Journal of Educational & Psychological Research(JEPR)

University of Georgia, College of Veterinary, Medicine and College of Pharmacy, Neurocognition Science Laboratory, United States

Publications

• Research Article   
  Adaptive Artificial Intelligence for Students with Specific Learning Disabilities in Reading Science Content
  Author(s): 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.. Read More»
  

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