THE INTEGRATION OF ARTIFICIAL INTELLIGENCE IN PHONICS-BASED BLENDING INSTRUCTION FOR STUDENTS WITH SPECIFIC LEARNING DISABILITIES AT THE PRIMARY LEVEL

Abstract

Early reading proficiency is a critical determinant of academic success. However, students with Specific Learning Disabilities (SLD), particularly dyslexia, frequently experience persistent difficulties in phonological processing and word decoding. Phonics-based blending instruction has long been recognized as an effective approach for supporting early literacy development, yet traditional classroom implementation often lacks the individualized feedback necessary for students with reading disabilities. Recent advances in Artificial Intelligence (AI) offer new opportunities to enhance phonics instruction through adaptive learning systems, automated speech recognition, and intelligent tutoring technologies. This paper examines the integration of AI-assisted tools in phonics-based blending instruction for primary school students with SLD. Drawing on theoretical frameworks including Cognitive Load Theory and the Zone of Proximal Development, the study explores how AI-supported systems can provide real-time feedback, reduce instructional barriers, and personalize learning pathways. Empirical findings from recent studies indicate that AI-assisted literacy interventions significantly improve phonemic awareness, decoding accuracy, and reading fluency among struggling readers. A quasi-experimental comparison of AI-supported instruction and traditional phonics instruction demonstrates measurable gains in reading performance among students using AI tools. The results suggest that AI technologies can function as effective supplementary instructional tools within inclusive classrooms. While concerns regarding data privacy, algorithmic bias, and digital access remain, the integration of AI in phonics-based instruction represents a promising direction for improving literacy outcomes among students with learning disabilities.