{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T16:05:29Z","timestamp":1784217929996,"version":"3.55.0"},"reference-count":61,"publisher":"BMJ","issue":"7","license":[{"start":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T00:00:00Z","timestamp":1784160000000},"content-version":"unspecified","delay-in-days":15,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["BMJ Open"],"accepted":{"date-parts":[[2026,7,1]]},"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Internationally, nursing students\u2019 awareness and familiarity with artificial intelligence (AI) remain a challenge as evidenced by the current literature. Interestingly, the Gulf Cooperation Council (GCC) region has earned a strong standing for driving national digital transformation; however, this ambition has not been translated into research. Despite growing interest in AI-driven healthcare, empirical studies examining nursing students\u2019 readiness in these countries to operate in healthcare environments remain limited, representing a critical gap in the literature. Our initial literature review found a high degree of heterogeneity among study designs, measurement tools and theoretical framing and highlighted an unequivocal need for a rigorous and systematic synthesis to uncover consistent patterns, methodological gaps and contextual factors that shape nursing students\u2019 engagement with AI.<\/jats:p>\n                    <jats:p>This protocol aims to provide a structured plan for combining the current evidence on nursing students\u2019 awareness, knowledge and attitudes regarding AI applications in nursing education.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and analysis<\/jats:title>\n                    <jats:p>This protocol is prepared in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) 2015 guidelines, and it is registered with the international Prospective Register of Systematic Reviews (PROSPERO). A comprehensive and systematic search of the literature will be undertaken across four key electronic databases: PubMed, CINAHL, Scopus and Web of Science, using Boolean search strings constructed from Medical Subject Headings-controlled terms and free-text keywords encompassing six predefined thematic domains: AI applications in nursing education, student awareness, attitudes, technology acceptance, adoption, ethical considerations and regional context. All studies published in English between January 2020 and June 2026 including cross-sectional, cohort, quasiexperimental and qualitative studies will be included in this review. The primary outcome is nursing students\u2019 awareness of and attitudes toward AI; secondary outcomes include AI-related knowledge, behavioural intention and ethical concerns. A 41-item standardised form will be used for data extraction across all included studies, systematically capturing study characteristics, instruments, theoretical frameworks, barriers, facilitators and the outcomes of interest. To assess the studies\u2019 quality, we will use the Joanna Briggs Institute (JBI) Critical Appraisal Tools for quantitative and qualitative studies, ensuring a comprehensive and methodologically consistent appraisal process across all included studies. Narrative synthesis will be performed complemented by meta-analysis where applicable, organised by the construct domains and geographic regions. This protocol provides an in-depth, systematic review plan that will report the most thorough synthesis to date regarding nursing students\u2019 awareness, knowledge levels and perceptions of the utilisation of AI within nursing education. The review will identify validated instruments for cross-cultural adaptation, establish benchmarks and estimate prevalence of awareness, knowledge and attitude. We will describe the theoretical and contextual contrived factors associated with these constructs in nursing students.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Ethics and dissemination<\/jats:title>\n                    <jats:p>As this systematic review is based exclusively on published literature and does not involve the collection of primary data from human participants or animals, formal ethical approval is not required. Findings from this review will be disseminated through publication in a peer-reviewed journal and presented at relevant national and international nursing and healthcare conferences. The review is expected to generate evidence-based insights that will inform nursing curricula, guide institutional policy on AI integration and highlight the critical evidence gap in the GCC region, including Oman, thereby contributing to the advancement of AI-ready nursing education internationally. A key focus will be mapping geographic variation, with particular attention to the GCC region where empirical evidence remains sparse.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>PROSPERO registration number<\/jats:title>\n                    <jats:p>CRD420261320108.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bmjopen-2026-121820","type":"journal-article","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T15:25:19Z","timestamp":1784215519000},"page":"e121820","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Awareness, knowledge and attitudes towards artificial intelligence among nursing students: a systematic review protocol"],"prefix":"10.1136","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6469-4430","authenticated-orcid":false,"given":"Manal Nasser","family":"AL Ghazali","sequence":"first","affiliation":[{"name":"School of Medicine, Cardiff University, Cardiff, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5512-1745","authenticated-orcid":false,"given":"Muhammad","family":"Riaz","sequence":"additional","affiliation":[{"name":"School of Medicine, Cardiff University, Cardiff, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"239","published-online":{"date-parts":[[2026,7,16]]},"reference":[{"key":"2026071608251189000_16.7.e121820.1","doi-asserted-by":"crossref","DOI":"10.1186\/s12912-025-03436-8","article-title":"Generative artificial intelligence acceptance, anxiety, and behavioral intention in the middle east: a TAM-based structural equation modelling approach","volume":"24","author":"Mohamed","year":"2025","journal-title":"BMC Nurs"},{"key":"2026071608251189000_16.7.e121820.2","doi-asserted-by":"crossref","unstructured":"Migdadi MK , Oweidat IA , Alosta MR , et al . 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