Algorithmic Recommendation Empowering Targeted Ideological and Political Education: Endogenous Logic, Value Goals, and Practical Pathways

Abstract

With the rapid development of artificial intelligence, algorithmic recommendation, as the core technology of intelligent information distribution, is profoundly reshaping the landscape of ideological and political education. Focusing on the integration of algorithmic recommendation into targeted ideological and political education, this paper systematically examines the internal mechanisms and practical pathways through which the technology enables this integration. The study argues that traditional ideological and political education faces a set of practical dilemmas, including the barriers created by information cocoons, the erosion of educators’ professional authority, the mismatch between content supply and demand, and the ethical risks that accompany the technology. Through an endogenous logic characterized by problem-driven necessity, functional complementarity, and process synergy, algorithmic recommendation provides technical support for realizing the value goals of personalized cultivation, intelligent teaching, and scientific evaluation. At the practical level, it is necessary to build a coordinated system spanning five dimensions, namely intelligent platforms, content ecosystems, teaching models, evaluation systems, and ethical governance, so as to balance technological empowerment with value guidance and, through human-machine collaboration, to advance the targeted transformation of ideological and political education, thereby offering theoretical reference and practical pathways for its innovation in the new era.