在医学学习中整合ChatGPT:对相互作用和矛盾的分析
Shih-Hsuan Tai1, Chi-Chuan Yeh2,3, Jann-Yuan Wang1,4,5
1School of Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan.
Medical teacher
|December 26, 2025
概括
医学教育中的生成人工智能存在系统矛盾,而不仅仅是性能问题. 解决这些问题需要改变学习系统,重新定义教育工作者.
科学领域:
- 医学教育 医学教育
- 人工智能的人工智能
- 教育技术的教育技术
背景情况:
- 目前在医学教育中对生成人工智能的研究往往侧重于性能或不可靠性等风险.
- 这些问题被视为由将新技术整合到学习环境中产生的系统矛盾.
- 需要一个理论框架来分析这些紧张局势,以便更深入地理解.
研究的目的:
- 分析从医疗教育中生成人工智能集成中出现的系统矛盾.
- 超越AI性能和风险的描述性报告.
- 应用文化-历史活动理论来理解学生-AI互动.
主要方法:
- 141名第一年实习医学生在观看"急性肝衰竭"概念视频后使用了ChatGPT.
- 定性数据包括学生与ChatGPT的对话,响应评估和自由文本反.
- 继感应性主题分析之后,使用文化历史活动理论进行了演性分析.
主要成果:
- 确定了四个系统矛盾: 1) 人工智能不可靠性与准确知识, 2) 学生技能差距与人工智能需求, 3) 不稳定的学生-人工智能劳动分工与专业规则, 4) 模两可的规则与专业规范.
- 这些矛盾凸显了学生-人工智能活动系统中的紧张关系.
- 需要专家验证和更明确的规则出现了.
结论:
- 人工智能的不可靠性和技能差距作为扩展性学习的催化剂.
- 系统性转型是必要的,包括及时的工程培训和重新定义教育工作者的角色.
- 教育人员应在协作实践中转向专家验证员.
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