Journal Paper Published

Abstract

This study adopts a cross-regional, task-based experimental design to explore interpreting students’ technology readiness and their perceptions of generative artificial intelligence (GenAI) feedback versus instructor feedback. Using the Technology Readiness Index 2.0 (TRI 2.0) alongside a six-item feedback-perception instrument covering clarity, accuracy, and actionability, it fills a critical empirical gap in digitally mediated feedback for interpreter education. Eighty-nine participants from four universities completed a simultaneous interpreting task via a self-developed LLM-powered web platform. They received immediate AI-generated feedback and subsequently evaluated it against the instructor feedback they typically received in interpreting classes. The findings from TRI 2.0 indicate participants generally have positive attitudes towards technology. However, the results from the feedback literacy scale show participants rated instructor feedback more favourably. In particular, instructor feedback was perceived as significantly clearer in articulating learning goals, more accurate in identifying specific errors, and more actionable in providing suggestions for improvement. Although technology readiness scores were generally high, they exhibited no correlation with students’ evaluations of AI-generated feedback, indicating that general techno-optimism does not necessarily translate into greater acceptance of algorithmic feedback. These findings underscore the pedagogical value of human feedback in interpreter training and support the use of GenAI for improved efficiency.

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