The impact of generative AI on health professional education: A systematic review in the context of student learning
Open accessAbstract
Background: Generative Artificial Intelligence (GenAI) is increasingly integrated into health professions education (HPE), offering new opportunities for student learning. However, current research lacks a comprehensive understanding of how HPE students actually use GenAI in practice. Laurillard's Conversational Framework outlines six learning types— acquisition, inquiry, practice, production, discussion and collaboration —commonly used to categorise learning activities supported by conventional and digital technologies. Gaining insight into how GenAI aligns with these six learning types could assist HPE academics in integrating it more thoughtfully and effectively into teaching and learning. Purpose: This systematic review investigates how HPE students utilise GenAI and examines how these uses align with Laurillard's six learning types compared to conventional and digital technologies. Material and Methods: A systematic review searching five major databases— ERIC, Education Database, Ovid Medline, Ovid Embase and Scopus including articles on HPE students' use of GenAI until 15th September 2024. Studies were included if they were conducted within formal HPE training programs in HPE and specifically mentioned how students interact with GenAI. Data were mapped to the six learning modes of the Laurillard's Framework. Study quality was assessed using the Medical Education Research Study Quality Instrument (MERSQI). Results: Thirty‐three studies met inclusion criteria. GenAI supported learning most frequently in practice (73%), inquiry (70%), production (67%) and acquisition (55%). These studies highlight GenAI's varied educational applications, from clarifying complex concepts to simulating clinical scenarios and generating practice materials. Discussion and collaboration were less represented (12% each), suggesting a shift toward more individualised learning with GenAI. The findings highlight benefits such as efficiency and accessibility, alongside concerns about critical thinking, academic integrity and reduced peer interaction. Conclusion: This review has provided insights into HPE students' learning aligned with Laurillard's existing six learning types. Although GenAI supports personalised and self‐directed learning, its role in collaborative modes is under‐explored.
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In medical education, students can use large language models to generate explanations and practice questions, summarise dense materials, improve academic prose, support literature searches, analyse data, and rehearse clinical scenarios.
“In medical education, students can use large language models (LLMs) to generate explanations and practice questions, summarise dense materials, improve academic prose, support literature searches, analyse data, and rehearse clinical scenarios (Pham et al., 2025; Cervantes et al., 2024; Izquierdo-Condoy et al., 2025; Dong et al., 2025).”
From Between Co-Intelligence and Cognitive Offloading: Educators’ Perspectives from Dutch Academic Medical Settings on Generative AI, Critical Thinking, and Adult Professional Learning (2026) · cites this paper as Pham et al., 2025
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“While studies have demonstrated growing interest in GenAI’s potential, few have empirically examined its ethical and pedagogical use within ME settings [9,12,13].”
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- Authors
- Pham, T. D., Karunaratne, N., Exintaris, B., Liu, D., Lay, T., Yuriev, E., & Lim, A.
- Affiliations
- Monash University (AU)
- The University of Sydney (AU)
- Royal Children's Hospital (AU)
- Corresponding author
- Lim, Angelina
- Venue
- Medical Education
- Publisher
- Wiley
- Published
- 2025-06-18
- Volume
- 59
- Issue
- 12
- Pages
- 1280-1289
- ISSN
- 0308-0110 (ISSN-L)
- 1365-2923
- PMC
- PMC12686775
- OpenAlex
- W4411403091
- Cited by
- 100
- References
- 25
- Citations by year
- 2026: 91 · 2025: 9
- FWCI
- 5.92
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Medical Education (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- Funders
- Monash University
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Keywords
- generative AI, health professions education, student use of AI, inquiry-based learning, hands-on learning, production-based learning, cooperative learning, self-directed learning, clinical simulation, academic integrity, critical thinking
- MeSH terms
- Artificial Intelligence, Education, Medical, Health Occupations, Humans, Learning, Students, Health Occupations
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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