Exploration of the "Human-Machine Collaboration" Teaching Model in Literary Theory Courses in the Era of AI-Generated Content

Abstract

The rapid advancement of AI-Generated Content (AIGC) has profoundly impacted the field of higher education. As a core component of traditional humanities disciplines, the teaching objectives, paradigms, and ecology of literary theory courses are facing an urgent need for structural reshaping. Based on the background of the AIGC era, this study aims to systematically explore the construction and application of the "Human-Machine Collaboration" teaching model in literary theory courses. The paper first analyzes how AIGC technology drives the shift in literary theory teaching from knowledge transmission to the cultivation of critical thinking and innovative ability. It then elaborates on the theoretical foundation of the "Human-Machine Collaboration" model in distributed cognition theory, explaining its theoretical implications and significant advantages in enhancing teaching effectiveness through the integration of intelligent and personalized approaches, as well as in reshaping the roles of teachers and students and their pedagogical relationships. Finally, the research constructs a comprehensive implementation path covering pre-class preparation, in-class discussion, and post-class evaluation, providing a solution with both theoretical depth and practical feasibility for the innovative transformation of literary theory education in the intelligent era.