A GenAI-Empowered Teaching Model for MTI Students’ Traditional Chinese Culture Translation: A Cultural Translation Theory Perspective

DOI: https://doi.org/10.62517/jhet.202615131

Author(s)

Na Zhao*, Ziting Niu

Affiliation(s)

Kunming University, Kunming, China *Corresponding author

Abstract

This study explores the application of Generative Artificial Intelligence (GenAI) in teaching the translation of China’s fine traditional culture to Master of Translation and Interpreting (MTI) students. Grounded in Bassnett’s cultural translation theory, it proposes a teaching model that deeply integrates GenAI into a reconstructed “teaching-learning-practice-evaluation” cycle. The model features a three-phase practical pathway: AI-aided resource preparation, human-machine collaborative classroom interaction, and a post-class personalized improvement loop. Employing a mixed-methods approach, empirical results demonstrate its effectiveness. Compared to a control group using traditional methods, the experimental class showed significantly greater improvement in core competencies like cultural term translation accuracy and cultural transmission integrity, while reducing instructors’ assessment workload by 40%. This approach effectively addresses key challenges in traditional teaching, such as cultural distortion and delayed feedback, offering a replicable model for the digital reform of cultural translation pedagogy.

Keywords

Cultural Translation Theory; GenAI; MTI; Traditional Culture Translation; Teaching-Learning-Practice-Evaluation Cycle

References

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