Transcultural adaptation and psychometric properties of the Chinese version of the Artificial Intelligence Attitude Scale for Nurses (AIASN)

Background The applications of artificial intelligence (AI) in nursing encompass a wide array of domains, including nursing practice, management, and education. Considering that nurses serve as the core group on the frontline of healthcare, their attitudes toward AI may be crucial for promoting the rational development of AI in the nursing field. However, there is currently no validated instrument available in China to assess nurses’ attitudes toward AI. This study aimed to translate the Artificial Intelligence Attitude Scale for Nurses (AIASN) and evaluate its psychometric properties among Chinese nurses. Methods This methodological study enrolled a sample consisting of 1309 nurses. The Chinese translation of the AIASN was generated using a commonly used instrument validation guideline. The validity of the instrument was assessed using content validity, structural validity (including Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Exploratory Structural Equation Modeling (ESEM)), convergent validity, and discriminant validity. The reliability of the instrument was assessed using internal consistency reliability and test-retest reliability. Results A five-factor model was obtained through EFA. This model showed satisfactory model fit in CFA and ESEM (CFI = 0.955, TLI = 0.926, RMSEA = 0.068, SRMR = 0.023). The Cronbach’s α coefficient for the instrument was 0.906. The intraclass correlation coefficient for the instrument was 0.819. Conclusions The Chinese version of the AIASN comprises five dimensions and 25 items, demonstrating robust psychometric properties for assessing Chinese nurses’ attitudes toward AI. It can help nursing educators develop AI-related education and training programs and identify nurses’ learning needs.