Psychometric validation of the Chinese generative artificial intelligence acceptance scale among university students

Generative AI use has become widespread among Chinese university students, but use does not necessarily indicate acceptance. This cross-sectional psychometric validation study adapted and evaluated the 20-item generative artificial intelligence acceptance scale for use in China.

Following translation, back-translation, expert review, and pilot testing, data were collected from a convenience sample of 604 university students at six institutions in Yunnan, Anhui, and Zhejiang provinces. Participants were randomly divided into exploratory and confirmatory subsamples of 302 each.

Corrected item-subscale correlations ranged from 0.555 to 0.774. Exploratory factor analysis and parallel analysis supported four dimensions-performance expectancy, effort expectancy, facilitating conditions, and social influence-and the unrotated four-factor solution explained 52.22% of the total variance. The correlated four-factor CFA model fitted the second subsample well, scaled χ²(164) = 180.41, p = 0.180, robust CFI = 0.993, robust TLI = 0.992, robust RMSEA = 0.020, and SRMR = 0.035, and fitted significantly better than the three-factor alternative, Δχ²(3) = 102.11, p < 0.001. A second-order model showed comparable fit. Cronbach's alpha ranged from 0.757 to 0.873, McDonald's omega from 0.810 to 0.905, and composite reliability from 0.773 to 0.888. AVE ranged from 0.494 to 0.615, with performance expectancy marginally below 0.50. HTMT estimates ranged from 0.542 to 0.775, and none of their bootstrap confidence intervals included 1.00; however, the Fornell-Larcker criterion was not fully satisfied, and the upper confidence limit for PE-FC was 0.868. Measurement invariance across gender was supported.

The findings provide preliminary support for primarily using the four subscales in similar Chinese university samples; a total score should be limited to broad descriptive use.