ArticleMedical education online2025
Ten tips to harnessing generative AI for high-quality MCQS in medical education assessment.
Article in Medical education online, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 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
- Human-Governed Validation of Artificial Intelligence-Generated Medical Assessment Artifacts: A Technical Report.Cureus · 2026Article
- Catalysts of Change: Using AI To Lower the Activation Energy for Developing Gamified Learning Experiences in Health Profession Education.Medical science educator · 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
- When Assessment Theory Meets Generative AI: Reimagining SBA Design in Medical Education.Perspectives on medical education · 2026Article
- Prompt engineering for single-best-answer multiple-choice questions in licensing examinations: a narrative review with a case study involving the Korean Medical Licensing Examination.Journal of educational evaluation for health professions · 2025Review
- Integrating large language models into medical undergraduate laboratory course to enhance bioethical competence: a quasi-experimental study.Frontiers in medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.