Maksat Bekkuliyev
Papers
1
Total Citations
2
H-Index
1
About
Maksat Bekkuliyev is a researcher at the intersection of human-robot interaction, language acquisition, and educational technology. His primary focus is on robot-assisted language learning (RALL), with a particular emphasis on how reciprocal peer tutoring (RPT) can enhance second-language acquisition. In his most-cited work, "Language Learning using Caption Generation within Reciprocal Multi-Party Child-Tutor-Tutee Interaction" (2023), Bekkuliyev explores a novel paradigm where children alternate between tutor and tutee roles, supported by a robot that generates real-time captions. This approach leverages collaborative, multi-party dynamics to improve speaking and comprehension skills. While his citation count is still growing—reflecting the early stage of his career—his work is notable for integrating natural language processing with pedagogical theory, offering a scalable, interactive solution for language classrooms. Bekkuliyev’s contributions are particularly relevant for researchers in educational robotics and AI-driven tutoring systems, as he bridges technical innovation with practical learning outcomes. His research holds promise for transforming how technology facilitates peer-to-peer language practice in diverse educational settings.
Research Focus
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Top Papers
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