Journal of Applied Science and Engineering

Published by Tamkang University Press

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Personalized Recommendation System for Ideological and Political Education in Universities Using AI and Virtual Reality

 2026

 Volume 32

Xu Jiang, Pingping Long, and Zeng Wang

College of Marxism, Chongqing Vocational and Technical University of Mechatronics. Chongqing 400000, Chongqing, China

Received: December 26, 2025
Accepted: March 27, 2026
Publication Date: April 25, 2026

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SEM diagram for hypothesis testing

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Background: Ideological and Political Education (IPE)based on the traditional teaching model typically offered in universities, is not always effective in attracting contemporary students due to its lack of interactivity and personalization. Objective: The proposed study will assess the effectiveness of an AIVR based hybrid personalized recommendation system for improving student engagement, satisfaction, and learning outcomes in IPE. Methods: 250 university students were used in a quantitative research design. Al based models customized learning material, whereas VR simulations gave context rich interactions. Pre and post assessments were used to collect data, and Structural Equation Modeling(SEM)is employed in the study to rigorously examine and validate the complex relationships among Al based personalization, VR immersion, student engagement, satisfaction, and learning outcomes within the proposed educational framework. SEM enables simultaneous testing of both the measurement and structural models, ensuring that observed variables reliably represent their underlying constructs and that directional relationships between them are assessed. Using goodness of fit indices and path coefficients, SEM assesses whether the conceptual model aligns with the empirical data and evaluates the
mediating role of student engagement in the relationship between Aland VRsupported teaching methods and improved learning effectiveness. This comprehensive analytical approach strengthens the reliability of the findings and supports the theoretical contributions of the hybrid AIVR learning model. Findings: The combination of AI and VR made a big difference in terms of student engagement and learning. Al based personalization and VR immersion increased motivation and knowledge retention, and student engagement mediated the relationship between teaching methods and outcomes Conclusion: The paper has demonstrated that AI and VR can transform IPE by providing more personalized, interactive, and immersive learning experiences and by serving as a better alternative to traditional learning.

Keywords: Artificial Intelligence, Virtual Reality, Ideological and Political Education, Personalization, Student Engagement, Learning Outcomes

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