Chinese Nurses’ AI Attitude Scale Validated in New Study

This methodological study addresses a significant gap in the nursing literature by providing a validated instrument specifically designed to assess the attitudes of Chinese nurses toward artificial intelligence. As AI applications expand across nursing practice, management, and education, understanding the perspectives of nurses, who are the core frontline group in healthcare, is considered crucial for promoting the rational development of AI within the nursing field.

The primary objective of the research was to translate the original AIASN and evaluate its psychometric properties among a sample of Chinese nurses. The study was designed as a methodological investigation, enrolling a total of 1309 nurses. The Chinese translation of the AIASN was generated using a commonly used instrument validation guideline.

To assess the validity of the Chinese AIASN, the researchers employed a comprehensive set of statistical methods. These included content validity, structural validity, convergent validity, and discriminant validity. Structural validity was examined using three distinct analytical approaches: Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Exploratory Structural Equation Modeling (ESEM). The use of EFA helped identify the factor structure, while CFA and ESEM provided further confirmation of the model’s fit to the data.

The results of the Exploratory Factor Analysis indicated that the Chinese version of the AIASN is best represented by a five-factor model. This model demonstrated satisfactory fit indices when tested using both CFA and ESEM. Specifically, the model fit was characterized by a chi-square to degrees of freedom ratio (χ2/df) of 3.733, a Comparative Fit Index (CFI) of 0.957, and a Tucker-Lewis Index (TLI) of 0.931. Additionally, the Root Mean Square Error of Approximation (RMSEA) was 0.065, with a 90% confidence interval ranging from 0.060 to 0.070, and the Standardized Root Mean Square Residual (SRMR) was 0.023. These metrics collectively suggest that the five-factor structure accurately reflects the data collected from the nurse sample.

Reliability assessments were also conducted to determine the consistency of the instrument. The internal consistency reliability, measured by Cronbach’s alpha coefficient, was found to be 0.912, indicating a high level of internal consistency among the items of the scale. Furthermore, the test-retest reliability was evaluated using the intraclass correlation coefficient (ICC), which yielded a value of 0.811. The 95% confidence interval for this ICC was reported as [0.629, 0.909].

The study concludes that the Chinese version of the AIASN comprises five dimensions and 26 items, demonstrating acceptable psychometric properties for assessing the attitudes of Chinese nurses toward AI within the specific sample studied. The instrument is intended to help nursing educators develop AI-related education and training programs and to identify specific learning needs among nurses.

Despite the positive findings, the authors acknowledge certain limitations that affect the generalizability of the results. The study utilized a convenience sampling design and was conducted in a single region. Consequently, the broader nationwide applicability of the scale remains to be further verified in future research.

Subject of Research: Validation of the Chinese version of the Artificial Intelligence Attitude Scale for Nurses

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

Article References: Lin, X., Qin, Y., Zeng, Y., Zhu, X., Li, J., Wang, Q., & Zhang, K. (2026). Transcultural adaptation and psychometric properties of the Chinese version of the Artificial Intelligence Attitude Scale for Nurses (AIASN). BMC Nursing. https://doi.org/10.1186/s12912-026-05521-y

Image Credits: AI Generated

DOI: 10.1186/s12912-026-05521-y

Keywords: nursing, artificial intelligence, psychometrics, attitude scale, Chinese nurses, healthcare technology, instrument validation, factor analysis, Transcultural, adaptation, psychometric, properties