Cross-sectional associations of generative AI acceptance with self-efficacy and well-being among students recruited from seven Chinese universities

Generative artificial intelligence (AI) is increasingly used in higher education, but its associations with students’ broader psychological outcomes remain uncertain. This cross-sectional study examined generative AI acceptance, general self-efficacy, and well-being among 495 students recruited from seven universities in China. Participants completed the Generative Artificial Intelligence Acceptance Scale, the New General Self-Efficacy Scale, and the WHO-5 Well-Being Index. Descriptive statistics, correlations, confirmatory factor analysis, and observed-variable path analysis were conducted in R. In unadjusted analyses, generative AI acceptance was positively associated with self-efficacy (β = 0.494, p < 0.001), and self-efficacy was positively associated with well-being (β = 0.217, p < 0.001). The direct association between AI acceptance and well-being was not statistically significant (β = −0.060, p = 0.240). The model accounted for 24.4% of the variance in self-efficacy and 3.8% in well-being. These coefficients were unadjusted for measured demographic characteristics. These findings should be interpreted within the context of the recruited non-probability sample. Universities should pair AI-related learning support with digital literacy, responsible-use guidance, and established student-support services.