基于案例的MCQ生成器:基于文献中公布的提示,定制的ChatGPT,用于自动生成项目
Yavuz Selim Kıyak1,2, Andrzej A Kononowicz2
1Department of Medical Education and Informatics, Faculty of Medicine, Gazi University, Ankara, Turkey.
Medical teacher
|February 10, 2024
概括
医疗教育工作者现在可以使用定制的GPT工具更有效地生成高质量,临床相关的多选择题 (MCQ). 这个工具简化了这个过程,克服了标准ChatGPT的局限性,以更好地开发医疗教育项目.
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,临床相关的多选择题 (MCQ) 是医学教育的一个重大挑战.
- 现有的基于ChatGPT的自动项目生成 (AIG) 方法需要特定的提示,但受到手动输入,缺乏用户专业知识以及标准AI模型的泛型性质的限制,这些模型往往缺乏医疗背景.
研究的目的:
- 引入一个定制的GPT,以案例为基础的MCQ生成器,旨在应对在医学教育中生成临床相关的MCQ的挑战.
- 为卫生专业的教育工作者提供一个工具,简化并提高创建基于案例的MCQ的效率.
主要方法:
- 基于案例的MCQ生成器是使用OpenAI的GPT Builder平台进行定制开发的.
- 该工具允许使用ChatGPT Plus订阅的用户选择提示,输入学习目标或测试点以生成MCQ.
主要成果:
- 定制的GPT提高了MCQ生成效率,并确保了上下文相关性,优于标准的ChatGPT.
- 它整合了已发布的医学教育提示,消除了手动快速输入的需要,并简化了创建过程.
结论:
- 基于案例的MCQ生成器为寻求创建相关MCQ的医学教育工作者提供了显著的改进.
- 未来的发展将侧重于可持续性,伦理考虑,全球受众的可访问性,以及整合新兴的教育文献.
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