Artificial Intelligence Literacy and Subjective Workload Among Clinical Nurses: A Cross-Sectional Study

Background: Although artificial intelligence (AI) is increasingly being integrated into nursing practice, little is known about the association between nurses' AI literacy and their subjective workload. This study examines the associations between three dimensions of AI literacy-technical understanding, critical appraisal, and practical application-and overall and dimension-specific subjective workload among clinical nurses. Methods: A cross-sectional survey was conducted among 305 clinical nurses recruited through convenience sampling from two hospitals in central Taiwan. AI literacy was assessed using the self-report Scale for the Assessment of Non-Experts' AI Literacy (SNAIL), with responses rated on a five-point Likert scale. Subjective workload was assessed using the self-report NASA Task Load Index (NASA-TLX). Pearson correlation and hierarchical multiple linear regression analyses were performed, adjusting for relevant demographic and work-related characteristics. Results: Higher technical understanding, critical appraisal, and practical application were each associated with lower temporal demand and frustration, and these associations remained statistically significant after correction for multiple testing. More limited associations were observed for physical demand and the overall NASA-TLX workload score, while no robust associations were found for mental demand, effort, or performance. Conclusions: The assessed dimensions of AI literacy are found to be associated with specific dimensions of nurses' subjective workload, rather than with workload uniformly, with the clearest relationships observed for temporal demand and frustration. Further longitudinal and intervention studies are needed to determine whether improvements in AI literacy are accompanied by meaningful changes in nurses' workload experiences.