Perceptions and attitudes of nursing students and academics toward artificial intelligence: A mixed-methods study

Abstract

Objective: Artificial intelligence (AI) is rapidly entering nursing education and practice, yet few studies examine students and academics together; this study mapped attitudes toward AI across these groups, identified predictors, and explained the underlying mechanisms to inform curriculum and policy. Methods: This study used an explanatory sequential mixed-methods design. In the quantitative phase, a cross-sectional survey (n = 282) administered the General Attitudes to AI Scale to test the associations between prior AI experience and self-rated technological affinity. In the qualitative phase, semi structured interviews explored use contexts, perceived benefits and risks, and educational implications. Results were integrated to derive the overarching insights. Results: The total mean score was 68.05 ± 7.44, and overall attitudes were found to be positive. More positive views were observed among participants with prior experience (U = 8349, P = .023) and with greater daily internet use (χ² = 18.549, P < .001). Differences in both positive (χ² = 20.714, P < .001) and nega tive (F = 3.227, P = .041) subscale scores were detected according to self-rated technological affinity. In the qualitative phase, facilitation and efficiency were emphasized and cautious, verification-oriented use was described as increasing with competence. Attitudes were characterized as favorable yet calibrated; advantages were most strongly attributed to standardized, repeatable tasks; human judgment was regarded as essential; privacy views were described as mixed; and role replacement was anticipated by a minority. Conclusion: Task-bound integration should be implemented in conjunction with verification-centered AI literacy and explicit privacy and governance safeguards.