Abstract
BACKGROUND/AIMS: Since the emergence of generative AI (GenAI) in fall 2022, its impact on higher education has been significant yet under-researched, leading to mixed reactions among nurse educators, ranging from enthusiasm to skepticism. A preliminary search of seven databases found no scoping reviews specifically that addressed nurse educators' concerns about using GenAI. Therefore, this study aims to map the existing literature on nurse educators' concerns regarding the use of GenAI in nurse education. INCLUSION CRITERIA: Included are any types of sources (peer-reviewed and nonpeer-reviewed) in English and from any country and were authored by an academic nurse educator that reported on "academic nurse educators," and "artificial intelligence" (such as GenAI, Generative AI, ChatGPT, large language models) in nursing education. Articles that did not report "nurse educator concerns," or were focused on clinical practice were excluded. METHODS: This protocol (see PRISMA-P in Appendix 1) establishes the study parameters for the planned scoping review, which will be conducted from April to July 2024. We will follow Joanna Briggs Institute, a comprehensive methodology, to ensure a rigorous approach. The final review will include relevant literature from eight academic databases published from Fall 2022 through April 2024. Data will be reported using the PRISMA-ScR checklist and flow diagram (2020) along with other visual diagrams to add validity to our findings. An inductive analysis approach will be used to code the evolving data, identify recurring themes, and pinpoint potential gaps in the literature. RESULTS: The final scoping review will present the search results, the study inclusion process, and the data analysis. CONCLUSION: Our planned review will potentially provide crucial insights into nurse educators' concerns on using GenAI, pinpointing gaps within the literature, and providing direction for future research. REVIEW REGISTRATION: This protocol was registered on May 8, 2024, on Open Science Framework (OSF). The registry number is OSF.IO/SZ8WR. This registration ensures the transparency and credibility of our research process, as it provides a public record of our study design and methods.