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Patterns, advances, and gaps in using ChatGPT and similar technologies in nursing education: a PAGER scoping review

datacite.subject.sdg04:Educação de Qualidade
datacite.subject.sdg03:Saúde de Qualidade
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorAmankwaa, Isaac
dc.contributor.authorEkpor, Emmanuel
dc.contributor.authorCudjoe, Daniel
dc.contributor.authorKobiah, Emmanuel
dc.contributor.authorDiebieri, Maximous
dc.contributor.authorFuseini, Abdul-Karim Jebuni
dc.contributor.authorGyamfi, Sebastian
dc.contributor.authorBrownie, Sharon
dc.date.accessioned2026-03-19T10:22:11Z
dc.date.available2026-03-19T10:22:11Z
dc.date.issued2025-10
dc.description.abstractBackground 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.eng
dc.identifier.doi10.1016/j.nedt.2025.106822
dc.identifier.eissn1532-2793
dc.identifier.issn0260-6917
dc.identifier.urihttp://hdl.handle.net/10400.1/28475
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.relation.ispartofNurse Education Today
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectChatGPT
dc.subjectNursing education
dc.subjectGenerative AI
dc.subjectArtificial intelligence
dc.subjectPAGER framework
dc.subjectScoping review
dc.subjectEducational technology
dc.titlePatterns, advances, and gaps in using ChatGPT and similar technologies in nursing education: a PAGER scoping revieweng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.startPage106822
oaire.citation.titleNurse Education Today
oaire.citation.volume153
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameFuseini
person.givenNameAbdul-Karim Jebuni
person.identifier.orcid0000-0002-6841-9495
relation.isAuthorOfPublicationb57f610a-567c-4049-8f78-bf7c4365bae8
relation.isAuthorOfPublication.latestForDiscoveryb57f610a-567c-4049-8f78-bf7c4365bae8

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