Survey on Current Status and Analysis of Influencing Factors of AI Self-Efficacy, AI Anxiety Level, and AI Acceptance in the Nurse Population: A Cross-Sectional Study

Objective : This study investigates the current status of self-efficacy, anxiety levels, and acceptance of artificial intelligence among nursing professionals, analyzes influencing factors, and provides a reference framework for enhancing nurses’ AI tool application capabilities and promoting the effective use of AI in clinical nursing practice. Method : Using convenience sampling, 240 clinical nurses in Guangxi Zhuang Autonomous Region were selected as study subjects from May to August 2025. Questionnaire surveys were conducted using a general information questionnaire, the AI Self-Efficacy Scale, the Artificial Intelligence Anxiety Scale (AIAS), and the Artificial Intelligence Acceptance Scale. Data analysis was performed using SPSS 26.0 statistical software, including descriptive statistics, t-tests, analysis of variance (ANOVA), Pearson correlation analysis, and multiple linear regression analysis. Results :The frequency of AI tool usage and AI-related research experience are statistically significant factors influencing nurses’ AI self-efficacy ( P  < 0.05). AI-related research experience is the primary factor affecting AI anxiety ( P  < 0.05). AI training experience is the primary factor influencing AI acceptance ( P  < 0.01). Furthermore, both nurses’ AI self-efficacy and AI acceptance showed weak positive correlations with AI anxiety (r = 0.343 > 0, P  < 0.01; r = 0.309, P  < 0.01); AI self-efficacy and AI acceptance demonstrated a moderate positive correlation (r = 0.570, P  < 0.01). Conclusion : The current nursing population exhibits a psychological state characterized by low self-efficacy, high pre-anxiety, and moderate acceptance. It is recommended to adopt a dual-pronged strategy of “empowerment” and “emotional support” to promote the application of AI tools in nursing. This approach aims to provide evidence and actionable guidance for healthcare institutions on how to advance the intelligent transformation of nursing in a people-centered and scientifically grounded manner.