Applications of generative artificial intelligence in undergraduate nursing education: A scoping review

Background: The rapid emergence of generative artificial intelligence (genAI) technologies has created new opportunities and challenges in undergraduate nursing education. As educators seek to integrate these tools into curricula, understanding their current applications and implications is essential. Objective: This scoping review aimed to explore how generative AI is being utilized in undergraduate nursing education. Methods: A comprehensive search was conducted across five databases for studies published between 2022 and March 2025. A total of 1641 records were identified, with 15 studies meeting the inclusion criteria following screening and full-text review. Included studies consisted of three qualitative studies, three teaching tips, and nine case studies. Results: Three primary themes emerged: (1) applications of generative AI in nursing education, including its use for generating course materials and academic administrative tasks; (2) ethical considerations such as academic integrity, bias, and equitable access; and (3) faculty role and readiness, highlighting the need for professional development, clear policies, and institutional support. Conclusion: This scoping review highlights the emerging role of genAI in undergraduate nursing education, revealing both its promise and its complexity. From enhancing classroom engagement and administrative efficiency to raising concerns around bias and academic integrity, the integration of genAI demands thoughtful and ethical implementation. Faculty readiness and institutional guidance are pivotal to ensuring that these tools are used to enrich student learning rather than compromise professional and academic integrity. As the field continues to evolve, future research should focus on evaluating outcomes, addressing gaps in faculty training, and establishing best practices to harness the full potential of genAI in shaping the next generation of nurses.