Disability and rehabilitation. Assistive technology · 2025
AI in autism education: a review of collaborative and longitudinal approaches.
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Adako Oyeyemi Patricia, Adeusi Oluwafemi Clement, Alaba Peter Adeniyi
Аннотация
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.
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.