The role of artificial intelligence in shaping nursing education: A comprehensive systematic review
Abstract
AIM: This systematic review assesses AI's application, effectiveness and impact on nursing education, while identifying research limitations. BACKGROUND: AI integration in nursing education is transforming traditional teaching and learning paradigms. DESIGN: A systematic review. METHODS: Following PRISMA 2020 guidelines, a search was conducted in PubMed, Web of Science, Embase, Cochrane Library and CINAHL from the inception of the databases to November 1, 2024, focusing on "Artificial Intelligence" and "nursing education." Two reviewers independently screened and assessed the literature. The quality was assessed using the Cochrane Risk of Bias 2.0 (RoB-2) tool for randomized controlled trials (RCTs), the Agency for Healthcare Research and Quality (AHRQ) tools evaluation for observational studies and the JBI Critical Appraisal Checklist for quasi-experimental studies. RESULTS: Fifteen studies involving 1464 nursing students and professionals were included. The application scenarios of AI technology in nursing education are diverse and varied and it has shown significant potential in many areas of nursing education, but conflicting results have also been observed. Evaluation of literature quality showed that there were seven high-quality studies and eight medium-quality studies. Artificial intelligence was found to have a positive impact on students at three levels: learning attitude and psychological effects, learning effectiveness and comprehensive clinical nursing competencies. Key research gaps were identified, including the lack of longitudinal studies, uneven study populations and the lack of measurement instrument validity and objectivity. CONCLUSION: AI positively impacts nursing education but requires further research to address gaps and ensure long-term effectiveness and privacy protection. REGISTRATION: PROSPERO ID: CRD42024562849.
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Virtual pediatric scenarios can support personalized learning and provide experiences that approximate real clinical settings while enhancing learner autonomy and immersion without time or space constraints.
“In addition, virtual pediatric scenarios can support personalized learning and provide experiences that approximate real clinical settings while enhancing learner autonomy and immersion without time or space constraints [9,11].”
Generative AI systems may embed algorithmic bias [9,25,35].
“embed algorithmic bias”
From Artificial Intelligence–Enabled Educational Tools and Clinical Reasoning in Undergraduate Nursing Education: A Systematic Review (2026) · cites this paper as [35]
Generative AI systems may produce inaccurate outputs [9,25,35].
“Generative AI systems may produce inaccurate outputs”
From Artificial Intelligence–Enabled Educational Tools and Clinical Reasoning in Undergraduate Nursing Education: A Systematic Review (2026) · cites this paper as [35]
Generative AI systems may obscure reasoning pathways due to limited transparency [9,25,35].
“obscure reasoning pathways due to limited transparency [9,25,35].”
From Artificial Intelligence–Enabled Educational Tools and Clinical Reasoning in Undergraduate Nursing Education: A Systematic Review (2026) · cites this paper as [35]
Virtual patients and AI-driven simulations may lack the emotional nuance and interpersonal complexity inherent in authentic clinical encounters [34,35].
“Virtual patients and AIdriven simulations may lack the emotional nuance and interpersonal complexity inherent in authentic clinical encounters [34,35].”
From Artificial Intelligence–Enabled Educational Tools and Clinical Reasoning in Undergraduate Nursing Education: A Systematic Review (2026) · cites this paper as [35]
The findings support a hybrid instructional model in which AI tools are integrated under faculty supervision and paired with explicit reasoning justification activities [25,35].
“Taken together, the findings support a hybrid instructional model in which AI tools are integrated under faculty supervision and paired with explicit reasoning justification activities [25,35].”
From Artificial Intelligence–Enabled Educational Tools and Clinical Reasoning in Undergraduate Nursing Education: A Systematic Review (2026) · cites this paper as [35]
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- Authors
- Ma, J., Wen, J., Qiu, Y., Wang, Y., Xiao, Q., Liu, T., Zhang, D., Zhao, Y., Lu, Z., & Sun, Z.
- Affiliations
- Nanjing University of Chinese Medicine (CN)
- Corresponding author
- Sun, Zhiling
- Venue
- Nurse Education in Practice
- Publisher
- Elsevier BV
- Published
- 2025-03
- Volume
- 84
- Pages
- 104345
- ISSN
- 1471-5953 (ISSN-L)
- OpenAlex
- W4409012083
- Cited by
- 73
- References
- 52
- Citations by year
- 2026: 50 · 2025: 23
- FWCI
- 4.37
- 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
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Educational Technology and Pedagogy
- Keywords
- artificial intelligence, nursing education, AI in teaching and learning, nursing students, learning effectiveness, learning attitude, research gaps, privacy preservation
- MeSH terms
- Artificial Intelligence, Education, Nursing, Humans, Students, Nursing
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