Harnessing Machine Intelligence to Bridge the Learning Divide for Students with Disabilities

Learning disabilities affect a significant number of students, often going undiagnosed until academic struggles become evident. Early identification and personalized interventions are crucial for improving educational outcomes, yet traditional assessment methods can be time-consuming and inconsistent. This research explores the potential of machine learning (ML) to detect learning disabilities such as dyslexia, ADHD, and dysgraphia by analysing student performance, cognitive abilities, and behavioural data. We implemented various ML models to predict the likelihood of these disabilities, comparing their performance based on accuracy, precision, and recall. Our findings highlight the effectiveness of ML in early detection and demonstrate how identified disabilities can inform tailored educational support, ultimately creating a more inclusive learning environment. This research emphasizes the transformative potential of ML in enhancing diagnostic accuracy and facilitating personalized interventions, leading to better outcomes for students with disabilities.