earticle

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This study aims to investigate the feasibility of TalkTrack, a hybrid feedback platform designed to enhance interpreter training by leveraging the crucial role of feedback. TalkTrack automatically detects disfluencies in interpreting and visualizes them, thereby providing immediate feedback to learners. In addition to AI-generated feedback, the platform allows instructors to offer qualitative evaluations, thereby enabling more detailed and comprehensive feedback. To assess the platform's feasibility, usability experiments were conducted with instructors and students from a graduate school of interpretation and translation. The findings indicate that users responded positively in terms of functional adequacy, overall satisfaction, and educational effectiveness, suggesting that the platform successfully met its intended objectives. The results of this study confirm that a hybrid feedback system can serve as an effective tool for enhancing both the quality and quantity of feedback in interpreter training. With continuous improvements, TalkTrack is expected to evolve into a technology-enhanced learning (TEL) tool that facilitates interaction between instructors and learners in interpreter education.