Exploration of Precise Teaching Pathways for Integrating Ideological and Political Education into University Economics and Management Courses via AI Technology

Abstract

The deepening implementation of educational digitalization strategies, coupled with the rapid advancement of artificial intelligence (AI) technology, has opened up new possibilities for achieving precision-oriented ideological and political education in economics and management courses. Currently, such education faces three practical challenges: value disconnection, fragmented competency development, and a lack of feedback mechanisms; consequently, traditional teaching approaches often fail to organically integrate value guidance, knowledge transmission, and competency cultivation. Building upon a rigorous definition of "precision-oriented teaching," this study constructs a three-stage integration model— "value–knowledge–competency" —and employs a data chain to vertically connect the value guidance layer, the knowledge construction layer, and the competency cultivation layer, thereby establishing a closed-loop, interconnected educational system. On this foundation, the study systematically designs AI-driven implementation pathways for precision-oriented teaching across three instructional phases—pre-class, in-class, and post-class—supported by a collaborative learning community comprising "teacher–student–AI" and corresponding safeguard mechanisms. This research demonstrates that AI technology serves as a pivotal variable in transitioning course-based ideological and political education from an "experience-driven" paradigm to a "data-evidenced" approach, and from a "linear additive" model to a "spiral symbiotic" framework. Furthermore, AI can provide systematic pathway references to overcome the three key difficulties in integrating such education into economics and management curricula: integration challenges, evaluation challenges, and coherence challenges.