The Effect of Personalization in Longer-Term Robot Tutoring

Daniel Leyzberg, Aditi Ramachandran, Brian Scassellati

Research output: Contribution to journalArticlepeer-review

32 Scopus citations


The benefits of personalized social robots must be evaluated in real-world educational contexts over periods of time longer than a single session to understand their full potential to impact learning outcomes. In this work, we describe a personalization system designed for longer-term personalization that orders curriculum based on an adaptive Hidden Markov Model (HMM) that evaluates students' skill proficiencies. We present a study investigating the effectiveness of this system in a five-session interaction with a robot tutor, taking place over the course of 2 weeks. Our system is evaluated in the context of native Spanish-speaking first-graders interacting with a social robot tutor while completing an English Language Learning educational task. Participants either received lessons: (1) ordered by our adaptive HMM personalization system which selects a lesson based on a skill that the individual participant needs more practice with ("personalized condition") or (2) ordered randomly from among the lessons the participant had not yet seen ("non-personalized condition"). We found that participants who received personalized lessons from the robot tutor outperformed participants who received non-personalized lessons on a post-test by 2.0 standard deviations on average, corresponding to a mean learning gain in the 98th percentile.

Original languageEnglish (US)
Article number19
JournalACM Transactions on Human-Robot Interaction
Issue number3
StatePublished - Dec 5 2018

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Artificial Intelligence


  • English Language Learning (ELL)
  • Human-robot interaction
  • personalization
  • tutoring


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