This review critically examines the integration of Virtual Reality (VR) and Augmented Reality (AR) in medical training across Low- and Middle-Income Countries (LMICs), offering a novel perspective by combining quantitative analysis with qualitative insights from medical students in Egypt and Ghana. Through a systematic review process, 17 peer-reviewed studies published between 2010 and 2023 were analysed. Altogether, these studies involved a total of 887 participants. The analysis reveals a growing interest in VR and AR applications for medical training in LMICs with a peak in published articles in 2023, indicating an expanding research landscape. A unique contribution of this review is the integration of feedback from 35 medical students assessed through questionnaires, which demonstrates the perceived effectiveness of immersive technologies over traditional 2D illustrations in understanding complex medical concepts. Key findings highlight that VR and AR technologies in medical training within LMICs predominantly focus on surgical skills. The majority of studies focus on enhancing surgical training, particularly general surgery. This emphasis reflects the technology’s strong alignment with the needs of LMICs, where surgical skills training is often a priority. Despite the promising applications and expanding interest in VR and AR, significant challenges such as accessibility and device limitations remain, demonstrating the need for ongoing research and integration with traditional methods to fully leverage these technologies for effective medical education. Therefore, this review provides a comprehensive analysis of existing VR and AR applications, their evaluation methodologies, and student perspectives to address educational challenges and enhance healthcare outcomes in LMICs.

In today’s interconnected world, teamwork and collaboration are becoming essential competencies across all disciplines. This review examines various pedagogical strategies aimed at nurturing these skills, with a specific focus on integrating algorithms into educational practices. While traditional approaches classify teamwork strategies as either instructor-led or student-led, this review introduces a third method that is based on ML algorithms, which are promising methods for optimizing team composition based on both static and dynamic student characteristics. We investigate the effectiveness of these algorithms in enhancing collaborative learning outcomes compared to conventional grouping methods. In fact, this review synthesizes the findings from 20 key studies on the implementation of these technologies in educational settings, evaluating their impact on learning outcomes, student motivation and overall satisfaction. Our findings suggest that computer-enhanced strategies not only improve the academic and collaborative experience but also pave the way for more personalized and dynamic educational environments. This review aims to provide educators and curriculum developers with comprehensive insights into leveraging advanced computational tools to foster effective teamwork and interdisciplinary collaboration, thereby enhancing the overall quality of education and preparing students for the collaborative demands of the professional world.