Aug 2026· Nursing and Health Sciences· Vol 28· 0 citations· 67 references
Medicine
TL;DR
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.
Abstract
ABSTRACT This scoping review follows the Arksey and O'Malley five‐stage framework: (1) identifying research questions, (2) identifying relevant studies, (3) selecting studies, (4) extracting, mapping, and charting data, and (5) collating, summarizing, and reporting findings. The review seeks to identify the extent and type of literature and gain insights into nurses' and nursing students' experiences with using Generative Artificial Intelligence (GenAI). A comprehensive search was conducted across five databases. The search was limited to studies published between January 2020 and July 2025. The systematic screening process identified 20 papers for inclusion in this review. Nurses' experiences with GenAI were mainly positive, while nursing students had mixed perceptions, though mostly positive. 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. An overarching analysis using the theory of planned behavior mapped these factors and experiences, yielding predictions of behavioral intentions and actions regarding GenAI use. Nurse leaders are vital in shaping institutional policies and ethical guidelines to encourage responsible use of GenAI.
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.
Natasha Hawkins, Anthea Fagan, Yumiko Coffey et al.· Journal of Advanced Nursing· 0 citations
AIM
To explore nursing students' generative artificial intelligence (GenAI) related practices during Bachelor Thesis (BT) preparation and to analyse their levels of technology integration, links with learning, and perceived barriers and facilitators.
METHODS
This descriptive qualitative study adopted a constructivist approach. Participants were 66 final-year nursing students who reported using GenAI tools during the preparation and defense of the BT. Data were collected using a qualitative open-ended questionnaire and analysed using thematic analysis. Findings were interpreted using the SAMR model (Substitution, Augmentation, Modification and Redefinition).
RESULTS
GenAI use spanned different levels of technology integration. At the Substitution and Augmentation levels, it primarily supported language correction, refinement of academic tone, and reference management. At the Modification and Redefinition levels, it supported critical reflection, knowledge reorganisation, integration of complex information, and co-creation of student-produced artefacts, with reports of a transformed learning experience. Nevertheless, students expressed concerns about the reliability of the information generated, the need for verification, and the ethical implications of using these tools.
CONCLUSIONS
GenAI significantly shapes how nursing students construct knowledge and develop academic practice in the BT. When integrated more fully, it may support autonomy and critical thinking, provided its use remains reflective, ethically grounded and aligned with the discipline's educational objectives. These findings have implications for curriculum design, academic supervision and pedagogical approaches that encourage responsible GenAI use in nursing education.
Olga Masot, Rosa Mar Alzuria-Alós, J. Roca et al.· Nurse Education Today· 0 citations
Objective To synthesize qualitative evidence on registered nurses’ experiences of using generative artificial intelligence (GAI) in clinical practice and nursing research, and to identify perceived benefits, challenges, and support needs for its standardized implementation in nursing. Methods A qualitative meta-synthesis was conducted using the Joanna Briggs Institute meta-aggregation approach. PubMed, CINAHL, Embase, PsycINFO, Scopus, Web of Science, the Cochrane Library, CNKI, Wanfang, VIP, and the China Biomedical Literature Database were searched from inception to April 25, 2026. Two reviewers independently screened studies, extracted data, and assessed methodological quality using the JBI Critical Appraisal Checklist for Qualitative Research. Synthesized findings were assessed using the JBI ConQual approach. Results Six qualitative studies involving 113 registered nurses were included. Thirty-eight findings were extracted and aggregated into eight categories, which generated three synthesized findings: (1) GAI may enhance work efficiency and professional competence; (2) nurses encounter ethical, cultural, and operational challenges when using GAI; and (3) nurses require training, institutional support, and clear guidance for standardized GAI use while maintaining positive expectations for future applications. Conclusion The available qualitative evidence suggests that registered nurses perceive GAI as a potentially supportive tool for improving efficiency, assisting clinical and research decision-making, and promoting professional development. However, the current evidence base remains limited, and the findings should be interpreted as preliminary. Further research across diverse healthcare systems and cultural contexts is needed to clarify how GAI can be safely and responsibly integrated into nursing practice. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/view/CRD420261365306, Identifier CRD420261365306.
