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Comfort with Robots Influences Rapport with a Social, Entraining Teachable Robot

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Artificial Intelligence in Education (AIED 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11625))

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Abstract

Teachable agents are pedagogical agents that employ the ‘learning-by-teaching’ strategy, which facilitates learning by encouraging students to construct explanations, reflect on misconceptions, and elaborate on what they know. Teachable agents present unique opportunities to maximize the benefits of a ‘learning-by-teaching’ experience. For example, teachable agents can provide socio-emotional support to learners, influencing learner self-efficacy and motivation, and increasing learning. Prior work has found that a teachable agent which engages learners socially through social dialogue and paraverbal adaptation on pitch can have positive effects on rapport and learning. In this work, we introduce Emma, a teachable robotic agent that can speak socially and adapt on both pitch and loudness. Based on the phenomenon of entrainment, multi-feature adaptation on tone and loudness has been found in human-human interactions to be highly correlated to learning and social engagement. In a study with 48 middle school participants, we performed a novel exploration of how multi-feature adaptation can influence learner rapport and learning as an independent social behavior and combined with social dialogue. We found significantly more rapport for Emma when the robot both adapted and spoke socially than when Emma only adapted and indications of a similar trend for learning. Additionally, it appears that an individual’s initial comfort level with robots may influence how they respond to such behavior, suggesting that for individuals who are more comfortable interacting with robots, social behavior may have a more positive influence.

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Acknowledgements

This work is supported by the National Robotics Initiative and the National Science Foundation, grant #CISE-IIS-1637809.

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Authors and Affiliations

  1. Arizona State University, Tempe, AZ, 85281, USA

    Nichola Lubold

  2. University of Pittsburgh, Pittsburgh, PA, 15260, USA

    Erin Walker

  3. Mount Holyoke College, South Hadley, MA, 01075, USA

    Heather Pon-Barry

  4. Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Amy Ogan

Authors
  1. Nichola Lubold
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  2. Erin Walker
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  4. Amy Ogan
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Corresponding author

Correspondence to Nichola Lubold .

Editor information

Editors and Affiliations

  1. University of Sao Paulo, Sao Paulo, Brazil

    Seiji Isotani

  2. University of Malaga, Málaga, Spain

    Eva Millán

  3. Carnegie Mellon University, Pittsburgh, PA, USA

    Amy Ogan

  4. DePaul University, Chicago, IL, USA

    Peter Hastings

  5. Carnegie Mellon University, Pittsburgh, PA, USA

    Bruce McLaren

  6. University College London, London, UK

    Rose Luckin

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Lubold, N., Walker, E., Pon-Barry, H., Ogan, A. (2019). Comfort with Robots Influences Rapport with a Social, Entraining Teachable Robot. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds) Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science(), vol 11625. Springer, Cham. https://doi.org/10.1007/978-3-030-23204-7_20

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  • DOI: https://doi.org/10.1007/978-3-030-23204-7_20

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-23204-7

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