AI literacy and competency in nursing education: preparing students and faculty members for an AI- enabled future-a systematic review and meta-analysis
Open accessAbstract
Introduction: Artificial Intelligence (AI) has made its way into every dimension of human life, and its impact is significant and multifaceted. Specifically, the effect of AI in nursing education has reshaped the healthcare system. However, this technological shift in nursing and the healthcare system still needs to be evaluated in multiple aspects to ensure the effective use of AI and to prepare future professionals. Methods: This PROSPERO-registered systematic literature review and meta-analysis explored the integration of AI literacy and competency within nursing curricula and the profession globally from January 2020 to June 2025. The study specifically aimed to: (1) examine the extent of AI integration within nursing curricula; (2) assess the awareness, attitudes, and readiness of nursing students, faculty, and practitioners toward AI; (3) evaluate the effectiveness of educational interventions designed to enhance AI literacy and competency; (4) identify ethical, institutional, and pedagogical challenges associated with AI adoption in nursing education; and (5) provide evidence-based recommendations for standardized and equitable AI education frameworks in nursing. Results: The review synthesizes evidence from 111 peer-reviewed articles, including 18 distinct quantitative studies, which have been further analyzed through meta-analytic techniques. PRISMA guidelines were followed to search for relevant articles and extract the required information. Meta-analysis reveals high levels of AI-related awareness (pooled estimate = 73%, 95% CI: 64–80%) and positive attitudes (71%, 95% CI: 63–78%) among various nursing groups. The implementation of AI-related skills remains highly variable (67%, 95% CI: 55–78%), especially in low-resource settings, which needs careful interpretation. Overall, meta-analysis findings highlight significant variations and reflect non-uniformity and disparities across regions, institutions, and nursing groups (students, staff, faculty). Conclusion: Thematic synthesis underscores the need for standardized AI education, tailored faculty development, and equitable access to digital tools. Although individual-level awareness and attitudes toward AI are promising, this review reveals a lack of institutional readiness, with many nursing programs lacking standardized curricula, faculty training, and infrastructural support. Moreover, findings emphasize the critical need for broader institutional and policy efforts to match individual enthusiasm with institutional capacity in preparing nurses for an AI-enabled healthcare landscape. Further, this review offers evidence-based recommendations for various stakeholders to ensure inclusive and future-ready nursing education. Systematic review registration: https://www.crd.york.ac.uk/prospero/ , identifier CRD420251090108.
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El-Banna et al. demonstrate the value of discipline specificity in nursing through a systematic review and meta-analysis.
“El-Banna et al. (2025) demonstrate the value of such specificity in nursing through a systematic review and meta-analysis”
From From AI Literacy to AI Fluency: A Developmental Framework for Higher Education (2026) · cites this paper as El-Banna et al., 2025
El-Banna et al. call for tailored faculty development alongside standardized AI education frameworks.
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Güven, H., Kewalramani, D., Benton, J., Repsha, C. L., Glogocheski, S., Starkweather, A. R., & Narayan, M. (2026) · Teaching and learning in nursing · doi:10.1016/j.teln.2026.09.019
Suhardin, S., Nubatonis, J. R., & Setu, Y. T. (2026) · Prosiding Seminar Nasional Sanata Dharma Berbagi · doi:10.24071/usdb.v4.586
Amin, S. M., Elgazar, H. E., Zoromba, M. A., Alrimawi, I., Atta, M. H. R., Ibrahim, R., Mohamed, H. S., Elbakry, M. A. A. E., Abozed, H. W., Ruksakulpiwat, S., et al. (2026) · Nurse Education Today · doi:10.1016/j.nedt.2026.107490
Alnomasy, N., Alrashedi, H., & Pangket, P. P. (2026) · JMIR Nursing · doi:10.2196/106044
Mohamed, M. G., Mahmoud, Z. A., & Sorour, M. S. (2026) · Scientific Reports · doi:10.1038/s41598-026-76573-y
Hemşirelik Eğitiminde Yapay Zeka ve Dijital Dönüşüm
Çağlar, O. (2026) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub1577.c6482
Trần, T. V., & Lê, T. H. (2026) · Tạp chí Khoa học Điều dưỡng · doi:10.54436/jns.2026.06.1364
Sunday, I. E., Nwogboji, E. U., Bamigbola, P. I., Atolagbe, U. K., & Ogbo, O. S. (2026) · IIAR Journal of Health Medicine and Biomedical Research · doi:10.68854/a5jte043
Yapay Zekâ Çağında Hemşirelik: Dijital Dönüşüm ve Bakım Uygulamaları
Altınbaş, Y. (2026) · Özgür Yayınları eBooks · doi:10.58830/ozgur.pub1577
- Authors
- El-Banna, M. M., Sajid, M. R., Rizvi, M. R., Sami, W., & McNelis, A. M.
- Affiliations
- University of Gujrat (PK)
- Santosh University (IN)
- Qatar University (QA)
- Vanderbilt University (US)
- Corresponding author
- Sami, Waqas
- Venue
- Frontiers in Medicine
- Publisher
- Frontiers Media SA
- Published
- 2025-11-26
- Volume
- 12
- Pages
- 1681784-1681784
- ISSN
- 2296-858X (ISSN-L)
- PMC
- PMC12689331
- OpenAlex
- W4416730328
- Cited by
- 62
- References
- 117
- Citations by year
- 2026: 62
- FWCI
- 3.67
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Medicine (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Qatar University QSpace (Qatar University) (submittedVersion)
- Funders
- Qatar University
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Information Systems › Digital literacy in education
- Keywords
- artificial intelligence in nursing education, AI literacy, AI competencies, nursing curriculum, nursing students, nursing faculty, attitudes toward AI, AI awareness, AI readiness, faculty development, institutional readiness, digital divide, resource-limited settings, ethics of AI in healthcare
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