Abstract:
This study examines the pedagogical focus of formative feedback provided to trainee TESOL teachers
following micro-teaching sessions in a university-based teacher education program in East Asia. The
dataset consists of approximately 220 post-lesson feedback emails (around 40,000 words) written by
a teacher educator across multiple cohorts and methodology courses, including teaching listening,
speaking, grammar, and teaching young learners through storytelling. The trainees were a mixed group
of pre-service and in-service teachers with varied levels of teaching experience, teaching contexts, and
professional trajectories. Using an AI-assisted qualitative analytic approach, the feedback was
systematically coded and analyzed to identify recurring pedagogical priorities and patterns. The
findings indicate that feedback overwhelmingly targets transferable teaching competencies—such as
instructional clarity, task design, scaffolding, alignment between lesson aims and enacted practice, and
learner engagement—rather than framework-specific techniques. Persistent developmental tendencies
included excessive teacher explanation, weak task setup, and misalignment between intended
instructional focus and classroom activity. The study argues that AI can function as a reflective analytic
tool for teacher educators, supporting large-scale pattern recognition while preserving human
interpretive authority. Implications are discussed for TESOL teacher education, formative feedback
practice, feedback literacy, and the ethical use of AI in pedagogical research. |
