Fuzzy Logic in Adaptive Learning Systems For Personalized Education
Dr. Koneti Krishnaiah,
Tallapally Mounika,
Dr. Tata Sivaiah
This study explores the integration of fuzzy logic into adaptive learning systems to enhance personalized
education. Traditional adaptive learning systems, while beneficial, often struggle with limitations in
personalization accuracy and adaptability. By incorporating fuzzy logic, which can manage uncertainty
and model nuanced learner needs, this research aims to improve the efficacy of adaptive learning
systems. Experimental results demonstrate that the fuzzy logic-enhanced system outperforms traditional
systems across several metrics. Specifically, it achieves a 10% increase in personalization accuracy, a
5% higher improvement in learning outcomes, and a 15-minute increase in average engagement time per
session. Additionally, user satisfaction ratings are higher by 1.5 points on a 10-point scale, and system
efficiency is improved with a reduction in processing time by 0.8 seconds. These findings underscore the
potential of fuzzy logic to provide more precise, engaging, and effective adaptive learning experiences,
making it a valuable advancement in personalized education technology.
