Integrative Model for Biomarker Identification of Cognitive Learning Barriers in ASD

This study proposes a model for identifying biomarkers that impede the conventional learning capability in children with Autism Spectrum Disorder (ASD). Through the analysis of brain signals, eye gaze and gait movements, this model aims to delineate measurable events associated with deficiencies in responsiveness, attention, and physiological aspects in children with ASD. Leveraging Artificial Intelligence (AI) and Machine Learning (ML), the study seeks to present innovative solutions for educational interventions tailored for children with ASD, facilitating enhanced cognitive development through the identification of specific biomarker patterns. The proposed model and algorithm provide a systematic approach to biomarker identification, enabling researchers to capture nuanced insights into the cognitive and physiological factors influencing learning abilities in children with ASD. Eventually, this study aims to contribute to the development of more effective educational strategies for individuals with ASD, thereby improving their overall learning outcomes.