通过生成问题模板而不是使用人工智能的问题,有效地让人类处于循环中:混合AIG的有效性证据
Yavuz Selim Kıyak1,2, Emre Emekli3, Özlem Coşkun1
1Department of Medical Education and Informatics, Faculty of Medicine, Gazi University, Ankara, Turkey.
Medical teacher
|November 28, 2024
概括
混合自动项目生成 (AIG) 有效地创建准确的多选题 (MCQ). 这种方法结合了基于模板和人工智能驱动的方法,确保教育评估的专家验证的结果.
科学领域:
- 医学教育 医学教育
- 教育中的人工智能
- 评估和评价的评估和评估.
背景情况:
- 手动创建多选题 (MCQ) 是耗时且低效的.
- 自动项目生成 (AIG) 提供可扩展的解决方案,采用基于模板 (准确但专家密集) 和人工智能驱动 (快速但可能不准确) 的方法.
- 混合AIG方法,结合两种方法,尚未用于MCQ生成或验证.
研究的目的:
- 使用混合AIG方法生成MCQ.
- 调查混合AIG生成的MCQ的有效性证据.
- 评估医疗专家是否能够识别混合AIG系统生成的正确答案.
主要方法:
- 使用自定义的ChatGPT.开发了一个项目模板.
- 使用基于模板的AIG软件Gazitor,从模板中生成MCQ.
- 一个由医生组成的小组审查并确定了生成的MCQ的正确答案.
主要成果:
- 混合AIG方法在105个决策中实现了软件正确答案和专家识别之间的96.2%匹配率.
- 专家们对所有生成的MCQ的正确答案达到了100%的共识.
- 这些发现与梅西克有效性框架的"与其他变量关系"组成部分保持一致.
结论:
- 混合AIG方法提高了MCQ生成效率,同时保持了准确性.
- 这种方法通过利用专家验证,有效地减轻了对人工智能"幻觉"的担忧.
- 混合AIG为在教育环境中开发高质量的MCQ提供了一个有希望的解决方案.
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