AI in autism education: a review of collaborative and longitudinal approaches

Abstract

PURPOSE: This study presents a comprehensive literature review examining the role of artificial intelligence (AI) in autism education. It explores how AI technologies-such as adaptive learning systems, natural language processing-based communication aids, emotion recognition algorithms, socially assistive robots, and immersive augmented and virtual reality platforms-support teaching, learning, and behavioral development for autistic individuals. METHODS: Peer-reviewed literature was critically synthesized to identify trends in AI tool development, including adaptive learning systems, natural language processing-based communication aids, emotion recognition algorithms, socially assistive robots, and immersive augmented and virtual reality platforms. Emphasis was placed on studies integrating co-design methodologies involving educators, therapists, families, and autistic learners. RESULTS: Findings reveal that AI enables personalized learning, enhances social and emotional skill development, and promotes inclusive educational environments. Co-design practices improve usability, accessibility, and cultural sensitivity in tool development. However, limitations include the scarcity of large, diverse datasets, challenges in gathering longitudinal multimodal data, limited cross-cultural validation, and insufficient focus on hardware innovation relative to software solutions. CONCLUSIONS: AI tools hold transformative potential for autism education, yet equitable and effective implementation requires addressing data diversity, ethical oversight, and interdisciplinary collaboration.