Sridharan Kannan, Sequeira Reginald P
Department of Pharmacology & Therapeutics, College of Medicine & Medical Sciences, Arabian Gulf University, Manama, Kingdom of Bahrain.
BMC Med Educ. 2024 Apr 22;24(1):431. doi: 10.1186/s12909-024-05365-7.
Artificial intelligence (AI) tools are designed to create or generate content from their trained parameters using an online conversational interface. AI has opened new avenues in redefining the role boundaries of teachers and learners and has the potential to impact the teaching-learning process.
In this descriptive proof-of- concept cross-sectional study we have explored the application of three generative AI tools on drug treatment of hypertension theme to generate: (1) specific learning outcomes (SLOs); (2) test items (MCQs- A type and case cluster; SAQs; OSPE); (3) test standard-setting parameters for medical students.
Analysis of AI-generated output showed profound homology but divergence in quality and responsiveness to refining search queries. The SLOs identified key domains of antihypertensive pharmacology and therapeutics relevant to stages of the medical program, stated with appropriate action verbs as per Bloom's taxonomy. Test items often had clinical vignettes aligned with the key domain stated in search queries. Some test items related to A-type MCQs had construction defects, multiple correct answers, and dubious appropriateness to the learner's stage. ChatGPT generated explanations for test items, this enhancing usefulness to support self-study by learners. Integrated case-cluster items had focused clinical case description vignettes, integration across disciplines, and targeted higher levels of competencies. The response of AI tools on standard-setting varied. Individual questions for each SAQ clinical scenario were mostly open-ended. The AI-generated OSPE test items were appropriate for the learner's stage and identified relevant pharmacotherapeutic issues. The model answers supplied for both SAQs and OSPEs can aid course instructors in planning classroom lessons, identifying suitable instructional methods, establishing rubrics for grading, and for learners as a study guide. Key lessons learnt for improving AI-generated test item quality are outlined.
AI tools are useful adjuncts to plan instructional methods, identify themes for test blueprinting, generate test items, and guide test standard-setting appropriate to learners' stage in the medical program. However, experts need to review the content validity of AI-generated output. We expect AIs to influence the medical education landscape to empower learners, and to align competencies with curriculum implementation. AI literacy is an essential competency for health professionals.
人工智能（AI）工具旨在通过在线对话界面根据其训练参数创建或生成内容。人工智能在重新定义教师和学习者的角色界限方面开辟了新途径，并有可能影响教学过程。
在这项描述性概念验证横断面研究中，我们探索了三种生成式人工智能工具在高血压药物治疗主题上的应用，以生成：（1）具体学习成果（SLOs）；（2）测试项目（A型多项选择题和病例组；简答题；客观结构化临床考试）；（3）医学生的测试标准设定参数。
对人工智能生成的输出进行分析，结果显示出高度的同源性，但在质量以及对优化搜索查询的响应方面存在差异。具体学习成果确定了与医学课程阶段相关的抗高血压药理学和治疗学的关键领域，并根据布鲁姆分类法使用了适当的行为动词进行表述。测试项目通常有与搜索查询中所述关键领域相关的临床案例。一些与A型多项选择题相关的测试项目存在结构缺陷、多个正确答案以及与学习者阶段的适用性存疑等问题。ChatGPT为测试项目生成了解释，这增强了对学习者自主学习的支持作用。综合病例组项目有重点突出的临床病例描述、跨学科整合以及针对更高水平能力的设计。人工智能工具在标准设定方面的响应各不相同。每个简答题临床场景的个别问题大多是开放式的。人工智能生成的客观结构化临床考试测试项目适合学习者阶段，并确定了相关的药物治疗问题。为简答题和客观结构化临床考试提供的标准答案可帮助课程教师规划课堂教学、确定合适的教学方法、制定评分标准，也可为学习者提供学习指南。文中概述了提高人工智能生成测试项目质量的关键经验教训。
人工智能工具是规划教学方法、确定测试蓝图主题、生成测试项目以及指导与医学课程中学习者阶段相适应的测试标准设定的有用辅助工具。然而，专家需要审查人工智能生成输出的内容效度。我们期望人工智能影响医学教育格局，赋能学习者，并使能力与课程实施保持一致。人工智能素养是卫生专业人员的一项基本能力。
