Technology-enhanced learning in nursing education: a systematic review

Abstract

Abstract Background Technology-enhanced learning (TEL), including artificial intelligence (AI), virtual reality (VR), high-fidelity simulation (HFS), and gamification, is increasingly used in nursing education to address limitations in clinical training and support the development of students’ clinical reasoning skills. This review aimed to synthesize current evidence on the educational outcomes associated with AI, VR, HFS, and gamification in nursing education, and to identify the factors that facilitate or hinder their successful implementation. Methods A systematic review was conducted in accordance with PRISMA 2020 and registered in PROSPERO (CRD420251048860). Five electronic databases were searched for English-language studies published between 2015 and 2025. Studies were eligible if they involved undergraduate or postgraduate nursing students and evaluated AI, VR, HFS, or gamification in educational settings. Methodological quality was assessed using the Mixed Methods Appraisal Tool (MMAT 2018). Results Forty studies met the inclusion criteria. HFS and VR were most frequently associated with improvements in knowledge, clinical judgment, and procedural skills. Gamification was associated with greater engagement, motivation, and knowledge retention. Evidence on AI was limited and preliminary, with a small number of studies suggesting possible benefits for critical thinking and problem-solving. Common barriers included technical problems, resource constraints, and inadequate faculty preparedness. Conclusions TEL methods appear to improve student learning and provide safe environments for practice. Successful integration of TEL into nursing education requires institutional support, evidence-based teaching strategies, and continuous faculty development. TEL should complement rather than replace traditional educational practices. Future research should examine the long-term impact of TEL on learning outcomes, its cost-effectiveness, and its implementation in resource-constrained settings.