ARTIFICIAL INTELLIGENCE IN ADDRESSING ENGLISH LEARNING DISABILITIES: A COMPREHENSIVE STUDY ON DYSLEXIA, ADHD, DEAFNESS AND VISUAL IMPAIRMENT

Abstract [English]

Background: English language acquisition, involving reading, writing, listening, and speaking, presents significant cognitive and sensory challenges for children with disabilities such as dyslexia, Attention Deficit Hyperactivity Disorder (ADHD), deafness, and visual impairment. Traditional pedagogical methods often assume uniform cognitive abilities, marginalizing these learners.
Objectives: This paper critically examines the role of Artificial Intelligence (AI) in creating inclusive, adaptive, and personalized English learning environments for these four disability groups.
Methods: Adopting a qualitative systematic review approach, the study synthesizes findings from peer-reviewed journal articles (2018–2026), AI in education research, and assistive technology studies, grounded in Cognitive Load Theory, Baddeley’s Model of Working Memory, and Universal Design for Learning (UDL).
Findings: AI-driven technologies including Natural Language Processing (NLP), speech recognition, computer vision, and adaptive learning systems significantly enhance early diagnosis, individualized instruction, and multimodal learning. For dyslexia, AI improves phonological decoding; for ADHD, it segments content to manage attention; for deaf learners, real-time captioning bridges auditory gaps; and for blind learners, screen readers and OCR enable text access.
Conclusion: AI, when aligned with inclusive pedagogy and human-centered design, holds transformative potential for equitable English language education. However, success depends on addressing ethical concerns (data privacy, algorithmic bias), technological inequities (digital divide), and pedagogical integration (teacher training). AI should empower, not replace, educators.