ArticleAdvances in medical education and practice2024
Comparing the Performance of ChatGPT-4 and Medical Students on MCQs at Varied Levels of Bloom's Taxonomy.
Article in Advances in medical education and practice, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Accuracy of Large Language Models When Answering Clinical Research Questions: Systematic Review and Network Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Impact of generative AI in medical education in India: a systematic review.Frontiers in artificial intelligence · 2025Pooled it
- Evaluating AI-generated examination papers in periodontology: a comparative study with human-designed counterparts.BMC medical education · 2025Trial
- Cognitive alignment and assessment validity in a high-stakes dental specialty examination: a Bloom's taxonomy-based analysis.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
- Accuracy, Reliability, and Bloom's Taxonomy Performance of Seven Large Language Models on Microbiology Questions.Advances in medical education and practice · 2026Article
- Beyond facilitation and inhibition: a configurational mechanism study of cognitive transitions in human-AI collaboration.Frontiers in psychology · 2026Article
- Comprehensive Evaluation of Large Language Models on Four Core Medical School Courses: A Cross-Sectional Comparative Study.Advances in medical education and practice · 2026Article
- Article
- When AI models take the exam: large language models vs medical students on multiple-choice course exams.Medical education online · 2025Article
- Applications, Challenges, and Prospects of Generative Artificial Intelligence Empowering Medical Education: Scoping Review.JMIR medical education · 2025Article
- Comparative evaluation of large language models performance in medical education using urinary system histology assessment.Scientific reports · 2025Article
- Large Language Models in Biochemistry Education: Comparative Evaluation of Performance.JMIR medical education · 2025Article
- Chatbot Underperformance in Biology and Image-Based Questions in Medical Education.Journal of CME · 2025Article
- Reframing individual roles in collaboration: digital identity construction and adaptive mechanisms for resistance-based professional skills in AI-human intelligence symbiosis.Frontiers in psychology · 2025Article
- Attitudes and perceptions of Thai medical students regarding artificial intelligence in radiology and medicine.BMC medical education · 2024Article
- The performance of OpenAI ChatGPT-4 and Google Gemini in virology multiple-choice questions: a comparative analysis of English and Arabic responses.BMC research notes · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: This research investigated the capabilities of ChatGPT-4 compared to medical students in answering MCQs using the revised Bloom's Taxonomy as a benchmark. Methods: A cross-sectional study was conducted at The University of the West Indies, Barbados. ChatGPT-4 and medical students were assessed on MCQs from various medical courses using computer-based testing. Results: The study included 304 MCQs. Students demonstrated good knowledge, with 78% correctly answering at least 90% of the questions. However, ChatGPT-4 achieved a higher overall score (73.7%) compared to students (66.7%). Course type significantly affected ChatGPT-4's performance, but revised Bloom's Taxonomy levels did not. A detailed association check between program levels and Bloom's taxonomy levels for correct answers by ChatGPT-4 showed a highly significant correlation (p<0.001), reflecting a concentration of "remember-level" questions in preclinical and "evaluate-level" questions in clinical courses. Discussion: The study highlights ChatGPT-4's proficiency in standardized tests but indicates limitations in clinical reasoning and practical skills. This performance discrepancy suggests that the effectiveness of artificial intelligence (AI) varies based on course content. Conclusion: While ChatGPT-4 shows promise as an educational tool, its role should be supplementary, with strategic integration into medical education to leverage its strengths and address limitations. Further research is needed to explore AI's impact on medical education and student performance across educational levels and courses.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.