ArticleNPJ digital medicine2025
A generative AI teaching assistant for personalized learning in medical education.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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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
13 citing papers in PubMed.
- Scaffolded AI-Supported Problem-Based Learning for Medical Interns: Exploratory Retrospectively Registered Randomized Controlled Evaluation With a Voluntary Feasibility Follow-Up.JMIR medical education · 2026Trial
- Development and Nationwide Multicentre Evaluation of Guideline-Grounded Large Language Model Chatbots to Support Patient Self-Management and Education in Rheumatology.Journal of medical systems · 2026Article
- 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
- Promise, Mimicry, and Surveillance: Responsibly Integrating Artificial Intelligence With Socratic Inquiry in Medical Education.JMIR medical education · 2026Article
- Impact of ChatGPT-assisted personalized learning on teaching acute abdomen to undergraduate medical students: A randomized crossover study.Pakistan journal of medical sciences · 2026Article
- Widely available, general-purpose generative AI applications for radiology education: determining the teaching quality of synthetic pediatric neuroradiology images.Pediatric radiology · 2026Article
- AI literacy mediates AI assisted diagnosis participation and critical thinking among medical students under supervision.NPJ digital medicine · 2026Article
- Generative artificial intelligence in medical education: from knowledge assessment to clinical reasoning and professional competence.Frontiers in medicine · 2026Review
- A primer on artificial intelligence for palliative care educators.Palliative care and social practice · 2026Review
- TCM-CoT-RAG: a chain-of-thought enhanced retrieval-augmented generation system for clinical decision support in Traditional Chinese Medicine rheumatology.Frontiers in pharmacology · 2026Article
- Article
- A generative AI teaching assistant for personalized learning in medical education.NPJ digital medicine · 2025Article
- Artificial Intelligence Generated Videos as Supportive Tools in Medical Education: A Scoping Review.Journal of medical education and curricular developmentReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Medical education faces a scalability crisis, where rising class sizes strain individualized instruction, while students increasingly adopt unvalidated Generative AI (GenAI) tools for individualized learning support. This study investigated how medical students integrate constrained GenAI systems into their self-directed learning practices using Retrieval-Augmented Generation (RAG), which limits large language model responses to instructor-curated materials, thereby reducing hallucinations while maintaining pedagogical utility. We deployed a RAG-based teaching assistant in a medical school basic science course across two consecutive cohorts, examining usage patterns, conversation content, and student feedback to understand adoption and learning behaviors. Students demonstrated strategic, context-dependent usage, with engagement intensifying during high-stakes assessment periods and substantial after-hours utilization. Users primarily sought clarification on foundational concepts and valued the system's continuous availability and source-grounded responses. However, knowledge-base constraints that ensured accuracy also limited broader inquiries, creating tension between reliability and comprehensiveness that shaped how students incorporated the tool into their study routines. These findings provide empirical evidence of how medical students navigate constrained AI tools for self-directed learning, informing institutional strategies for integrating these technologies into pedagogical frameworks.
Identifiers
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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.