ArticlePloS one2026
Evaluating cognitive depth of AI-generated multiple-choice questions with Bloom's Taxonomy.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Cognitive-level analysis of dentomaxillofacial radiology questions in the Turkish dentistry specialization examination: a Bloom's revised taxonomy analysis.BMC oral health · 2026Article
- Evaluating the clinical decision-making performance of large language models in clinically oriented thoracic anatomy scenarios: a comparative evaluation study.BMC medical education · 2026Article
- Assessing multiple-choice question quality in internal medicine: a comparative analysis of three large language models against expert consensus.Frontiers in medicine · 2026Article
- Correction: Evaluating cognitive depth of AI-generated multiple-choice questions with Bloom's Taxonomy.PloS one · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionWhile LLMs are used to generate medical and dental MCQs, their alignment with Bloom's Taxonomy remains unexplored. MATERIALS AND
methodsFive widely used LLMs, including ChatGPT-4o (OpenAI), Copilot Pro (Microsoft), Claude Sonnet 4 (Anthropic), Grok 3 (xAI), and DeepSeek R1 (DeepSeek) were evaluated. Each model generated 60 MCQs (total 300) based on content from an oral and maxillofacial anatomy textbook across the five cognitive levels of Bloom's Taxonomy. Two independent investigators assessed each item using a 5-point Likert scale for remembering, understanding, applying, analyzing, and evaluating/creating. Inter-rater reliability was measured using weighted Cohen's kappa. Model performance and inter-model differences were analyzed using the Kruskal-Wallis test.
resultsInter-rater reliability was moderate to strong (kappa = 0.74-0.86). Median scores for remembering, understanding, applying, and evaluating/creating were above 4 across all LLMs, while the analyzing level scored a median of 3.5 for ChatGPT-4o and DeepSeek R1. No significant difference was found between models in remembering and understanding levels (p > 0.05). Claude Sonnet 4 outperformed the other models at the applying, analyzing, and evaluating/creating levels (p = 0.01, 0.003, and 0.005, respectively). Within-model analysis showed that only Copilot Pro and Claude Sonnet 4 consistently aligned with Bloom's cognitive levels across all categories. In contrast, ChatGPT-4o, DeepSeek R1, and Grok 3 performed significantly better at the lower cognitive levels (p = 0.00, 0.00, and 0.001, respectively).
conclusionsAll LLMs performed well at lower cognitive levels, while Claude Sonnet 4 achieved the highest alignment at higher-order levels.
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