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About

Ezgi Mamus is a leading researcher at the intersection of human-robot interaction and developmental language acquisition. Her work focuses on designing and evaluating social robots as effective second-language tutors for young children. Mamus’s major contributions include demonstrating that robots can successfully scaffold early language learning, with her large-scale studies showing that children can acquire new vocabulary through structured, multi-lesson interactions with robots like the NAO. Her highly cited papers—including “Guidelines for Designing Social Robots as Second Language Tutors” (160 citations) and “Social Robots for Early Language Learning” (139 citations)—provide foundational frameworks for the field. Notably, her large-scale study involving 192 Dutch children learning English words (137 citations) is a landmark in child-robot interaction research. Through her work with the L2TOR project, Mamus has shown that robot tutors can be as effective as human instruction in certain contexts, paving the way for scalable, engaging educational tools. Her research continues to shape how we think about technology’s role in early childhood education and language development.