فهرست نشریات علمی دسترسی آزاد ایران

Reflective practice, vital in teacher professional learning, faces systemic barriers in Iran including insufficient mentoring, large class sizes, and focusing on exam-dominated pedagogies. This mixed-method research study aims to examine how the use of artificial intelligence (AI) in providing AI-driven feedback can combat these barriers and enhance Iranian English teachers' reflective practices. Using a quasi-experimental pretest-posttest design, we experimented with the impact of AI-driven feedback using Mote and ChatGPT, voice feedback tools, on 10 secondary and senior high school teachers for six weeks. Participants were purposively selected to differ by experience and context; they recorded lessons, received AI-supported feedback, and reflected on their teaching practices in journals, supplemented by peer discussions. Reflective quality, which refers to the effectiveness of reflection, was assessed quantitatively via an adapted rubric (Farrell, 2022) on five dimensions: descriptive, critical, perspective-taking, action-oriented, and depth of analysis. Quantitative results showed that the reflective practice quality, measured by Farrell’s (2022) rubric, improved significantly (mean increase = 3.6, p < 0.001, Cohen's d = 3.3). Qualitative thematic analysis provided main themes: actionable feedback enabling targeted improvements, cultural appropriateness with Iranian classroom realities, improvements in professional development, and collaborative reflection, which fosters professional communities. These findings suggest that available AI technologies like Mote and ChatGPT can improve reflection, bridge resource gaps, and promote professional development in Iran's resource-constrained education settings. Limitations include small sample size and short duration, which necessitate larger-scale studies to improve generalizability.