From Assistive Technology to Intelligent Pedagogy: How AI Can Transform Individualized Instruction for Learners with Disabilities

Abstract

Artificial intelligence (AI) is shifting assistive technology from a largely tool-centered model toward increasingly adaptive, data-informed and pedagogically responsive forms of support. It's a significant integrative offer for considering that transition and the potential that 'AI' may have in creating more individualized teaching that doesn't repeat patterns of exclusion, surveillance, bias and dependence. It draws on recent systematic reviews, meta-analysis, international policy guidance, accessibility standards and fundamental research relating to universal design for learning (UDL), assistive technology and personalized learning and integrates evidence from a range of learning disabilities, including autism, sensory disabilities, physical disabilities and intellectual/developmental disabilities. Giving to the evaluation, AI eased systems can enhance to the purposes of modified education by adaptive job sequencing, multimodal depiction, dialog and linguistic ropes, computerized feedbacks, brainy education, insides alteration finished convenience, communiqué provision and nursing development and contextually support. This signal is talented, as a 2026 meta-analysis originate an intermediate result scope (Hedges' g = 0.588) for general effect of AI interferences for scholars with infirmities, with advanced consequence dimensions when the consequence degree was a theoretical attainment amount, and a 2025 methodical appraisal of AI interferences for pupils with erudition incapacities comprised 11 revisions with optimistic conclusions, though there is noteworthy jeopardy of prejudice. These exposes are altogether about protected yet confident hopefulness in its place of high-tech determinism. In the midpoint of the discussion there is a trust that no one should expression at AI as an another for the singular teachers or counsellors, peoples or agency of beginners. Instead, it requirements to be an ingenious coating of teaching in a situation where admittance is at the central and the humanoid issue is leading. The artefact essences on the Intelligent Individualized Pedagogy (IIP) outline which contains seven values of communication: (1) pupil intervention, (2) convenience by strategy, (3) active personalization, (4) multimodal framework, (5) human-AI co-regulation, (6) evidence-based adaptation, and (7) principled answerability. The system distills these concepts into a pedagogical process – from learner profiling to setting goals, adapting the learning process with the help of AI to formative assessment, teacher feedback, and subsequent learning process adjustments – which can be repeated. Implications for teachers, teacher educators, school leaders, and policy makers and researchers, especially in low resource environments, are provided. At the end of the article, the authors say that the transition to be enacted is not “From teacher to AI” but rather “From static accommodation to responsive pedagogy” where AI can “broaden the scope, precision, and speed of the accommodation that can be provided to a diverse group of students.