一个生成的人工智能教学助理,用于医疗教育中的个性化学习
Thomas Thesen1,2, Soo Hwan Park3
1Department of Medical Education, Geisel School of Medicine at Dartmouth, Hanover, NH, USA. thomas.thesen@dartmouth.edu.
NPJ digital medicine
|November 4, 2025
概括
医学学生战略性地使用受约束的人工智能工具 (检索增强生成) 进行自我指导的学习,在高风险时期优先考虑准确性和可用性. 这种方法平衡了可靠性和医疗教育中全面信息的需求.
科学领域:
- 医疗教育 技术 技术 医学教育
- 人工智能在医疗保健教育中的应用
- 学生学习策略 学生学习策略
背景情况:
- 医疗教育面临着随着班级规模的增加而面临的可扩展性挑战.
- 学生越来越多地使用未经验证的生成人工智能 (GenAI) 工具来支持学习.
- 医疗培训中需要可靠,在教学上健全的AI工具.
研究的目的:
- 研究医学学生如何将受约束的GenAI系统集成到自主学习中.
- 用基于RAG的AI教学助理分析使用模式和学习行为.
- 了解AI工具限制对学生学习策略的影响.
主要方法:
- 在医学院基础科学课程中部署Retrieval-Augmented Generation (RAG) AI教学助理.
- 检查两个队伍的使用模式,对话内容和学生反.
- 对学生互动的分析,以了解受约束AI的采用和学习行为.
主要成果:
- 学生使用RAG系统的战略和上下文依赖,特别是在评估.
- 在课后学习期间使用率很高,主要用于概念澄清.
- 学生们重视AI的持续可用性和基于源的响应.
- 知识库的限制,虽然确保了准确性,但限制了调查的范围,影响了全面性.
结论:
- 医学学生适应受限制的人工智能工具,在自主学习中平衡可靠性和全面性.
- RAG系统提供了一种可行的方法,可以在医学教育中提供可靠的AI支持.
- 调查结果为整合人工智能到医学教学框架中的机构战略提供了信息.
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