How AI Adoption Improves Student Performance in K-12 After-School Education in China

Abstract

This study investigates how AI adoption in K-12 after-school education in China influences student performance, with digital literacy, classroom management, learning styles, and inclusivity as mediating variables. Based on a conceptual framework, AI adoption is a key mechanism linking educational factors to student performance. A positivist, cross-sectional design was employed, using structured surveys administered to 360 teachers at a Hai Dian District after-school education branch. Structural Equation Modeling (SEM) was applied to examine direct and indirect relationships among the constructs. Descriptive results indicate moderate to favorable perceptions across constructs, with acceptable reliability and discriminant validity, although some multicollinearity concerns require further examination. Direct effects of predictors on AI adoption are modest (0.109–0.179), whereas digital literacy demonstrates the strongest indirect effect (0.385) through AI adoption via student performance, underscoring the primacy of student outcomes as a conduit to AI integration. The findings suggest that AI implementation can benefit from enhancing digital skills, classroom pedagogies, and inclusive environments. Subsequent research ought to explore different contexts, long-term effects, and moderated relationships, using additional validation methods.