Assistive Technologies for Learning Disabilities: A Systematic Review of Trends and Impact

Aroudra Syamantak Thakur

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Conference: 2025 ASEE -GSW Annual Conference
Location: Arlington, TX, Texas
Publication Date: March 9, 2025
Start Date: March 9, 2025
End Date: March 11, 2025
Tagged Topic: Diversity
Page Count: 2
Permanent URL: https://peer.asee.org/55030

Paper Authors

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Aroudra Syamantak Thakur The University of Texas at Arlington

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Aroudra Syamantak Thakur is currently an undergraduate student at the University of Texas at Arlington, pursuing a BSc Honors in Computer Science with minors in Mathematics and Business Administration. His research interests include artificial intelligence (AI) and computer vision. Aroudra has experience applying various machine learning algorithms and models for human-computer interaction and assistive technologies, and he is particularly interested in how AI-assisted technologies can support adaptive learning tools for diverse learning styles.

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Abstract

Special education aims to equip students with learning disabilities or differences with the academic and social skills necessary to thrive independently in society. However, there are several problems that arise while delivering special education. Special education teachers are often in short supply due to the need for specialized training. Additionally, stigmatization and bias impact the care provided to students with special needs. Woodcock & Moore (2021) found that teachers tend to lower expectations for these students, offering positive feedback despite failures and prioritizing empathy over the child’s futures. Artificial Intelligence offers a potential solution to this problem. Free from social biases, AI not only helps alleviate the shortage of specially trained teachers, but it also provides necessary, personalized learning. Our study is a review of AI-assisted learning for students with learning disabilities and its potential enhance inclusive learning methods, helping these students become fully integrated members of society.

AI models such as, DreamBox Learning on Math Achievement (Wang & Woodworth, 2011), Computer Based Speech Therapy (Schipor et al., 2003) have been proven to aid in special education and target specific disabilities of a student, helping develop social and academic skills. Other AI tutoring systems, such as Intelligent Tutoring Systems (Koedinger et al. (2013), can also be implemented to aid these children by providing personalized learning. These models focus on completion time, accuracy, and holistic clarity to ensure that students not only finish the problems assigned but also gain knowledge from it. By providing regular input and performance analysis, while ensuring progress, AI-assisted learning models may improve student engagement in special education.

This review will analyze several studies exploring the benefits of Artificial Intelligence and AI-assisted teaching methods in special education. The first step would be to define clear inclusion and exclusion criteria and conduct a literature search across relevant data sites and resource pages, focusing on studies in the last two decades that measure outcomes such as academic performance, behavioral improvements, and student engagement. Primary focus would be on AI models that benefit learning and engagement among students with learning disabilities in the classroom. Effect sizes would be calculated using statistical models such as Cohen’s d to assess the quality of the study. The results can then be analyzed for heterogeneity and generalizability to determine the overall impact the AI-model had on the learning of the children.

By examining the effectiveness of personalized learning for students with ADHD, ADD, dyslexia, autism, and other learning disabilities, this review aims to highlight how AI can contribute to inclusive education and emphasize the benefits of using AI-assisted learning in special education. While AI cannot replace teachers, it has immense potential to enhance traditional methods by helping design more effective curricula and supporting teacher training in special education.

Citation
Format

Thakur, A. S. (2025, March), Assistive Technologies for Learning Disabilities: A Systematic Review of Trends and Impact Paper presented at 2025 ASEE -GSW Annual Conference, Arlington, TX, Texas. https://peer.asee.org/55030

TY - CPAPER
AB - Special education aims to equip students with learning disabilities or differences with the academic and social skills necessary to thrive independently in society. However, there are several problems that arise while delivering special education. Special education teachers are often in short supply due to the need for specialized training. Additionally, stigmatization and bias impact the care provided to students with special needs. Woodcock &amp;amp; Moore (2021) found that teachers tend to lower expectations for these students, offering positive feedback despite failures and prioritizing empathy over the child’s futures. Artificial Intelligence offers a potential solution to this problem. Free from social biases, AI not only helps alleviate the shortage of specially trained teachers, but it also provides necessary, personalized learning. Our study is a review of AI-assisted learning for students with learning disabilities and its potential enhance inclusive learning methods, helping these students become fully integrated members of society.

AI models such as, DreamBox Learning on Math Achievement (Wang &amp;amp; Woodworth, 2011), Computer Based Speech Therapy (Schipor et al., 2003) have been proven to aid in special education and target specific disabilities of a student, helping develop social and academic skills. Other AI tutoring systems, such as Intelligent Tutoring Systems (Koedinger et al. (2013), can also be implemented to aid these children by providing personalized learning. These models focus on completion time, accuracy, and holistic clarity to ensure that students not only finish the problems assigned but also gain knowledge from it. By providing regular input and performance analysis, while ensuring progress, AI-assisted learning models may improve student engagement in special education.

This review will analyze several studies exploring the benefits of Artificial Intelligence and AI-assisted teaching methods in special education. The first step would be to define clear inclusion and exclusion criteria and conduct a literature search across relevant data sites and resource pages, focusing on studies in the last two decades that measure outcomes such as academic performance, behavioral improvements, and student engagement. Primary focus would be on AI models that benefit learning and engagement among students with learning disabilities in the classroom. Effect sizes would be calculated using statistical models such as Cohen’s d to assess the quality of the study. The results can then be analyzed for heterogeneity and generalizability to determine the overall impact the AI-model had on the learning of the children.

By examining the effectiveness of personalized learning for students with ADHD, ADD, dyslexia, autism, and other learning disabilities, this review aims to highlight how AI can contribute to inclusive education and emphasize the benefits of using AI-assisted learning in special education. While AI cannot replace teachers, it has immense potential to enhance traditional methods by helping design more effective curricula and supporting teacher training in special education.
AU - Aroudra Syamantak Thakur
CY - Arlington, TX, Texas
DA - 2025/03/09
PB - ASEE Conferences
TI - Assistive Technologies for Learning Disabilities: A Systematic Review of Trends and Impact
UR - https://peer.asee.org/55030
ER -