Unpacking the Links between AI-Based Teaching Tools and Teacher Burnout: Evidence from Teachers in Basic Education

Artificial intelligence (AI) tools are undergoing rapid development within K-12 education, with institutions increasingly embracing this innovative integration. While teacher awareness remains a fundamental determinant of technology adoption, it is equally critical to balance the advantages of these tools against the potential for work-related stress and burnout. Against this backdrop, this study examines how teachers' adoption of AI tools for teaching relates to their burnout in basic education. In line with the TALIS 2024 questionnaire, AI tools are measured in a broad way; in this paper, we refer to them as AI-based digital tools used in instruction. This study draws upon the Technology Acceptance Model (TAM) to evaluate the impact of perceived usefulness on primary and secondary teachers' AI adoption. Concurrently, utilizing the Job Demands-Resources (JD-R) framework, the analysis investigates a competitive mediation mechanism linking perceived usefulness, AI usage, and teacher burnout. Hypotheses were tested via Structural Equation Modeling (SEM) using data from the 2024 Teaching and Learning International Survey (TALIS 2024). The findings confirm the positive influence of perceived usefulness on AI adoption. Furthermore, a distinct suppression effect was identified: while perceived usefulness acts as a cognitive resource that buffers teacher burnout, actual AI usage functions as a job demand that exacerbates it. The study concludes by discussing the theoretical and practical implications of these findings.