Term of Award
Spring 2025
Degree Name
Master of Science in Computer Science (M.S.)
Document Type and Release Option
Thesis (open access)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Department of Computer Science
Committee Chair
Andrew Allen
Committee Member 1
Vijayalakshmi Ramasamy
Committee Member 2
Weitian Tong
Abstract
This study explores enhanced methods for accurately identifying Attention Deficit Hyperactivity Disorder (ADHD) indicators in college students. ADHD, a neurodevelopmental disorder, impacts attention, impulse control, and emotional regulation, often leading to academic and social difficulties. Many students remain undiagnosed due to symptom overlap with stress and other factors. Traditional tools like the IVA-2 assess behavioral responses but may not fully capture ADHD complexity. This research integrates IVA-2 data with multimodal metrics from the Non-Intrusive Classroom Attention Tracking System (NiCATS), which monitors facial expressions, eye movements, and computer interactions. Preliminary results show that combining these tools improves ADHD detection, reduces false negatives, and distinguishes ADHD from anxiety-related symptoms. The findings support personalized interventions and highlight the potential of data-driven approaches to enhance diagnostic accuracy and student support.
Recommended Citation
@article{shabneen2025adhddeveloping, title={Enhancing ADHD Diagnosis in College Students Using Multimodal Integration of NiCATS and IVA-2 Tools}, author={Shabneen, Rushmila}, organization = {Georgia Southern University} year={2025} }
Research Data and Supplementary Material
No
Included in
Artificial Intelligence and Robotics Commons, Disability Studies Commons, Educational Technology Commons
