Knowledge, attitudes, and practices related to the use of artificial intelligence in nursing education and practice: a systematic review and meta-analysis.
Integrated AI education that emphasizes attitude formation, targeted training to address implementation barriers, and organizational support systems for AI integration are recommended, highlighting the significant association between AI knowledge, attitude, and clinical implementation.
Abstract
Background
The integration of artificial intelligence (AI) into nursing education and practice has demonstrated significant potential to enhance efficiency, reduce errors, and optimize healthcare delivery. However, the successful implementation of AI requires careful consideration of the knowledge, attitudes, and practices of nursing professionals and students.
Aim
To examine the association between knowledge, attitudes, and practices (KAP) regarding AI use in nursing education and clinical practice.
Method
Systematic review and meta-analysis. Following PRISMA guidelines, a comprehensive search was conducted across three major databases (SCOPUS, Web of Science, and PubMed) for studies published between January 2019 and April 2025.
Results
Ten qualified cross-sectional studies were identified from an initial pool of 283 records. A random-effects meta-analysis revealed a moderate positive correlation between knowledge and attitudes {Pearson's correlation (r) = 0.43, 95% confidence interval (CI) = [0.31, 0.53]}, a moderate attitude-practice relationship {Pearson's correlation (r) = 0.46, 95% CI = [0.32, 0.59]}, and a weak knowledge-practice association {Pearson's correlation (r) = 0.26, 95% CI = [0.13, 0.38]}. Significant heterogeneity was observed in the analysis [between-study variance (τ²) = 0.03-0.05; inconsistency (I²) = 89.88%-95.02%; Cochran's Q (Q) = 16.44-157.00, p < 0.001), thereby suggesting that contextual factors such as institutional policies and prior AI exposure may influence these relationships.
Conclusions
These findings partially aligned with the KAP theoretical framework, thus highlighting the significant association between AI knowledge, attitude, and clinical implementation. We recommend integrated AI education that emphasizes attitude formation, targeted training to address implementation barriers, and organizational support systems for AI integration. Future research should employ longitudinal designs to establish causal relationships and examine the contextual factors influencing AI use across diverse healthcare settings.
The findings highlight the need for a supportive educational environment with guidance to enable nursing students to use artificial intelligence appropriately and responsibly when needed, particularly among vocational college students and those from socioeconomically disadvantaged backgrounds.
Hui-Ying Fan, Qing Zhou, Lili Deng et al.· BMC Nursing· 0 citations
Aim: This meta-analysis examined the effectiveness of artificial intelligence (AI)-based educational interventions on learning outcomes in nursing students.Material and Method: A systematic search of PubMed, Scopus, CINAHL, and Web of Science identified studies published between January 2021 and May 2025. Following PRISMA guidelines, 11 studies with 1,539 students were included, comprising randomized controlled trials and quasi-experimental designs. Outcomes assessed were knowledge, clinical reasoning, satisfaction, clinical performance, attitude, and self-efficacy. Risk of bias was evaluated with the RoB 2 tool, and random-effects models were used for meta-analysis.Results: AI-based interventions significantly improved knowledge acquisition (MD = 4.47, 95% CI [2.60, 6.34], p
Orkun Erkayıran· Bandırma Onyedi Eylül Üniver...· 0 citations
The weak negative correlation between knowledge and attitudes suggests that greater awareness of AI may be accompanied by increased concerns regarding its use, and further educational initiatives are needed to enhance nurses’ preparedness for AI integration in clinical practice.
R. Elsayed, A. Nagy, Eman Sobhy El-Said Hussein et al.· BMC Nursing· 1 citation
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
Healthcare students had unsatisfactory awareness of AI, and their overall attitude was negative, highlighting an urgent need for an integrated AI curriculum tailored to their actual needs.
Waghachavare Vivek B., Dhobale Randhir V., Gore Alka D. et al.· International journal of com...· 0 citations