使用Bloom的分类学评估人工智能生成的多选择题的认知深度
Trang Thi Nguyen1, Linh Nguyen2, Ha Thi Nguyet Do2
1Faculty of Dentistry, Phenikaa University, Hanoi, Vietnam.
PloS one
|February 27, 2026
概括
大型语言模型 (LLM) 在生成较低级别的Bloom's Taxonomy多选题 (MCQ) 方面表现强. 克劳德·索内特4在医学教育中表现出与更高阶认知技能的优越对齐.
科学领域:
- 医学教育 医学教育
- 教育中的人工智能
- 认知科学 认知科学
背景情况:
- 大型语言模型 (LLM) 越来越多地用于生成教育内容,包括多选题 (MCQ).
- 在LLM产生的MCQ与像Bloom的分类学这样的既定教育框架的对齐尚未被彻底调查.
- 这项研究的重点是评估LLMs在口腔和大面部解剖学的不同认知水平上创建MCQ的能力.
研究的目的:
- 评估多种广泛使用的大型语言模型 (LLM) 与布鲁姆的分类学认知水平的一致性.
- 在记忆,理解,应用,分析和评估/创建等级之间比较不同LLM在生成MCQ方面的表现.
- 确定哪些LLM最能支持医学教育内容生成中更高层次的思维技能.
主要方法:
- 五位领先的法学士 (ChatGPT-4o,Copilot Pro,Claude Sonnet 4,Grok 3,DeepSeek R1) 从口腔和大面部解剖学教科书中生成了300个MCQ.
- MCQ的设计是为了针对Bloom的分类学中的五个认知层次.
- 两个独立的调查人员使用5点的利克特级别对每个MCQ进行了评分,通过加权的科恩卡帕评估了评分者之间的可靠性.
主要成果:
- 评价者之间的可靠性是中等到强的 (kappa = 0.740.86).
- 所有LLM在较低的认知水平 (记忆,理解,应用,评估/创造) 上都表现良好,平均得分高于4.
- 与其他模型相比,克劳德·索内特4在更高的认知水平 (应用,分析,评估/创造) 上表现优越.
- 聊天GPT-4o,DeepSeek R1和Grok 3显示了对较低认知水平的显著偏差.
结论:
- 法律学士通常在生成Bloom's Taxonomy较低认知水平的MCQ方面表现良好.
- 克劳德·索内特4在评估的法学士中显示出与更高层次思维技能的最高对齐.
- 这些发现表明,LLM在医疗教育中支持全面的认知技能发展方面具有不同的能力.
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