教育工作者能否区分医学生和人工智能创作的创造性反思?
Constance Wraith1, Alasdair Carnegy1, Celia Brown1
1Imperial College School of Medicine, London, UK.
Medical education
|July 2, 2025
概括
教师们很难将人工智能产生的反射与学生的工作区分开来,这表明在医学教育中检测生成人工智能 (GenAI) 的使用方面存在挑战. 这项研究建议利用GenAI来教授反射练习,而不是只专注于检测.
科学领域:
- 医学教育 医学教育
- 教育中的人工智能
背景情况:
- 书面反思是医学院课程的关键组成部分.
- 生成型人工智能 (GenAI) 越来越多地用于学术任务,包括医学教育.
研究的目的:
- 调查医学教育工作者是否能区分GenAI撰写的和学生撰写的反思.
- 为了确定教育工作者在试图区分作者时使用的特征.
主要方法:
- 一项涉及28名教育工作者的混合方法研究.
- 教育工作者参加了"大声思考"采访,同时评估了一组四个反思 (学生撰写,GenAI撰写或混合).
- 对敏感性和特异性的定量分析,以及对采访成绩单的定性主题分析.
主要成果:
- 教育人员表现出无法可靠地区分学生和GenAI撰写的反思.
- 敏感度和特异性值表明,在准确的作者识别方面存在重大挑战.
- 主题分析显示,判断是基于写作风格,反思性内容和教育工作者先入为主的观点.
结论:
- 对教育工作者来说,区分学生和GenAI撰写的反思提出了相当大的挑战.
- 该研究建议利用GenAI作为一种工具,以提高医学学生的反思性实践技能.
- 探索将GenAI整合到医学教育中可能会为学生带来好处,特别是那些发现书面反思困难的学生.
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