Health professions educators’ use of generative artificial intelligence to support student learning in medicine, nursing, midwifery and allied health: A scoping review

Abstract

Background: Generative artificial intelligence (Gen-AI) is rapidly revolutionising health professions education (HPE), offering new opportunities to enhance teaching, assessment and feedback across disciplines. Health professions educators (HPErs) play a central role in the effective integration of Gen-AI into educational practices. Although the use of Gen-AI among HPErs is increasing, research on how it is used remains limited. Understanding HPErs’ practices is essential to guide professional development, identify needs and provide the necessary support for effective adoption. Aim: This scoping review aims to map and synthesise the existing literature on how HPErs use Gen-AI to support student learning in HPE, specifically medicine, nursing, midwifery and allied health. Method: Arksey and O’Malley’s (2005) framework was used. The research question and search strategy were developed using the PICO framework (population, intervention, context and outcome). Six databases were searched: CINAHL, ERIC, MEDLINE, Embase, Scopus and PsycINFO. Studies were screened using selection criteria. Inductive content analysis was used to identify key themes. Result: Nineteen studies were included for review. Five key themes were identified: (1) HPErs’ perceptions and attitudes, (2) use of Gen-AI in HPE, (3) HPErs’ knowledge, awareness and digital literacy, (4) factors influencing Gen-AI adoption among HPErs and (5) students’ expectations of HPErs. Conclusion: This review highlights HPErs’ perceptions, practices and knowledge related to Gen-AI use in HPE. The findings highlight the need for targeted professional development and institutional support for effective Gen-AI adoption. A key gap is the limited focus on HPErs’ perspectives in Australia and the lack of studies specifying the key elements of training and support needed to adopt Gen-AI in HPE.