Personalized Robot Tutoring Using the Assistive Tutor POMDP (AT-POMDP)


  • Aditi Ramachandran Yale University
  • Sarah Strohkorb Sebo Yale University
  • Brian Scassellati Yale University



Selecting appropriate tutoring help actions that account for both a student’s content mastery and engagement level is essential for effective human tutors, indicating the critical need for these skills in autonomous tutors. In this work, we formulate the robot-student tutoring help action selection problem as the Assistive Tutor partially observable Markov decision process (AT-POMDP). We designed the AT-POMDP and derived its parameters based on data from a prior robot-student tutoring study. The policy that results from solving the AT-POMDP allows a robot tutor to decide upon the optimal tutoring help action to give a student, while maintaining a belief of the student’s mastery of the material and engagement with the task. This approach is validated through a between-subjects field study, which involved 4th grade students (n=28) interacting with a social robot solving long division problems over five sessions. Students who received help from a robot using the AT-POMDP policy demonstrated significantly greater learning gains than students who received help from a robot with a fixed help action selection policy. Our results demonstrate that this robust computational framework can be used effectively to deliver diverse and personalized tutoring support over time for students.




How to Cite

Ramachandran, A., Sebo, S. S., & Scassellati, B. (2019). Personalized Robot Tutoring Using the Assistive Tutor POMDP (AT-POMDP). Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 8050-8057.



AAAI Technical Track: Robotics