Bridging the Theory–Practice Gap in Foreign Language Teacher Education: A Descriptive-Analytical Study of an AI-Empowered Reflective Training Model

Foreign language teacher preparation frequently struggles with a persistent disconnect between university-based linguistic training and classroom-ready pedagogical practice. This gap contributes to increased cognitive load, instructional uncertainty, and early-career attrition among novice teachers. The rapid integration of artificial intelligence into educational environments adds another layer of complexity to this transition, demanding innovative frameworks that integrate technological literacy with pedagogical decision-making and psychological readiness. This paper introduces a competency-based training model, The AI-Empowered Reflective Teacher, designed to shift focus from theoretical language knowledge to applied classroom competence. Drawing on implementation records, participant feedback, and reflective documentation from ۲۲۰ training cycles involving approximately ۳,۰۰۰ pre-service teachers across English, French, and German programs, the study employs a descriptive-analytical approach. Preliminary patterns indicate that the ۱۳۰-hour blended structure may foster faster pedagogical readiness, shorten preparation time, and support teacher self-efficacy through guided AI integration and structured mentorship. Analytical discussion highlights how the model addresses commonly neglected competencies, mitigates affective strain, and fosters sustainable reflective practice. The paper explicitly positions this work as a foundational descriptive contribution that precedes rigorous quasi-experimental validation, offering policy-relevant insights for teacher training institutions and educational counseling units.

کلیدواژه ها:

AI-TPACK ، foreign language teacher education ، competency-based training ، reflective practice ، teacher self-efficacy ، educational technology