CSE Students Secure First Place Awards at CENG Capstone Design Competition 2026
Two Computer Science and Engineering (CSE) senior project teams proudly received first-place awards at the CENG Capstone Design Competition 2026, held on June 2, 2026. The competition featured two categories: Best Prototype and Best Marketable Design, highlighting innovative projects developed by graduating students across the College of Engineering.
The first-place award in the Best Prototype category was presented to the project “ITA’AM – Autonomous Assistive Feeding Robot: An AI-Powered Feeding Assistant for People with Upper-Limb Disabilities.” The project was developed by Computer Engineering students Abdallah Alkanani, Ali Ghazi, Khaled Qarawi, and Abdulrahman Shabban under the supervision of Dr. Ahmed Badawy.
ITA’AM addresses the challenges faced by individuals with upper-limb disabilities who often have limited options for independent eating. The project introduces a low-cost autonomous feeding assistant that combines robotic manipulation, computer vision, facial-gesture interaction, reinforcement learning, and ROS 2 communication to support safe, hygienic, and dignified independent feeding. Through its innovative design and AI-powered capabilities, the project demonstrates the potential of assistive robotics to enhance quality of life and accessibility for individuals with disabilities.
The first-place award in the Best Marketable Design category was awarded to “A’BER – AI-Powered Dysarthria Speech Therapy System.” The project was developed by Computer Engineering students Yahya Abdulselam, Aiman Alhetari, Abdelrahman Kotb, and Ahmed Mohamed under the supervision of Dr. Moutaz Saleh.
A’BER is an AI-driven speech therapy platform designed to support individuals with dysarthria, a motor speech disorder commonly caused by neurological conditions such as stroke, cerebral palsy, and Parkinson’s disease. The system integrates speech impairment severity detection, phoneme-level error analysis, and personalized therapy planning using advanced machine learning models and large language models. Delivered through an accessible web application, the platform provides patients with adaptive therapy exercises and AI-generated feedback, offering a scalable solution that can improve access to speech rehabilitation services.
These achievements reflect the creativity, technical excellence, and dedication of our students and faculty supervisors. The awarded projects demonstrate how innovative engineering solutions can address real-world challenges in healthcare and accessibility while creating meaningful societal impact.
