放射学检查中的人工智能:问题生成方法的心理测量比较
Emre Emekli1,2, Betül Nalan Karahan1
1Eskişehir Osmangazi University Faculty of Medicine, Department of Radiology, Eskişehir, Türkiye.
Diagnostic and interventional radiology (Ankara, Turkey)
|July 21, 2025
概括
像ChatGPT-4o和基于模板的自动项目生成 (AIG) 等人工智能 (AI) 方法显示出创造放射学教育MCQ的潜力. 教师撰写的问题仍然优越,但人工智能工具可以帮助教育工作者高效地开发评估.
科学领域:
- 医学教育 医学教育
- 医疗保健中的人工智能
- 放射学教育 放射学教育
背景情况:
- 为医学教育制定高质量的多选题 (MCQ) 是耗时的.
- 人工智能 (AI) 为自动化问题生成提供了潜在的解决方案.
- 与传统方法相比,评估人工智能生成的MCQ的有效性至关重要.
研究的目的:
- 评估基于人工智能的问题生成方法的可用性,特别是ChatGPT-4o和基于模板的自动项目生成 (AIG) 系统.
- 为了比较AI生成的MCQ的心理测量特性,感知质量和教育适用性,与放射学教育中的教师编写的MCQ.
- 确定人工智能工具在支持放射学评估开发方面的有效性.
主要方法:
- 对115名第五年级医学学生进行了15个问题的考试,其中包括放射科医生的MCQ,ChatGPT-4o和基于模板的AIG方法.
- 对学生的回答进行了分析,以确定难度和歧视指数.
- 学生通过使用利克特尺度对问题的清晰度,难度,分心因素的可信性和学习目标的对齐性进行了评分.
主要成果:
- 教师写的问题显示了最高的正确答案率,其次是基于模板的AIG和ChatGPT-4o.
- 基于模板的AIG和ChatGPT-4o问题表现出可接受的心理测量特性,一些问题表现非常好.
- 关于问题的清晰度和内容对齐,学生的反是有利的,尽管考试成绩与实际表现的相关性很弱.
结论:
- 聊天GPT-4o和基于模板的AIG都能为放射学教育提供可接受的心理特征的MCQ.
- 人工智能产生的问题显示出对补充传统方法的希望,可能提高MCQ开发的效率和一致性.
- 由于这个单一机构研究的探索性和局限性,需要在更大的多中心研究中进一步验证.
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