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<p>This paper proposes a personalised recommendation system for Ideological and Political Education using Reinforcement Learning techniques. The primary objective is to create a dynamic, adaptable learning system that provides content tailored to individual learners’ needs, enhancing engagement and knowledge retention. We integrate Deep Q-Networks, Proximal Policy Optimisations, and Contextual Bandits to develop a Hybrid Deep Reinforcement Learning (HDRL) framework. This system dynamically adjusts to learners’ evolving needs by incorporating emotion analysis, knowledge graphs, and bias mitigation strategies. The system outperforms traditional recommendation systems, demonstrating a 30% increase in engagement and a 25% improvement in knowledge retention. The integration of emotional feedback and real-time personalisation leads to a more engaging and effective educational experience. Our HDRL-based system offers a significant advancement in personalised ideological and political education, addressing the limitations of static educational models. By ensuring content neutrality and adapting to individual learner profiles, the system fosters better engagement and balanced learning outcomes.</p>

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Design of a personalized recommendation system for ideological and political education using reinforcement learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月23日

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  • Zhiyong Li

摘要

This paper proposes a personalised recommendation system for Ideological and Political Education using Reinforcement Learning techniques. The primary objective is to create a dynamic, adaptable learning system that provides content tailored to individual learners’ needs, enhancing engagement and knowledge retention. We integrate Deep Q-Networks, Proximal Policy Optimisations, and Contextual Bandits to develop a Hybrid Deep Reinforcement Learning (HDRL) framework. This system dynamically adjusts to learners’ evolving needs by incorporating emotion analysis, knowledge graphs, and bias mitigation strategies. The system outperforms traditional recommendation systems, demonstrating a 30% increase in engagement and a 25% improvement in knowledge retention. The integration of emotional feedback and real-time personalisation leads to a more engaging and effective educational experience. Our HDRL-based system offers a significant advancement in personalised ideological and political education, addressing the limitations of static educational models. By ensuring content neutrality and adapting to individual learner profiles, the system fosters better engagement and balanced learning outcomes.