Esmaeel Saeedy Robat

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Esmaeel Saeedy Robat is a leading researcher in the field of educational technology, with a primary focus on robot-assisted language learning (RALL) and second language acquisition. His most-cited work, the 2024 meta-analysis "Robot-Assisted Language Learning: A Meta-Analysis," synthesizes 27 empirical studies involving 2,637 participants to evaluate the effectiveness of RALL. By analyzing 64 effect sizes, Robat identified significant variability in outcomes, revealing crucial moderating factors such as learner age, robot type, and instructional design—insights that have reshaped how educators and technologists deploy social robots in language classrooms. With 42 citations already, this study has become a foundational reference for researchers exploring human-robot interaction in education. Robat’s contributions extend beyond meta-analysis; his work bridges cognitive science and applied linguistics, offering evidence-based guidance for integrating autonomous systems into language curricula. His rigorous methodological approach and clear synthesis of complex data have made him a sought-after voice in conferences on AI in education. For students and scholars alike, Robat’s research illuminates the promise and pitfalls of using robots as conversational partners, paving the way for more adaptive, learner-centered technologies in global language education.