ArticleBMC nursing2026
Applications, attitudes and ethical considerations of Generative Artificial Intelligence (Gen AI) in nursing education: a scoping review.
Article in BMC nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- The Associations of AI Literacy, AI Self-Efficacy and AI Attitudes Among Nursing Students: A Cross-Sectional Path Analysis.Nursing reports (Pavia, Italy) · 2026Article
- Awareness, knowledge and attitudes towards artificial intelligence among nursing students: a systematic review protocol.BMJ open · 2026Article
- Development of vignette-based evaluation with generative artificial intelligence (VEGA) for nursing students: a pilot study.BMC medical education · 2026Article
- The influence of AI literacy on fear of negative evaluation among students and researchers: a multisite cross-sectional study.BMC medical education · 2026Article
- Exploring factors associated with nursing students' artificial intelligence literacy: insights from a national mixed methods study.BMC nursing · 2026Article
- Article
- Understanding Time Availability and Format Preferences for AI Professional Development in Health Professions Education.Journal of CME · 2026Article
- Clinical nursing interns' perceptions of artificial intelligence-assisted tools in human-AI collaboration: a qualitative persona-based study.Frontiers in public health · 2026Article
- Effectiveness of a Mobile Application with an AI-Powered Chatbot to Enhance Nursing Students' Awareness of Substance Abuse Prevention: An Interventional Study.F1000Research · 2026Article
- Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGenerative Artificial Intelligence (Gen AI) is a type of artificial intelligence that can learn from and mimic large amounts of data to create content such as text, images, music, videos, code, and more, based on inputs or prompts. Gen AI technologies are being increasingly integrated into healthcare education, including the field of nursing, where they are utilised to support a range of pedagogical activities. PURPOSE: This scoping review examined and described the application of Gen AI as a teaching, learning and assessment strategy in Nursing education and examined the ethical implications of and attitudes towards its implementation.
methodsWe conducted a scoping review using a combination of methodological approaches, including Arksey and O'Malley's 5-step framework, the PRISMA-ScR guidelines, and JBI evidence synthesis methods and searched five databases: EMBASE (Elsevier), Web of Science Core (Clarivate), CINAHL & Medline (EBSCO), Applied Social Science Index and Abstracts, and ERIC (ProQuest). A wide search of grey literature was also conducted. Literature published in English between January 1st 2014, and July 1st 2025 was included in the review.
resultsOf the 1,251 articles retrieved, we identified 103 articles for inclusion in the review. There were 44 discussion/opinion/conference papers and 59 empirical research papers. Gen AI has predominantly been used for content creation simulation, personalised learning, tutoring, skill development and assessment. Students and Educators describe mixed attitudes towards the implementation of Gen AI, with several ethical concerns regarding the application of Gen AI in nursing education evident, including privacy, transparency, bias, and accountability issues.
conclusionWhile there is growing openness to Gen AI, a body of work remains regarding ethical and educational challenges. Recommendations for educational practice and curriculum development include a need for clear policies and guidelines to ensure the ethical use of Gen AI resources by educators and students. Further research is needed to understand long-term effects and promote responsible implementation within the context of nursing education.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.