Computer assisted language learning (CALL) becomes more realistic and motivating for learners through introduction of humanoid robots. A robot assisted language learning (RALL) system is expected to provide an immersive environment for a second language (L2) learner to prepare for real face-to-face communication. We are developing a joining-in type RALL system using two humanoid robots, one playing the role of a teacher and the other playing the role of an advanced peer learner. The interaction between the two robots and learner is designed to smoothly switch between two learning modes, that is, tutoring and implicit learning, for effective language learning. In this paper, we measured the effect ofimplicit learning with repetitive queries quantitatively with 37 learners divided into two groups with and without interaction for implicit learning. The experimental results showed that the repetitive queries of specific grammatical expressions consistently improved the correct use of the expressions, and the improvement was significantly greater when the peer learner robot presented an answer for implicit learning compared with when there was no assistance from the robot.

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.

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This paper examines how eye gaze activities are different in between human-human and humanrobot conversations in second language (L2). The results show that the mainly-gazed-at listener gazes more at the speaker and he/she takes more often a floor in L2 conversations than in L1 conversations, whereas the speaker's eye gaze activity is almost the same in both conversations. The result shows that there is a significant positive correlation between the mainly-gazed-at listener's gazing ratio and the ratios of mainly-gazed-at listener taking a floor. Comparative analyses of eye gaze activities between human-human and human-robot conversations are also conducted. The results show that the listener gazes more at the speaker in human-robot conversations than in human-human conversations, whereas the robots do not provide the nonverbal information related to the contents of the utterances. These results may show that listeners gaze more at the speaker to show their intention to take a floor in both human-human and human-robot conversations.