The impact of generative AI on health professional education: A systematic review in the context of student learning

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.