Usef Faghihi

Sul Ross State University, University of Memphis, Université du Québec à Montréal

Papers

Total Citations

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About

Usef Faghihi is a researcher specializing in intelligent tutoring systems (ITS), cognitive modeling, and human-computer interaction, with a particular focus on applying artificial intelligence to educational and training environments. His work sits at the intersection of cognitive science, robotics, and adaptive learning technologies, exploring how machines can be designed to support human learners in complex problem-solving tasks. Faghihi's most notable contribution is his development of multiparadigm intelligent tutoring systems, most prominently demonstrated in his 2013 paper on robotic arm training, which has garnered 30 citations. This work addressed a critical challenge in ITS design — how to effectively represent domain knowledge — by integrating multiple paradigms, including cognitive modeling and expert systems, to deliver richer and more adaptive tutoring services. His 2012 work on CELTS introduced a particularly innovative dimension to the field by incorporating human-like learning capabilities and emotional modeling into a cognitive tutoring agent, accumulating 14 citations. Together, these contributions demonstrate Faghihi's commitment to building more naturalistic, responsive, and cognitively grounded tutoring technologies. His research offers meaningful insights for educators, AI developers, and researchers working to make automated learning systems more effective and human-centered.