Fed-RLCL: Federated Reinforcement Learning with Curriculum Learning for Adaptive Content Sequencing

As education increasingly shifts to digital platforms, the need for adaptive learning systems to personalize content to individual learners has grown. Traditional models often struggle to balance personalization with data privacy and scalability, particularly in large, diverse learning environments. This paper proposes a Federated Reinforcement Learning model with Curriculum Learning (Fed-RLCL) designed to deliver adaptive content sequencing that responds to each student’s unique learning progression. Our model uses reinforcement learning to adjust content difficulty dynamically, with curriculum learning techniques ensuring that material builds on prior knowledge. Implementing a federated learning framework allows the model to learn from a distributed set of student interactions without transferring sensitive data to a centralized server. This federated setup enhances privacy, enabling secure adaptation to individual performance patterns across diverse groups. Extensive experimentation shows that Fed-RLCL optimizes the learning experience, providing tailored content recommendations that improve engagement and learning outcomes. Our approach paves the way for scalable, privacy-preserving adaptive learning solutions, with potential applications in large classrooms and online education platforms.