Amankwaa, IsaacEkpor, ECudjoe, DKobiah, EFuseini, AKJDiebieri, MGyamfi, SBrownie, S2025-08-062025-08-062025-07-05Nurse Education Today, ISSN: 0260-6917 (Print); 1532-2793 (Online), Elsevier BV, 153, 106822-. doi: 10.1016/j.nedt.2025.1068220260-69171532-2793http://hdl.handle.net/10292/19643Background and aim: Generative AI (GenAI) can transform nursing education and modernise content delivery. However, the rapid integration of these tools has raised concerns about academic integrity and teaching quality. Previous reviews have either looked broadly at artificial intelligence or focused narrowly on single tools like ChatGPT. This scoping review uses a structured framework to identify patterns, advances, gaps, evidence, and recommendations for implementing GenAI in nursing education. Methods: This scoping review followed the JBI methodology and PRISMA-ScR guidelines. We searched PubMed, CINAHL, SCOPUS, ERIC, and grey literature (October to November 2024). Data synthesis utilised the PAGER framework as a mapping tool to organise and describe patterns, advances, gaps, evidence for practice, and recommendations. Results: Analysis of 107 studies revealed GenAI implementation across four key domains: assessment and evaluation, clinical simulation, educational content development, and faculty/student support. Three distinct implementation patterns emerged: restrictive, integrative, and hybrid approaches, with hybrid models demonstrating superior adoption outcomes. Technical advances showed significant improvement from GPT-3.5 (75.3 % accuracy) to GPT-4 (88.67 % accuracy) in NCLEX-style assessments, with enhanced capabilities in multilingual assessment, clinical scenario generation, and adaptive content creation. Major gaps included limited methodological rigour (29.0 % of empirical studies), inconsistent quality control, verification challenges, equity concerns, and inadequate faculty training. Geographic distribution showed North American (42.1 %) and Asian (29.9 %) dominance, with ChatGPT representing 83.2 % of tool implementations. Key recommendations include developing institutional policies, establishing quality verification protocols, enhancing faculty training programs, and addressing digital equity concerns to optimise GenAI integration in nursing education. Conclusions: GenAI has transformative potential in nursing education. To realise its full potential and ensure responsible use, research should focus on developing standardised governance frameworks, empirically validating outcomes, developing faculty in AI literacy, and improving technical infrastructure for low-income contexts. Such efforts should involve international collaboration, highlighting the importance of the audience's role in the global healthcare community.© 2025 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).http://creativecommons.org/licenses/by/4.0/Artificial IntelligenceChatGPTEducational technologyGenerative AINursing educationPAGER frameworkScoping review4205 Nursing42 Health SciencesSocial Determinants of HealthNetworking and Information Technology R&D (NITRD)4 Quality Education1110 Nursing1302 Curriculum and PedagogyNursing3901 Curriculum and pedagogy4204 Midwifery4205 NursingJournal ArticleOpenAccess10.1016/j.nedt.2025.106822