Foreign Language Teaching Motivation Shifts of University English Teachers in the Generative AI Era

In the context of big data and artificial intelligence, large language models (LLMs) have become pivotal tools for university English teachers around the world to reshape pedagogical concepts and optimize instructional pathways. This study investigates the motivational logic and behavioral features of AI-empowered teaching, focusing on Mr. L from a university in southwestern China. A mixed-methods approach was adopted, combining questionnaires, classroom observations, and in-depth interviews, to capture both quantitative and qualitative dimensions of teaching practice. The findings reveal that English teachers in university demonstrate diversified motivations, staged processes, and forward-looking goals in AI-assisted translation instruction. Their key motivations include enhancing students' translation competence, cultivating their AI literacy, and integrating technological applications. At the same time, teacher motivation is influenced by multiple factors, such as individual technological awareness, societal and institutional demands, and the teacher’s professional agency and pedagogical consciousness. The study concludes that AI-driven teaching motivations not only stimulate innovation in classroom methodology but also create new opportunities for reflective practice and professional growth. By presenting an empirical case, the research offers practical insights for foreign language educators seeking to navigate the challenges and opportunities of AI technologies, and provides useful references for promoting effective integration of AI into higher education. 在大数据与人工智能背景下，大语言模型成为高校英语教师转变教学观念、优化教学路径的重要工具。研究以西南地区某高校具备AI教学经验的L教师为个案，结合问卷调查、课堂观察与访谈等方法，从多维度呈现其教学动机的形成与演变。结果表明，高校英语教师在AI辅助的翻译教学中呈现出动机多元化、过程阶段性与目标前瞻性等特征，主要动机包括提升学生翻译能力、培养AI素养以及探索技术应用等。影响教师动机的关键因素既包括技术认知与社会需求，也体现出教师的教育主体性与教学自觉。研究为高校外语教师应对AI技术变革提供了实证样本与经验参考。