Comfort with robots influences rapport with a social, entraining teachable robot

Nichola Lubold, Erin Walker, Heather Pon-Barry, Amy Ogan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Education - 20th International Conference, AIED 2019, Proceedings
EditorsSeiji Isotani, Eva Millán, Amy Ogan, Bruce McLaren, Peter Hastings, Rose Luckin
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783030232030
StatePublished - 2019
Externally publishedYes
Event20th International Conference on Artificial Intelligence in Education, AIED 2019 - Chicago, United States
Duration: Jun 25 2019Jun 29 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11625 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference20th International Conference on Artificial Intelligence in Education, AIED 2019
Country/TerritoryUnited States


  • Entrainment
  • Learning
  • Loudness
  • Pitch
  • Rapport
  • Teachable agent

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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