Papers
4
Total Citations
17
H-Index
2
About
Mustapha Riad is a pioneering researcher at the intersection of educational technology, human-robot interaction, and cognitive computing. His work centers on leveraging humanoid robots—particularly the NAO platform—as intelligent teaching assistants capable of detecting and classifying learning disabilities such as dysgraphia. Riad’s major contributions include developing convolutional neural network (CNN) models that enable robots to identify handwriting disorders in real time, as well as creating novel “knowledge primitives” for digit recognition that enhance children’s visual learning. His most cited paper, “Dysgraphia detection based on convolutional neural networks and child-robot interaction” (2023, 9 citations), demonstrates how robotic systems can serve as scalable diagnostic tools in classrooms. Another influential work, “A New Knowledge Primitive of Digits Recognition for NAO Robot Using MNIST Dataset and CNN Algorithm” (2023, 4 citations), advances the integration of deep learning with embodied agents. Riad’s research has direct implications for inclusive education, offering teachers automated support for students with motor and expressive difficulties. His achievements include prototyping and deploying functional robot assistants that classify dysgraphia types, a step toward personalized, technology-driven learning environments.
Research Focus
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Top Papers
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