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微调的大型语言模型用于产生麻醉学多选择题:与教师撰写的项目进行心理测量比较

Carlos Ramon Hölzing¹, Charlotte Meynhardt¹, Patrick Meybohm¹

¹Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, University Hospital Würzburg, Oberdürrbacher Str. 6, Würzburg, 97080, Germany.

JMIR formative research

|February 18, 2026

概括

精心调整的大型语言模型 (LLM) 可以在麻醉学中创建多选择题 (MCQ),其心理测量特性与教师专家撰写的相似. 自动化项目生成可以补充,而不是取代,开发高质量的医学教育评估的传统方法.

科学领域:

医学教育医学教育
人工智能在评估中的作用
心理测量心理测量心理测量

背景情况:

多选题 (MCQ) 对于标准化医疗评估至关重要.
开发高质量的MCQ需要专业知识和严格的方法.
大型语言模型 (LLM) 为自动化MCQ生成提供了机会,但评估是有限的.

研究的目的:

评估是否精心调整的LLM可以产生麻醉学MCQ,其心理测量特性与教师撰写的项目相当.

主要方法:

一个微调的GPT-4模型被训练在麻醉学材料.
该模型生成了15个MCQ,并与15个教师编写的MCQ一起进行分析.
项目分析遵循心理测量标准,比较难度,点-二次相关性和歧视指数.

主要成果:

在LLM生成的MCQ和教师编写的MCQ之间,在难度,点-双序列相关性或歧视指数方面没有发现显著差异.
两组MCQ都显示了整体心理测量质量的适度.
由LLM生成的项目 (平均难度0.79,点位序列0.17,歧视0.08) 与专家项目 (平均难度0.81,点位序列0.19,歧视0.09) 相似.

结论:

监督精细调整的LLM可以产生与专家教师相似的心理测量质量的MCQ.
自动项目生成应该补充,而不是取代手动的MCQ开发.
需要进一步的研究,以实现概括性和优化LLM在评估中的整合.

关键词:

麻醉学麻醉学人工智能的人工智能是人工智能.评估评估的评估评估的评估.精细调整精细调整大型语言模型.医学教育医学教育多选题的问题是多选题.心理测量是指心理测量.

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