"Perceptions of artificial intelligence in nursing students: A qualitative meta-synthesis based on the UTAUT2 model"

Artificial intelligence (AI) is rapidly transforming nursing education and clinical practice. Understanding students' perceptions is essential for designing effective and equitable AI-enhanced learning strategies. The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) offers a robust model for examining factors that shape acceptance and use.

To integrate qualitative evidence on nursing students' perceptions of incorporating AI into academic education, using the UTAUT2 model to interpret constructs influencing acceptance and use.

A qualitative deductive meta-synthesis was conducted following Dixon-Woods' framework. A systematic search (October 2024-July 2025) in PubMed, CINAHL, and Web of Science identified qualitative studies and qualitative findings from mixed-method research published between 2020 and 2025. Methodological quality was appraised using ENTREQ guidelines and the Joanna Briggs Institute's Qualitative Assessment and Review Instrument (QARI). Data were thematically synthesized according to UTAUT2 constructs.

Nursing students perceive AI as a tool to enhance learning and efficiency (performance expectancy) yet concerns about the complexity and digital competencies persist (effort expectancy). Faculty support and institutional context emerge as key factors in promoting acceptance (social influence and facilitating conditions). Intention to use relates to interest and prior experience (hedonic motivation). Cost remains a barrier (price value), while accumulated experience strengthens adoption (habit).

AI represents a valuable resource in nursing education, offering opportunities for personalized learning, improved access to information, and enhanced competency development. Effective integration requires addressing technical, ethical, equity-related, and training barriers. Importantly, AI should complement, rather than replace, the human dimension of nursing education.