Conflict and Reconstruction of Foreign Language Teachers' Professional Identity in Human-Machine Collaborative Teaching Models

Abstract

With the deep integration of artificial intelligence into foreign language classrooms and the rapid proliferation of human-machine collaborative education models, some foreign language teachers are experiencing professional identity conflicts, including weakened professional authority, confused role positioning, digital competence anxiety, and emotional alienation. This paper examines the practical conflicts faced by foreign language teachers in human-machine collaborative scenarios from four dimensions—role, competence, value, and emotion—analyzes their underlying causes, and proposes reconstruction pathways for teachers' professional identity from four aspects: cognitive renewal, competence rebuilding, role transformation, and institutional support. The study argues that human-machine collaboration is not a replacement of teachers by technology but a process of reconstructing teachers' professional identity. Teachers should uphold the humanistic core of foreign language education, achieve complementary advantages between humans and intelligent technology, and provide human capital support for the high-quality development of smart foreign language education.