The deep iteration of generative artificial intelligence (GAI) and educational large models has pushed university foreign language education into the deep waters of digital transformation. Foreign language teachers, as the core entities in the implementation of teaching and the integration of technology, are directly determined by their AI application capabilities in determining the effectiveness of digital empowerment in foreign language education. This study is based on the Technology Pedagogical Content Knowledge (TPACK) theory and the teacher’s professional resilience theory. This paper systematically analyzes the hierarchical distribution, scene characteristics, and core pain points of foreign language teachers’ AI applications. The study finds that current foreign language teachers’ AI applications present four characteristics: shallow tool application, fragmented scenes, structural imbalance in capabilities, and absence of ecological support. The acceptance of technology, TPACK literacy, and perceived organizational support are the core variables restricting deep application. This paper starts from four aspects: technology empowerment, professional development, teaching reconfiguration, and institutional support, and establishes a four-dimensional collaborative response system of “sophistication advancement - scene integration - risk prevention - ecological support”. It proposes operational strategies such as differentiated ability cultivation, AI integration of teaching, learning, and assessment, and ethical compliance control. This provides theoretical support and operational approaches for university foreign language teachers to solve technical application problems, achieve role transformation in their profession, and update teaching paradigms.