsive to the demands of the learner and the larger economy (McGreal, 2024). In recent years, several studies have explored the transformative potential of AI in education, particularly in micro-credentialing. Pirkkalainen et al. describe the key features of higher education micro-credential platforms, pointing out ethical implications regarding data privacy and algorithmic bias. This points to the fact that ethical concerns have to be dealt with carefully to ensure a responsible AI implementation within educational institutions (Pirkkalainen et al., 2023). Ahmat et al. review the challenges and opportunities for higher education to move towards micro-credential certification in a manner curator-oriented, focusing on how such innovations could resolve skill gaps and increase workforce readiness. Their work highlighted economic benefits and has the potential of microcredentials in providing better employability (Ahmat et al., 2021). Orman et al. intend to scrutinize the role of AI in democratizing education and mitigating the digital divide. It requires specific efforts to enhance infrastructure and literacy in the digital aspect of underserved communities. It highlights the possible contributions AI can make to bridge educational gaps (Orman et al., 2023). Tian et al. discuss the integration of AI into blockchain technology to increase credibility and enhance micro-credential security. Their study outlines how such integration in technology would lead to heightened security and trustworthiness of educational credentials (Tian et al., 2022). Brown et al. give an overview of the complete global landscape of micro-credentialing in their paper, reviewing how micro-credentials fit within the interrelationship of lifelong learning and employability. Their work presents an opportunity for micro-credentials to become a more established component of education in the 21st century (Brown et al., 2021). Zawacki-Richter et al. conducted a systematic review of AI applications in higher education settings that define the most relevant key areas: profiling, assessment, adaptive systems, and intelligent tutoring systems. The study underlines the challenges and opportunities that AI brings to educational contexts (Zawacki-Richter et al., 2019). Rajabalee proposes a systematic analysis of the literature about implementing micro-credentials within academic contexts and underlines their potential: namely, becoming recognized credits that would be transferable in formal higher education (Rajabalee, 2023).In light of the above, and although the application of AI in micro-credentialing seems promising, this raises some pertinent questions and issues, such as data privacy, access to equity, and long-run effects on employment. This paper interacts with the AI revolution in micro-credentialing to posit that, though not void of challenges, it is an imperative and, indeed, developmental phase education needs to go through and is likely to birth overwhelmingly positive benefits for learners worldwide if thoughtfully managed.to birth overwhelmingly positive benefits for learners worldwide if thoughtfully managed.This use of artificial intelligence in the integration of micro-credentialing systems heralds a new era of personalized education: a transformation in how we learn and how we are assessed in skills and knowledge. This will bring the strength of AI to be harnessed and tapped into personalized learning in response to individual learners' needs, optimizing their educational paths and probably accelerating their career advancement.The benefits of AI in micro-credentialing lie at the foundation. Primary among them is the fact that AI allows for dynamic learning environments that evolve in real time with a learner's progress, preferences, and performance. Such learning analytics will allow AI to understand how learners interact with material, tune task difficulty, or suggest more resources to better assist learners with the struggles they face. This kind of customization was never before possible within traditional educational settings, in which one-size-fits-all approaches too frequently predominate.Most importantly, AI will be used for the identification of the gaps in the skills pool and the trends in industry which are emerging, enabling the programs to be updated regularly and issue microcredentials that are most relevant to the market need today. This will keep learners competitive in the workforce and address skill shortages that have left industries in a tough spot, aligning education more with economic requirements. For example, job listings, industry releases, professional development trends, and the like can all go into an algorithm that would then present to or automatically enroll learners into micro-courses that enhance their employability. More generally, automation of the processes of assessment and feedback through AI will reduce the administrative overhead and will enable scalable solutions in credentialing. Automated grading systems and AI-enhanced evaluations will provide instantaneous, detailed feedback to learners-a capability missing from today's skillsbased educational tools, primarily due to the resource-intense nature of providing human-generated feedback on many assignments.However, AI in micro-credentialing is not without its challenges. It must address issues regarding data privacy, algorithmic bias, and the digital divide if it is going to live up to its potential in this space. Nevertheless, with the benefits of increased access, more personalized learning, and closer alignment to labor market demand, AI-driven micro-credentialing is bound to be a transforming force in the education system-an improvement in the way qualifications are to be achieved and recognized in the digital age.Most of the criticism in this regard focuses on data privacy and the risk for increased digital divide in AI for education. The proponents of skepticism argue that AI and digital platforms might skew reliance toward the better-off in terms of the access of technology, leaving the low-income population behind. Moreover, because data collection is pervasive, huge privacy concerns are attached to AI applications. However, strong data protection regulations such as those contained in the EU GDPR, which offers solid privacy protections, might mitigate these concerns. Moreover, with enhanced efforts to expand digital infrastructure and literacy into low-income and rural areas, the disparities of AI-enhanced learning will be low, hence making the distribution of educational benefits more equal. On the one hand, the integration of AI into micro-credentialing has various advantages for education; on the other, it has created a whole set of concerns and opposition that has to be taken into account to make sure that the innovations go in the best interests of all the stakeholders. Chief among such concerns are problems related to data privacy, the digital divide, and potential biases in the AI algorithms.Data privacy seems to be one of the major concerns, as AI needs access to huge volumes of personal information to be able to tailor the learning experience effectively. As others argue, data collection and analysis might lead to possible privacy breaches in case it is not handled properly. The good thing is that this kind of situation necessitates the existence of good data protection measures. This would mean that there are strict policies in handling data and security protocols that ensure the information regarding the learners is protected. Further, clearly stating how the data would be used and getting consent from the users can help reduce issues of privacy and ensure trust in AI systems.Another critical issue is that AI-driven micro-credentialing will exclude some people who are maybe not in a position to fully exploit the programs on account of lack of access to the technological infrastructure for the same. People of low socioeconomic status or in remote locations may not have the technological infrastructure that would enable them to take full advantage of those programs. There is a need for targeted initiatives in this regard, as this will increase both digital literacy and access to technology. Partnerships among governments, educational institutions, and the private sector will ensure the deployment of affordable technology and internet access, thus guaranteeing that AI's benefits are more evenly distributed among the people.Algorithmic bias is one of the main issues that need to be addressed. AI is as neutral as the data it learns from; hence, bad data will generate bad decisions and therefore perpetuate discrimination. This serves to underscore the need to make investments in AI systems developed using diverse and inclusive data sets, and continuously monitor and adapt the systems to steer clear of practices considered unfair. Furthermore, the embedding of regulatory frameworks in the process-to be fair in its algorithms-can even be more protective against bias in ensuring that AI-driven micro-credentialing becomes an enabler for educational equity. All these issues, addressed proactively, will pave the way for the full realization of AI potential for micro-credentialing, enabling very personalized, efficient, and fair educational opportunities fit for the modern workforce.A wealth of literature and practical applications testify to the practical effect and potential of AI in micro-credentialing. For instance, according to one research by MIT scholars, AI-based individualized learning systems can reduce the time taken to achieve fluency in a subject by up to 50%. Table 1 shows a comparison of AI-driven micro-credentialing platforms, highlighting the AI technologies used, the level of personalization features, their relevance to industry needs, and the success rates achieved. AIdriven micro-credentialing programs are now being rolled out across corporations like IBM, which