Mapping the evidence on artificial intelligence–based clinical decision support systems for undergraduate nursing students: a scoping review protocol

Objective: – This scoping review will map the scope and nature of the available evidence on the use of artificial intelligence–based clinical decision support systems (AI-CDSSs) for undergraduate nursing students. Introduction: – AI is increasingly integrated into nursing education to support students’ clinical reasoning and decision-making. Yet the literature on AI-CDSSs remains fragmented and heterogeneous, with variability in technologies, educational applications, and reported outcomes, as well as concerns regarding bias and ethical implications. To date, no comprehensive synthesis has mapped the evidence on AI-CDSS use for undergraduate nursing students. Eligibility criteria: – This review will map empirical research involving undergraduate nursing students using AI-based tools to support clinical reasoning, diagnostic reasoning, prioritization, or the nursing process. It will consider sources published in English, Spanish, French, Italian, or Chinese, provided that the title and abstract are in English and the record is considered relevant. Searches will be limited to sources published from 2017 onward, aligning with the rapid evolution and only recent widespread availability of modern generative AI tools. Methods: – This review will follow the JBI methodology for scoping reviews. Searches will be conducted in PubMed, Scopus, CINAHL (EBSCOhost), the Cochrane Library, ERIC (EBSCOhost), and Web of Science Core Collection, while gray literature will be retrieved from ProQuest Dissertations and Theses Global (ProQuest), OpenAlex, Google Scholar, and professional body and regulatory sources. Screening will be performed independently by 2 reviewers, and data will be synthesized descriptively and thematically. Reporting will be documented according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. Review registration: – OSF https://osf.io/eh9bt/