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Summary: AgentX: Using Reinforcement Learning to
Improve the Effectiveness of Intelligent Tutoring
Systems
Kimberly N. Martin and Ivon Arroyo
Department of Computer Science
University of Massachusetts
140 Governors Drive
Amherst, MA 01003
{kmartin,ivon}@cs.umass.edu
Abstract
Reinforcement Learning (RL) can be used to train an agent to comply with the
needs of a student using an intelligent tutoring system. In this paper, we introduce
a method of increasing efficiency by way of customization of the hints provided by
a tutoring system, by applying techniques from RL to gain knowledge about the
usefulness of hints leading to the exclusion or introduction of other helpful hints.
Students are clustered into learning levels and can influence the agents method
of selecting actions in each state in their cluster of affect. In addition, students
can change learning levels based on their performance within the tutoring system
and continue to affect the entire student population. The RL agent, AgentX, then
uses the cluster information to create one optimal policy for all students in the clus-
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