Constructing a "Teacher–Machine–Student" Collaborative Symbiosis: The Logic and Practical Pathways of a New Teaching Relationship in the AI Era

Abstract

The rapid advancement of generative AI (AIGC) has brought unprecedented opportunities for improving teaching quality in higher education, yet unchecked reliance on technology alone breeds risks of alienation such as algorithmic servitude, emotional deficit, and a weakened teacher–student connection. Drawing on the theory of collaborative symbiosis, this paper develops a conceptual framework that reconceives the teaching relationship as a "Teacher–Machine–Student" triadic Collaborative Symbiosis. It constructs the PAU Collaborative Framework, in which the teacher (Professional) leads value guidance, AI (AI-agent) provides cognitive support, and the student (User) is repositioned as a co-creative subject, with an "emotion–cognition dual axis" division of labor as its core operating mechanism. On this basis, the paper argues that collaborative symbiosis can systematically reshape teaching along three dimensions—teaching culture, teacher–student interaction, and learning atmosphere. The argument is illustrated with practitioner observations and with empirical findings drawn from the author's prior quasi-experimental study (Zhang, 2025), in which the accuracy of students' English expression of policy concepts rose from 44.7% to 82.7%; these figures are used illustratively rather than as new experimental evidence. The paper further proposes four recommendations centered on "Human–Machine Value Addition"—building a diversified evaluation system, establishing a three-level verification mechanism, driving a teacher-development loop, and moving from control to co-construction in cultural governance—thereby cultivating a new ecosystem of Quality Culture oriented toward the AI era.