AI-mediated instruction and novice language teachers' identity: Reinforcing and disrupting factors

Abstract

This study examines how artificial intelligence (AI)-mediated instruction shapes novice language teachers' identity by highlighting both reinforcing and disrupting forces. Seven novice English as a Foreign Language (EFL) teachers at a private language school participated in a three-phase qualitative design: (a) demographic interviews to contextualize backgrounds and expectations; (b) observations of three lessons per teacher from an 18-session course in which AI tools were implemented; and (c) stimulated-recall interviews anchored to recorded AI episodes. Transcripts from all phases were analyzed through iterative open, axial, and selective coding, with reflexive memoing and member checking to enhance credibility. Findings reveal AI as a double-edged driver of identity. Reinforcing factors included enhanced classroom dynamism and efficiency, increased creativity, boosted self-confidence, and the emergence of a tech-savvy teacher identity. Disrupting factors involved challenged pedagogical knowledge, a perceived lack of AI expertise, limited recognition and appreciation within the institution, and moments of professional authenticity crisis. Overall, AI-mediated episodes prompted oscillations rather than linear change in identity, positioning novices alternately as empowered innovators and uncertain practitioners; targeted AI literacy, mentored experimentation, and recognition structures are recommended to stabilize preferable positions.