Dyslexic Student

Dyslexia research focuses on understanding and mitigating the challenges faced by dyslexic students, primarily in reading and language processing. Current research employs machine learning models, including recommendation systems, neural networks (e.g., denoising autoencoders), and sequence models, to personalize support, diagnose dyslexia through objective measures like EEG and eye-tracking data, and develop assistive technologies such as VR-based training and AI-powered speed reading tools. These advancements aim to improve early identification, provide tailored interventions, and ultimately enhance educational outcomes for dyslexic students, impacting both the scientific understanding of this learning disorder and its practical management in educational settings.