ENHANCING INTELLIGIBILITY AND LEARNER AUTONOMY IN EFL PRONUNCIATION INSTRUCTION THROUGH AI-DRIVEN PEDAGOGICAL FEEDBACK
Abstract
Modern English as a Foreign Language (EFL) pronunciation instruction has shifted its primary goal from native-like accent imitation to functional intelligibility and comprehensibility. Despite this paradigm shift, traditional classroom settings often struggle to provide personalized, real-time diagnostic feedback due to large class sizes and time constraints. This study investigates the pedagogical integration of Artificial Intelligence (AI)-driven speech analysis and automated acoustic feedback in EFL pronunciation teaching. Synthesizing contemporary literature on Computer-Assisted Pronunciation Training (CAPT) and feedback literacy, this paper examines how AI feedback impacts learner autonomy, segmental clarity, and suprasegmental awareness. The review reveals that AI tools provide consistent, low-anxiety practice environments that facilitate cognitive noticing and self-correction. However, over-reliance on automated metrics without explicit pedagogical framing can result in mechanical practice detached from communicative contexts. To reconcile these dynamics, this article introduces a conceptual Scaffolded Pedagogical Feedback Model (SPFM) that integrates AI-generated acoustic diagnostic data, structured peer dialogue, and teacher-mediated instructional intervention. The framework highlights the necessity of developing student feedback literacy to transform automated output into actionable learning strategies.
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