ArticleJMIR medical education2024
Using ChatGPT in Psychiatry to Design Script Concordance Tests in Undergraduate Medical Education: Mixed Methods Study.
Article in JMIR medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 28 citations in OpenAlex.
- Mixed-methods research in medical education: Lessons from a meta-study of methodological practice.Medical education · 2026Review
- Artificial Intelligence-Assisted Generation of Case Scenarios and Multiple-Choice Questions in Psychiatry: A Pilot Study.Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry · 2026Article
- Strengths and Potential Pitfalls of the Use of Artificial Intelligence in Psychiatric Education and Practice.Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry · 2026Article
- AI- vs Human-Based Assessment of Medical Interview Transcripts in a Generative AI-Simulated Patient System: Cross-Sectional Validation Study.JMIR medical education · 2026Article
- Enhancing Medical Education: The Role of Artificial Intelligence Tools in the Teaching-Learning Process.Advances in medical education and practice · 2026Review
- Rethinking AI in clinical decision support: a framework for reciprocal human-AI interaction.Frontiers in digital health · 2026Article
- Large language models for generating key-feature questions in medical education.Medical education online · 2025Article
- AI in Psychiatric Education and Training From 2016 to 2024: Scoping Review of Trends.JMIR medical education · 2025Article
- Teaching Clinical Reasoning in Health Care Professions Learners Using AI-Generated Script Concordance Tests: Mixed Methods Formative Evaluation.JMIR formative research · 2025Article
- Applications, Challenges, and Prospects of Generative Artificial Intelligence Empowering Medical Education: Scoping Review.JMIR medical education · 2025Article
- Applications of Artificial Intelligence in Psychiatry and Psychology Education: Scoping Review.JMIR medical education · 2025Article
- The role of generative artificial intelligence in psychiatric education- a scoping review.BMC medical education · 2025Article
- Review
- FonoTCS: validation of a tool for assessing clinical reasoning in Speech-Language pathology.CoDAS · 2025Article
- Beginner-Level Tips for Medical Educators: Guidance on Selection, Prompt Engineering, and the Use of Artificial Intelligence Chatbots.Medical science educator · 2024Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Undergraduate medical studies represent a wide range of learning opportunities served in the form of various teaching-learning modalities for medical learners. A clinical scenario is frequently used as a modality, followed by multiple-choice and open-ended questions among other learning and teaching methods. As such, script concordance tests (SCTs) can be used to promote a higher level of clinical reasoning. Recent technological developments have made generative artificial intelligence (AI)-based systems such as ChatGPT (OpenAI) available to assist clinician-educators in creating instructional materials. Objective: The main objective of this project is to explore how SCTs generated by ChatGPT compared to SCTs produced by clinical experts on 3 major elements: the scenario (stem), clinical questions, and expert opinion. Methods: This mixed method study evaluated 3 ChatGPT-generated SCTs with 3 expert-created SCTs using a predefined framework. Clinician-educators as well as resident doctors in psychiatry involved in undergraduate medical education in Quebec, Canada, evaluated via a web-based survey the 6 SCTs on 3 criteria: the scenario, clinical questions, and expert opinion. They were also asked to describe the strengths and weaknesses of the SCTs. Results: A total of 102 respondents assessed the SCTs. There were no significant distinctions between the 2 types of SCTs concerning the scenario (P=.84), clinical questions (P=.99), and expert opinion (P=.07), as interpretated by the respondents. Indeed, respondents struggled to differentiate between ChatGPT- and expert-generated SCTs. ChatGPT showcased promise in expediting SCT design, aligning well with Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria, albeit with a tendency toward caricatured scenarios and simplistic content. Conclusions: This study is the first to concentrate on the design of SCTs supported by AI in a period where medicine is changing swiftly and where technologies generated from AI are expanding much faster. This study suggests that ChatGPT can be a valuable tool in creating educational materials, and further validation is essential to ensure educational efficacy and accuracy.
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