Optimizing Educational Strategies for Dyslexia Through Multimodal Machine Learning

Abstract

Dyslexia is a prevalent learning disability that affects reading, writing, and comprehension skills. Traditional interventions often rely on single-modality approaches, which may not fully address the diverse cognitive needs of dyslexic learners. This research explores the integration of multimodal machine learning as an optimized strategy for dyslexia interventions. By integrating visual, auditory, and textual inputs, multimodal AI systems can personalize learning experiences, enhance engagement, improve comprehension, and facilitate real-time adaptation of educational materials to match individual cognitive patterns. This research highlights the potential of AI-driven educational strategies in enhancing accessibility and inclusivity, paving the way for more effective dyslexia interventions in modern learning environments. It also shows how to optimize educational Strategies for Dyslexia through multimodal machine learning. A narrative synthesis and systematic literature review conducted under PRISMA guidelines, the research evaluates adaptive systems that personalize content in real time. Findings indicate that MML enhances reading fluency, knowledge retention, and learner engagement. These results highlight the potential of AI-driven multimodal frameworks to improve accessibility and inclusivity in dyslexia education.