ADAPTIVE REINFORCEMENT LEARNING FRAMEWORK FOR PERSONALIZED EDUCATIONAL TRAJECTORIES IN AI-DRIVEN HIGHER EDUCATION SYSTEMS

In the rapidly evolving landscape of digital higher education, personalized learning has become a critical component in enhancing student performance and engagement. Traditional self-paced learning systems, while flexible, often lack dynamic adaptability and fail to respond effectively to real-time student behavior. This study proposes an Adaptive Reinforcement Learning (ARL) framework designed to optimize individualized learning trajectories in AI-driven educational environments.

The proposed model conceptualizes the learning process as a Markov Decision Process (MDP), where student knowledge states, learning actions, and reward mechanisms interact dynamically. By employing Q-learning algorithms, the system continuously refines its decision-making strategy based on student performance, engagement, and retention metrics. Unlike static rule-based systems such as Fuzzy Logic, the ARL framework enables continuous learning and autonomous adaptation.

Experimental simulations indicate that the proposed approach significantly improves learning efficiency, reduces cognitive overload, and enhances long-term knowledge retention. This research demonstrates the transformative potential of reinforcement learning in developing next-generation intelligent tutoring systems and adaptive educational platforms.

在快速发展的数字化高等教育领域，个性化学习已成为提升学生学习表现与参与度的核心要素。传统的自定进度学习系统虽具备灵活性，但往往缺乏动态适配能力，无法有效响应学生的实时学习行为。本研究提出一种自适应强化学习（Adaptive Reinforcement Learning, ARL）框架，旨在优化人工智能驱动教育环境下的个性化学习路径。
该模型将学习过程建模为马尔可夫决策过程（Markov Decision Process, MDP），学生知识状态、学习行为与奖励机制在此框架中实现动态交互。系统通过引入Q学习算法，基于学生的学习表现、参与度及知识留存率指标，持续优化其决策策略。与模糊逻辑（Fuzzy Logic）这类基于静态规则的系统不同，ARL框架可实现持续学习与自主适配。
实验仿真结果表明，所提方法可显著提升学习效率、降低认知负荷，并增强长期知识留存效果。本研究证实了强化学习在开发下一代智能导学系统与自适应教育平台方面的变革性应用潜力。