在基于案例的学习中使用大型语言模型进行快速定量反:试点研究
Carolyn Qian1, Christina Gao2, Sang-O Park1
1Harvard Medical School, Harvard University, Boston, MA 02138 USA.
Medical science educator
|July 8, 2025
概括
大型语言模型 (LLM) 可以有效地评分医学学生的案例交互. GPT-4o与专家评估有很高的相关性,校准提高了准确性.
科学领域:
- 医学教育技术技术 医学教育技术
- 医疗保健中的人工智能
背景情况:
- 交互式基于案例的学习对医学学生来说是有价值的.
- 学生互动的自动评分可以增强反.
- 大型语言模型 (LLM) 为教育评估提供了潜力.
研究的目的:
- 评估GPT-4o在评分医学学生与虚拟病例互动方面的有效性.
- 评估LLM产生的分数和专家评估之间的相关性.
- 为了确定校准是否可以提高LLM评分的准确性.
主要方法:
- 医学学生与LLM提出的虚拟案例进行了互动.
- GPT-4o对学生的互动进行了评分.
- 研究人员将LLM成绩与专家人类得分者进行了比较.
- 应用了校准方法来解决差异.
主要成果:
- GPT-4o与专家得分评估的相关性很高.
- 在LLM和专家评分之间观察到一个显著的协议.
- 校准有效地减少了LLM和专家得分之间的差异.
结论:
- 法学学位,特别是GPT-4o,显示出在基于案例的场景中评估医学学生表现的重大前景.
- 通过LLM的自动评分可以成为传统专家评估的可靠补充.
- 校准是一种可行的策略,可以提高基于LLM的教育评分的准确性.
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