Language teacher AI competence self-efficacy, emotion regulation, and mindfulness in relation to teacher digital burnout: a mediation model

This study explores the relationships between language teachers’ AI competence self-efficacy (TAICS), emotion regulation, mindfulness, and digital burnout. By examining these variables in AI-mediated teaching environments, the study proposes a mediation model where emotion regulation and mindfulness act as statistical mediators (indirect pathways) between AI competence self-efficacy and digital burnout. The research employs Structural Equation Modeling (SEM) to test the hypothesized relationships, using data collected from 672 language teachers. The results indicate that while AI competence self-efficacy is significantly associated with emotion regulation and mindfulness, it is not significantly associated with digital burnout via the direct path specified in the model. However, both emotion regulation and mindfulness carry significant indirect associations between AI competence self-efficacy and digital burnout. These findings highlight the importance of emotional resilience and mindfulness as psychological resources that are associated with lower digital burnout among educators in AI-enhanced classrooms. The study concludes with practical recommendations for integrating emotional regulation and mindfulness training into AI competency development programs to support teacher well-being.