Research on the Application of Artificial Intelligence in the Digital Transformation of University Ideological and Political Work

Ming Jin Ming Jin
2025 Journal of Contemporary Educational Research 0 citations

Abstract

With the continuous penetration of artificial intelligence (AI) in higher education, the organizational mechanisms and implementation logic of ideological and political education are undergoing new evolutionary trends. These technologies have been widely embedded in key aspects such as teaching management, student services, and value guidance, playing an increasingly prominent role in student status recognition, precise information distribution, and dynamic behavioral interventions. In the practice of ideological and political education in universities, intelligent systems have had a profound impact on the way educational content is supplied and the modes of interaction, shifting the approach from being experience-driven to data-guided. Meanwhile, the task structure of university education scenarios has become more complex, with highly heterogeneous student behaviors, value orientations, and demand preferences, leading to significant challenges for traditional ideological education models in terms of response efficiency and intervention accuracy. Against this background, this paper, based on the trend of digital evolution in ideological and political work in universities, systematically outlines the logical structure and key pivot points in the process of AI integration. It further proposes a fusion strategy composed of “intelligent perception–collaborative services–data-driven” to provide theoretical support and practical pathways for the intelligent transformation of the education system in the new era.

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Year
2025
Type
article
Volume
9
Issue
11
Pages
403-409
Citations
0
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Closed

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Ming Jin (2025). Research on the Application of Artificial Intelligence in the Digital Transformation of University Ideological and Political Work. Journal of Contemporary Educational Research , 9 (11) , 403-409. https://doi.org/10.26689/jcer.v9i11.12822

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DOI
10.26689/jcer.v9i11.12822