人工智能辅助案例构建器:使PBL更具可扩展性和适应性
Enjy Abouzeid1, Inas Zakaria2,3, Mohammed Alshobaily4
1School of Medicine, Ulster University, Londonderry, UK.
Medical teacher
|February 14, 2026
概括
开发医学教育案例是很困难的. 像PBL案例构建器这样的AI驱动工具可以帮助教育工作者更有效地创建基于问题的学习 (PBL) 案例,减少工作量并提高一致性.
科学领域:
- 医学教育 医学教育
- 教育中的人工智能
背景情况:
- 高质量的基于问题的学习 (PBL) 案例开发是医疗机构的资源密集和挑战.
- 教师的时间限制影响了积极学习的足够案例的产生.
研究的目的:
- 引入一个人工智能辅助的工具,PBL案例构建器,以简化医学教育案例的创建.
- 减少教师的工作量,提高PBL案例开发的效率和一致性.
主要方法:
- 开发了一个定制的ChatGPT应用程序,PBL案例构建器.
- 建设者要求教育工作者输入目标受众,课程上下文,核心主题和复杂性.
- 该工具生成具有渐进式触发器,学习目标和辅导员提醒改进的案例.
主要成果:
- PBL案例构建器确保了生成的案例的上下文化和教学调整.
- 教育者的角色从案例作者转移到审稿人,大大减少了工作量.
- 人工智能辅助的案例构建提高了教学设计的效率,一致性和透明度.
结论:
- 人工智能辅助案例构建为医学教育中的挑战提供了可行的解决方案.
- 这种方法简化了案例开发,促进了适应性,并提高了教学透明度.
- 未来的研究应该评估教育影响,用户感知,并通过存储库探索全球扩展.
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