Application of AI-Assisted Translation Tools in the Management of Translation Teaching Resources in Colleges and Universities

Artificial intelligence translation tools have gradually become integrated into translation teaching practice in colleges and universities. However, tool application often faces the dilemma of high use and low trust, and existing research largely ignores the systematic investigation of the dynamic management of teaching resources and the learning process. This study focuses on core pain points in translation teaching, such as mistranslation and text disconnection, and constructs and verifies a closed-loop model of artificial intelligence translation teaching, with a dynamic terminology database and text consistency detection at its core. The empirical results show that the closed-loop mechanism can not only efficiently manage key teaching resources, such as terminology, and realize the dynamic precipitation of knowledge, but they also accurately intervene for teachers and help students reflect on their learning. This study provides a paradigm for designing translation courses and for intelligently managing teaching resources in colleges and universities in the era of artificial intelligence.