Understanding Mathematical Story Problems in Students with Dyslexia In the Context of Artificial Intelligence

Abstract

This study was driven by the challenges dyslexic students face in understanding mathematical word problems, stemming from decoding, language processing, and neurological difficulties. The research aims to describe and examine how dyslexic students comprehend math word problems with support from the Game-Math TCM AI, which provides text-to-speech and visual manipulative features. This study employed a descriptive qualitative case study involving two sixth-grade students with dyslexia selected through screening tests. Before completing five addition and subtraction word problems, the students were introduced to the AI features used in the learning activities. Data were gathered from worksheets, screen recordings of student AI interactions, and semi-structured interviews. The analysis focused on four comprehension indicators: interpreting, classifying, inferring, and comparing. Findings show that text-to-speech assisted students in understanding the problem content (interpreting), while visual manipulatives supported their ability to identify and select the appropriate operations (classifying). Both features also helped students outline the steps toward conclusions (inferring), and the visual tools enabled them to verify their understanding (comparing). Overall, the combination of text-to-speech, visual manipulatives, and guided support reduced cognitive demands and strengthened dyslexic students’ understanding of math word problems through the Game-Math TCM platform.