Transforming Nursing Education with Artificial Intelligence: A Systematic Review (2010–2025)

Abstract

Introduction: This systematic review provides the first comprehensive synthesis of empirical studies on Artificial Intelligence (AI) integration in nursing education, offering actionable insights for nurse educators and clinical leaders. It highlights how AI transforms learning environments by enhancing personalization, feedback, and instructional efficiency. Aims: To examine how AI is applied across nursing education settings and its impact on learning outcomes. Methods: A systematic search of PubMed, CINAHL, IEEE Xplore, and Scopus identified peer-reviewed studies published from January 2010 to April 2025. Eligible studies focused on empirical AI applications in academic, clinical, or hybrid nursing education contexts. Studies were appraised using the Critical Appraisal Skills Programme (CASP) checklist, and findings were synthesized thematically. Results: Twenty-eight studies met the inclusion criteria. AI-enhanced nursing education in four main areas: (a) personalized learning systems tailored content to individual needs, (b) simulation-based training improved decision-making in high-acuity scenarios,(c) automated assessment tools provided immediate, unbiased feedback, and (d) at the institutional level, AI supported curriculum management and predictive analytics. Common risks included technological inequities, faculty preparedness gaps, and ethical concerns around privacy and bias. Conclusion: To support implementation, this study recommends: (a) integrating AI-powered simulation into emergency care training, (b) deploying adaptive platforms to support at-risk learners, and (c) using automated tools for real-time formative feedback. Diagnostic accuracy is proposed as a measurable outcome to assess impact. The next step for educators is to initiate multi-site pilot programs over 6-12 months, evaluating improvements in learning outcomes, trust, and system integration.

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