ArticleBMC medical education2025
Comparison of AI-generated and clinician-designed multiple-choice questions in emergency medicine exam: a psychometric analysis.
Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07522658 (Comparison of Artificial Intelligence-Generated and Academician-Developed Multiple True/False Questions in Anesthesiology Education), which is not on this map. Cited by 11 papers.
What it found
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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.
Comparison of Artificial Intelligence-Generated and Academician-Developed Multiple True/False Questions in Anesthesiology Education: A Prospective Cohort Study
Who cites it
11 citing papers in PubMed.
- 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
- Can AI Write MCQs? A Comparison of ChatGPT-Generated and Educator-Authored Multiple-Choice Questions in Graduate Nursing Education.Nursing reports (Pavia, Italy) · 2026Article
- AI Integration in Undergraduate Medical Education: Qualitative Study of Faculty Perspectives in the United Arab Emirates.JMIR medical education · 2026Article
- Human-Edited Generative AI-Assisted Multiple-Choice Questions in Postgraduate Family Medicine: Blinded Cross-Sectional Comparative Psychometric Study.JMIR medical education · 2026Article
- Human-Governed Validation of Artificial Intelligence-Generated Medical Assessment Artifacts: A Technical Report.Cureus · 2026Article
- Fine-Tuned Large Language Models for Generating Multiple-Choice Questions in Anesthesiology: Psychometric Comparison With Faculty-Written Items.JMIR formative research · 2026Article
- Quality of Large Language Model-Generated MCQs Across Three Medical Disciplines: An Expert Rater-Based Comparison of Gemini, GPT-4 and Perplexity Pro.Advances in medical education and practice · 2026Article
- Large language models in emergency medicine education: opportunities, challenges, and implementation pathways.Frontiers in public health · 2026Review
- When Assessment Theory Meets Generative AI: Reimagining SBA Design in Medical Education.Perspectives on medical education · 2026Article
- Evaluation of a Hybrid Case-Based Learning and Small-Group Teaching Model Using Artificial Intelligence-Assisted Case Generation: An Innovative Dental Education Study.Advances in medical education and practice · 2026Article
- Measuring Emotion Perception Ability Using AI-Generated Stimuli: Development and Validation of the PAGE Test.Journal of Intelligence · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) has shown promise in generating multiple-choice questions (MCQs) for medical education, yet the psychometric quality of such items remains underexplored. This study aimed to compare the psychometric properties of MCQs created by ChatGPT-4o and those written by emergency medicine clinicians.
methodsEighteen emergency medicine residents completed a 100-item examination comprising 50 AI-generated and 50 clinician-authored questions across core emergency medicine topics. Each item was analyzed for difficulty (P_index), discrimination (D_index), and point-biserial correlation (PBCC). Items were also categorized based on standardized index classifications.
resultsChatGPT-4o-generated questions exhibited a higher mean difficulty index (P_index: 0.76 ± 0.23) compared to those created by clinicians (0.65 ± 0.24; p = 0.02), indicating that the AI-generated items were generally easier. Participants achieved significantly higher scores on AI-generated items (76.8 ± 8.18) than on clinician-authored questions (67.3 ± 9.65; p = 0.003). The mean discrimination index did not differ significantly between AI-generated (0.172 ± 0.23) and clinician-generated items (0.196 ± 0.26; p = 0.634). Likewise, the mean point-biserial correlation coefficient (PBCC) was nearly identical between the two groups (AI: 0.23 ± 0.28; clinicians: 0.23 ± 0.25; p = 0.99), suggesting similar internal consistency. Categorical analysis revealed that 56% of AI-generated items were classified as "easy," compared to 36% of clinician-designed items. Furthermore, based on PBCC values, 36% of AI-generated items and 24% of clinician items were identified as "problematic" (p = 0.015), indicating a higher rate of psychometric concerns among AI-generated questions.
conclusionThe findings suggest that AI-generated questions, while generally easier and associated with higher participant scores, may pose psychometric limitations, as evidenced by a greater proportion of items classified as problematic. Although the overall internal consistency and discrimination indices were comparable to clinician-authored items, careful quality control and validation are essential when integrating AI-generated content into assessment frameworks.
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