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

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.