Artificial intelligence anxiety and carbon footprint awareness in relation to sustainability attitudes among nurses and nursing students: a cross-sectional study

Background Digital transformation and environmental sustainability are increasingly interconnected priorities in healthcare. Nurses and nursing students are directly exposed to artificial intelligence technologies while also being expected to engage in sustainable healthcare practices. However, the relationships between artificial intelligence anxiety, environmental awareness, and sustainability attitudes remain underexplored, and it is not known whether such relationships operate similarly among practising nurses and nursing students. This study examined the associations of artificial intelligence anxiety and individual carbon footprint awareness with sustainability attitudes among nurses and nursing students, and whether these associations differ between the two groups. Methods This quantitative, descriptive, correlational, cross-sectional study was conducted with 431 participants, including 142 registered nurses and 289 nursing students, in the Aegean Region of Türkiye. A convenience sample was recruited from one nursing faculty, one university hospital, and one state hospital between August 2025 and February 2026, using anonymous self-report questionnaires administered both online and in person according to participant preference. The instruments included a demographic information form, the Artificial Intelligence Anxiety Scale, the Carbon Footprint Awareness Scale, and the Sustainability Attitudes in Nursing Survey. Data were analyzed using IBM SPSS Statistics version 26. Hierarchical multiple linear regression was performed. Results Artificial intelligence anxiety was negatively correlated with individual carbon footprint awareness ( r =-.254, p < .001) and sustainability attitudes ( r =-.392, p < .001). Individual carbon footprint awareness was positively correlated with sustainability attitudes ( r = .664, p < .001). After adjustment for covariates, both variables were independently associated with sustainability attitudes: individual carbon footprint awareness B = 0.939, 95% CI [0.773, 1.105], p < .001, and artificial intelligence anxiety B =-0.289, 95% CI [-0.398, -0.179], p < .001. Together they accounted for 48.9% of explained variance beyond covariates (ΔR² =0.489, F(2, 422) = 220.122, p < .001). A significant participant group × carbon footprint awareness interaction was identified (B = 0.554, 95% CI [0.245, 0.863], p < .001): the association with carbon footprint awareness was stronger among registered nurses (β = 0.621) than among nursing students (β = 0.520), and overall model fit differed markedly between groups (R² =0.755 vs. 0.389). The corresponding interaction for artificial intelligence anxiety was not significant ( p = .216). The final model explained 55.7% of the variance (R² =0.557, F(10, 420) = 52.737, p < .001). The interaction analyses were exploratory rather than pre-specified. Conclusions Artificial intelligence anxiety and individual carbon footprint awareness were each independently associated with sustainability attitudes among nurses and nursing students. The association involving carbon footprint awareness was not equivalent across the two groups, being stronger among practising nurses, whereas the association involving artificial intelligence anxiety operated similarly in both. Because of the cross-sectional design, these associations cannot establish temporal ordering or causal influence. The findings identify artificial intelligence-related psychological responses and environmental awareness as attitudinal correlates that may warrant separate consideration for practising nurses and for students, and provide a basis for hypothesis generation in future longitudinal and interventional research.