基因智能和教师合作支持可行发展课程一致的开放式访问板式问题
Ronaé McLin1, Colleen M Croniger1,2, Amy L Wilson-Delfosse3
1Case Western Reserve University School of Medicine, Cleveland, OH 44106 USA.
Medical science educator
|March 9, 2026
概括
一个生成的人工智能聊天机器人创建USMLE风格的解剖问题,其中83%被专家认为是可用的. 该工具为医学教育评估开发提供了一种具有成本效益的方法.
科学领域:
- 医学教育 医学教育
- 教育中的人工智能
- 评估设计 评估设计
背景情况:
- 为医学教育开发高质量的评估项目,例如美国医学执照考试 (USMLE) 样式的解剖学多选择题,是资源密集的.
- 现有的评估创建流程可能耗时且昂贵,可能会限制当地发展和公平.
研究的目的:
- 开发和评估一个生成的人工智能 (AI) 聊天机器人,用于创建与课程一致的解剖学多选择题.
- 由主题专家评估人工智能产生的问题的可用性和质量.
- 探索AI在识别课程差异和支持医学教育中评估发展方面的潜力.
主要方法:
- 一个生成的人工智能聊天机器人旨在使用开放式教育资源制作USMLE风格的解剖学多选题.
- 一百个由人工智能产生的问题被主题专家审查了可用性和所需的修改.
- 在采访主题专家时进行了情绪分析,以衡量他们对人工智能产生的评估项目的看法.
主要成果:
- 在100个评估的人工智能生成解剖问题的83%被发现是潜在的可用的,许多只需要微小的修改或不需要修改.
- 情绪分析表明,专家们的情绪轻微积极,他们认识到AI在评估创建中的好处和局限性.
- 人工智能聊天机器人发现了教师期望与既定学习目标之间的差异,并建议了课程增强的领域.
结论:
- 一个伦理设计的生成AI聊天机器人为创建医学教育评估项目提供了一个可扩展和具有成本效益的解决方案.
- 人工智能产生的问题可以支持当地评估的发展,提高教育公平性,并可能揭示课程改进的机会.
- 这种方法证明了人工智能工具在增强医疗教育和评估传统方法方面的价值.
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