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评估LLM生成的临床解剖学MCQ与专家创建的临床解剖学MCQ:医学教育中的基于学生感知的比较研究

Maram Elzayyat¹, Janatul Naeim Mohammad¹, Sami Zaqout¹

¹Department of Basic Medical Sciences, College of Medicine, QU Health, Qatar University, Doha, Qatar.

Medical education online

|August 30, 2025

概括

大型语言模型 (LLM) 可以生成与专家撰写的医学教育多选择题 (MCQ). 人工智能生成的MCQ有助于识别知识缺口,但专家审查确保了质量和课程一致性.

科学领域:

医疗教育
教育中的人工智能
解剖学评估

背景情况:

大型语言模型 (LLM) 越来越多地用于医疗环境中的教育内容.
在解剖学教育中,与专家撰写的问题相比,LLM生成的多选择题 (MCQ) 的有效性尚未得到充分证实.

研究的目的:

通过ChatGPT,Google-Gemini和临床解剖学家生成的MCQ的质量和有效性进行比较.
评估学习者对人工智能产生的看法与专家撰写的解剖学MCQ.

主要方法:

这是一项涉及48名医学生的横截式混合方法研究.
参与者根据17个质量标准完成并评估了三组匿名的MCQ (ChatGPT,Gemini,专家撰写).
使用描述性统计,奇方位分析和定性反的专题审查.

主要成果:

大多数学生认为三种来源的MCQ都同样有效.
通过具有挑战性的分心因素来识别知识缺口, 频繁使用ChatGPT.
专家撰写的问题在促进更深入的分析思维方面获得了最高评分; 发现了显著的偏好差异 (χ2(64) = 688.79, p < .001).

结论:

通过LLM生成的解剖学MCQ显示了与专家撰写的问题相比的学习者感知价值.
人工智能产生的MCQ可以提高参与度, 但专家监督对于清晰度和课程一致性至关重要.
混合人工智能-人类方法为高质量的医学教育评估设计提供了可扩展的解决方案.

关键词:

人工智能产生的内容人体结构评估质量大型语言模型医疗教育

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