Advancing Natural Language Processing For Adaptive Assistive Technologies In Reading And Writing Disabilities

Abstract

The present research endeavors to explore the application of Natural Language Processing (NLP)-driven assistive technologies in the context of individuals afflicted with reading and writing impairments within the Kingdom of Saudi Arabia. By employing both descriptive and inferential statistical analyses, we aim to explore the disparities in reading and writing proficiencies among students afflicted with dyslexia and dysgraphia. Additionally, we endeavor to investigate their inclinations towards Natural Language Processing (NLP)-based assistive technologies. The present study encompasses a cohort of 150 individuals, comprising 100 students diagnosed with dyslexia and an additional 50 individuals diagnosed with dysgraphia. The findings indicate a lack of statistically significant disparities in the reading and writing proficiencies exhibited by both cohorts. Nevertheless, it is worth noting that the inclinations towards NLP-driven assistive technologies manifest a pronounced predilection for the utilization of text-to-speech functionalities, word prediction algorithms, and language simplification tools. The discoveries emphasize the imperative for tailored methodologies in the creation and execution of adaptive assistive technologies for individuals who experience challenges in reading and writing.