Aug 2026· Journal of Advanced Nursing· 0 citations· 37 references
Medicine
Abstract
Aim
To examine nursing academics' perceptions and experiences of artificial intelligence (AI) integration in nursing education.
Design
Scoping review.
DATA SOURCES
MEDLINE, CINAHL, ERIC, Scopus, and Web of Science were searched in August 2025.
Methods
A scoping review using Joanna Briggs Institute methodology. Peer-reviewed original research and reviews published in English (2019-2025) were included if they examined nursing educators' perspectives, attitudes, or experiences with AI in nursing education across undergraduate, postgraduate, and professional contexts. The Substitution, Augmentation, Modification, Redefinition (SAMR) framework was used to classify pedagogical integration levels.
Results
Fifteen studies from eight countries, encompassing 2004 nursing academics, were included. A pattern described as an "adoption paradox" was identified: whilst most academics believe AI will revolutionise nursing education, implementation remains conservative. Two-thirds of applications operate at the augmentation level, with none achieving transformative redefinition. Nursing academics use AI selectively, predominantly for academic productivity and research writing but rarely for student assessment. Primary barriers included knowledge gaps, institutional policy vacuums, and pronounced global access inequities. Academics expressed concerns regarding critical thinking erosion and professional identity threats whilst acknowledging efficiency benefits.
Conclusions
Nursing academics appear to adopt AI selectively, prioritising preservation of core professional values while embracing applications perceived to enhance, rather than replace, educational practice. The absence of transformative integration suggests perceived incompatibilities between artificial intelligence and nursing's relational foundations, signalling a need for more active pedagogical engagement to bridge this widening gap.
IMPACT
This review addresses the critical gap in understanding how nursing academics integrate artificial intelligence while maintaining professional values. Despite high optimism, actual implementation remains basic, with multiple barriers limiting transformative adoption. Findings provide evidence for nursing education programs globally regarding faculty development, institutional policy frameworks, and curriculum design strategies integrating technological advancement whilst maintaining person-centred values.
NO PATIENT OR PUBLIC CONTRIBUTION
Not applicable, as no patients or public were involved.
Aims: This study aimed to synthesize the scientific literature on the integration of artificial intelligence (AI) into nursing education to significantly enhance learning outcomes. The application of AI in clinical teaching can enhance nursing students' preparation for a technologically advanced healthcare environment.
Methods: This study used a narrative literature review. Key electronic databases, including CINAHL, MEDLINE, Scopus, and Google Scholar, were searched according to the PRISMA guidelines. The review included articles published between 2020 and 2024, written in English, and employing qualitative and quantitative research designs. The search items included AI, ChatGPT, challenges, opportunities, nursing education, technology, students, teaching, and learning. Data were synthesized by summarizing the main results of the included studies.
Results: Ten studies met the inclusion criteria and were included in the review. The findings showed that AI can enhance clinical teaching, improve nursing students' self-efficacy, and support teaching and learning. However, challenges related to academic integrity, assessment quality, unequal access to AI, and inadequate skill development were also identified.
Conclusion: The findings of this study revealed that the use of AI in nursing education is instrumental in improving the acquisition of clinical skills and teaching and learning. Nursing education institutions should create awareness of the safe use of AI. Furthermore, policies should be implemented to ensure that AI use is controlled and adequately monitored. All stakeholders, including patients, students, nurses, and nurse educators, should be developed and provided with adequate resources for effective AI implementation.
S. Khunou, Carine Prinsloo· Indonesian Contemporary Nurs...· 0 citations
Thematic analysis revealed that various factors underpinning their attitudinal, normative, and control beliefs are critical determinants of nurses' and students' overall experiences with GenAI and their intentions to use GenAI technologies.
Ming Wei Jeffrey Woo, Adrian Heng Tsai Tan· Nursing and Health Sciences· 0 citations
BACKGROUND
The rapid digital transformation of healthcare is reshaping the capabilities required of the nursing workforce. Technologies such as artificial intelligence (AI), clinical decision-support systems, and digital health technologies are increasingly embedded within healthcare delivery. Consequently, professional and policy frameworks increasingly emphasise the need for nurses to develop AI and digital health competencies. However, the extent to which these capabilities are integrated within undergraduate nursing education remains unclear.
AIM
To map and synthesise the evidence on how artificial intelligence and digital health competencies are integrated within undergraduate nursing education to prepare graduates for practice in increasingly digital healthcare environments.
METHODS
A scoping review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) was conducted across six databases. Studies examining the integration of AI or digital health competencies within undergraduate nursing education were included. Data were charted and synthesised using thematic analysis.
