The rapid integration of generative artificial intelligence (genAI) into higher education has introduced both unprecedented opportunities and pressing challenges, particularly in fields like nursing education that require high levels of critical thinking, ethical reasoning, and clinical competency (Summers et al., 2024). As genAI tools become increasingly accessible, educators, and students alike are grappling with how to effectively incorporate them into the learning environment (Archibald & Clark, 2023). GenAI refers to a subset of artificial intelligence (AI) that uses algorithms to learn from large datasets and generate new content—including text, images, and code—based on patterns found in existing data (Glauberman et al., 2023; University, 2026). Examples of these include ChatGPT, Claude, Gemini and Copilot. Traditional artificial intelligence focuses on intelligently completely specific tasks intelligently based on the inputted rules and data whereas genAI can create new content. GenAI is trained on large datasets, and when a user enters a prompt, the system identifies patterns in the data to predict outcomes and generate human-like responses (Marr, 2023). GenAI is trained on large datasets, and when a user enters a prompt, the system identifies patterns in the data to predict outcomes and generate human-like responses. Within the context of higher education, these advanced technologies are transforming how learners access information, complete assignments, and engage with curricular content.

Undergraduate nursing students represent a population uniquely situated at the intersection of emerging technology and traditional learning. As the next generation of healthcare professionals, they must learn to use and critically evaluate genAI such as, ChatGPT and Gemini while developing foundational clinical knowledge and professional behaviors (Nashwan & Abujaber, 2025). Early research suggests that students are already using genAI tools to summarize readings, prepare study materials, and complete some academic assignments (Crompton & Burke, 2023). However, the educational frameworks guiding students' use of genAI are still in development and many nursing programs lack explicit policies or training regarding AI literacy (Topaz et al., 2024).

Concerns about over-reliance on AI, ethical misuse, and the erosion of critical thinking are rising, especially given the limited guidance available to both faculty and students (Topaz et al., 2024). Moreover, there are gaps in understanding how genAI may support or hinder key nursing competencies such as clinical reasoning, professional communication, and ethical care. With the American Association of Colleges of Nursing (AACN) calling for a transformation in nursing education through its updated The Essentials: Core Competencies for Professional Nursing Education (The Essentials) (2021), there is a timely need to examine how emerging technologies align or conflict with these evolving educational goals (Carrington et al., 2024).

Despite the growing presence of genAI in higher education, little is known about how faculty are utilizing these tools in undergraduate nursing programs, what educational outcomes they may influence, and how faculty are adapting their teaching strategies to accommodate for these major advancements. Wing Yan Yeung et al.'s (2025) scoping review about the use of artificial intelligence (AI) in communication training for undergraduate nursing students identified few published reports about faculty experiences of utilizing AI in their teaching. In general, preliminary studies and commentaries about the use of AI in undergraduate teaching have focused more broadly on its use in health professions education (Wing Yan Yeung, 2025). Consequently, a systematic mapping of applications within nursing education remains absent from the literature. Given the evolving nature of both genAI technology and nursing education frameworks, a comprehensive review of current applications is critical to inform faculty, curriculum developers, and institutional administrators and nursing faculty about existing best practices for application of genAI in undergraduate nursing courses.

In response, the purpose of this scoping review is to systematically explore and map the current applications of genAI in undergraduate nursing education. This review will identify the types of tools used, the educational activities that these applications support, the outcomes measured, and the challenges and opportunities noted across studies. Understanding how genAI is currently integrated into nursing education will inform future research, guide faculty development, and support ethical and effective use of technology in undergraduate nursing curricula.