Research on Practical Dilemmas and Paradigm Construction of AI-Enabled Precise Learning Situation Analysis in Ideological and Political Courses

Abstract

With the in-depth advancement of the national education digitalization strategy in the new era, the deep integration of artificial intelligence (AI) and ideological and political education has become a crucial direction for the teaching innovation of ideological and political courses. Traditional ideological and political teaching suffers from delayed feedback, one-sided analysis, passive adjustment, and insufficient precision in education, which restrict the effectiveness of fostering virtue and morality. This paper systematically examines the theoretical logic, current research landscape, and practical dilemmas associated with AI-enabled ideological and political teaching. It identifies key challenges including data fragmentation, algorithmic bias, and the lack of standardized evaluation frameworks that hinder the effective deployment of intelligent technologies in educational contexts. To address these issues, this study constructs a closed-loop application paradigm comprising four interconnected stages: intelligent collection, precise analysis, dynamic feedback, and iterative optimization. The proposed paradigm leverages machine learning algorithms, natural language processing, and educational data mining techniques to enable real-time monitoring and adaptive adjustment of teaching strategies. Furthermore, this paper discusses the ethical considerations and governance mechanisms necessary to ensure the responsible application of AI in ideological and political education. The findings provide a theoretical foundation and practical reference for enhancing the precision, effectiveness, and scientific rigor of ideological and political course instruction in the digital era, contributing to the broader goal of modernizing educational methodologies through intelligent technologies.