The Mediating Effect of Fixed Teaching Mindset on the Relationship Between AI Anxiety and Professional Burnout Among English as a Foreign Language Teachers

This study investigates the relationships among artificial intelligence anxiety (AIA), fixed teaching mindset (FTM), and professional burnout (BO) among English language teachers in Türkiye, specifically examining the mediating role of FTM in the AIA-BO relationship. Based on the Job Demands-Resources (JD-R) Model and Mindset Theory, the study employed a quantitative, cross-sectional survey design with 238 EFL teachers. Data were collected using the Teacher Artificial Intelligence Anxiety Scale, an adaptation of the Maslach Burnout Inventory-Educators Survey, and the Teacher Mindset Scale. Results confirmed that AIA acts as a significant job demand, showing positive direct effects on emotional exhaustion (EE) and depersonalization (DP) as core burnout dimensions. Moreover, AIA was found to positively predict FTM. The central hypothesis of mediation was partially supported. FTM significantly and partially mediated the relationship between AIA and both EE and DP. This indicates that FTM functions as a key psychological mechanism through which AI-related anxiety is associated with these core burnout symptoms. However, the indirect effect on the personal accomplishment was not significant. These findings extend the JD-R model by integrating FTM as a depleted psychological resource that amplifies teachers’ vulnerability to technological demands, offering a novel theoretical lens for understanding burnout in AI-integrated educational contexts. The persistence of significant direct effects suggests that while FTM is an important pathway, other mechanisms also contribute to burnout. Practically, the study highlights the need for extensive professional development that includes both AI literacy training and mindset cultivation strategies to enhance teacher well-being and resilience against technological stress.