ArticleFrontiers in public health2026
Artificial intelligence literacy, anxiety, and attitudes among registered nurses across different hospital tiers: a multi-center cross-sectional study.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.
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Corrections and comments
- Erratum issued
Authors and funding
4 authors.
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Abstract
Background: The rapid advancement of artificial intelligence is driving an unprecedented technological transformation in nursing. However, the successful integration of these technologies depends largely on the proficiency and perspectives of registered nurses. Consequently, there is an urgent need to examine the psychological and behavioral responses of this workforce. Methods: A multi-center, cross-sectional survey was conducted from March to May 2026. a stratified convenience sampling method was employed to recruit 1,392 registered nurses from tertiary hospitals, secondary hospitals, and community health centers in Chongqing. Data collection instruments included a general demographic questionnaire, the Artificial Intelligence Literacy Scale, the Artificial Intelligence Anxiety Scale, and the General Attitudes Towards Artificial Intelligence Scale. Statistical analyses, including descriptive statistics, Spearman correlation, and multiple linear regression. Results: A total of 1,392 registered nurses participated in the study, with a mean age of 34.69 ± 7.13 years. AI literacy scored 5.27 ± 0.90 (75.29% scoring rate). AI anxiety was moderate (51.29%), with the highest concerns appearing in socio-technical blindness (56.43%) and job replacement (55.14%). Overall, nurses maintained a positive attitude toward AI (74.00%). AI literacy was negatively correlated with anxiety ( Conclusion: Nurses demonstrated high AI literacy and positive attitudes; however, anxiety remained prominent. Enhancing AI literacy may alleviate psychological anxiety, with device accessibility and usage patterns also playing critical roles. To facilitate the effective integration of artificial intelligence into clinical practice, administrators should strengthen institutional support mechanisms alongside providing facility resources and conventional education, thereby promoting the full utilization and translation of available resources.
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