Reinforcement Learning Approaches for Adaptive Curriculum Design and Delivery in Higher Education

Abstract

The growing diversity of student populations and the increasing demand for personalized learning experiences have highlighted the limitations of conventional curriculum delivery in higher education. Adaptive curriculum design driven by Reinforcement Learning (RL) offers a promising approach to optimize learning pathways, dynamically adjusting content sequencing and instructional strategies based on real-time student performance and engagement metrics. This chapter explores the theoretical foundations and practical implementation of RL-based adaptive learning systems, emphasizing state representation, reward function design, curriculum sequencing, and integration with Learning Management Systems (LMS). Techniques for embedding high-dimensional student data, managing uncertainty in knowledge states, and balancing exploration and exploitation in learning paths are discussed in detail. Case studies and application scenarios illustrate the potential of RL to enhance student engagement, knowledge retention, and academic outcomes while providing scalable solutions for diverse learning environments. Ethical considerations, including fairness, transparency, and policy refinement through continuous monitoring, are examined to ensure responsible deployment of AI-driven adaptive curricula. The chapter provides a comprehensive framework for leveraging RL to create dynamic, personalized, and data-driven higher education experiences, advancing the state-of-the-art in educational technology.</jats:p>