Effectiveness, Risks, and Pedagogical Reconstruction in College English Translation Teaching via a ChatGPT-Based Tri-Dimensional Integration Model

This study addresses three persistent challenges in college English translation instruction: the imbalance between student and teacher ratios, inefficiencies in analyzing complex sentence structures, and frequent cultural mistranslations. To tackle these issues, a tri-dimensional integration model—comprising Technology, Cognition, and Ethics—and a PREP teaching framework were developed. A mixed-method empirical study was conducted involving 60 non-English majors. Findings reveal that ChatGPT-assisted instruction significantly improves translation accuracy by 27% and complex sentence processing efficiency by 41%. However, it also introduces a 38% rate of cultural mistranslation, a 27% risk of overreliance on technology, and potential deviations in rendering political terms. To address these risks, the study advocates for multi-engine comparisons and a four-stage task chain to enhance learners’ cultural decision-making competence. It also recommends building a dedicated cultural terminology database and implementing a dual-review mechanism for quality assurance. Furthermore, a “Three-Three Curriculum Model” (30% AI literacy, 30% cross-cultural analysis, 40% human-led revision) is proposed to support the transformation of teachers into operators, instructional designers, and ethical stewards. Ultimately, the study underscores a “Human-led, AI-empowered” principle: while machines can convert language on the surface, the true mission of education lies in interpreting the untranslatable depths of culture—steering with technology, illuminating with humanity, and fostering a dialogue of the soul.