实施大型语言模型以支持基于误解的协作学习在医疗保健教育中
Brandon C J Cheah1,2, Shefaly Shorey3, Jun Hong Ch'ng1
1Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, 5 Science Drive 2, Singapore, 117545, Singapore, 65 81503939.
JMIR medical education
|January 16, 2026
概括
大型语言模型 (LLM) 可以产生医疗保健教育的误解,以促进协作学习和批判性思维. 这个框架指导教育工作者使用人工智能产生的误解来增强学生的概念变化.
科学领域:
- 医学教育 医学教育
- 医疗保健中的人工智能
- 教育技术的教育技术
背景情况:
- 误解往往会对学习产生不利影响.
- 人工智能 (AI) 和大型语言模型 (LLM) 是教育中的新兴技术.
- 需要探索使用人工智能在医疗保健教育中的新型教学方法.
研究的目的:
- 提出一个框架,用于在医疗保健教育中使用LLM产生的误解.
- 展示AI产生的误解如何可以用于协作学习.
- 促进医疗保健学生的概念变化和批判性思维.
主要方法:
- 为LLM产生的误解使用制定框架.
- 概述了临床和基础科学医疗保健学科的用例.
- 为教育工作者提供10步指导,帮助他们实施框架.
主要成果:
- 在结构化的同行讨论中使用LLM产生的误解,可以促进概念变化.
- 该框架为教育工作者提供了实际的实施指南.
- 确定需要进一步研究人工智能支持学习的长期影响.
结论:
- 一个新的框架支持医疗保健教育工作者整合人工智能技术.
- 由LLM产生的误解可以成为协作学习的宝贵工具.
- 需要进一步的研究来评估对学生成绩的影响.
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