Adaptive Artificial Intelligence for Students with Specific Learning Disabilities in Reading Science Content

Abstract

Richard Lamb, Tosha Owens, Danielle Malone and Sai Kala Sagar Nookarapu

Purpose: This study investigated whether real-time adaptation of science text complexity using neurocognitive data can improve reading comprehension and performance for students with dyslexia.

Materials and Methods: One hundred participants (50 with dyslexia, 50 neurotypical) completed science reading tasks while functional near-infrared spectroscopy (fNIRS) recorded hemodynamic responses. A deep Convolutional Neural Network (CNN) classified cognitive demand into high, moderate, or low levels. Textual features such as reading level, lexical density, and complexity were dynamically adjusted based on these classifications.

Results: The CNN achieved an accuracy of 0.86 in classifying cognitive demand. Adaptive text adjustments significantly improved comprehension scores for students with dyslexia (55% to 75%) and test performance (60% to 78%) (p < .001). Neurotypical students showed modest gains. The approach demonstrated that real-time adaptation based on cognitive load can reduce overload and enhance accessibility.

Conclusions: Integrating neurocognitive data with adaptive AI systems offers a promising pathway to personalize science education for students with reading disabilities. This method improves comprehension and performance while supporting inclusive learning environments. Future research should explore multimodal supports and long-term impacts of adaptive AI technologies in diverse educational contexts.

PDF