RESULTS
Seventeen studies met the inclusion criteria. Integration of AI and digital health within undergraduate nursing education remains fragmented and largely exploratory, most commonly occurring through isolated courses, pilot initiatives, or educator-led activities rather than coordinated, program-wide curriculum design. Educational approaches emphasise scaffolded learning through case-based learning, simulation, and reflective activities to support the development of AI and digital health competencies. These competencies are predominantly conceptualised as professional, ethical, and evaluative capabilities, emphasising critical thinking, accountability, and patient-centred care rather than technical mastery. While students and educators generally report positive attitudes towards AI technologies, variation in readiness, confidence, and digital literacy persists. Systemic barriers, including limited faculty preparedness, curriculum constraints, governance ambiguity, and resource disparities, continue to limit the sustainable integration of AI and digital health competencies within nursing curricula.
CONCLUSION
These findings highlight the need for longitudinal, program-wide integration of AI and digital health competencies, explicit alignment with recognised competency frameworks, and investment in educator capability to prepare graduates for safe and effective practice in increasingly AI-enabled healthcare environments.
Dianne Stratton-Maher, T. Shaik, Yan Li et al.· Nurse Education Today· 0 citations
The evidence showed that, virtual reality simulations with varying immersive abilities were the most popular innovative educational technology used in undergraduate mental health nursing education and both immersive and non‐immersive VRS were equally effective in improving student learning experiences compared to traditional learning modalities.
Sini Jacob, Sebastian Samuel, Greety Antony et al.· International Journal of Men...· 0 citations
BACKGROUND
Artificial intelligence (AI) is becoming an integral part of nursing education; however, the perspectives of graduate nursing students on its use remain underexplored.
AIM
This study aimed to examine graduate nursing students' views on AI in nursing education.
METHODS
A qualitative phenomenological design incorporating the photovoice method was adopted. Fifteen graduate nursing students from a state university were recruited between 01 March 2025 and 30 May 2025. Data were analyzed using thematic analysis in accordance with Braun and Clarke's approach.
RESULTS
Analysis yielded five main themes and 17 subthemes: (1) Areas of AI application in nursing education, (2) Perceived advantages of AI, (3) Perceived disadvantages of AI, (4) Recommendations for effective AI integration in nursing education, and (5) Future directions for AI in nursing education.
CONCLUSIONS
Participants viewed AI as a tool to enhance the quality and effectiveness of nursing education, while emphasizing the importance of ethical sensitivity and protection of professional identity. The use of photovoice method enriched and deepened these insights.
AIM
To map and analyse how artificial intelligence technologies interact with and support clinical judgement processes in nursing across practice and educational contexts.
DESIGN
Scoping review.
METHODS
JBI methodology for scoping reviews.
DATA SOURCES
An electronic search was conducted on 1 July 2025 across MEDLINE, CINAHL, Scopus, Web of Science, and IEEE Xplore to identify studies published since January 2015. Additional sources of grey literature included ProQuest Dissertations & Theses Global, preprint servers (medRxiv and arXiv), and websites of relevant organisations.
RESULTS
Eleven studies were included. Mapped against Tanner's Clinical Judgement Model, AI applications predominantly supported early cognitive phases (noticing and interpreting) through predictive models and decision support systems, while responding and reflecting phases received minimal attention.
CONCLUSION
Current AI research in nursing concentrates on computational pattern recognition, with the reflective processes central to expertise development remaining largely unexamined. Future research should examine how AI influences nurses' cognitive and interpretative processes across all phases of clinical judgement, with primary studies in nursing education representing a particularly underdeveloped priority.
IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE
The integration of artificial intelligence into nursing practice should be guided by a clear understanding of how these technologies support clinical judgement. Artificial intelligence-enabled tools must be rigorously developed, implemented, and evaluated to enhance nurses' reasoning processes while safeguarding patient safety, professional autonomy, and quality of care.
IMPACT
Research on artificial intelligence in nursing rarely employs theoretical frameworks of clinical judgement, limiting understanding of how these technologies interact with cognitive processes central to professional expertise. This review provides a theoretically grounded synthesis, identifying research gaps and implementation priorities for AI development aligned with nursing clinical judgement.
REPORTING METHOD
PRISMA-ScR.
PATIENT OR PUBLIC CONTRIBUTION
This study did not include patient or public involvement in its design, conduct or reporting.
TRIAL REGISTRATION
Protocol registered in Open Science Framework (https://osf.io; DOI: https://doi.org/10.17605/OSF.IO/UH7RA), and subsequently published in a peer-reviewed journal, DOI: https://doi.org/10.62741/ahrj.v2i4.73.
J. Alves, Ana Rita Ribeiro de Azevedo, R. Encarnação et al.· Journal of Advanced Nursing· 0 citations