was able to reskill its workforce in essential areas like cybersecurity. This has not only brought about improved performance for these job variants but also saw employee satisfaction and retention rates rising equally as well (Alam, 2022;Robert et al., 2024). According to the studies, not only has there been improved performance for these job variants, but employee satisfaction and retention rates have also risen equally well. Besides, Coursera's AI-powered learning path personalization enables higher course completion rates and superior learner outcomes. For instance, the University of Queensland leveraged AI to analyze data on student performance and enable personalized interventions that drastically reduced dropout rates (Crawford et al., 2024). This collection of case studies illustrates very clearly the opportunities brought about by AI in increasing learning efficiency, improving educational outcomes, and streamlining education trajectories to meet labor-market needs better. Using concrete examples of successful implementations allows us to shift this appreciation toward revealing AI's potential as applied to micro-credentialing. The use of AI in education has to be balanced in a manner such that it becomes an instrument of empowerment but not exclusion. Policies and practices have to be aimed at putting AI tools within the reach of all demographic and socioeconomic groups without exception so as not to worsen already existing inequalities. Those must include, but will not be limited to, the deployment of necessary hardware and software, as well as training and support to educators and learners. Only such inclusive strategies can make full use of the potential of AI to democratize education so that advanced, personalized learning opportunities are within everyone's reach, regardless of background or resources.Spotlighting the Understudied Aspects of AI in Micro-CredentialingIdentifying and mitigating this research gap is necessary to maximize the benefits that will accrue to the educational frameworks. In fact, it can even be called for longitudinal research into these dynamics, pinpointing the necessity of assessing the long-term implications that micro-credentials have on the equity in career growth, income increments, and job security. Finally, equity and access around these programs warrant further research. With AI-driven micro-credentialing, it needs to be understood how people from underrepresented groups are affected, which includes minorities, those in rural areas, and those with low economic statuses. This calls for understanding the possible barriers such groups may face and aiming to reduce them in fostering inclusive educational opportunities.Further research also needs to be carried out on the equity and potential biases of AI algorithms used within micro-credentialing to ensure that the recommendations and assessments are bias-free. In addition, AI-enhanced learning outcomes need to be compared to their traditional counterparts to establish the contexts in which micro-credentials offer unique advantages or otherwise fall short. There is also a need to investigate the psychological impact of the personalized AI learning systems in terms of motivation and anxiety in order to ensure that such innovations promote learner well-being effectively. Other areas of critical research will include acceptance of micro-credentials in different sectors, development of appropriate regulatory and ethical frameworks, economic analysis of implementation costs and benefits of introducing AI, changing roles for educators, and technical challenges of integrating AI within existing infrastructure. Filling these gaps would significantly help in enhancing understanding and applications of AI in education towards accelerating the power of micro-credentials to revolutionize learning and career development in a holistic manner.Challenges of data privacy, equity, and algorithmic bias associated with AI-driven micro-credentialing demand a multi-dimensional approach. Robust encryption techniques, transparency about disposing of data, and explicit consent of the user at each point are some measures to allay concerns over data privacy. Following the principles of privacy by design, it is incumbent upon educational institutions and platforms to rigorously work along the lines of regulations like GDPR. Particularly in this sphere, such equity issues relate to the digital divide and raise focused initiatives for increasing access to technology and digital literacy within acting communities. The collaboration of the government, educational institutions, and tech companies can facilitate the spreading of more affordable devices and reliable internet access. To address algorithm bias, diversified representative datasets have to be developed for AI systems' training. Continuous monitoring and auditing of AI algorithms would help detect and rectify biases, thereby arriving at impartial judgments. Moreover, such policy regulatory frameworks for this field and ethical guidelines will also enhance transparency and accountability for AI uses. If all these aspects are kept in mind with appropriate care, the use of AI-based microcredentialing can be broadly inclusive, fair, privacy-preserving, and hence more effective and trustworthy.The integration of AI into micro-credentialing will be one of the breakthrough elements in the push toward more personalized, efficient, and inclusive education systems. It provides a more dynamic and responsive learning environment through the closer alignment of educational pathways with the needs and realities of the individual within the market. The bottom line: Issues of privacy, equity, and access need to be part of the new paradigm but are surmountable with well-planned policy and careful, ethical technology use. All these players-policy makers, educators, technologists, and learners themselvesneed to converge their efforts in order to realize the promise of AI in making educational experiences increasingly personal, accessible, and effective for all. What the AI revolution in micro-credentialing portends, therefore, is not just the business of new technologies but a full reinvention of how education can be the bridge to opportunity and success in a world of increasing complexity.
49Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education.PubMed
Russell Franco D'Souza, Mary Mathew, Vedprakash Mishra, et al.
Med Educ Online. 2024 Dec 31;29(1):2330250. doi: 10.1080/10872981.2024.2330250. Epub 2024 Apr 3.
Artificial Intelligence (AI) holds immense potential for revolutionizing medical education and healthcare. Despite its proven benefits, the full integration of AI faces hurdles, with ethical concerns standing out as a key obstacle. Thus, educators should be equipped to address the ethical issues that arise and ensure the seamless integration and sustainability of AI-based interventions. This article presents twelve essential tips for addressing the major ethical concerns in the use of AI in medical education. These include emphasizing transparency, addressing bias, validating content, prioritizing data protection, obtaining informed consent, fostering collaboration, training educators, empowering students, regularly monitoring, establishing accountability, adhering to standard guidelines, and forming an ethics committee to address the issues that arise in the implementation of AI. By adhering to these tips, medical educators and other stakeholders can foster a responsible and ethical integration of AI in medical education, ensuring its long-term success and positive impact.
50Integrating AI in education: Opportunities, challenges, and ethical considerationsOpenAlex
Chima Abimbola Eden, Onyebuchi Nneamaka Chisom, Idowu Sulaimon Adeniyi
Integrating Artificial Intelligence (AI) in education presents a promising frontier with manifold opportunities, yet it also poses significant challenges and necessitates ethical considerations. This review explores the multifaceted landscape of AI integration in education, highlighting its potential to revolutionize traditional pedagogical approaches, personalize learning experiences, and streamline administrative tasks. However, it also addresses the challenges pertaining to implementation, including issues related to accessibility, data privacy, and the digital divide. The opportunities afforded by AI in education are vast and transformative. AI-driven technologies have the capacity to adapt instruction to individual learning styles, thereby enhancing student engagement and academic outcomes. Additionally, AI-powered tools can automate administrative tasks, allowing educators to allocate more time to meaningful interactions with students. Moreover, AI holds promise in facilitating the creation of immersive learning environments through virtual reality and augmented reality applications, enriching the educational experience. Nevertheless, the integration of AI in education presents ethical considerations that warrant careful examination. Concerns regarding data privacy and security arise as educational institutions collect and analyze vast amounts of student data. Moreover, there are apprehensions about the potential for AI algorithms to perpetuate biases or reinforce inequalities if not implemented with conscientious oversight. Furthermore, questions surrounding the ethical use of AI in assessing student performance and making consequential decisions underscore the importance of establishing transparent and accountable practices. While the integration of AI in education offers unprecedented opportunities for innovation and improvement, it is imperative to navigate the associated challenges with diligence and ethical foresight. By addressing these challenges thoughtfully, stakeholders can harness the full potential of AI to cultivate equitable, inclusive, and effective educational environments.
