AISTY: An Explainable AI-Driven Vision-Based Adaptive Learning System for Children with Autism Spectrum Disorder

Abstract

The educational needs of children with autism spectrum disorder (ASD) require individualized approaches because they face major difficulties with communication and social skills and learning processes. The diverse requirements of children with ASD remain unmet by conventional educational approaches because these methods fail to provide suitable flexibility and expandability. The research develops Adaptive Interface System for Tracking Engagement (AISTY) as an explainable AI-based learning module which solves the current educational needs of children with ASD. The system AISTY uses computer vision to track behavioural data through real-time analysis while delivering interactive learning content that adjusts to student needs. The system uses a custom Convolutional Neural Network (CNN) model to analyse facial expressions for engagement and emotional state classification (Happy, Sad, Neutral, Surprise, Fear, Angry, Disgust) with 78% accuracy while operating at 14-16 frames per second on typical computer equipment. The system contains three learning modules (Mathematics, Sing-Along, and Mini-Games) which modify their content according to the child's current state. The system includes an explainable AI (XAI) dashboard that uses SHAP-inspired visualizations to show caregivers exactly what elements influence the system's adaptation decisions. The usability assessment with parents and a special education teacher validated the system's operational capabilities and user-friendly interface and confirmed its worth. The research shows AISTY offers a flexible autism education system which proves that explainable adaptive AI can be effectively used in special needs education to boost student participation and academic success.