Background and Aim: Artificial intelligence (AI) is rapidly changing clinical practice, and consequently, the knowledge, skills, and competencies of the modern nursing workforce. Despite this, nursing education programs around the world still do not have a consistent approach to preparing students for safe practice in AI-driven healthcare environments, especially in terms of technical competencies, clinical decision-making, and ethical considerations. The purpose of this scoping review was to map the current evidence on the use of AI in pre-licensure and graduate nursing education, to identify the most common pedagogical approaches, and to identify key gaps that could guide curricular changes for digital healthcare.

Materials and Methods: The review was carried out following the Arksey and O'Malley framework, adapted by Levac et al., “and reported according to the PRISMA-ScR guidelines. Peer-reviewed literature was searched systematically in PubMed, CINAHL, Scopus, and ERIC between January 2018 and December 2025. Articles covering AI in teaching, learning, curriculum, or competency development in undergraduate or postgraduate nursing programs were considered.

Results: Twenty-eight studies met the inclusion criteria. The key strategies were AI-based simulation, AI-assisted clinical reasoning, natural language processing (NLP)-based virtual patients, and AI-based virtual reality. Learner outcomes included improved clinical decision-making, more empathetic communication, greater digital literacy, and increased confidence in AI-powered tools. Identified barriers included faculty unpreparedness, inadequate infrastructure, and ethical issues. Few studies reported formal AI competency frameworks specific to nursing practice.

Conclusions: The integration of AI in nursing education is still in its infancy and remains fragmented. Structured competency frameworks, continuous faculty development, and curricula based on ethical principles are needed to prepare a digitally competent nursing workforce.