51The 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.
52Building 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.
53A Comparative Analysis of AI Privacy Concerns in Higher Education: News Coverage in China and Western CountriesOpenAlex
Yujie Xue, Vinayagum Chinapah, Chang Zhu
This study examines how Chinese and Western news media covered artificial intelligence (AI) privacy issues in higher education from 2019 to 2024. News articles were retrieved from Nexis Uni. First, non-negative matrix factorization (NMF) was employed to identify core AI privacy topics in university teaching, administration, and research. Next, a time trend analysis investigated how media attention shifted in relation to key events, including the COVID-19 pandemic and the emergence of generative AI. Finally, a sentiment analysis was conducted to compare the distribution of positive, negative, and neutral reporting. The findings indicate that AI-driven proctoring, student data security, and institutional governance are central concerns in both Chinese and English media. However, the focus and framing differ: some Western outlets highlight individual privacy rights and controversies in remote exam monitoring, while Chinese coverage more frequently addresses AI-driven educational innovation and policy support. The shift to remote education after 2020 and the rise of generative AI from 2023 onward have intensified discussions on AI privacy in higher education. The results offer a cross-cultural perspective for institutions seeking to reconcile the adoption of AI with robust privacy safeguards and provide a foundation for future data governance frameworks under diverse regulatory environments.
54The evolving role of nursing informatics in the era of artificial intelligence.PubMed
Abdulqadir J Nashwan, Jc A Cabrega, Mutaz I Othman, et al.
Int Nurs Rev. 2025 Mar;72(1):e13084. doi: 10.1111/inr.13084.
AIM: This narrative review explores the integration of artificial intelligence (AI) into nursing informatics and examines its impact on nursing practice, healthcare delivery, education, and policy. BACKGROUND: Nursing informatics, which merges nursing science with information management and communication technologies, is crucial in modern healthcare. The emergence of AI presents opportunities to improve diagnostics, treatment, and healthcare resource management. However, integrating AI into nursing practice also brings challenges, including ethical concerns and the need for specialized training. SOURCES OF EVIDENCE: A comprehensive literature search was conducted from January 2013 to December 2023 using databases like PubMed, Google Scholar, and Scopus. Articles were selected based on their relevance to AI's role in nursing informatics, particularly in enhancing patient care and healthcare efficiency. DISCUSSION: AI significantly enhances nursing practice by improving diagnostic accuracy, optimizing care plans, and supporting resource allocation. However, its adoption raises ethical issues, such as data privacy concerns and biases within AI algorithms. Ensuring that nurses are adequately trained in AI technologies is essential for safe and effective integration. IMPLICATIONS FOR NURSING PRACTICE AND POLICY: Policymakers should promote AI literacy programs for healthcare professionals and develop ethical guidelines to govern the use of AI in healthcare. This will ensure that AI tools are implemented responsibly, protecting patient rights and enhancing healthcare outcomes. CONCLUSION: AI offers promising advancements in nursing informatics, leading to more efficient patient care and improved decision-making. Nonetheless, overcoming ethical challenges and ensuring AI literacy among nurses are critical steps for successful implementation.
55Artificial intelligence and obesity management: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2023OpenAlex
Harold Bays, Angela Fitch, Suzanne Cuda, et al.
Background: This Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) provides clinicians an overview of Artificial Intelligence, focused on the management of patients with obesity. Methods: The perspectives of the authors were augmented by scientific support from published citations and integrated with information derived from search engines (i.e., Chrome by Google, Inc) and chatbots (i.e., Chat Generative Pretrained Transformer or Chat GPT). Results: Artificial Intelligence (AI) is the technologic acquisition of knowledge and skill by a nonhuman device, that after being initially programmed, has varying degrees of operations autonomous from direct human control, and that performs adaptive output tasks based upon data input learnings. AI has applications regarding medical research, medical practice, and applications relevant to the management of patients with obesity. Chatbots may be useful to obesity medicine clinicians as a source of clinical/scientific information, helpful in writings and publications, as well as beneficial in drafting office or institutional Policies and Procedures and Standard Operating Procedures. AI may facilitate interactive programming related to analyses of body composition imaging, behavior coaching, personal nutritional intervention & physical activity recommendations, predictive modeling to identify patients at risk for obesity-related complications, and aid clinicians in precision medicine. AI can enhance educational programming, such as personalized learning, virtual reality, and intelligent tutoring systems. AI may help augment in-person office operations and telemedicine (e.g., scheduling and remote monitoring of patients). Finally, AI may help identify patterns in datasets related to a medical practice or institution that may be used to assess population health and value-based care delivery (i.e., analytics related to electronic health records). Conclusions: AI is contributing to both an evolution and revolution in medical care, including the management of patients with obesity. Challenges of Artificial Intelligence include ethical and legal concerns (e.g., privacy and security), accuracy and reliability, and the potential perpetuation of pervasive systemic biases.
56Critical 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.
57ARTIFICIAL INTELLIGENCE IN STUDENT PRIVACY AND DATA SECURITYOpenAlex
Ambar Dutta
The rapid digitization of education has revolutionized data management practices, yet it concurrently escalates risks to student data privacy and security. This paper examines the dual role of Artificial Intelligence (AI) in both exacerbating and mitigating these challenges. While AI-driven tools such as learning analytics and biometric systems enhance educational outcomes, they introduce vulnerabilities like adversarial data manipulation, over-collection of sensitive information, and algorithmic bias. Traditional security models, reliant on rule-based systems and manual oversight, prove inadequate against evolving cyber threats, underscoring the need for adaptive solutions. AI-based approaches—including federated learning, differential privacy, and anomaly detection—offer proactive mechanisms to safeguard data through decentralized training, noise-injected anonymization, and real-time threat detection. However, these technologies face implementation barriers such as high computational costs, regulatory conflicts, and ethical dilemmas. Regulatory frameworks like GDPR, FERPA, and COPPA further complicate compliance, as divergent mandates on data retention, consent, and transparency challenge global institutions. Through a comparative analysis of AI and traditional models, this study advocates for hybrid frameworks that integrate AI’s scalability with human oversight to balance innovation and accountability. Case studies highlight AI’s efficacy in reducing breaches (e.g., 75% fewer FERPA violations via automated redaction tools) but also expose risks like biased facial recognition systems. The paper concludes with strategic recommendations: prioritizing ethical AI governance, fostering regulatory harmonization, and investing in infrastructure to democratize access. By addressing these imperatives, educational stakeholders can harness AI’s potential while upholding the trust and privacy essential to equitable learning environments
58Navigating ethical considerations in the use of artificial intelligence for patient care: A systematic review.PubMed
Walaa Badawy, Haithm Zinhom, Mostafa Shaban
Int Nurs Rev. 2025 Sep;72(3):e13059. doi: 10.1111/inr.13059. Epub 2024 Nov 15.
