Cognitive Status of Nursing Postgraduates Toward Generative Artificial Intelligence: A Qualitative Study Based on the UTAUT Framework

Abstract Background Generative Artificial Intelligence has the potential to enhance research efficiency and reduce clinical workload for nursing postgraduates, gradually transforming the development of the healthcare and nursing sectors. Understanding nursing postgraduates' experiences and perceptions of Generative Artificial Intelligence tools is essential for promoting their proper application. Aim To comprehensively explore Chinese nursing postgraduates' perceptions, attitudes, and needs regarding GenAI using qualitative interviews. Design A qualitative study design. Methods Semi-structured interviews was conducted among 16 nursing postgraduates. Purposeful sampling selected master's degree nursing students with experience in the use of artificial intelligence. Thematic analysis was performed to identify recurring patterns and codes. Results Five major themes emerged from the analysis: performance expectancy, effort expectancy, social influence, usage attitudes and behaviors, and boundaries to Generative Artificial Intelligence adoption. The findings revealed nursing postgraduates’ generally positive perceptions and usage behaviors toward Generative Artificial Intelligence among participants, alongside their barriers and concerns in its application. Conclusions Generative Artificial Intelligence is increasingly integrated into research and practice in healthcare and nursing. Nursing students should approach Generative Artificial Intelligence tools rationally and apply them appropriately. This study demonstrates that nursing postgraduates hold a relatively positive attitude and cognitive stance toward Generative Artificial Intelligence, which differs significantly from that of undergraduate students. In light of the current lack of Generative Artificial Intelligence-related education, the study also proposes educational strategies tailored to the Chinese context.