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About

Fouad Sakr is a researcher whose work bridges the cutting edge of artificial intelligence with the critical need for human-centered validation. His research spans two distinct yet interconnected domains: the development of efficient, deployable AI for resource-constrained hardware, and the rigorous psychometric assessment of how people perceive and interact with AI. In hardware AI, Sakr has made notable contributions to edge computing, including a study on memory-efficient binary convolutional neural networks for microcontrollers (10 citations) and a tiny CNN designed for embedded electronic skin systems (5 citations), pushing the boundaries of AI on limited-resource devices. Simultaneously, he has pioneered the cross-cultural validation of AI literacy and perception scales. His most-cited work, a multinational validation of the Arabic version of the Artificial Intelligence Literacy Scale (AILS) among university students in four Arab countries (14 citations, 2024), addresses a critical gap by providing the first Arabic tool to measure AI literacy. He has also validated an Arabic translation of the Fear of Autonomous Robots and AI scale (4 citations) and explored the mediating role of a dark future between personality traits and AI fear (3 citations). Sakr’s dual focus on making AI both technically efficient and socially understood marks him as a versatile and impactful scholar.