Self-efficacy using fuzzy control for long-term communication in robot-assisted language learning

Recently, language education has a great demand from elementary school to adults. Robots are used as teaching assistants in Robot-Assisted Language Learning. It is very effective to use robots for language education. However, the robots may have some problems. One of the problems is to get bored when interacting with robots. This paper deals with this issue by using a method based on social cognitive theory. We discuss the role of robots based on mutual learning in language education. Next, we explain the concept of self-efficacy for evaluating the learning condition of robots. We propose a method to express self-efficacy using fuzzy control. The essence of the proposed method is to adapt to human's state. The experimental results show the effectiveness of the proposed method for long-term communication between a human and a robot.

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