AIM: To explore the ethical considerations and challenges faced by nursing professionals in integrating artificial intelligence (AI) into patient care. BACKGROUND: AI's integration into nursing practice enhances clinical decision-making and operational efficiency but raises ethical concerns regarding privacy, accountability, informed consent, and the preservation of human-centered care. METHODS: A systematic review was conducted, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Thirteen studies were selected from databases including PubMed, Embase, IEEE Xplore, PsycINFO, and CINAHL. Thematic analysis identified key ethical themes related to AI use in nursing. RESULTS: The review highlighted critical ethical challenges, such as data privacy and security, accountability for AI-driven decisions, transparency in AI decision-making, and maintaining the human touch in care. The findings underscore the importance of stakeholder engagement, continuous education for nurses, and robust governance frameworks to guide ethical AI implementation in nursing. DISCUSSION: The results align with existing literature on AI's ethical complexities in healthcare. Addressing these challenges requires strengthening nursing competencies in AI, advocating for patient-centered AI design, and ensuring that AI integration upholds ethical standards. CONCLUSION: Although AI offers significant benefits for nursing practice, it also introduces ethical challenges that must be carefully managed. Enhancing nursing education, promoting stakeholder engagement, and developing comprehensive policies are essential for ethically integrating AI into nursing. IMPLICATIONS FOR NURSING: AI can improve clinical decision-making and efficiency, but nurses must actively preserve humanistic care aspects through ongoing education and involvement in AI governance. IMPLICATIONS FOR HEALTH POLICY: Establish ethical frameworks and data protection policies tailored to AI in nursing. Support continuous professional development and allocate resources for the ethical integration of AI in healthcare.
59Integrating AI in medical education: a comprehensive study of medical students’ attitudes, concerns, and behavioral intentionsOpenAlex
Shuo Duan, Chunyu Liu, Tianhua Rong, et al.
BACKGROUND: To analyze medical students' perceptions, trust, and attitudes toward artificial intelligence (AI) in medical education, and explore their willingness to integrate AI in learning and teaching practices. METHODS: This cross-sectional study was performed with undergraduate and postgraduate medical students from two medical universities in Beijing. Data were collected between October and early November 2024 via a self-designed questionnaire that covered seven main domains: Awareness of AI, Expectations and concerns about AI, Importance of AI in education, Potential challenges and risks of AI in education and learning, The role and potential of AI in education, Perceptions of generative AI, and Behavioral intentions and plans for AI use in medical education. RESULTS: A total of 586 students participated in the survey, 553 valid responses were collected, giving an effective response rate of 94.4%. The majority of participants reported familiarity with AI concepts, whereas only 43.5% had an understanding of AI applications specific to medical education. Postgraduate students exhibited significantly higher levels of awareness of AI tools in medical contexts compared with undergraduate students (p < 0.001). Gender differences were also observed, with male students showing more enthusiasm and higher engagement with AI technologies than female students (p < 0.001). Female students expressed greater concerns regarding privacy, data security, and potential ethical issues related to AI in medical education than male students (p < 0.05). Male students or postgraduate students showed stronger behavioral intentions to integrate AI tools in their future learning and teaching practices. CONCLUSIONS: Medical students exhibit optimistic yet cautious attitudes toward the application of AI in medical education. They acknowledge the potential of AI to enhance educational efficiency, but remain mindful of the associated privacy and ethical risks. Strengthening AI education and training and balancing technological advancements with ethical considerations will be crucial in facilitating the deep integration of AI in medical education. TRIAL REGISTRATION: Not clinical trial.
60Navigating Ethical Dilemmas in AI-Enhanced EducationOpenAlex
Jing Li, Quanwei Huang
With the rapid advancement of artificial intelligence (AI) in education, AI-enhanced learning is driving profound pedagogical shifts, offering personalized teaching and resource optimization while raising ethical concerns such as data privacy, algorithmic bias, intellectual property, transparency, and equity. Using bibliometric methods, the authors of this study systematically analyzed global research on AI ethics in education over the past decade, revealing its dynamic evolution. AI ethics in education research has grown exponentially, shifting from early technical feasibility studies to the ethical risks of generative AI in specific scenarios. However, research remains technology-centric, lacking focus on appropriate educational contexts. The international network is dominated by the United States, China, and European Union countries, with limited participation from developing nations. This study also examines ethical dilemmas and gaps in current research frameworks, aiming to provide insights for academics, policymakers, and future studies.
61ChatGPT is not capable of serving as an author: ethical concerns and challenges of large language models in educationOpenAlex
This research delves into the dynamic role of ChatGPT and similar large language models within the realm of education.It sheds light on their set of limitations, ethical concerns, and challenges that must be addressed thoughtfully, offering a comprehensive exploration of their implications in various educational contexts and the evolving landscape of teaching, research, and scholarly communication.The paper initiates its exploration by investigating how ChatGPT can be applied in scientific writing and publishing.Furthermore, the paper critically assesses the constraints associated with utilizing ChatGPT in education.It acknowledges the model's limitations in generating authoritative content, comprehending complex subject matter, and ensuring information accuracy.These limitations, thoroughly examined, present substantial obstacles to the integration of ChatGPT into educational practices.The research also addresses the ethical dilemmas and potential pitfalls that arise from a heavy reliance on generative AI in education.It delves into issues of bias, accountability, and the dissemination of misinformation.These considerations emphasize the importance of maintaining human agency and oversight in educational settings, promoting the responsible use of AI.The paper further explores the impact of ChatGPT on academic research, both in terms of augmenting research productivity and potential risks to the rigor and authenticity of scholarly work.Strategies and tools for detecting and mitigating instances of academic misconduct involving AI-generated content are examined in detail.Additionally, the research investigates the role of ChatGPT in enhancing critical thinking skills among students, educators, and researchers.It explores the potential for innovative pedagogical methods that leverage generative AI to foster improved critical thinking.Moreover, the paper considers the implications of ChatGPT on educational policy, encompassing issues such as privacy concerns, intellectual property rights, and the necessity for regulations in the evolving landscape of AI in education.These insights are invaluable for educators, researchers, policymakers, and stakeholders seeking to harness the benefits of generative AI while navigating the associated challenges in the realm of education.
62Cyber-Ethical Leadership in Higher EducationOpenAlex
Ryma Abassi
The rapid digitization of higher education has introduced complex ethical challenges that demand new forms of leadership. This paper explores the concept of cyber-ethical leadership as a critical response to evolving issues such as data privacy, AI-generated academic content, digital surveillance, and cybersecurity crisis management. Drawing from real-world case studies and institutional practices, we propose a practice-based framework that emphasizes ethical reflexivity, participatory governance, and multi- stakeholder engagement. By reframing leadership as ethical agency rather than hierarchical authority, this study highlights how leaders in academia can navigate digital dilemmas through context-aware, inclusive decision-making. The findings call for the integration of digital ethics into institutional policy, leadership training, and crisis preparedness, contributing to a more resilient and ethically grounded digital transformation of higher education.
