AI for Students with Learning Disabilities: A Systematic Review

 Panjwani-Charani, S. & Zhai, X. (in press). AI for Students with Learning Disabilities: A Systematic Review. In X. Zhai & J. Krajcik (Eds.), Uses of Artificial Intelligence in STEM Education (pp. xx-xx). Oxford, UK: Oxford University Press.

28 Pages Posted: 2 Nov 2023 Last revised: 8 Feb 2024

Date Written: October 30, 2023

Abstract

This review study aims to uncover how artificial intelligence (AI) has been employed to support students with learning disabilities (SWLDs). Of the 16 reviewed studies, 10 were focused on dyslexia, with only one focused on dyscalculia and the remaining fo- cused 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 robots, communication as- sistants, mastery learning, intelligent tutors, and interactive robots. 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 (4 substitution, 6 augmentation, 2 modification, and 4 redefinition lev- els). 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.

Keywords: learning disabilities, artificial intelligence (AI), dyslexia, dyscalculia, adaptive learning, SAMR-LD

Suggested Citation

Panjwani-Charania, Sahrish and Zhai, Xiaoming, AI for Students with Learning Disabilities: A Systematic Review (October 30, 2023).  Panjwani-Charani, S. & Zhai, X. (in press). AI for Students with Learning Disabilities: A Systematic Review. In X. Zhai & J. Krajcik (Eds.), Uses of Artificial Intelligence in STEM Education (pp. xx-xx). Oxford, UK: Oxford University Press. , Available at SSRN: https://ssrn.com/abstract=4617715

Sahrish Panjwani-Charania

The University of Georgia

Xiaoming Zhai (Contact Author)

AI4STEM Education Center ( email )

110 Carlton Street
Athens, GA GA 30602
United States
7065424548 (Phone)

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