Presentation Helping Learners Notice Their Speech Through AI Video-Synchronized Feedback more

When practicing speaking with a practice partner, language learners rarely remember what they actually said, or how they said it, after a session ends. Without this recall, feedback loses context. To address this, the researchers developed Pecha, a web application that records learners speaking via webcam, transcribes their speech with speaker diarization, and generates AI feedback across customizable categories like grammar and cohesion. What sets Pecha apart is that feedback links directly to video timestamps, so learners see replays of the exact moments errors occur. Combined with both written and spoken advice, learners are able to reflect on and improve their speaking skills. This design draws on Schmidt's Noticing Hypothesis, which holds that learners must consciously attend to linguistic features for acquisition to occur. Timestamp-synchronized feedback makes errors salient in a way that delayed correction cannot. The approach also aligns with Video-Stimulated Recall methodology, where reviewing recorded performance promotes deeper reflection and self-correction. This session will demonstrate the application, discuss its theoretical basis, and share early observations from use at universities in Japan and the United States, with students practicing English and Japanese respectively. Attendees will leave with practical ideas for integrating AI-assisted feedback into autonomous speaking practice.