Abstract
BACKGROUND: The 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. OBJECTIVE: This 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. METHODS: A 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. RESULTS: Among 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). CONCLUSION: ChatGPT-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.