医学教育中的大型语言模型:比较聊天GPT与人类生成的考试问题
概括
医学教育者创造了比人工智能更好的考试问题. 人类编写的问题比大型语言模型 (LLM) 问题具有更高的歧视力,尽管难度相似.
科学领域:
- 医学教育 医学教育
- 教育中的人工智能
背景情况:
- 创建高质量的医学考试问题是劳动密集型的.
- 通过测试增强的学习提高了学生的成绩.
- 使用大型语言模型 (LLM) 自动生成问题可能是有益的,但缺乏比较研究.
研究的目的:
- 为了比较学生在ChatGPT (LLM) 产生的多选题 (MCQ) 的表现,与医学教育工作者创建的表现.
- 评估LLM产生的MCQ与人为产生的MCQ的项目难度和歧视力.
主要方法:
- 创建了两组25个MCQ:一个由医学教育家创建,另一个由ChatGPT 3.5.5.创建.
- 161名学生在他们的神经生理学考试前完成了46个MCQ (25人,21个LLM) 的形成性测试.
- 学生们表示他们对每个问题的感知来源 (人或LLM).
主要成果:
- 人类和LLM问题之间没有观察到项目难度的显著差异.
- 人类生成的问题在统计学上显示出明显更高的歧视力 (平均值=0.36),而不是LLM生成的问题 (平均值=0.24).
- 学生只有57%的时间正确识别了问题的来源.
结论:
- 虽然LLM可以产生医学考试问题,但人类创建的问题目前表现出优越的歧视力.
- 需要进一步的研究,以探索不同类型的问题和在不同的教育背景下LLM的能力.
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