This is one of the achievements of the 2024 teaching reform project “Exploration and Practice of the ‘Lead-Learn-Produce-Evaluate’ Teaching Model for AI-Empowered Chinese Culture International Communication Translation Courses” (2024JY18).
Published: November 30, 2025
Article history: Updated on June 29, 2026
Abstract
This article examines a classroom problem that has become increasingly visible in translation training: students often bring AI-generated versions to class before they have learned how to question them. Focusing on Chinese political discourse translation, the study draws on one semester of undergraduate teaching practice, including AI-produced drafts, student revisions, classroom discussion records, and selected reflective journals. Three recurrent problems are analyzed in detail. First, politically loaded items ending in “主义” were often converted into ready-made English “-ism” labels, which changed the evaluative force of the source text. Second, normatively dense terms such as “道德” were flattened into a single common equivalent, reducing the legal and ethical relation implied in the original. Third, culturally familiar metaphors such as “打虎,” “拍蝇,” and “猎狐” were translated too literally for international readers. The classroom evidence suggests that the central pedagogical risk is not merely that AI makes errors, but that fluent errors are easily mistaken for acceptable translations. On this basis, the article proposes a human-AI collaborative teaching framework in which AI supplies provisional options, students conduct accountable post-editing, and teachers organize reflection around terminology, stance, genre, and audience. The frame-work treats AI as a trigger for interpretive judgment rather than a substitute for translator agency.
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How to cite this paper
Integrating AI into the Teaching of Chinese Political Discourse Translation: Challenges and a Human—AI Collaborative Framework
How to cite this paper: Jing Zhang. (2025). Integrating AI into the Teaching of Chinese Political Discourse Translation: Challenges and a Human—AI Collaborative Framework. Translation and Foreign Language Learning, 1(4), 693-698.
DOI: http://dx.doi.org/10.26855/tfll.2025.11.016