Papers
10
Total Citations
56
H-Index
4
About
Mohammed Qbadou is a researcher whose work sits at the compelling intersection of artificial intelligence, human-robot interaction, and assistive technology for children with special educational needs. His scholarship is primarily focused on socially assistive robotics, machine learning, and the development of intelligent systems to support therapeutic and pedagogical interventions for children with autism spectrum disorders and learning disabilities such as dysgraphia. Qbadou's most influential contribution — "Towards Robot-Assisted Therapy for Children With Autism" (2022, 24 citations) — demonstrates his pioneering application of ontological knowledge models and reinforcement learning algorithms to robot-assisted therapy, positioning him as a forward-thinking voice in rehabilitation robotics. His sustained focus on dysgraphia has yielded multiple notable works, including convolutional neural network-based detection systems and humanoid robot assistants capable of classifying students by dysgraphia type, bridging the gap between clinical diagnosis and classroom support. Beyond therapeutic applications, Qbadou has explored multilingual human-robot communication, head pose estimation using fuzzy lattice reasoning, and the deployment of humanoid platforms like NAO for visual learning enhancement. With over 50 cumulative citations across a focused body of work, his research offers meaningful and growing contributions to the future of inclusive, technology-driven education.
Research Focus
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