在临床复杂的MCQ创作中,GPT-4与人类作者对比:对项目质量的盲目分析
Hannah Wu1,2, Toby Zerner2, Daniel Lee3
1Adelaide Medical School, University of Adelaide, Adelaide, Australia.
Medical teacher
|May 29, 2025
概括
像GPT-4这样的人工智能 (AI) 可以生成与专家人类作家相比的多选择题 (MCQ). 然而,人类监督对于完善人工智能生成的医疗评估MCQ至关重要.
科学领域:
- 医学教育 医学教育
- 医疗保健中的人工智能
- 评估和评价的评估和评估.
背景情况:
- 越来越多的人工智能 (AI) 的使用需要评估其在生成教育内容方面的能力.
- 大型语言模型 (LLM) 显示了创建评估工具的潜力,但它们的质量需要与人类专业知识进行严格的比较.
研究的目的:
- 将GPT-4产生的多选题 (MCQ) 的结构质量与由人类专家和新手撰写的问题进行比较.
- 评估AI产生的MCQ在医学教育评估中的表现.
主要方法:
- 对124个MCQ进行了盲目分析,包括来自GPT-4的项目,新手人类作家和专家人类作家.
- 一个标准化的评分系统评估了内容有效性,范围,物品解剖学,认知技能水平,缺陷,反,临床推理和整体适用性.
- 一个共识小组客观地评估了每个项目,盲目对作者进行评估.
主要成果:
- 在所有评估的类别中,专家项目表现优于初学者项目.
- 人工智能生成的MCQ在全球印象中与专家撰写的项目总体上具有可比性.
- 专家项目在内容有效性,反真实性,临床推理和更高阶认知技能测试方面显示出轻微优势,尽管两者都符合可接受的标准.
- 由于错误的答案或偏见的答案定位,人工智能项目需要更高的修订率.
结论:
- 在医疗评估中,GPT-4可以生成适合测试复杂临床概念的MCQ.
- 虽然人工智能生成的MCQ在结构上与专家撰写的项目可比,但人类监督对于确保内容有效性和优化质量至关重要.
- 人工智能对MCQ产生有希望,但需要仔细验证和改进.
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