63Generative AI and future education: a review, theoretical validation, and authors’ perspective on challenges and solutionsOpenAlex
Wali Khan Monib, Atika Qazi, Rosyzie Anna Awg Haji Mohd Apong, et al.
Generative AI (Gen AI), exemplified by ChatGPT, has witnessed a remarkable surge in popularity recently. This cutting-edge technology demonstrates an exceptional ability to produce human-like responses and engage in natural language conversations guided by context-appropriate prompts. However, its integration into education has become a subject of ongoing debate. This review examines the challenges of using Gen AI like ChatGPT in education and offers effective strategies. To retrieve relevant literature, a search of reputable databases was conducted, resulting in the inclusion of twenty-two publications. Using Atlas.ti, the analysis reflected six primary challenges with plagiarism as the most prevalent issue, closely followed by responsibility and accountability challenges. Concerns were also raised about privacy, data protection, safety, and security risks, as well as discrimination and bias. Additionally, there were challenges about the loss of soft skills and the risks of the digital divide. To address these challenges, a number of strategies were identified and subjected to critical evaluation to assess their practicality. Most of them were practical and align with the ethical and pedagogical theories. Within the prevalent concepts, "ChatGPT" emerged as the most frequent one, followed by "AI," "student," "research," and "education," highlighting a growing trend in educational discourse. Moreover, close collaboration was evident among the leading countries, all forming a single cluster, led by the United States. This comprehensive review provides implications, recommendations, and future prospects concerning the use of generative AI in education.
64Framing Artificial Intelligence in Higher Education: A Narrative Policy Analysis Study of Higher Education Institutions with Particular Reference to MissouriOpenAlex
Taylor D. Corlee
This study examines how higher education institutions narratively frame students’ use of artificial intelligence through policy-related documents, focusing on how these stories shape AI policy and how they can guide Missouri-based research. Using the Narrative Policy Framework and directed content analysis, 59 documents from 12 US public universities were coded for characters, plots, and morals and analyzed with descriptive statistics, Pearson correlations, and difference-of-means tests. Findings reveal that institutions frame AI policy through narratives that cast faculty as guides, students as both capable and vulnerable, and AI as both tools and threat. For Missouri, recognizing how narratives shape AI governance highlights the need for statewide baseline policies that balance regulation with education, incorporating disclosure norms, faculty discretion, training, and equity safeguards, while also including student voices and tracking narrative evolution over time to ensure clarity, fairness, and responsible AI integration.
65Ethical 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
66Federated Learning for Privacy-Preserving Big Data Analytics in Cloud EnvironmentsOpenAlex
Arshiya Shirdi, Sumeer Basha Peta, Nirmal Sajanraj, et al.
The rapid increase of big data in cloud systems unlocks plentiful intelligent analysis chances but it needs immediate attention for data privacy protection and security management alongside regulatory adherence. The centralized educational approach forces sensitive information to gather at a single station thus creating potential risks of data theft and privacy violations. This paper evaluates Federated Learning (FL) because it functions as a decentralized privacy-preserving approach that trains models collectively based on distributed data without sharing actual data values. Our framework stands as a novel FL design meant for heterogeneous cloud systems where it implements secure aggregation approaches with adaptive client pick and differential privacy functionalities to deliver strong security while building scalability. The proposed framework demonstrates superior or equal predictive abilities to centralized models through extensive real-world large-scale dataset experiments while upholding rigorous privacy conditions. The research confirms Federated Learning functions as a workable solution for protecting big data analytics across compliant and large-scale cloud environments.
67A Breakthrough in Producing Personalized Solutions for Rehabilitation and Physiotherapy Thanks to the Introduction of AI to Additive ManufacturingOpenAlex
Emilia Mikołajewska, Dariusz Mikołajewski, Tadeusz Mikołajczyk, et al.
The integration of artificial intelligence (AI) with additive manufacturing (AM) is driving breakthroughs in personalized rehabilitation and physical therapy solutions, enabling precise customization to individual patient needs. This article presents the current state of knowledge and perspectives of using personalized solutions for rehabilitation and physiotherapy thanks to the introduction of AI to AM. Advanced AI algorithms analyze patient-specific data such as body scans, movement patterns, and medical history to design customized assistive devices, orthoses, and prosthetics. This synergy enables the rapid prototyping and production of highly optimized solutions, improving comfort, functionality, and therapeutic outcomes. Machine learning (ML) models further streamline the process by anticipating biomechanical needs and adapting designs based on feedback, providing iterative refinement. Cutting-edge techniques leverage generative design and topology optimization to create lightweight yet durable structures that are ideally suited to the patient’s anatomy and rehabilitation goals .AI-based AM also facilitates the production of multi-material devices that combine flexibility, strength, and sensory capabilities, enabling improved monitoring and support during physical therapy. New perspectives include integrating smart sensors with printed devices, enabling real-time data collection and feedback loops for adaptive therapy. Additionally, these solutions are becoming increasingly accessible as AM technology lowers costs and improves, democratizing personalized healthcare. Future advances could lead to the widespread use of digital twins for the real-time simulation and customization of rehabilitation devices before production. AI-based virtual reality (VR) and augmented reality (AR) tools are also expected to combine with AM to provide immersive, patient-specific training environments along with physical aids. Collaborative platforms based on federated learning can enable healthcare providers and researchers to securely share AI insights, accelerating innovation. However, challenges such as regulatory approval, data security, and ensuring equity in access to these technologies must be addressed to fully realize their potential. One of the major gaps is the lack of large, diverse datasets to train AI models, which limits their ability to design solutions that span different demographics and conditions. Integration of AI–AM systems into personalized rehabilitation and physical therapy should focus on improving data collection and processing techniques.
68Cross-border higher education cooperation under the dual context of artificial intelligence and geopolitics: opportunities, challenges, and pathwaysOpenAlex
Yaoshun Zhu, Yaoshun Zhu, Zhitao Zhu, et al.
This paper examines the profound impact of artificial intelligence (AI) and geopolitics on cross-border higher education cooperation. AI has the potential to enhance educational accessibility and collaboration efficiency by enabling personalized learning, virtual classrooms, open resource platforms, and open-source research collaborations, ultimately helping bridge global educational gaps. However, significant challenges arise, such as techno-nationalism (e.g., semiconductor export controls), data sovereignty conflicts (e.g., GDPR restrictions), divergent algorithmic values, and the expanding digital divide. To address these challenges, this study proposes several solutions: the creation of an inclusive technological ecosystem (including open-source platforms, shared computing power, and cross-cultural models); the development of mutual recognition mechanisms (such as data stratification and standard harmonization); the strengthening of South-South cooperation through digital public goods; and the reconstruction of ethical consensus, emphasizing cultural diversity and human-in-the-loop principles. Notably, China has actively contributed to these efforts through technological empowerment (e.g., National Smart Education Platform, Luban Workshops), regulatory input (e.g., Global Governance Initiative), and infrastructure support. Looking ahead, the paper argues for the establishment of an “Intelligent Education Community,” guided by the principles of “extensive consultation, joint contribution, shared benefits, and wise governance,” to ensure that AI advances global educational equity and promotes human progress.
