Adapting a Teachable Robot’s Dialog Responses Using Reinforcement Learning: Cross-Cultural User Study Exploring Effect on Engagement

Abstract

Teachable robots in education have the ability to increase student engagement and learning through personalised interactions and the use of social behaviours, such as speech, gaze and emotional expressions. Adaptation of these behaviours is motivated by an interest in tailoring the learning experience to an individual user and improving user outcomes. This work proposes an adaptive response-selection algorithm for a teachable robot which aims to increase user task engagement. A Q-learning algorithm learns an individualised policy and is rewarded based on the user’s paraphrasing behaviour and response time when teaching the robot. A user study is conducted across two user groups, recruited from Australia and Japan. This study evaluates the performance of the adaptive approach for response selection against a non-adaptive method and explores the differences in response and perception of the teaching task between the two participant groups. The results show that measures of task engagement increase more when using the adaptive approach compared to a non-adaptive method of response selection, but that this difference is not consistent across both participant groups. The adaptive approach is also shown to have a positive effect on user perceptions of the interaction.