Architectural Design and Implementation Methodology for Reinforcement Learning-Based Adaptive Educational Systems.

Triveni Subhash Rathod; Shailesh T Khandare

doi:10.36948/ijfmr.2025.v07i06.62534

Architectural Design and Implementation Methodology for Reinforcement Learning-Based Adaptive Educational Systems.

Author(s)	Ms. Triveni Subhash Rathod, Prof. Dr. Shailesh T Khandare
Country	India
Abstract	Contemporary educational systems struggle to deliver personalized learning experiences that accommodate diverse learner characteristics and preferences. This paper presents a comprehensive architectural design and implementation methodology for an adaptive learning framework leveraging Reinforcement Learning (RL) and Markov Decision Processes (MDP). The proposed system architecture integrates intelligent components including learner assessment, student modeling, Q-learning-based recommendation engine, and generative AI content delivery. We formalize the learning process as an MDP with 11 states and 8 actions, implementing Q-learning algorithms for sequential learning path optimization. The three-tiered architecture comprises React-based frontend, Node.js backend, and MySQL database, integrated with Gemini API for content generation. The methodology incorporates Felder-Silverman Learning Style Model (FSLSM) with hybrid machine learning classification combining K-means clustering, Artificial Neural Networks, and Bayesian inference. This work addresses critical implementation challenges including cold-start problems, scalability considerations, and privacy-preserving design principles.
Keywords	Adaptive Learning Systems, Reinforcement Learning, Q-Learning, Markov Decision Process, System Architecture, Learning Style Classification, Educational Technology, Implementation Methodology
Field	Engineering
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-12-10
DOI	https://doi.org/10.36948/ijfmr.2025.v07i06.62534

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Architectural Design and Implementation Methodology for Reinforcement Learning-Based Adaptive Educational Systems.

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