Pathways to teacher wellbeing: AI pedagogy self-efficacy, workload, and anxiety in a structural model

Abstract

As artificial intelligence (AI) tools become more common in K–12 classrooms, questions remain about whether AI competence supports teacher wellbeing. Grounded in social cognitive theory, this study examined relationships among teachers’ AI pedagogy efficacy, instructional and engagement self-efficacy, workload, anxiety, and mental wellbeing. Survey data were collected from a nationally representative sample of 400 U.S. K–12 teachers. Hierarchical regression and structural equation modeling (SEM) were used to examine direct and indirect pathways. Results indicated that AI pedagogy efficacy strengthened teacher self-efficacy, which was associated with lower workload and anxiety and, in turn, higher mental wellbeing. Findings suggest that building teachers’ AI competence may support psychological wellbeing as AI becomes embedded in instructional practice.