评估人工智能产生的跨专业教育的老年病例研究:在5个平台上的系统分析
Nicole Ruggiano1, Sudikshya Sahoo1, Ava Brashear2
1School of Social Work, University of Alabama, Tuscaloosa, AL, United States.
JMIR medical education
|January 30, 2026
概括
生成型人工智能平台在为卫生专业教育创建老年人模拟案例研究方面表现不同. 聊天GPT表现最好,而Grok得分最低,强调需要仔细选择人工智能工具和快速工程.
科学领域:
- 医疗教育 技术 技术 医学教育
- 医疗保健中的人工智能
- 基于模拟的学习
背景情况:
- 基于模拟的学习 (SBL) 对医疗保健专业人员的培训至关重要.
- 开发各种SBL场景对教育工作者来说是耗时的.
- 生成型人工智能为高效的案例研究创建提供了潜力.
研究的目的:
- 使用五个人工智能平台生成老年病例场景.
- 系统地评估人工智能产生的案例的质量,准确性和偏见.
- 为了比较不同AI平台在创建跨专业教育材料方面的表现.
主要方法:
- 每个人工智能平台生成了10个老年病例研究 (N=50个总共).
- 这些平台包括ChatGPT,Claude,Copilot,Gemini和Grok.
- 经验丰富的SBL教育工作者使用模拟场景评估工具 (SSET) 评估案例.
主要成果:
- 案例质量在人工智能平台之间和人工智能平台内部有很大差异.
- 聊天GPT获得了最高的整体分数 (平均3.27),Grok获得了最低的 (平均1.61).
- 人工智能平台在生成学习目标方面表现出色,但在供应/材料描述方面遇到了困难.
结论:
- 这项研究为SBL病例生成提供了第一个对生成AI的系统评估.
- 调查结果为教育工作者选择和使用人工智能工具提供了基于证据的指导.
- 有效的快速工程是提高人工智能生成的SBL资源用于跨专业教育的关键.
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