ARTIFICIAL INTELLIGENCE IN AUTISM SPECTRUM DISORDER: A SYSTEMATIC REVIEW OF AI-SUPPORTED SCREENING, EDUCATION, AND INTERVENTION TECHNOLOGIES

Ilias Vasileiou

Abstract


Artificial intelligence (AI) technologies are increasingly transforming research and practice in Autism Spectrum Disorder (ASD). Advances in machine learning, deep learning, computer vision, and natural language processing have enabled the development of computational tools capable of identifying behavioral markers, supporting personalized educational environments, and enhancing therapeutic interventions for individuals on the autism spectrum. Despite the rapid growth of this interdisciplinary field, the literature remains fragmented across domains including computer science, clinical medicine, psychology, and educational research. The present study provides a systematic review of empirical research examining the application of artificial intelligence technologies in autism spectrum disorder. Following the PRISMA 2020 guidelines, a comprehensive search was conducted in major international databases, including Scopus, Web of Science, PubMed, ERIC, and PsycINFO, for studies published between 2015 and 2025. After duplicate removal, screening, and eligibility assessment, 49 empirical studies were included in the final synthesis. The findings indicate that artificial intelligence applications in autism research cluster into four primary domains: AI-supported diagnostic screening and early identification, AI-based educational technologies and adaptive learning systems, socially assistive robotics for social communication training, and AI-supported therapeutic monitoring and behavioral intervention systems. Across these domains, AI technologies demonstrated promising potential to improve early detection of autism, support individualized learning environments, and enhance the effectiveness of therapeutic interventions. However, the reviewed studies also revealed substantial methodological heterogeneity, limited sample sizes, and variability in algorithmic approaches and outcome measures. In addition, ethical considerations related to data privacy, algorithmic transparency, and responsible clinical implementation remain critical challenges for the field. Overall, the evidence suggests that artificial intelligence technologies may play an increasingly important role in the future of autism screening, education, and intervention, although further interdisciplinary research and large-scale validation studies are required to ensure their reliability, effectiveness, and ethical deployment.

Keywords


artificial intelligence; autism spectrum disorder; machine learning; digital health; educational technology; systematic review

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References


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DOI: http://dx.doi.org/10.46827/ejse.v12i5.6741

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