ArticleKorean journal of medical education2026
From setting to vetting: using artificial intelligence for Single Best Answer questions review.
Article in Korean journal of medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- The new wave of generative artificial intelligence in medical education.Korean journal of medical education · 2026Article
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
purposeMaintaining the quality of Single Best Answer (SBA) questions remains a challenge in medical education, especially as artificial intelligence (AI)-generated items become more common. While considerable attention has been paid to AI question generation, the vetting process is under-explored and difficult to scale.
methodsThis study investigates the feasibility and reliability of using a large language model to support the vetting of SBA questions. An AI-based reviewer, QA-bot, was developed using custom GPT and embedded with 25 criteria aligned with Bloom's taxonomy (Levels 1-3). QA-bot and two experienced educators independently evaluated 32 AI-generated SBA questions using the shared evaluation rubric.
resultsThe rubric showed high internal consistency (Cronbach's alpha=0.878), and strong inter-rater reliability between human reviewers (intraclass correlation coefficient [ICC]=0.893). QA-bot demonstrated good alignment with human raters (ICC=0.861 and 0.840). While the AI performed well on objective, rule-based criteria, it was less consistent in detecting irrelevant complexity and accurately judging difficulty.
conclusionThese findings suggest that AI can function as an efficient first-pass reviewer, improving consistency and reducing workload, with human oversight remaining essential for educational and clinical relevance.
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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.