Inclusive Learning Through AI: A Comprehensive Review of Assistive Educational Technologies for Students with Neurodevelopmental Disorders and other Disabilities

Abstract

Globally, millions of students with neurodevelopmental disorders and other disabilities face persistent barriers to equitable education that conventional teaching methods rarely overcome. This investigation is unique as it links AI-assisted learning tools to student's needs, to the student's academic results, which fills an important void in inclusive education research. A systematic review of 30 peer reviewed articles (IEEE Xplore; Scopus; PubMed) on AI methods (machine learning; natural language processing; computer vision) and student characteristics (autism spectrum disorder; dyslexia; ADHD; sensory/motor issues), and results were reported using the PRISMA guidelines. The prominent contributions of the study are, an empirical mapping of AI enabled learning tools to student needs and measurable outcomes; a practical guide for aligning the capabilities provided by AI systems to pedagogical requirements and institutional readiness. The findings of this review show that AI-based learning systems can increase student participation, understanding, and ability to develop skills, when they are combined with effective teaching practices and adequate teacher training. Real-world applications have been found to provide increased communication independence for augmentative/alternative communication (AAC) users; to increase reading proficiency among individuals who use text-to-speech and/or speech-to-text technologies; and to enhance understanding in mathematics among students who utilize adaptive learning environments. However, the review also identified several persistent barriers to the adoption of AI-assisted learning systems, such as limited funding; lack of necessary infrastructure; lack of preparation of educators; and mismatch between existing policies and the capabilities provided by AI systems. The report includes recommendations for implementing AI-assisted learning systems at the local level, building educator capacity, ensuring the development of safe and ethical AI systems, and evaluating the long-term impacts on student learning to support scalable and equitable adoption in schools.