Teachable robots learn what to say: Improving child engagement during teaching interaction

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Abstract

Teachable robots are a promising technology to promote engagement in the classroom for young students. They are capable of displaying and adapting different social behaviours and characteristics, such as speech, gaze, and vocal pitch, to personalise the teaching interaction, and sustain interest for students over time. Research in this field shows a growing use of reinforcement learning to achieve this adaptation, however there is limited research on adaptive dialog behaviours for teachable robots. Our work proposes an adaptive dialog selection algorithm, implemented using Q-learning, which aims to personalise the dialog choices of a teachable robot in order to optimise for task engagement, measured by the time taken per teaching input, and the amount paraphrasing in the user’s response. We investigate the effect of this approach in a case study with children aged 9–10 years old. The results show that this demographic responds positively to the teaching interaction, and provide useful insights into their preferences and abilities.

Original languageEnglish
Title of host publicationSocial Robotics - 15th International Conference, ICSR 2023 Doha, Qatar, December 3–7, 2023 Proceedings, Part II
EditorsAbdulaziz Al Ali, John-John Cabibihan, Nader Meskin, Silvia Rossi, Wanyue Jiang, Hongsheng He, Shuzhi Sam Ge
Place of PublicationSingapore Singapore
PublisherSpringer
Pages27-37
Number of pages11
ISBN (Electronic)9789819987184
ISBN (Print)9789819987177
DOIs
Publication statusPublished - 2024
EventInternational Conference on Social Robotics 2023 - Doha, Qatar
Duration: 3 Dec 20237 Dec 2023
Conference number: 15th
https://link.springer.com/book/10.1007/978-981-99-8718-4 (Proceedings)
https://icsr23.qa/ (Website)

Publication series

NameLecture Notes in Computer Science
Publisher Springer
Volume14454
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Social Robotics 2023
Abbreviated titleICSR 2023
Country/TerritoryQatar
CityDoha
Period3/12/237/12/23
Internet address

Keywords

  • Adaptive Behaviours
  • Child-Robot Interaction (CRI)
  • Engagement
  • Teachable Robots

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