Patterns, Advances, and Gaps in Using ChatGPT and Similar Technologies in Nursing Education: A PAGER Scoping Review

Abstract

<title>Abstract</title> Background 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 GenIA 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). Six reviewers independently screened and extracted data using Covidence software. Data synthesis used the PAGER framework to derive patterns, advances, gaps, evidence for practice, and recommendations. Team meetings and cross-validation ensured analytical rigour. Results Analysis of 107 studies revealed structured implementation of GenAI across key domains. Usage patterns emerged in high-stakes assessment, clinical competency development, and evidence-based content creation. Implementation approaches varied through restrictive, integrative, or hybrid models. Technical advances showed GPT-4 achieved 88.67% accuracy in nursing-specific assessments compared to 75.3% in GPT-3.5, with enhanced capabilities in clinical scenario generation and multilingual support. Key challenges included limited methodological rigour (29.0% of empirical studies), lack of implementation frameworks, and inequitable access. The evidence is dominated by publications from North America (42.1%) and Asia (29.9%). 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.

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