69Generative 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.
70Advancing equity and inclusion in educational practices with <scp>AI</scp> ‐powered educational decision support systems ( <scp>AI</scp> ‐ <scp>EDSS</scp> )OpenAlex
Olga Viberg, René F. Kizilcec, Alyssa Friend Wise, et al.
Abstract A key goal of educational institutions around the world is to provide inclusive, equitable quality education and lifelong learning opportunities for all learners. Achieving this requires contextualized approaches to accommodate diverse global values and promote learning opportunities that best meet the needs and goals of all learners as individuals and members of different communities. Advances in learning analytics (LA), natural language processes (NLP), and artificial intelligence (AI), especially generative AI technologies, offer potential to aid educational decision making by supporting analytic insights and personalized recommendations. However, these technologies also raise serious risks for reinforcing or exacerbating existing inequalities; these dangers arise from multiple factors including biases represented in training datasets, the technologies' abilities to take autonomous decisions, and processes for tool development that do not centre the needs and concerns of historically marginalized groups. To ensure that Educational Decision Support Systems (EDSS), particularly AI‐powered ones, are equipped to promote equity, they must be created and evaluated holistically, considering their potential for both targeted and systemic impacts on all learners, especially members of historically marginalized groups. Adopting a socio‐technical and cultural perspective is crucial for designing, deploying, and evaluating AI‐EDSS that truly advance educational equity and inclusion. This editorial introduces the contributions of five papers for the special section on advancing equity and inclusion in educational practices with AI‐EDSS. These papers focus on (i) a review of biases in large language models (LLMs) applications offers practical guidelines for their evaluation to promote educational equity, (ii) techniques to mitigate disparities across countries and languages in LLMs representation of educationally relevant knowledge, (iii) implementing equitable and intersectionality‐aware machine learning applications in education, (iv) introducing a LA dashboard that aims to promote institutional equality, diversity, and inclusion, and (v) vulnerable student digital well‐being in AI‐EDSS. Together, these contributions underscore the importance of an interdisciplinary approach in developing and utilizing AI‐EDSS to not only foster a more inclusive and equitable educational landscape worldwide but also reveal a critical need for a broader contextualization of equity that incorporates the socio‐technical questions of what kinds of decisions AI is being used to support, for what purposes, and whose goals are prioritized in this process.
71ADVANCING 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
72Empowering 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.
73Advancing 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.
74The 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—creativity, critical thinking, and empathy—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.
75Factors influencing digital literacy among the 9185 elderly in South Korea: A machine learning approach.PubMed
Haewon Byeon
Medicine (Baltimore). 2026 Jan 2;105(1):e46761. doi: 10.1097/MD.0000000000046761.
This study aims to identify key factors influencing digital literacy among the elderly individuals in South Korea using advanced machine learning techniques, focusing on enhancing their quality of life and social participation. Data from the 2018 to 2019 National Survey of Older Koreans were utilized, encompassing responses from 9185 individuals aged 65 and above. Digital literacy was assessed through the ability to use various digital devices and applications. Predictors included demographic, socioeconomic, health, and social support variables. CatBoost, a gradient boosting algorithm, was employed for feature selection and importance ranking, followed by logistic regression analysis to determine the relationships between these predictors and digital literacy levels. The analysis revealed that higher education levels, employment status, economic status, social participation, subjective health, and informal support positively influenced digital literacy among the elderly individuals. Conversely, age and depression were negatively associated with digital literacy. The CatBoost model demonstrated superior performance in identifying significant predictors, with education level emerging as the most influential factor, followed by age and employment status. The findings highlight the multifaceted nature of digital literacy among the elderly individuals, emphasizing the importance of educational attainment, social engagement, and economic conditions. Targeted interventions addressing these factors can effectively bridge the digital divide, ensuring that the elderly population can fully participate in the digital age and enhance their overall well-being.
76Large Language Models in Medical Education: Opportunities, Challenges, and Future DirectionsOpenAlex
Alaa Abd‐Alrazaq, Rawan AlSaad, Dari Alhuwail, et al.
The integration of large language models (LLMs), such as those in the Generative Pre-trained Transformers (GPT) series, into medical education has the potential to transform learning experiences for students and elevate their knowledge, skills, and competence. Drawing on a wealth of professional and academic experience, we propose that LLMs hold promise for revolutionizing medical curriculum development, teaching methodologies, personalized study plans and learning materials, student assessments, and more. However, we also critically examine the challenges that such integration might pose by addressing issues of algorithmic bias, overreliance, plagiarism, misinformation, inequity, privacy, and copyright concerns in medical education. As we navigate the shift from an information-driven educational paradigm to an artificial intelligence (AI)-driven educational paradigm, we argue that it is paramount to understand both the potential and the pitfalls of LLMs in medical education. This paper thus offers our perspective on the opportunities and challenges of using LLMs in this context. We believe that the insights gleaned from this analysis will serve as a foundation for future recommendations and best practices in the field, fostering the responsible and effective use of AI technologies in medical education.
77The dark side of generative artificial intelligence: A critical analysis of controversies and risks of ChatGPTOpenAlex
Krzysztof Wach, Cong Doanh Duong, Joanna Ejdys, et al.
Objective: The objective of the article is to provide a comprehensive identification and understanding of the challenges and opportunities associated with the use of generative artificial intelligence (GAI) in business. This study sought to develop a conceptual framework that gathers the negative aspects of GAI development in management and economics, with a focus on ChatGPT. Research Design & Methods: The study employed a narrative and critical literature review and developed a conceptual framework based on prior literature. We used a line of deductive reasoning in formulating our theoretical framework to make the study's overall structure rational and productive. Therefore, this article should be viewed as a conceptual article that highlights the controversies and threats of GAI in management and economics, with ChatGPT as a case study. Findings: Based on the conducted deep and extensive query of academic literature on the subject as well as professional press and Internet portals, we identified various controversies, threats, defects, and disadvantages of GAI, in particular ChatGPT. Next, we grouped the identified threats into clusters to summarize the seven main threats we see. In our opinion they are as follows: (i) no regulation of the AI market and urgent need for regulation, (ii) poor quality, lack of quality control, disinformation, deepfake content, algorithmic bias, (iii) automation-spurred job losses, (iv) personal data violation, social surveillance, and privacy violation, (v) social manipulation, weakening ethics and goodwill, (vi) widening socio-economic inequalities, and (vii) AI technostress. Implications & Recommendations: It is important to regulate the AI/GAI market. Advocating for the regulation of the AI market is crucial to ensure a level playing field, promote fair competition, protect intellectual property rights and privacy, and prevent potential geopolitical risks. The changing job market requires workers to continuously acquire new (digital) skills through education and retraining. As the training of AI systems becomes a prominent job category, it is important to adapt and take advantage of new opportunities. To mitigate the risks related to personal data violation, social surveillance, and privacy violation, GAI developers must prioritize ethical considerations and work to develop systems that prioritize user privacy and security. To avoid social manipulation and weaken ethics and goodwill, it is important to implement responsible AI practices and ethical guidelines: transparency in data usage, bias mitigation techniques, and monitoring of generated content for harmful or misleading information. Contribution & Value Added: This article may aid in bringing attention to the significance of resolving the ethical and legal considerations that arise from the use of GAI and ChatGPT by drawing attention to the controversies and hazards associated with these technologies.
