The Role of Artificial Intelligence in Developing a Future‐Ready Nursing Workforce: A Systematic Review
Abu Zafor Md Shaleah, Rajib Kumar Biswas, Moustaq Karim Khan Rony, Most. Tahmina Khatun, Salma Akhter, Most Baby Najnin, Sujoy Biswas, Maryam AlharrasiBackground
Artificial intelligence (AI) is increasingly transforming health care through advanced technologies that support clinical practice, education, workforce management, and decision making. As healthcare systems face growing challenges related to workforce shortages, increasing service demands, and rapid digital transformation, there is a need to understand how AI can contribute to the development of a future‐ready nursing workforce equipped with the competencies required for modern healthcare environments.
Aim
This systematic review aimed to examine the role of AI in developing a future‐ready nursing workforce and to synthesize current evidence regarding its applications, benefits, challenges, and implications for workforce preparedness.
Methods
A systematic review was conducted in accordance with PRISMA 2020 and was registered in PROSPERO. PubMed, Scopus, Web of Science, CINAHL, and IEEE Xplore were searched for studies published between January 2015 and February 2026. Eligible primary studies examined an identifiable AI technology or an AI‐specific construct in nursing education, clinical practice, workforce development, competency development, workforce planning, or professional preparedness. Methodological quality was assessed using the Mixed Methods Appraisal Tool, and findings were synthesized narratively.
Results
A total of 38 primary empirical studies satisfied the AI‐specific eligibility criteria. The evidence was concentrated in cross‐sectional ( n = 15) and qualitative ( n = 11) designs, with fewer development/prototype ( n = 6), mixed methods ( n = 3), randomized or quasi‐experimental ( n = 2), and methodological ( n = 1) studies. AI‐supported education, clinical decision support, workforce forecasting, and competency development showed promising benefits, but much of the evidence was based on perceptions, simulations, prototypes, or single‐site evaluations rather than long‐term implementation. Recurrent barriers included limited AI literacy, ethical and privacy concerns, algorithmic bias, infrastructure constraints, workflow integration, and organizational readiness.
Conclusion
AI has promising, but still uneven, potential to support a future‐ready nursing workforce. Current evidence supports cautious and staged integration rather than uncritical scale‐up. Standardized AI and digital competency frameworks, human oversight, governance, infrastructure readiness, and prospective evaluation of effectiveness, equity, and cost are needed before widespread deployment.
Implications for Nursing Management
Nursing managers should prioritize standardized AI and digital competency development, readiness assessment, pilot testing, and governance before organization‐wide implementation. Decisions about AI adoption should also consider infrastructure, training, workflow redesign, total cost of ownership, and sustainability, particularly in resource‐constrained settings.