Intelligent Teaching of Higher Ideological and Political Education Using Artificial Intelligence

Abstract

With the rapid development of artificial intelligence technology, ideological and political education in Chinese universities is undergoing profound change. This article is based on a three-layer concept of value resonance, situational perception, and adaptive presentation of educational resources. It explores how to use natural language processing, knowledge graphs, and reinforcement learning to construct an intelligent education framework to enhance the effectiveness of ideological and political education. This framework captures students’ interest signals and emotional clues through multisource data, dynamically optimizes the order of resource presentation using a strategy network, and promotes value internalization and behavior transformation through a closed-loop feedback mechanism. The results show that the system demonstrated significant advantages at accelerating reinforcement learning convergence, accurately reaching emotional resonance, and enhancing classroom interaction. This study introduces new possibilities for the deployment of intelligent teaching systems.