在医学教育评估中,利用生成性人工智能为高质量的MCQS提供十个建议
Mohi Eldin Magzoub1, Imran Zafar1,2, Fadi Munshi2,3
1Department of Medical Education, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.
Medical education online
|July 17, 2025
概括
使用人工智能 (AI) 可以简化生成高质量的医学教育多选题 (MCQ). 本指南为医学教育工作者提供了10个实用技巧,以有效利用人工智能,以高效和经济高效地创建MCQ.
科学领域:
- 医学教育 医学教育
- 人工智能的人工智能
- 评估设计 评估设计
背景情况:
- 高质量的MCQ生成是必不可少的,但往往耗时且昂贵.
- 现有的策略包括项目银行,作家培训和自动项目生成 (AIG).
- 生成性AI提供了一个有前途的解决方案,可以在不牺牲质量的情况下降低成本和时间.
研究的目的:
- 为了解决医疗教育工作者在使用人工智能进行MCQ生成时所面临的挑战.
- 为改善人工智能辅助的MCQ开发提供实际指导.
- 协助本科和研究生医学教育工作者创建高质量的MCQ.
主要方法:
- 在文献中搜索最近的医学教育文章,关于人工智能在MCQ生成.
- 在AI驱动的评估开发中纳入机构经验.
- 开发一套实用,可操作的10个技巧.
主要成果:
- 识别了使用人工智能来生成高质量的MCQ的常见障碍.
- 从文献和实践经验中综合发现.
- 为有效的AI辅助MCQ生成创建了10个提示框架.
结论:
- 生成型人工智能,当精确地应用时,可以显著减少MCQ生产的时间和成本.
- 这10个技巧旨在赋予医学教育工作者克服AI实施障碍的能力.
- 有效地使用人工智能可以提高医学教育评估的质量和效率.
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