Ten tips to harnessing generative AI for high-quality MCQS in medical education assessment

Bibliographic Data

ID15328237
AuthorsM Magzoub (0000-0002-6721-4500, College of Medicine and Health Sciences, United Arab Emirates University, corresponding author), Imran Zafar (0000-0002-4044-5093, College of Medicine and Health Sciences, United Arab Emirates University), Fadi Munshi (United Arab Emirates University), Fouzia Shersad (0000-0003-0991-811X, United Arab Emirates University)
Year2025
Volume30
Issue1
Pages2532682-2532682
Publication date2025-07-17
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueMedical Education Online (JOURNAL)
Journal identifiersISSN: 1087-2981 • E-ISSN: 1087-2981
PublisherTaylor & Francis (PUBLISHER • GB)
DOI10.1080/10872981.2025.2532682
PMID40674697
OpenAlexW4412492505
LanguageEN
References cited38

Generating high quality MCQs is time consuming and expensive. Many strategies are applied to produce high quality items including sharing of item banks, training of item writers and automatic item generation (AIG). Generative AI, when used with precision, has proven to reduce significantly both cost and time without compromising quality. Medical educators encounter numerous obstacles when using AI to generate MCQs of good quality. We searched the fast and recent growing medical education literature for articles related to the use of AI in generating high quality MCQs. Additionally, the development of these tips was guided by our own institutional experience. We created 10 tips for MCQ generation using AI to assist MCQ item writers in both undergraduate and graduate medical education

Generative grammar · Generative model · Graduate medical education · Medical education · Multiple choice · Quality (philosophy · Artificial Intelligence in Healthcare and Education · Computer Science · Innovations in Medical Education · Medicine · Topic Modeling · Artificial Intelligence · Internal Medicine

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