Artificial Intelligence in Inclusive Education and Its Relationship with Socioeducational Participation in Students with Autism Spectrum Disorder

Background: The integration of artificial intelligence (AI) in education offers new opportunities for personalized learning and inclusive practices. However, empirical evidence on its relationship with engagement and participation processes in students with Autism Spectrum Disorder (ASD) remains limited in real classroom contexts. Methods: A non-experimental observational design was used, based on secondary analysis of anonymized educational data from 60 students with ASD in inclusive schools. Participants were classified according to their regular use of AI-based tools, defined as at least three sessions per week over one academic term. Student participation was assessed through a composite index including behavioral engagement, social interaction, academic autonomy, and self-regulation. Independent samples t-tests and Cohen’s d were applied. Results: Students using AI-based tools showed significantly higher levels across all participation dimensions. The strongest differences were found in behavioral engagement and social interaction, with large effect sizes, while autonomy and self-regulation showed moderate differences. Conclusions: AI-based tools are associated with improved engagement and involvement in students with ASD, particularly in observable and interactional dimensions, highlighting their potential for inclusive education.