L2 Teachers' Work Engagement in AI‐Enhanced Teaching Environments: Harnessing a Phenomenological Approach to Uncover Its Personal and Contextual Determinants

ABSTRACT

Adopting a descriptive phenomenological approach, this study sought to unravel the determinants of L2 teachers' work engagement within artificial intelligence (AI)‐assisted instructional contexts. To this end, 49 English language teachers were purposefully selected from Chinese educational contexts to complete an open‐ended questionnaire online. Data were thematically analysed using MAXQDA (v. 2024), revealing that teachers' work engagement has been shaped by specific personal traits— adaptability , AI literacy , self‐efficacy , resilience and emotional regulation —and contextual conditions, including leadership behaviours , professional development opportunities , colleague support and learner engagement . These findings highlight how these individual and environmental factors together have influenced teachers' ability to remain engaged while navigating AI‐mediated teaching demands. By uncovering these personal and contextual determinants, the study offers practical insights for educational leaders and policymakers seeking to support L2 teachers in effectively integrating AI tools into instruction.