为临床前医学学生创建的LLM多选项实践测验;项目写作缺陷的普遍性
Troy Camarata1, Lise McCoy2, Robert L Rosenberg3
1Baptist University College of Osteopathic Medicine.
Advances in physiology education
|June 14, 2025
概括
人工智能可以创建医学考试问题,但专家审查至关重要. 大型语言模型在主题专家的监督下显示出产生实践测试的前景.
科学领域:
- 医学教育 医学教育
- 教育中的人工智能
背景情况:
- 多选题 (MCQ) 对于医学教育评估至关重要.
- 自动化MCQ生成可以减少教师的工作量,并扩大学生的实践资源.
研究的目的:
- 评估使用ChatGPT生成USMLE/COMLEX-USA风格的医疗实践问题的可行性.
- 评估人工智能产生的医疗评估项目的质量和准确性.
主要方法:
- 二年级医学学生使用ChatGPT生成的脏生理测试.
- 独立专家根据NBME/NBOME指南评估了问题质量.
主要成果:
- 49%的AI产生的问题有写作缺陷;22%有事实/概念错误.
- 91%的问题被认为是适合作为修订的起点.
结论:
- 大型语言模型 (LLM) 是可行的产生医学教育实践问题.
- 专家监督和项目写作培训对于有效使用医学评估中的LLM至关重要.
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