The Role of Artificial Intelligence in Developing a Future‐Ready Nursing Workforce: A Systematic Review
8 affiliations
- 1School of Graduate Studies Rutgers University Newark New Jersey USA rutgers.edu
- 2Nell Hodgson Woodruff School of Nursing Emory University Atlanta USA emory.edu
- 3Miyan Research Institute International University of Business Agriculture and Technology Dhaka Bangladesh iubat.edu
- 4Faculty of Graduate Studies University of Kelaniya Colombo Sri Lanka kln.ac.lk
- 5Department of Nursing West Najran Hospital for Maternity and Children Najran Saudi Arabia
- 6Graduate School of Biomedical and Health Science Hiroshima University Hiroshima Japan hiroshima-u.ac.jp
- 7Nursing Service Dhaka Medical College Hospital Dhaka Bangladesh dmc.edu.bd
- 8College of Nursing Sultan Qaboos University Muscat Oman squ.edu.om
Abstract
Background: 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.
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BibTeX
@article{shaleah2026role,
title = {{The Role of Artificial Intelligence in Developing a Future‐Ready Nursing Workforce: A Systematic Review}},
author = {Abu Zafor Md Shaleah and Rajib Kumar Biswas and Moustaq Karim Khan Rony and Most. Tahmina Khatun and Salma Akhter and Most Baby Najnin and Sujoy Biswas and Maryam Alharrasi},
journal = {Journal of Nursing Management},
year = {2026},
month = nov,
doi = {10.1155/jonm/8272597},
url = {https://doi.org/10.1155/jonm/8272597},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - The Role of Artificial Intelligence in Developing a Future‐Ready Nursing Workforce: A Systematic Review
AU - Abu Zafor Md Shaleah
AU - Rajib Kumar Biswas
AU - Moustaq Karim Khan Rony
AU - Most. Tahmina Khatun
AU - Salma Akhter
AU - Most Baby Najnin
AU - Sujoy Biswas
AU - Maryam Alharrasi
PY - 2026
DA - 2026/11/01
JO - Journal of Nursing Management
DO - 10.1155/jonm/8272597
UR - https://doi.org/10.1155/jonm/8272597
AB - Background: 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.
SN - 0966-0429
SN - 1365-2834
ER -