Kairat Balkibekov

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About

Kairat Balkibekov is a researcher at the forefront of human-robot interaction, with a specific focus on educational robotics and child-robot learning dynamics. His work challenges traditional paradigms by exploring how robots can serve not as all-knowing tutors, but as fallible, social peers. Balkibekov’s major contribution lies in investigating the strategic use of robot failure—specifically, whether a robot that occasionally loses in games can enhance a child’s learning experience. His most-cited paper, “Should robots win or lose? Robot's losing playing strategy positively affects child learning” (2016, 21 citations), demonstrates that a peer-like robot, which also learns a foreign language alongside the child, creates a more engaging and effective educational environment. This work, along with his closely related study “You win, I lose”: Towards adapting robot's teaching strategy” (2016, 16 citations), provides empirical evidence that a robot’s perceived vulnerability can foster deeper cognitive and social engagement in young learners. By shifting the focus from robot competence to collaborative growth, Balkibekov’s research offers a compelling framework for designing more empathetic and effective educational technologies.