Authors - Sanket Shah, Jenice Bhavsar, Bhumi Shah, Jishan Shaikh, Khevana Raval, Ekta Vyas Abstract - Dyslexia is a neurodevelopmental condition that impairs reading fluency and phonological processing across languages. Early identification in school settings remains difficult because the Dyslexia Assessment for Languages of India (DALI) assessment tool requires expert administration which makes it difficult to implement in practice. The latest developments in artificial intelligence allow researchers to evaluate reading patterns through inexpensive devices which people commonly use. The research presents a system framework that uses multiple methods to combine webcam-based eye-tracking with voice analysis and machine learning methods for early dyslexia detection. The system examines tabular gaze and speech features through gradient-boosted models while using convolutional neural networks to encode spatial gaze patterns which include a meta-learning layer for multimodal fusion. The proposed framework enables practical implementation through its web-based interface which connects to secure backend services, thus providing schools with a privacy-protected and scalable method to conduct dyslexia assessments and provide personalized learning assistance in their resource-limited classrooms.
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