ArticleMedical science educator2025
Answering Patterns in SBA Items: Students, GPT3.5, and Gemini.
Article in Medical science educator, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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Who cites it
3 citing papers in PubMed.
- From setting to vetting: using artificial intelligence for Single Best Answer questions review.Korean journal of medical education · 2026Article
- Beyond Chatbots: Moving Toward Multistep Modular AI Agents in Medical Education.JMIR medical education · 2025Article
- Student Perspective Matters for GenAI in Question Setting in Medical Education.Medical science educator · 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While large language models (LLMs) are often used to generate and answer exam questions, limited work compares their performance across multiple iterations using item statistics. This study aims to fill that gap by investigating answering patterns of how LLMs respond to single-best answer (SBA) questions, comparing their performance to that of students. Forty-one SBA questions for first-year medical students were assessed using the most easily assessable and free-to-use GPT3.5 and Gemini across 100 iterations. Both LLMs exhibited more repetitive and clustered answering patterns compared to students, which can be problematic as it may compound mistakes by repeating error selection. Distractor analysis revealed that students performed better when managing multiple options in the SBA format. We found that these free-to-use LLMs are inferior to well-trained students or specialists in handling technical questions. We have also highlighted concerns on LLMs' contextual interpretation of these items and the need of human oversight in the medical education assessment process.
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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.