The readiness-anxiety paradox in artificial intelligence adoption among nursing students: a multidimensional...

This study examined the relationship between medical artificial intelligence readiness and AI-related anxiety among nursing students following surgical nursing education.

A descriptive cross-sectional study was conducted with 252 nursing students from two universities who had completed surgical diseases nursing courses. Data were collected between February and July 2024 using the Artificial Intelligence Anxiety Scale and the Medical Artificial Intelligence Readiness Scale. Group comparisons, correlation analyses, and hierarchical regression analysis models were performed.

Our findings indicate a notable "awareness-apprehension paradox": higher ability (technical readiness) was associated with lower learning anxiety (B = - 0.25, p < .01), whereas higher ethics (ethical readiness) was associated with greater concerns regarding job replacement (B = 0.41, p < .001) and anxiety. Female students had significantly higher anxiety scores, and regular AI use was associated with greater readiness. AI utilization and sociotechnical perceptions together accounted for variance in readiness outcomes. Higher levels of medical AI readiness were not uniformly associated with lower anxiety; instead, increased readiness coexisted with elevated concerns in specific anxiety dimensions, indicating a complex association between technological preparedness and psychological adaptation.

The results suggest that developing technical competence alone may not be sufficient to align with confident technology adoption. They further indicate that, in the absence of corresponding organizational safeguards, ethical awareness may function more as a "cognitive demand" than as a resource. The study adds to the Job Demands-Resources (JD-R) framework by illustrating the dual nature of AI readiness. Nursing education programs may therefore benefit from integrating ethical reflection, critical technological awareness, and psychological preparedness alongside AI skill development to support sustainable implementation of AI-supported clinical practice.