Intelligent Adaptive Learning Platforms Powered by Hybrid Soft Computing Techniques

Abstract

Adaptive learning platforms increasingly leverage AI to tailor educational content. Yet, integrating soft computing methods—fuzzy reasoning, neural networks, and evolutionary optimization—remains underexplored. This study presents a hybrid architecture combining fuzzy inference, client–server neural adaptation, and genetic optimization to enhance learning personalization. Deployed in a university mathematics course (N=180), the platform improved post-test scores by 20% and reduced dropout by 15%. Performance metrics, user satisfaction, and system utility demonstrate measurable gains over static and single-method systems.