ArticleJMIR medical education2026
AI-Assisted Angoff Standard Setting for Multiple-Choice Examinations in Medical Education: Comparative Study.
Article in JMIR medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
Background: Standard setting is essential for a defensible assessment in medical education. The modified Angoff method requires several expert judges, and determining the minimally competent candidate is cognitively challenging. However, empirical evidence on the role of AI in standard setting is unclear. Objective: This study aimed to examine the role of large language models (LLMs) in the modified Angoff standard setting method for multiple-choice questions in a basic medical science examination compared with faculty judges. Methods: This study was conducted in year 2 of the MD program in the United Arab Emirates. Ten faculty judges and 5 LLMs (GPT-5.2, Grok, DeepSeek, MedGemma, and Claude 4.5 Sonnet) determined the Angoff cutoff scores for a summative examination (120 multiple-choice questions). Standardized prompts were used for the LLMs to mimic the same information provided for faculty judges. Generalizability ( Results: Faculty-generated Angoff estimates (mean 69.13, SD 10.92) were comparable to LLM-generated estimates (mean 68.94, SD 12.39). A 2-tailed paired-sample Conclusions: In this single-institution study, the evaluated LLMs generated modified Angoff estimates that were broadly comparable to those of faculty judges. Generalizability theory analyses demonstrated higher
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