ReviewCurrent research in physiology2025
AI-generated multiple-choice questions in health science education: Stakeholder perspectives and implementation considerations.
Review in Current research in physiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07522658 (Comparison of Artificial Intelligence-Generated and Academician-Developed Multiple True/False Questions in Anesthesiology Education), which is not on this map. Cited by 5 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.
Comparison of Artificial Intelligence-Generated and Academician-Developed Multiple True/False Questions in Anesthesiology Education: A Prospective Cohort Study
Who cites it
5 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
- Education Research: Quality of Narrative Feedback Generated by a Large Language Model Compared With Expert Faculty for Case-Based Learning in Neurology Education.Neurology. Education · 2026Article
- Catalysts of Change: Using AI To Lower the Activation Energy for Developing Gamified Learning Experiences in Health Profession Education.Medical science educator · 2026Article
- Evaluating the ability of AI models to generate level-specific medical MCQs with variable difficulty.BMC research notes · 2026Article
- Editorial: A global perspective on current research in physiology education.Current research in physiology · 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multiple-choice questions (MCQs) are widely used in health science education because they are an efficient way to evaluate knowledge from simple recall to complex clinical reasoning. The creation of high-quality MCQs, however, can be time-consuming and requires expertise in question composition. Advancements in artificial intelligence (AI), especially large language models (LLMs), offer the potential to allow for the rapid generation of high-quality, consistent, and course-specific MCQs. Here we discuss the potential benefits and drawbacks of the use of this technology in the generation of MCQs, including ensuring the accuracy and fairness of questions, along with technical, ethical, and privacy considerations. We offer practical guiding principles for the implementation of AI-generated MCQs and outline future research areas related to their impact on student learning and educational quality.
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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.