TY - JOUR AU - Ramachandran, Aditi AU - Sebo, Sarah Strohkorb AU - Scassellati, Brian PY - 2019/07/17 Y2 - 2024/03/28 TI - Personalized Robot Tutoring Using the Assistive Tutor POMDP (AT-POMDP) JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 33 IS - 01 SE - AAAI Technical Track: Robotics DO - 10.1609/aaai.v33i01.33018050 UR - https://ojs.aaai.org/index.php/AAAI/article/view/4807 SP - 8050-8057 AB - <p>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 (<em>n</em>=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.</p> ER -