ReviewSAGE open nursing
Transforming Nursing Education with Artificial Intelligence: A Systematic Review (2010-2025).
Review in SAGE open nursing. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- The AI Adaptation Paradox: Navigating Professional Relevance and Sustainable Integration in Nursing.Nursing inquiry · 2026Article
- Enhancing Learning in Graduate Nursing Education Through a Co-Designed AI Virtual Tutor: A Mixed-Methods Evaluation.Journal of clinical nursing · 2026Article
- Effects of AI on Nursing Education: Protocol for a Systematic Review and Meta-Analysis.JMIR research protocols · 2026Article
- Integrating Artificial Intelligence into Community Health Nursing Education and Practice: Opportunities, Ethical Challenges, and Future Directions.Healthcare (Basel, Switzerland) · 2026Review
- Core competencies of clinical nursing educators in the AI era: a qualitative study.BMC nursing · 2026Article
- Promoting Critical Thinking and Digital Literacy in Nursing Students Through AI-Powered Podcasting: A Mixed-Methods Study.Nursing reports (Pavia, Italy) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.