A Novel Fuzzy Logic System for Real-Time Text Difficulty Assessment in Mobile Reading Apps for Dyslexia

Automated text difficulty assessment in mobile reading applications remains an underexplored challenge for dyslexia support systems. This study presents the development and validation of an intelligent fuzzy logic system engineered for real-time text complexity analysis in mobile web environments. Our approach integrates six computational variables: sentence length patterns, lexical complexity metrics, syllabic density analysis, visual layout parameters, and punctuation distribution algorithms. The implemented system combines a FastAPI-based backend with a responsive React frontend, enabling cross-device accessibility through progressive web application technologies. Technical validation demonstrates 94.2% accuracy in difficulty classification compared against expert assessments, with processing speeds averaging 0.3 seconds per text analysis. Usability evaluation with 40 participants across mobile and desktop interfaces yielded SUS scores of 82.6, while 85% expressed frequent usage intention. The mobile web architecture achieves 98% device coverage with WCAG 2.1 AA compliance standards. This work establishes the first fuzzy inference engine specifically optimized for Spanish-language dyslexia support applications, creating a new technical foundation for intelligent reading assistance platforms.