Academic Performance in Nursing Education Through Digital Competencies and AI Integration: A Systematic Review

Abstract

Digital transformation and artificial intelligence are reshaping nursing education by changing how students access information, complete academic tasks, and engage with technology-mediated learning. However, evidence on digital competencies, AI-related constructs, mediating mechanisms, and academic performance remains fragmented and methodologically uneven. This systematic review of empirical studies synthesized how digital competencies and AI-related constructs are associated with academic performance and learning-related outcomes in nursing education. Following PRISMA 2020 guidelines adapted to social science research, searches were conducted in Scopus and Web of Science Core Collection in March 2026, covering 2022–2026. Twenty-five empirical studies were included: 18 quantitative, 4 qualitative, and 3 mixed-methods studies. The evidence was concentrated in the Middle East and North Africa, Asia, and Europe. Findings suggest that digital competencies are associated with academic and learning-related outcomes mainly through self-efficacy, academic motivation, cognitive presence, and learning flow. AI-related evidence remains emerging, mixed, and context-dependent. Although some AI-assisted interventions reported favorable outcomes, one experimental study found greater knowledge gains with traditional text-based study than with ChatGPT-assisted learning. Therefore, AI integration should not be considered universally beneficial, but contingent on pedagogical design, task type, teacher guidance, AI literacy, responsible use, and critical verification.