Artificial Intelligence Empowering English Translation Teaching: Empirical Study on the Optimization of Traditional Chinese Culture Text Translation and Cross Cultural Communication Efficiency

Abstract

Under the dual background of globalization and cultural confidence building, the English translation of traditional Chinese cultural texts has become an important carrier of cross-cultural communication. Current translation teaching faces problems such as insufficient translation accuracy, weak transmission of cultural connotations, and inadequate cross-cultural communication awareness. This study constructs an AI-enabled teaching model for traditional cultural text translation, integrating machine translation, natural language processing, cultural orientation, and teaching adaptation. The research focuses on university English translation teaching involving Chinese traditional cultural texts, including classic literature, folk culture, and traditional art. A 16-week teaching experiment is conducted with four English-major classes from two universities, divided into experimental and control groups. Results show that the experimental group achieves relative improvements of 40.8% in translation accuracy and 28.9% in cultural connotation transmission compared with the control group. Students’ cross-cultural communication cognition and practical ability also improve significantly. The findings demonstrate that AI-supported translation teaching can reduce cultural loss, improve sentence adaptation, and enhance translation effectiveness for traditional cultural texts.