AI-Driven and Peer Feedback as Mediators of Student Language Teachers’ Reflective Practice, Autonomy, and Agency: A Sociocultural Perspective

Abstract

Grounded in a sociocultural theoretical framework, this study examined the interplay between AI-driven feedback and peer feedback in shaping the reflective practice, autonomy, and agency of student language teachers. The study employed a case study design to explore the experiences of 14 student language teachers enrolled in a teacher education course at one teacher education university in Iran. Drawing on semi-structured interviews, reflective journals, peer feedback sessions, and AI-generated feedback logs, the study findings indicated that AI-driven feedback functioned as an objective, data-driven tool, recognising pedagogical patterns and areas for development, while peer feedback offered contextualised, empathetic critiques, enhancing co-constructed meaningmaking. The findings also highlighted that in combination, these factors create a meaningful feedback ecosystem that provides reflective depth, promotes teachers’ self-regulated learning and their exercise of professional agency. The study emphasises that technological and social mediation in teacher education be balanced to support development of AI literacy and the adaptation of culturally responsive tools through scaffolded peer collaboration.