Professional identity in AI-supported EFL teaching: a comparative qualitative study of pre-service and in-service teachers

Abstract

In recent years, AI tools such as ChatGPT have become increasingly visible in EFL education; however, how teachers construct professional identity within AI-supported teaching remains underexplored, particularly across career stages. This study aimed to compare pre-service and in-service EFL teachers' identity sense-making in AI-supported contexts, drawing on a developmental perspective of teacher identity. Adopting a qualitative design, data were collected through two online focus group discussions: one with 11 in-service teachers working in public schools in an eastern province of Türkiye and one with 15 pre-service teachers studying in a single department at a public university. The data were analyzed using thematic analysis. The findings revealed three interconnected dimensions: (1) a shift from knowledge authority toward pedagogical mediation, (2) changes in professional legitimacy and confidence linked to AI/digital literacy, and (3) divergent identity orientations across career stages, with pre-service teachers constructing more future-oriented and flexible identities, while in-service teachers negotiated continuity within established professional roles. These findings suggest that AI does not produce a uniform transformation in teacher identity; rather, its meaning is shaped by teachers' position within their professional trajectory. By directly comparing pre-service and in-service teachers, the study offers a career-stage-sensitive explanation of identity negotiation in AI-supported teaching and highlights the need for identity-informed approaches in AI-related teacher education and professional development.