Artificial Intelligence Integration in Nursing Education: A Mixed Methods Review with a Focus on Faculty Experiences

This project aims to conduct a novel sequential explanatory mixed methods review to systematically investigate the authentic experiences, perceptions, and challenges of nursing faculty in integrating Artificial Intelligence (AI) into teaching and learning. While existing research predominantly focuses on technological applications or student outcomes, a deep understanding of the faculty remains underdeveloped. The research design comprises two consecutive phases. Phase One involves text mining and network analysis of the extant literature to objectively map the field's macro-level intellectual structure and identify core research clusters and gaps through quantitative methods. Building on these findings, Phase Two entails an in-depth thematic analysis of the full texts of purposefully selected literature to interpret the rich, qualitative dimensions of faculty experiences. Expected outcomes include: 1) A knowledge map visualising the research foci and evolution of the field; 2) A thematic framework elucidating faculty cognition, emotions, barriers, and support needs; 3) Evidence-based insights to inform the development of faculty training programmes, technology integration strategies, and supportive institutional policies. By integrating macro-level mapping with micro-level depth, this study will address a critical gap in current understanding and provide a pivotal foundation for promoting the sustainable and human-centred adoption of AI in nursing education.