"AI as Colleague": A Study on the Restructuring of Vocational Undergraduate English Teaching Models Driven by Agent AI

Abstract

The rise of intelligent agent (Agent AI) technology, which possesses autonomous task planning and cognitive collaboration capabilities, has made the metaphor of "AI as colleague" a novel theoretical perspective and practical pathway for reforming vocational undergraduate English teaching. This study aims to explore the restructuring logic of vocational undergraduate English teaching models driven by Agent AI. It first clarifies the theoretical connotation of Agent AI as a "collaborative subject" and its alignment with the essential demands of vocational English teaching. Subsequently, the paper systematically elaborates on the directions for restructuring from three core dimensions: teaching objectives, content, and process. Teaching objectives should shift from cultivating singular language proficiency to incorporating human-machine collaboration literacy; teaching content should establish a real-time evolution mechanism based on dynamic corpora and agent feedback; and the teaching process should rely on a multi-agent architecture to achieve layered interaction and adaptive learning path design. Finally, the study proposes an evolutionary direction toward the normalization of "AI colleagues," which includes the transformation of the teacher's role into that of a learning ecosystem architect and coordinator, the construction of a blended learning space integrating embodied and distributed cognition, and the establishment of an iterative optimization cycle based on data intelligence and pedagogical reflection. This study provides a systematic theoretical framework and analytical perspective for understanding the transformation of vocational language education paradigms in the intelligent era.