Leveraging Support Vector Machines to Develop Technology-Driven Educational Tools for Children with ASD

Autism Spectrum Disorder (ASD) presents unique challenges in learning, communication, and social interaction. The integration of machine learning techniques, such as Support Vector Machines (SVM), has the potential to revolutionize personalized educational interventions for autistic children. This research aims to utilize SVM algorithms to identify patterns in behavioural, cognitive, and learning preferences of children with ASD. By analysing large datasets from behavioral studies, educational performance, and interaction with technology, SVM can classify and predict individualized learning needs, thereby enabling the development of tailored educational tools.