JMIR Publications

ABSTRACT

Background:

The global health workforce faces persistent shortages, maldistribution, and unequal access to education and continuing professional development, particularly in low- and middle-income countries (LMICs). Digital education has emerged as an important mechanism for expanding access to health professions education and strengthening workforce capacity. The rapid emergence of artificial intelligence (AI), including generative AI, intelligent tutoring systems, automated feedback, adaptive learning, and AI supported assessment, is further changing how health professionals acquire and apply knowledge and skills. However, evidence from LMICs remains fragmented, and it is unclear what types of digital and AI enabled educational interventions have been implemented, what outcomes have been assessed, and whether capabilities relevant to AI mediated practice are being considered.

Objective:

This systematic review aims to map and synthesize empirical evidence on digital and AI enabled health professions education in LMICs. Specifically, it will characterize intervention and implementation contexts, summarize reported educational and implementation outcomes, examine how digital and AI related capabilities are defined and measured, and identify evidence gaps requiring further research.

Methods:

This review will follow Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidance and will be reported in accordance with PRISMA 2020 where applicable. Studies evaluating or describing formal digital or AI enabled educational interventions involving health professions students, trainees, educators, or practising professionals in LMICs will be eligible, including medicine, nursing, midwifery, pharmacy, dentistry, and allied health professions. Searches will be conducted in PubMed, Scopus, Web of Science, and EBSCOhost. Quantitative, qualitative, and mixed-methods studies will be considered. Data will be extracted on intervention characteristics, context, participants, technology, educational function, pedagogy, delivery mode, outcomes, implementation factors, and digital and AI related capabilities. The conceptual framework developed by Tudor Car et al for digital health professions education will provide the overarching organizational structure. Reported educational outcomes will be categorized using the Kirkpatrick model where sufficient information is available, while implementation outcomes will be classified using the taxonomy proposed by Proctor et al. Quantitative findings will be synthesized descriptively, with statistical pooling considered only where sufficient methodological and statistical homogeneity exists; qualitative findings will be synthesized narratively.

Results:

The protocol was registered with PROSPERO (CRD42021273419) as a review of online nursing education in LMICs. This protocol represents a planned amendment extending the population to health professions education and incorporating digital and AI enabled educational approaches. Database searching will commence following finalization of the amended eligibility criteria and search strategy.

Conclusions:

This review will provide an evidence map of how digital and AI enabled education is being used across health professions in LMICs, including what educational outcomes and implementation conditions have been reported, and how emerging digital and AI related capabilities are conceptualised and measured. Rather than assuming a uniform effect across heterogeneous interventions, the review will identify patterns, gaps, and priorities for more rigorous comparative, longitudinal, and capability oriented research. Clinical Trial: PROSPERO (CRD42021273419)