Yan Deng, Yidan Zhu, Jiaqi Li et al.· Frontiers in Public Health· 0 citations
Objective To systematically evaluate the real experiences of nursing students participating in generative artificial intelligence-assisted learning. Methods Electronic searches were conducted in the China National Knowledge Infrastructure (CNKI), VIP Database, Scopus, Wanfang Data, and the China Biomedical Literature Database (CBM), Web of Science, PubMed, Cochrane, Embase, and CINAHL databases for qualitative studies on the experiences of nursing students with generative AI-assisted learning from the establishment of the databases to March 2026. Qualitative studies on nursing students’ experiences with generative AI-assisted learning were screened, appraised, and synthesized using thematic synthesis. Results Eighteen studies were included in the synthesis. A total of 49 themes were identified and organized into 13 categories, leading to four integrated findings: (1) dual experience of empowerment and challenges, (2) internal conflict between technology and nursing humanism, (3) user experience differentiation amid Practical Constraints, (4) general demand for supporting systems and educational reform. Conclusion This study found that nursing students’ experiences with generative AI are shaped by both the opportunities and challenges associated with its use in learning. These findings highlight the need for nursing educators to strengthen students’ AI literacy, critical thinking, and ethical awareness, for curriculum designers to integrate AI-related competencies into nursing curricula while maintaining a strong emphasis on humanistic care, and for policymakers to establish clear governance frameworks and educational guidelines to support the responsible use of AI in nursing education. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/view/CRD420261367837, identifier CRD420261367837.
Shanshan Du, Sha Wang, Feng-ming Yan et al.· Frontiers in Medicine· 0 citations
BACKGROUND
Integrating generative artificial intelligence (GenAI) into course learning has emerged as a significant trend in the future development of nursing education. Students are central to learning activities, and their authentic experiences with GenAI-assisted learning are essential for understanding how educational innovations are perceived and implemented. However, there remains a paucity of qualitative studies focusing on students with GenAI-assisted learning experiences in the classroom environment. This study therefore aimed to explore nursing undergraduates' experiences, perceptions, and opinions following their participation in structured GenAI-assisted learning in a formal classroom environment involving both instructors and peers.
METHODS
Based on the guidance of the Unified Theory of Acceptance and Use of Technology (UTAUT), a semi-structured interview outline was designed. Using purposive sampling, 12 nursing undergraduates with learning experiences assisted by GenAI in the classroom environment were recruited for semi-structured interviews, and the data were analyzed using the six-phase reflexive thematic analysis method.
RESULTS
Analysis yielded four themes: (1) Perceived usefulness and ease of use, (2) Social facilitation and resource support, (3) Risk concerns, and (4) Expectations and suggestions.
CONCLUSION
Structured GenAI-assisted learning was perceived to support nursing undergraduates' classroom learning and skill development. Future efforts may focus on optimizing teaching strategies, strengthening ethical guidance, and developing nursing-specific GenAI tools to promote the responsible and effective integration of GenAI into nursing education.
Dan Song, Yue Zhang, Xia Xu et al.· BMC Nursing· 0 citations
ABSTRACT Mental health nursing requires specialised training in therapeutic communication and related soft skills. Innovative, digitally enhanced strategies may support competency development, particularly amid clinical placement shortages. This article outlines insights from a scoping review of technology use in undergraduate mental health nursing education. The objectives were to (a) explore the impact of using these technologies in students' experience and (b) develop insights that can contribute to the development of a curriculum that embed innovative educational technology with strong theoretical foundations and pedagogical strategies. This review was guided by the modified Arksey and O'Malley methodological framework. The PCC (Population, Concept, Context) framework was used to formulate the eligibility criteria. A review protocol was registered with the Open Science Framework (OSF). The databases for searching included CINAHL, MEDLINE, Scopus, PsycINFO, Web of Science. The results were reported according to the PRISMA‐ScR checklist. The review included 35 studies and the evidence showed that, virtual reality simulations (VRS) with varying immersive abilities were the most popular innovative educational technology used in undergraduate mental health nursing education. Interestingly, no articles were identified that used other innovative educational technologies as outlined in the search strategies. Both immersive and non‐immersive VRS were equally effective in improving student learning experiences compared to traditional learning modalities. The main benefits included enhanced learning experiences and comprehension and the development of necessary skills and confidence before entering clinical settings. Advanced technologies offer future‐focused benefits for mental health nursing education; however, core therapeutic skills remain essential. Virtual learning should therefore complement, not replace, realistic learning experiences.
Sini Jacob, Sebastian Samuel, Greety Antony et al.· International Journal of Men...· 0 citations