Exploring Robot's Playing Strategy with a Language Learning Robot Companion

Talgat Tursynbekov, Kairat Balkibekov, Alisher Asatarov, Anara Sandygulova

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper presents a preliminary study exploring playing strategy of a robot and the way it can affect a learning outcome of young adults. We developed a social robot that plays the role of a learning companion for a foreign language. We conducted a preliminary study where we explored two robot playing strategies, always-winning and always-losing, on the learning outcomes of the participant. In addition, we also wanted to find out whether the personality of participants might have an effect on their learning performance. This pilot study was conducted with 20 participants aged 17-25 years old. Our preliminary findings suggest that extroverts learn more when they win the robot while introverts learn more when the game ends in a draw.

Original languageEnglish
Title of host publicationHRI 2018 - Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction
PublisherIEEE Computer Society
Pages261-262
Number of pages2
ISBN (Electronic)9781450356152
DOIs
Publication statusPublished - Mar 1 2018
Event13th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2018 - Chicago, United States
Duration: Mar 5 2018 → Mar 8 2018

Conference

Conference13th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2018
CountryUnited States
CityChicago
Period3/5/18 → 3/8/18

Keywords

  • adaptive strategies
  • educational robotics
  • human-robot interaction
  • social robotics

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Electrical and Electronic Engineering

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