Exploring the Impact of AI Usage on Academic Anxiety Among Vocational Education Students: A Mixed-Methods Approach Using SEM and fsQCA

While the application of artificial intelligence (AI) in education has garnered growing scholarly attention, research specific to vocational education remains limited. In China, vocational education plays a critical role in cultivating skilled workers, yet students in this context often face distinctive academic anxiety and mental health challenges. This study, grounded in the conservation of resources theory, investigates how AI usage influences academic anxiety among Chinese vocational students. Drawing on data from 511 questionnaires, we employed structural equation modeling (SEM) and fuzzy set qualitative comparative analysis (fsQCA) to examine both linear and configurational relationships. The results reveal that AI usage significantly and negatively predicts academic anxiety, with class engagement serving as a key mediating variable. Moreover, teacher support for AI usage, as a conditional resource, positively moderates the link between AI usage and class engagement. The fsQCA further identifies three distinct configurational pathways leading to low academic anxiety. These findings contribute to the literature on AI in vocational education by uncovering its psychological impact and offering theoretical and practical insights for educational management in this domain.