Research on the Reform and Innovative Pathways of Translation Teaching in the Era of Artificial Intelligence

Abstract

The rapid advancement of artificial intelligence, particularly machine translation and large language models, is reshaping translation practice and redefining competency requirements in the translation industry. Traditional teaching models that focus primarily on linguistic knowledge are increasingly insufficient for addressing these changes. While AI technologies have improved translation efficiency and accessibility, they also present new challenges for translator competence and pedagogical design. Situated within the context of the AI era, this study examines the impact of artificial intelligence on translation teaching and identifies key problems in current university-level education, including misaligned teaching objectives, limited instructional content, and inadequate evaluation systems. In response, the paper proposes reform principles centered on competency development, human–machine collaboration, and the integration of humanistic values. It further explores practical approaches such as curriculum restructuring, teaching model innovation, and diversified assessment methods. The study concludes that the effective integration of artificial intelligence can promote a shift from single-skill language training to comprehensive competence development, thereby enhancing teaching quality and the practical relevance of translation education.