78Opportunities and Challenges of Integrating Generative Artificial Intelligence in EducationOpenAlex
Rommel AlAli, Yousef Wardat
This paper thoroughly examines both the opportunities and obstacles associated with integrating Generative Artificial Intelligence (AI) into educational settings. It explores how Generative AI has the potential to enrich learning experiences, customize education for individuals, and foster creativity. However, it also confronts several challenges including ethical dilemmas, safeguarding data privacy, mitigating algorithmic biases, and reshaping the role of educators. Through a synthesis of theoretical frameworks and empirical research, the paper offers valuable insights into effective strategies for navigating these challenges. It emphasizes the importance of establishing ethical guidelines, ensuring transparency in algorithms, and adopting inclusive design principles during AI integration. Furthermore, the paper underscores the importance of providing educators with adequate training and professional development opportunities to effectively utilize AI tools. Additionally, it advocates for ongoing dialogue among stakeholders—such as educators, policymakers, technologists, and students—to steer responsible AI integration in education. Ultimately, the paper advocates for a collaborative approach that prioritizes human-centric values, equity, and diversity. While Generative AI holds promise for revolutionizing educational practices, its integration requires thoughtful consideration of ethical, social, and pedagogical implications. Through proactive collaboration and partnership, educators can leverage AI's potential to create more immersive, tailored, and equitable learning environments.
79Higher 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 & 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.
80Empowering Education with Generative Artificial Intelligence Tools: Approach with an Instructional Design MatrixOpenAlex
Lena Ivannova Ruiz-Rojas, Patricia Acosta-Vargas, Javier De-Moreta-Llovet, et al.
This study focuses on the potential of generative artificial intelligence tools in education, particularly through the practical application of the 4PADAFE instructional design matrix. The objective was to evaluate how these tools, in combination with the matrix, can enhance education and improve the teaching–learning process. Through surveys conducted with teachers from the University of ESPE Armed Forces who participated in the MOOC course “Generative Artificial Intelligence Tools for Education: GPT Chat Techniques”, the study explores the impact of these tools on education. The findings reveal that generative artificial intelligence tools are crucial in developing massive MOOC virtual classrooms when integrated with an instructional design matrix. The results demonstrate the potential of generative artificial intelligence tools in university education. By utilizing these tools in conjunction with an instructional design matrix, educators can design and deliver personalized and enriching educational experiences. The devices offer opportunities to enhance the teaching–learning process and tailor educational materials to individual needs, ultimately preparing students for the demands of the 21st century. The study concludes that generative artificial intelligence tools have significant potential in education. They provide innovative ways to engage students, adapt content, and promote personalized learning. Implementing the 4PADAFE instructional design matrix further enhances the effectiveness and coherence of educational activities. By embracing these technological advancements, education can stay relevant and effectively meet the digital world’s challenges.
81Benefits, Challenges, and Methods of Artificial Intelligence (AI) Chatbots in Education: A Systematic Literature ReviewOpenAlex
Şahin Gökçearslan, Cansel Tosun, Zeynep Gizem Erdemir
In many fields, AI chatbots continue to be popular with new tools and attract the attention of universities, K12 schools, educational organizations, and researchers. The aim of this research is to review the research on AI chatbots by restricting it to the category of education and to examine this research from a methodological point of view. Therefore, we performed a systematic literature review with a sample of 37 SSCI articles published in the educational context. Within the scope of the selected studies, the advantages and disadvantages of AI chatbots in education for students and educators, as well as the types of chatbots used, year, keywords, and method were analyzed. According to the research results, increased motivation to learn and language skill development are advantages for students, while cost-effectiveness and reduced workload are advantages for educators. Limited interaction, misleading answers for learners, originality, and plagiarism are the most common disadvantages for educators. The study also includes research results and recommendations related to the methodological review.
82Current practices and future direction of artificial intelligence in mathematics education: A systematic reviewOpenAlex
Liz Aliza Awang, Farrah Dina Yusop, Mahmoud Danaee
Mastering mathematics is often challenging for many students; however, the rise of artificial intelligence (AI) offers numerous advantages, including enhanced data analysis, automated feedback, and the potential for creating more interactive and engaging learning environments. Despite these benefits, there is a need for comprehensive reviews that provide an overview of AI's role in mathematics education to help educators identify the best AI tools, and to inform researchers about current trends and future directions. This study conducts a systematic literature review (SLR) to investigate the applications and trends of AI in mathematics education by examining articles published in reputable journals indexed in Web of Science and Scopus. The review categorizes AI tools into those narrowly addressing mathematical problems, such as solving equations and visualizing geometry, and those offering broader pedagogical support, including adaptive learning systems and generative AI platforms. Key aspects analyzed include the distribution of AI in Mathematics Education (AIME) studies across different educational levels, the types and categories of AI tools used, the functionality of commercialized AIME tools available on the internet, and the emerging trends and future directions in AIME based on recent literature. The insights from this SLR are crucial for educators, policymakers, and researchers, enabling them to integrate AI effectively into mathematics education and tailor tools to specific teaching strategies and learning needs.
83Exploring the Role of LLMs Like ChatGPT in Pharmacy Education for Supporting Students' Therapeutic Decision-making.PubMed
Paola Carou-Senra, Irene Delgado-Taboada, Carmen Alvarez-Lorenzo, et al.
Am J Pharm Educ. 2025 Aug;89(8):101462. doi: 10.1016/j.ajpe.2025.101462. Epub 2025 Jul 4.
OBJECTIVE: The aim of this study was to evaluate the utility of large-scale language models as training strategies for clinical student pharmacists, exploring their potential use in drug dosage adjustment while analyzing their limitations. METHODS: ChatGPT, Gemini, and Copilot were evaluated for predicting the appropriate drug dose using common pharmacokinetic problems. Three narrow therapeutic drugs-tacrolimus, vancomycin, and lidocaine-were selected, and 3 different therapeutic scenarios were tested for each drug with the models. The prompt structure was modified to analyze its impact on the results achieved. The performance of the models in each scenario was rated using a numerical scale from 0 to 2. The potential benefits of the model as support tools for students, as well as the identification of the current limitations, were evaluated. RESULTS: ChatGPT achieved the highest score and had the greatest number of correct answers. Tacrolimus inputs produced the most correct answers, likely because its calculations were less complex. Moreover, modifications in the prompt structure led to significant changes in the results for most models, highlighting the critical role of prompt design. CONCLUSION: While the results indicate room for improvement, the successful cases highlight promising directions. With more rigorous study of the models, enhanced data quality, and a deeper understanding of prompt design, these artificial intelligence tools could offer substantial support to students. Educating users on these emerging technologies will further enhance their application in health care, maximizing benefits and mitigating potential risks and limitations.
