Sep 2026· Journal of Nursing Reports in Clinical Practice· 0 citations
TL;DR
Generative AI in nursing education is an emerging field, and future research should address policy, long-term outcomes, and institutional adoption to ensure responsible integration to ensure responsible integration.
Abstract
Generative artificial intelligence (AI), particularly large language models such as Chat Generative Pre-Trained Transformer (ChatGPT), is rapidly transforming higher education, including nursing. This study mapped global research trends on the integration of generative AI in nursing education using a bibliometric approach. Articles indexed in Scopus between 2020 and 2025 were retrieved with keywords related to generative AI, ChatGPT, and nursing education. A total of 149 English-language journal articles were analyzed, and bibliometric visualization was conducted using VOSviewer version 1.6.20 to examine publication patterns, leading authors, journals, institutions, countries, and thematic clusters. Results showed a steady rise in publications, with significant growth in 2023–2024 following the widespread adoption of ChatGPT. The most prolific author is from Taipei Medical University, while Nurse Education in Practice was the top journal with 13 articles and 113 citations. Taipei Medical University and NUS Yong Loo Lin School of Medicine were the most productive institutions, and the United States led in overall output and international collaborations. Keyword analysis revealed four thematic clusters: technological foundations, pedagogical applications, competency and critical thinking, and nursing informatics. Generative AI in nursing education is an emerging field, and future research should address policy, long-term outcomes, and institutional adoption to ensure responsible integration.
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
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
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
Background: Traditional clinical skills training faces challenges such as limited standardized patient resources, high costs of simulation equipment, and restricted opportunities for repeated practice. Generative artificial intelligence (GAI), particularly large language models, has attracted increasing attention as a potential tool for simulation, feedback, and adaptive learning in medical education. However, the research landscape and thematic development of GAI in clinical skills training remain insufficiently mapped. Methods: A bibliometric analysis was conducted using literature retrieved from the Web of Science Core Collection from January 1, 2011 to April 1, 2025. VOSviewer, the Bibliometrix R package, and CiteSpace were used to analyze publication trends, country and institutional contributions, journal distribution, collaboration networks, keyword co-occurrence, co-cited references, and citation bursts. Results: A total of 322 publications were included. Research activity remained limited before 2023 but increased rapidly thereafter. The United States contributed the largest number of publications, followed by China and India. Major contributing institutions included the National University of Singapore, Gazi University, and Nova Southeastern University. Frequently publishing journals included JMIR Medical Education, Medical Teacher, and BMC Medical Education. Keyword and thematic analyses showed that current research attention is mainly concentrated on ChatGPT, large language models, natural language processing, clinical reasoning simulation, personalized learning, virtual patient interaction, and ethical governance. Conclusions: Research on GAI in clinical skills training is in an early but rapidly expanding stage, with growing scholarly attention to language-model-driven educational applications and related ethical issues. The bibliometric findings reflect research activity, knowledge structure, and thematic priorities rather than direct evidence of educational effectiveness. Future studies should adopt rigorous empirical designs and standardized evaluation frameworks to assess the effectiveness, safety, and appropriate boundaries of GAI applications in clinical skills training.
Jia Zhang, Yuanzhou Liu, Bo Wang· Medicine· 0 citations
AIM
This study aimed to map evidence regarding the role of generative artificial intelligence (GenAI) in developing higher-order thinking skills (HOTS) among nursing students.
BACKGROUND
GenAI is revolutionizing medical education by enabling innovative pedagogical approaches and showing promise in cultivating HOTS. However, its implementation and evidence on its role in fostering HOTS in nursing students remain unclear.
DESIGN
A scoping review.
METHODS
The review was conducted in accordance with the PRISMA-ScR checklist and Arksey and O'Malley methodological framework. Searches were performed in October 2025 and updated in February 2026 across eight databases: PubMed, Ovid EMBASE, CINAHL, Web of Science, PsycINFO, Cochrane Library, Education Source and ERIC. Two reviewers independently screened the studies in a blinded manner. Seventeen articles were included. Data were analyzed using interpretive description to summarize study characteristics, followed by thematic analysis to identify relevant themes.
RESULTS
Studies showed the dual role of GenAI in cultivating HOTS in nursing education. Three key themes were identified, including: (1) the paradoxical reconstruction of cognitive dynamics; (2) tensions and frictions at the human-AI interface; and (3) reshaping learning and professional identity in the AI era.
CONCLUSIONS
The dual role of GenAI in HOTS development indicates that nursing education should integrate GenAI judiciously. While GenAI offers potential benefits for HOTS development, it also introduces risks related to cognitive dependence and reduced critical engagement. Furthermore, efforts should focus on improving the quality of human-AI collaboration, with particular attention to humanistic care, thereby helping students positively reshape their professional perceptions.
Yan Ning, Shanshan Sun, Yi Lu et al.· Nurse Education in Practice· 0 citations
Introduction: The rapid advancement of Artificial Intelligence (AI) in medical education is driving a shift from traditional instructional design methods toward personalized and adaptive learning models. Despite numerous promising applications, the available evidence remains limited and fragmented; therefore, a comprehensive synthesis of the evidence is needed to support robust conclusions. Methods: This study employed the four-phase meta-synthesis framework proposed by Sandelowski and Barroso. A systematic search was conducted across Medline, Embase, CINAHL, PsycINFO, PubMed, Web of Science, ScienceDirect, Wiley Online Library, SpringerLink, Taylor & Francis Online, SAGE Journals, and Scopus, covering publications from 2010 to 2025. Studies were screened according to predefined inclusion and exclusion criteria, and their methodological quality was evaluated using the Critical Appraisal Skills Programme (CASP). Coding reliability was assessed through a test–retest procedure, resulting in a reliability coefficient of 0.81. Results: A total of 273 records were identified, of which 16 studies met the inclusion criteria and obtained CASP scores exceeding the threshold of 30. Content analysis revealed five principal domains: faculty-related applications (21%), student-related applications (28%), applications in the learning process (15%), curriculum development (13%), and assessment mechanisms (23%). Student-related applications constituted the largest proportion, highlighting the pivotal role of learner-centered personalization in AI-driven medical education. Conclusion: The integration of Artificial Intelligence (AI) into individualized educational experiences represents a transformative model for medical education. AI enables adaptive learning pathways, dynamic assessment methods, and data-driven instructional environments, thereby enhancing student engagement, fostering faculty innovation, and promoting equity in learning outcomes. This synthesis proposes an overarching conceptual framework to inform policy, research, and implementation in the context of AI-supported personalized medical education.
Ava Taghavi Monfared, Maryam Hojati, Zohreh Farahmandpour et al.· Journal of Advances in Medic...· 0 citations
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
