From Passive Correction to Active Decision-Making: AI-Empowered Terminology Consistency and Standardization in Collaborative Translation Teaching

Abstract

Terminology inconsistency is a persistent and critical challenge in collaborative translation, often remaining hidden until the final revision, at which point rectification becomes costly and time-consuming. This article presents a teaching innovation that integrates Artificial Intelligence (AI) into a translation technology course to address this issue. Based on a real classroom project, namely the collaborative English translation of a 22-page Chinese text on Tao Xingzhi’s educational philosophy, the research designed a three-step closed-loop pedagogical model: (1) AI-assisted term extraction, (2) student-led group discussion and decision-making, and (3) AI-assisted post-translation consistency checking. The findings demonstrate that this approach reduces terminology management time by approximately 80% (from 90–135 minutes to 17–26 minutes), improves term consistency to 95%, and fundamentally shifts students’ roles from passive error correctors to active decision-makers. The article argues that AI should not replace students’ critical thinking but instead take over mechanical tasks, allowing learners to focus on higher-order skills such as justification, evaluation, and cultural mediation.