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

Synthesis: Alrazeeni et al. (2026) provide the first comprehensive systematic review of empirical AI applications in nursing education (28 studies, 2010–April 2025, from PubMed, CINAHL, IEEE Xplore, and Scopus). They find AI enhances nursing education in four areas — personalized learning, simulation-based training, automated assessment, and institutional curriculum management/predictive analytics — while surfacing recurring risks (technological inequity, faculty preparedness gaps, privacy and bias concerns). They offer concrete implementation recommendations and propose diagnostic accuracy as a measurable outcome.

Key Findings

1. Four application areas. AI in nursing education centers on: (a) personalized learning systems tailoring content to individual needs; (b) simulation-based training improving decision-making in high-acuity scenarios; (c) automated assessment providing immediate, unbiased feedback; (d) institutional-level AI for curriculum management and predictive analytics.
2. Recurring risks. Technological inequities, faculty preparedness gaps, and ethical concerns around privacy and bias are common across studies.
3. Actionable recommendations. Integrate AI-powered simulation into emergency-care training; deploy adaptive platforms to support at-risk learners; use automated tools for real-time formative feedback; and adopt diagnostic accuracy as a measurable outcome for assessing impact.
4. Next step. Initiate multi-site pilot programs over 6–12 months, evaluating improvements in learning outcomes, trust, and system integration.

What this means for practice

• Instructors. Integrate AI-powered simulation into emergency- and high-acuity care training, and use automated assessment tools for real-time formative feedback so students receive immediate, unbiased correction.
• Instructors. Deploy adaptive platforms to identify and support at-risk learners before high-stakes clinical placements.
• Faculty developers. Close the faculty-preparedness gap the review finds recurring across studies by training nurse educators to adopt, interpret, and critique these systems.
• Administrators. Run multi-site pilot programs over 6–12 months that evaluate learning outcomes, trust, and system integration, treat diagnostic accuracy as a measurable outcome, and address the recurring risks of technological inequity, privacy, and bias before scaling.

Limitations

• As a qualitative systematic review of 28 studies (January 2010–April 2025), its findings reflect the quality and heterogeneity of those primary studies rather than any new primary data.
• Coverage is bounded by the search period and by four databases (PubMed, CINAHL, IEEE Xplore, Scopus), and the review is restricted to empirical AI applications — conceptual and Gray-literature sources are excluded.
• The included studies vary in design and setting and were appraised with the CASP checklist rather than pooled, so the recommendations — including the 6–12 month multi-site pilots — are forward-looking proposals rather than demonstrated effects.

Citation

Alrazeeni, D. M., Alharrasi, M., Rony, M. K. K., Biswas, R. K., Tama, I. J., Halder, C. R., Deb, B., Bashar, F., & Akter, F. (2026). Transforming nursing education with artificial intelligence: A systematic review (2010–2025). SAGE Open Nursing, 12, 1–31. https://doi.org/10.1177/23779608261424597