AI-Driven Educational Activity Recommender for Children with Autism

Autism Spectrum Disorder (ASD) is estimated to affect about 1% of children globally. While there is currently no cure, early detection and targeted interventions can significantly enhance the well-being and daily functioning of children with ASD. This paper presents an intelligent, content-based recommender system designed to suggest personalized activities aligned with each child’s preferences and developmental needs. The proposed system integrates social stories, educational videos, and interactive exercises supported by machine learning techniques to foster communication, social interaction, emotional regulation, and cognitive development—while reducing the need for constant parental supervision. Unlike traditional content-based systems, our approach incorporates the child’s emotional state (mood) to provide more diverse and context-aware recommendations, avoiding the filter bubble effect and enhancing personalization and engagement. A key contribution of this work lies in its focus on personalized and interactive learning experiences, made possible through the combination of multiple assistive technologies. Additionally, the study addresses the problem of data scarcity by providing a publicly available dataset to facilitate further research in ASD-focused intelligent systems. Preliminary feedback from therapists and parents indicates that the system holds strong potential to substantially improve the educational, communicative, and emotional skills of children with ASD. These promising results motivate future large-scale empirical evaluations to validate its effectiveness and establish it as a valuable tool for ASD intervention and inclusive education.