From "Technical Assistance" to "Human-Machine Synergy": Reconstruction and Innovation of Scenario-based Teaching Models in Civics Courses from the Perspective of Generative AI

Abstract

With the breakthrough progress of Generative Artificial Intelligence (AIGC) technology, the teaching of Ideological and Political Theory Courses (hereinafter referred to as "Civics Courses") in universities is facing a critical node of transformation from digitization to intelligence. For a long time, technology has mostly played the role of an "auxiliary tool" in Civics teaching, suffering from pain points such as solidified scenarios, superficial interactions, and insufficient personalization. Generative AI, with its unique knowledge generation capabilities, multimodal context construction abilities, and human-like interaction logic, provides an opportunity for Civics teaching to shift from the paradigm of "Technical Assistance" to "Human-Machine Synergy." Based on the practical needs of the reform and innovation of Civics Courses in the new era, this paper deeply analyzes the theoretical connotation of the "Human-Machine Synergy" teaching model. It systematically reconstructs the practical model of scenario-based teaching in Civics Courses from three dimensions: the "Dynamic Knowledge Graph" in theoretical teaching, the "Virtual-Real Twinning" in practical teaching, and "Intelligent Decision-Making" in social services. Furthermore, addressing the potential loss of teacher subjectivity, algorithmic bias, and ethical risks in human-machine synergy, this paper proposes building a governance mechanism of "Value Leadership, Dual-Teacher Synergy, and Ethical Regulation," aiming to provide theoretical support and practical solutions for promoting the high-quality development of university Civics Courses.