84Education in the AI era: a long-term classroom technology based on intelligent roboticsOpenAlex
Francisco Bellas, M. Naya-Varela, Alma Mallo, et al.
Artificial Intelligence (AI) will have a major social impact in the coming years, affecting today’s professions and our daily routines. In the short-term, education is one of the most impacted areas. The autonomous decision making that can be achieved with tools based on AI implies that some of the traditional methodologies associated with the fundamentals of the learning process in students, must be reviewed. Consequently, the role of teachers in the classroom may change, as they will have to deal with such AI tools performing parts of their work, and with students making a common use of them. In this scope, the AI in Education (AIEd) community agrees on the key relevance of developing AI literacies to train teachers and students of all educational levels in the fundamentals of this new technological discipline, so they can understand how these tools based on AI work and pilot the adaptation in an informed way. This implies teaching students about the fundamentals of topics like perception, representation, reasoning, learning, and the impact of AI, with the aim of delivering a solid formation in this area. To support them, formal teaching and learning resources must be developed and tested with students, properly adapted to different educational levels. The main contribution of this proposal lies in the presentation of the Robobo Project, a technological tool based on intelligent robotics that supports such formal AI literacy training for a wide range of ages, from secondary school to higher education. The core part of this paper is focused on showing the possibilities the Robobo Project offers to teachers in a simple way, and how it can be adapted to different levels and skills, leading to a long-term educational proposal. Validation results that support the feasibility of this technology in the education about AI, obtained with students and teachers in different educational levels during a period of six years, are presented and discussed.
85AI-Powered Innovations Transforming Adaptive Education for Disability SupportOpenAlex
Munikrishnaiah Sundara Ramaiah, Sevinthi Kali Sankar Nagarajan, Pawan Whig, et al.
This chapter examines artificial intelligence's (AI) transformative impact on adaptive education for students with disabilities. This chapter explores how AI-driven technologies, including intelligent tutoring systems, speech recognition tools, and personalized learning algorithms, are reshaping educational experiences to be more inclusive and effective. By leveraging these innovations, educators can tailor content and support to meet individual learning needs, enhance accessibility, and overcome barriers to education. Drawing on case studies and practical examples, the chapter highlights the ability of AI to revolutionize traditional educational models by delivering customized, responsive support for diverse disabilities. Emphasizing the potential of AI to foster equity and inclusion, the chapter underscores its role in advancing opportunities for all learners and creating a more accessible educational landscape.
86The use of generative AI by students with disabilities in higher educationOpenAlex
Xin Zhao, Andrew Cox, Xuanning Chen
The use of generative AI is controversial in education largely because of its potential impact on academic integrity. Yet some scholars have suggested it could be particularly beneficial for students with disabilities. To date there has been no empirical research to discover how these students use generative AI in academic writing. Informed by a prior interview study and AI-literacy model, we surveyed students regarding their use of generative AI, and gained 124 valid responses from students with disabilities. We identified primary conditions affecting writing such as ADHD, dyslexia, dyspraxia, and autism. The main generative AI used were chatbots, particularly ChatGPT, and rewriting applications. They were used in a wide range of academic writing tasks. Key concerns students with disabilities had included the inaccuracy of AI answers, risks to academic integrity, and subscription cost barriers. Students expressed a strong desire to participate in AI policymaking and for universities to provide generative AI training. The paper concludes with recommendations to address educational disparities and foster inclusivity. • Students with disabilities are using generative AI to overcome barriers they face in academic writing. • The main generative AI used by students with disabilities are chatbots, rewrite applications, and translation software. • The main concerns are the inaccuracy of answers, the risk of academic integrity breaches, and the digital divide created by subscription costs. • There is a strong desire among students to be involved in generative AI policymaking and for universities to provide training on generative AI.
87Exploring the role of generative AI in higher education: Semi-structured interviews with students with disabilitiesOpenAlex
Oriane Pierrès, Alireza Darvishy, Markus Christen
Abstract The release of a free generative artificial intelligence (GAI), ChatGPT, in November 2022 has opened up numerous opportunities for students with disabilities in higher education. While the transformative impact of GAI on teaching and learning in general is being debated intensively, little attention has been given to its potential for fostering or hindering inclusion. In news and blog articles, disability advocates have provided insights into the benefits and uses of GAI. However, a comprehensive understanding from a broader sample remains lacking. In order to address this gap, this study raises the question: “How do students with disabilities use and perceive ChatGPT as a tool in higher education?”. Semi-structured interviews were conducted with students with disabilities to gain insights into their current utilization of GAI, identify limitations and challenges, and explore their expectations. A total of 33 participants took part, including neurodiverse students as well as students with visual impairments, chronic diseases, hearing impairments, and mental health conditions. Results suggest that ChatGPT brings significant opportunities as an assistant in teaching, writing, reading and research, or self-organization. Based on this study, higher education institutions are recommended to consider the opportunities the tool represents for students with disabilities in their AI policies. They also have a responsibility to train and inform students to harness the potential of GAI. Developers are encouraged to address accessibility issues and to include the opinions of individuals with disabilities in their research. More practically, the results of this study can be used to design future applications that bear in mind the expectations and concerns of students with disabilities.
88Using AI to Improve Accessibility and Inclusivity in Higher Education for Students with DisabilitiesOpenAlex
Xhulio Mitre, Muhamet Zeneli
Around 1.3 billion, or more than 15% of the global population, suffer from a significant form of disability, with students and researchers in this category being underrepresented in higher education. This paper examines the use of Interactive Artificial Intelligence solutions to improve the inclusivity and accessibility of students with disabilities in higher education. The paper addresses the failure of traditional education methods to accommodate the diverse needs of people with disabilities. Through an exploratory literature review, several AI-driven solutions, like assistive technologies, adaptive-learning systems, and generative AI chatbots and virtual assistants, have been reviewed to understand how AI could be leveraged to create an inclusive learning environment. Key results show AI-driven solutions' potential to transform the learning process of people with disabilities by creating personalized learning paths, increasing access to educational resources, and supporting real-time communication. Additionally, the research highlights ethical concerns about the integration of AI in education, emphasizing the need for the participation of disabled individuals in the developmentprocess.
89Using ChatGPT in the Secondary Gifted Classroom for Personalized Learning and MentoringOpenAlex
Keri M. Guilbault, Yibin Wang, Kimberly M. McCormick
Artificial intelligence (AI) has the potential to revolutionize education by promoting personalized learning experiences and enhancing educators’ efficiency. Generative AI tools like ChatGPT are increasingly used by K-12 teachers for lesson planning, differentiated instruction, and automated feedback. This article examines how ChatGPT, in alignment with the National Association for Gifted Children (NAGC) standards, can be used to address the unique cognitive and affective needs of secondary gifted learners. Case studies provide examples of how to use ChatGPT to facilitate differentiation, personalized learning, support independent research, and provide virtual mentorship. The importance of prompt engineering is emphasized and encouragement of critical thinking skills when using AI tools. By integrating human expertise with AI capabilities, educators can enhance the mentorship experience, foster reflective learning, and cultivate future-ready leaders .