AgentX: Using reinforcement learning to improve the effectiveness of intelligent tutoring systems

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 cluster and begin to customize the help given to the cluster based on that optimal policy.