The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Artificial intelligence in nursing education: A scoping review
Abstract
Aim To explore recent empirical studies on implementation of artificial intelligence in nursing education in hospital settings through the prism of the Strengths, Weaknesses, Opportunities and Threats (SWOT) model.Background In the last decade, artificial intelligence has markedly influenced healthcare and nursing domains, particularly in improving care and educational processes for nursing staff. Despite its ongoing integration in nursing education, an understanding of its impact remained limited.Design Scoping review.Methods A systematic search using PubMed and ScienceDirect databases, following PRISMA guidelines, identified relevant studies. The main inclusion criteria were empirical studies from 2018 onwards and a focus on nursing students/registered nurses in hospital settings. The exclusion criteria were non-empirical documentation such as abstracts, editorials and opinion-related articles, as well as studies in surgical, pediatric, gynecological and mental health nursing.Results In total, 15 articles were selected from a pool of 6517 documents. The aspects mentioned in the employed literature highlighted the positive impact of artificial intelligence on educational experiences, knowledge acquisition and mental safety. Challenges of the artificial intelligence implementation in the nursing education field, such as technical issues, language barriers and limited realistic experience were also identified.Conclusions The findings of the review suggest that artificial intelligence provides significant benefits for nursing education. However, continuous evaluation managing weaknesses and maximizing the educational potential of artificial intelligence in the nursing field is crucial.
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“The 18 included studies in this umbrella review collectively explored the diverse applications of AI in nursing across research, practice, and education [32-49].”
From The Role of AI in Nursing Education and Practice: Umbrella Review (2025) · cites this paper as [35]
Citations
Cites
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Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians
Asan, O., Bayrak, A. E., & Choudhury, A. (2020) · Journal of Medical Internet Research · doi:10.2196/15154 · Cited by 825
Predicted Influences of Artificial Intelligence on Nursing Education: Scoping Review
Buchanan, C., Howitt, M. L., Wilson, R., Booth, R. G., Risling, T., & Bamford, M. (2021) · JMIR Nursing · doi:10.2196/23933 · Cited by 326
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Castonguay, A., Farthing, P., Davies, S., Vogelsang, L., Kleib, M., Risling, T., & Green, N. (2023) · Nurse Education Today · doi:10.1016/j.nedt.2023.105916 · Cited by 203
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Ng, Z. Q. P., Ling, L. Y. J., Chew, H. S. J., & Lau, Y. (2022) · Journal of Nursing Management · doi:10.1111/jonm.13425 · Cited by 184
Communication skills training using virtual reality: a descriptive qualitative study
Shorey, S., Ang, E., Ng, E. D., Yap, J., Lau, L. S. T., & Chui, C. K. (2020) · Nurse Education Today · doi:10.1016/j.nedt.2020.104592 · Cited by 152
Research works printed but not linked: 1
- Peters, 2020, Chapter 11: Scoping reviews. JBI Manual for Evidence, Synthesis, 169, 467
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The Role of AI in Nursing Education and Practice: Umbrella Review
El Arab, R. A., Al Moosa, O. A., Abuadas, F. H., & Somerville, J. (2025) · Journal of Medical Internet Research · doi:10.2196/69881 · Cited by 202
“Lifshits and Rosenberg [35] contributed to this discussion by pointing out usability issues arising from language and cultural mismatches in AI tools, which hinder effective adoption, especially in diverse patient populations.”
The role of artificial intelligence in shaping nursing education: A comprehensive systematic review
Ma, J., Wen, J., Qiu, Y., Wang, Y., Xiao, Q., Liu, T., Zhang, D., Zhao, Y., Lu, Z., & Sun, Z. (2025) · Nurse Education in Practice · doi:10.1016/j.nepr.2025.104345 · Cited by 73
The Role of Artificial Intelligence in Nursing Care: An Umbrella Review
Rony, M. K. K., Das, A. K., Khalil, M. I., Peu, U. R., Mondal, B., Alam, M. S., Shaleah, A. Z. M., Parvin, M. R., Alrazeeni, D. M., & Akter, F. (2025) · Nursing Inquiry · doi:10.1111/nin.70023 · Cited by 67
Saleh, Z. T., Rababa, M., Elshatarat, R. A., Alharbi, M., Alhumaidi, B. N., Al-Za'areer, M. S., Jarrad, R. A., niarat, T. F. A., Almagharbeh, W. T., Al‐Sayaghi, K. M., et al. (2025) · BMC Nursing · doi:10.1186/s12912-025-03082-0 · Cited by 65
El-Banna, M. M., Sajid, M. R., Rizvi, M. R., Sami, W., & McNelis, A. M. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1681784 · Cited by 62
Metadata
- Authors
- Lifshits, I., & Rosenberg, D.
- Affiliations
- Hebrew University of Jerusalem (IL)
- ORCID
- Rosenberg, Dennis
- Venue
- Nurse Education in Practice
- Publisher
- Elsevier BV
- Published
- 2024-10
- Volume
- 80
- Pages
- 104148
- ISSN
- 1471-5953 (ISSN-L)
- OpenAlex
- W4403244162
- Cited by
- 121
- References
- 32
- Citations by year
- 2026: 68 · 2025: 51 · 2024: 1
- FWCI
- 2.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nurse Education in Practice (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Education › Simulation-Based Education in Healthcare
- Keywords
- artificial intelligence, nursing education, hospital-based nursing education, SWOT analysis, implementation barriers, nursing students, registered nurses, knowledge acquisition, technical challenges, language barriers, simulation-based training
- MeSH terms
- Artificial Intelligence, Clinical Competence, Education, Nursing, Education, Nursing, Baccalaureate, Humans, Students, Nursing
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