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AI-Driven EST Translation Teaching: A Human-AI Synergy Framework for New Engineering Talents
0
Zitationen
2
Autoren
2026
Jahr
Abstract
Amid China’s "New Engineering" initiative and growing global technical communication needs, EST translation teaching demands enhanced efficiency and precision. This study proposes an AI-assisted teaching framework integrating core computing technologies, including NMT engines, context-aware terminology retrieval algorithms, and LLMs. Targeting bottlenecks in specialized translation instruction for engineering students, the framework combines these technologies with tailored pedagogical strategies. Experimental results indicate the model boosts translation accuracy by 26.5% and terminology standardization by 46.2% compared to traditional methods. This work provides a scalable technical solution for EST teaching optimization, offering empirical evidence for human-AI synergy in engineering-oriented language education.
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