ArticleBMC medical education2025
AI versus human-generated multiple-choice questions for medical education: a cohort study in a high-stakes examination.
Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
40 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The performance of ChatGPT and other large language models on multiple-choice questions in biomedical disciplines: A meta-analysis.Anatomical sciences education · 2026Pooled it
- Psychometric performance and student perceptions of AI- versus student-generated multiple-choice questions: a single-center randomized controlled trial.BMC medical education · 2026Trial
- Evaluating AI-generated examination papers in periodontology: a comparative study with human-designed counterparts.BMC medical education · 2025Trial
- Can students identify AI? - A cross-sectional quantitative study about AI recognition in tablet-based MCQ assessment among fifth-year undergraduate medical students at Saarland University, Germany.BMC medical education · 2026Article
- Artificial intelligence in radiology examinations: a psychometric comparison of question generation methods.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- Can AI Write MCQs? A Comparison of ChatGPT-Generated and Educator-Authored Multiple-Choice Questions in Graduate Nursing Education.Nursing reports (Pavia, Italy) · 2026Article
- Human-Edited Generative AI-Assisted Multiple-Choice Questions in Postgraduate Family Medicine: Blinded Cross-Sectional Comparative Psychometric Study.JMIR medical education · 2026Article
- Article
- AI-Generated Microlearning for Plastic Surgery Residency: Single-Arm Pre-Post Feasibility Study.JMIR medical education · 2026Article
- Education Research: Quality of Narrative Feedback Generated by a Large Language Model Compared With Expert Faculty for Case-Based Learning in Neurology Education.Neurology. Education · 2026Article
- Scaling Multimodal Agentic AI in Medical Education: Multisite Cross-Sectional Study of Simulation Effectiveness in Primary Care.JMIR formative research · 2026Article
- Exploring the length and time related concepts for scenario-based multiple-choice questions through perspectives of medical education experts: a mixed-method approach.Korean journal of medical education · 2026Article
- Fine-Tuned Large Language Models for Generating Multiple-Choice Questions in Anesthesiology: Psychometric Comparison With Faculty-Written Items.JMIR formative research · 2026Article
- Evaluating the ability of AI models to generate level-specific medical MCQs with variable difficulty.BMC research notes · 2026Article
- Large language models in Chinese anesthesiology residency examinations: a comparative analysis of performance, reliability and clinical reasoning.BMC medical education · 2026Article
- Evaluation of three artificial intelligence chatbots for generating clinical hematology multiple choice questions for medical students.Scientific reports · 2026Article
- Comparative performance evaluation of ChatGPT-4 Omni and Gemini Advanced in the Turkish Dentistry Specialization Exam.BMC medical education · 2026Article
- Evaluating cognitive depth of AI-generated multiple-choice questions with Bloom's Taxonomy.PloS one · 2026Article
- The potential of LLMs in generating questions and answers with EHRs.Frontiers in digital health · 2026Article
- Evaluating large language models for diabetic retinopathy multiple-choice question generation in clinical ophthalmic education.Frontiers in medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe creation of high-quality multiple-choice questions (MCQs) is essential for medical education assessments but is resource-intensive and time-consuming when done by human experts. Large language models (LLMs) like ChatGPT-4o offer a promising alternative, but their efficacy remains unclear, particularly in high-stakes exams.
objectiveThis study aimed to evaluate the quality and psychometric properties of ChatGPT-4o-generated MCQs compared to human-created MCQs in a high-stakes medical licensing exam.
methodsA prospective cohort study was conducted among medical doctors preparing for the Primary Examination on Emergency Medicine (PEEM) organised by the Hong Kong College of Emergency Medicine in August 2024. Participants attempted two sets of 100 MCQs-one AI-generated and one human-generated. Expert reviewers assessed MCQs for factual correctness, relevance, difficulty, alignment with Bloom's taxonomy (remember, understand, apply and analyse), and item writing flaws. Psychometric analyses were performed, including difficulty and discrimination indices and KR-20 reliability. Candidate performance and time efficiency were also evaluated.
resultsAmong 24 participants, AI-generated MCQs were easier (mean difficulty index = 0.78 ± 0.22 vs. 0.69 ± 0.23, p < 0.01) but showed similar discrimination indices to human MCQs (mean = 0.22 ± 0.23 vs. 0.26 ± 0.26). Agreement was moderate (ICC = 0.62, p = 0.01, 95% CI: 0.12-0.84). Expert reviews identified more factual inaccuracies (6% vs. 4%), irrelevance (6% vs. 0%), and inappropriate difficulty levels (14% vs. 1%) in AI MCQs. AI questions primarily tested lower-order cognitive skills, while human MCQs better assessed higher-order skills (χ² = 14.27, p = 0.003). AI significantly reduced time spent on question generation (24.5 vs. 96 person-hours).
conclusionChatGPT-4o demonstrates the potential for efficiently generating MCQs but lacks the depth needed for complex assessments. Human review remains essential to ensure quality. Combining AI efficiency with expert oversight could optimise question creation for high-stakes exams, offering a scalable model for medical education that balances time efficiency and content quality.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.