Validity of AI-generated multiple-choice questions in medical education: a systematic review

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Authors: Yavuz Selim Kıyak, Abdullah Bedir Kaya, Emre Emekli
Cited by: 5
References: 75

Abstract

Large language models (LLMs) are increasingly used to generate multiple-choice questions (MCQs) in medical education. We conducted a systematic review following PRISMA 2020, searching PubMed, Web of Science, Scopus, and ERIC through 15 February 2026. Two reviewers independently screened 1352 records, extracted data and assessed methodological quality using the Joanna Briggs Institute checklist. Findings were synthesized according to Messick's five sources of validity evidence. Seventy-one studies from 24 countries were included. Most relied on expert review rather than learner-based testing. All studies reported content evidence, whereas fewer addressed relations to other variables (40/71), response process (35/71), internal structure (31/71) or consequences (25/71). Error rates ranged from

Fields of Study & Subjects

Artificial Intelligence in Healthcare and Education Clinical Reasoning and Diagnostic Skills Innovations in Medical Education

Publication details

Journal / source
Postgraduate Medical Journal
Publisher
Oxford University Press (OUP)
Publication date
1 May 2026
Language
EN