AI IN EDUCATION: PERSONALIZED LEARNING PATHWAYS USING REINFORCEMENT LEARNING AND STUDENT BEHAVIOR MODELING

Abstract : The integration of Artificial Intelligence (AI) into education has opened transformative pathways for personalized learning, particularly through the use of Reinforcement Learning (RL) and student behavior modeling. This research explores the design and implementation of adaptive learning systems that tailor educational content dynamically based on real-time engagement metrics and predictive progress modeling. By leveraging RL algorithms, the system functions as an intelligent tutor that continuously optimizes learning trajectories for individual students, treating content selection as a sequential decision-making problem. Student behavior, captured through interaction data such as time on task, quiz responses, clickstream analysis, and emotional engagement (via affective computing), is modeled to infer cognitive states and learning preferences. These models inform the RL agent, enabling it to predict the likely impact of content types and pedagogical strategies on a learner’s future performance. The feedback loop established between the student's actions and the system's adaptive responses facilitates personalized interventions that are both timely and contextually relevant. Furthermore, the use of predictive analytics allows the system to anticipate learning plateaus, disengagement, or concept mastery, thereby adjusting content difficulty, presentation style, or pacing proactively. Experimental evaluations on simulated and real-world datasets indicate that RL-driven personalization outperforms traditional static learning pathways in terms of student engagement, retention, and academic achievement. Importantly, this approach also supports equity in learning by accommodating diverse learner needs, including those of students with varying levels of prior knowledge or learning disabilities. The framework proposed contributes to the broader field of intelligent tutoring systems by introducing a scalable and data-driven method for curriculum adaptation, grounded in robust AI methodologies. In conclusion, the fusion of RL with student behavior modeling represents a significant advancement in educational technology, promising not only to enhance individual learning experiences but also to redefine how educational content is delivered in both formal and informal settings. Future work may focus on refining behavior inference models using multimodal data and extending RL strategies to support collaborative and project-based learning environments.

Keyword Personalized learning, Reinforcement Learning, Student behavior modeling, Adaptive educational systems, Engagement analytics, Predictive learning pathways