Learning Effect of Implicit Learning in Joining-in-type Robot-assisted Language Learning System

AlBara Khalifa, Tsuneo Kato, Seiichi Yamamoto


The introduction of robots into language learning systems has been highly useful, especially in motivating learners to engage in the learning process and in letting human learners converse in more realistic conversational situations. This paper describes a novel robot-assisted language learning system that induces the human learner into a triad conversation with two robots through which he or she improves practical communication skills in various conversational situations. The system applies implicit learning as the main learning style for conveying linguistic knowledge, in an indirect way, through conversations on several topics. A series of experiments was conducted using 80 recruited participants to evaluate the effect of implicit learning and the retention effect in a joining-in-type robot-assisted language learning system. The experimental results show positive effects of implicit learning and repetitive learning in general. Based on these experimental results, we propose an improved method, integrating implicit learning and tutoring with corrective feedback in an adaptive way, to increase performance in practical communication skills even for a wide variety of proficiency of L2 learners.


Computer Assisted Language Learning (CALL), Robot Assisted Language Learning (RALL), Implicit Learning, Corrective Feedback

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Copyright (c) 2019 AlBara Khalifa, Tsuneo Kato, Seiichi Yamamoto

International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
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