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About

Thorsten Schodde is a leading researcher at the intersection of social robotics and second-language acquisition, whose work has fundamentally shaped how robots can serve as adaptive tutors for young children. His primary research areas include human-robot interaction, intelligent tutoring systems, and affective computing in educational contexts. Schodde’s most significant contribution is the development of adaptive robot language tutoring systems that leverage Bayesian knowledge tracing and predictive decision-making to personalize instruction in real time. His large-scale study involving 192 Dutch five-year-olds learning English from a NAO robot—published in 2019 with 137 citations—demonstrated that social robots can effectively teach vocabulary over multiple sessions, while his 2018 guidelines for designing social robot tutors (160 citations) have become a foundational reference in the field. Schodde also pioneered the integration of iconic gestures and adaptive scaffolding to enhance learning outcomes, and he explored how robots can manage children’s affective states during tutoring. His work, supported by the L2TOR project, has not only advanced theoretical understanding but also provided practical frameworks for deploying robots in real-world classrooms, making him a pivotal figure in educational robotics.