AI for Students with Learning Disabilities

Abstract

Abstract This review study aims to uncover how artificial intelligence (AI) has been employed to support students with learning disabilities (SWLDs). Of the sixteen reviewed studies, ten were focused on dyslexia, with only one focused on dyscalculia and the remaining focused on learning disabilities in general. The study suggests that only 50% of studies focused on school-age children. Seven types of AI applications were used to support SWLDs, including adaptive learning, facial expression, chat robot, communication assistant, mastery learning, intelligent tutor, and interactive robot. The adaptive learning was the most widely used. Employing the SAMR-LD (i.e., substitute, augment, modify, and redefine—learning disability) model, we found that AI had been utilized in various ways to support SWLDs (four substitutions, six augmentations, two modifications, and four redefinition levels). Findings revealed the potential of AI in supporting SWLDs, but the small number of empirical studies also implies significant gaps and the need for more research on how AI can support SWLDs beyond just identifying and diagnosing a learning disability.