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July 3, 2026

Artificial intelligence for inclusive education of neurodiverse learners: a systematic review of psychological and social factors

Key Points

This review aims to examine the role of psychological and social factors in AI-powered inclusive education for neurodiverse learners.
Conducted a systematic review following PRISMA guidelines across five databases (Scopus, Web of Science, IEEE Xplore, ACM Digital Library, ERIC).
Screened 871 records, resulting in 16 included studies after quality appraisal based on a threshold of 8/10.
Focused on studies from 2014 to 2025 analyzing psychological and social factors in education for neurodiverse learners.
Identified eight psychological factors and eight social factors that influence educational experiences for neurodiverse learners.
AI was found to potentially improve social communication and emotional learning in some learners, though responses varied significantly.
Overall, small sample sizes and methodological differences limit the generalizability of the findings.

Abstract

Objectives This systematic review examines psychological and social factors in AI-powered inclusive education for neurodiverse learners, including those with autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), dyslexia, and other developmental variations.Methods Following PRISMA guidelines, we searched five databases (Scopus, Web of Science, IEEE Xplore, ACM Digital Library, and ERIC) for peer-reviewed studies published 2014–2025. From 871 initial records, 37 duplicates were removed and 456 excluded as ineligible, leaving 378 for title/abstract screening. Of these, 290 were excluded and 88 reports sought for retrieval; 32 could not be retrieved, leaving 56 full texts assessed for eligibility. Thirty-four were excluded and 6 after quality appraisal (threshold ≥ 8/10), yielding 16 included studies.Results Studies addressed eight psychological factors (attention and engagement, emotional recognition, communication processing, learning motivation, cognitive flexibility, behavioral regulation, memory processing, and anxiety regulation) and eight social factors (social communication, skills training, educational environment, peer relationships, family involvement, cultural context, professional support, and inclusive design). Findings suggest AI may improve social communication, emotional learning, and adaptive skills in some neurodiverse learners. However, responses varied between and within individuals, reflecting the heterogeneity of autism, ADHD, and dyslexia. Effect sizes were inconsistent and samples small, limiting generalizability.Conclusions This review examined a small and still-developing evidence base for AI to support individualized education recognizing neurodiversity as natural variation. However, geographic concentration (East Asia 43.8%, North America 31.3%), small sample sizes, and methodological heterogeneity limit generalizability. Future research should prioritize larger samples, longitudinal designs, and inclusion of underrepresented regions.

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