Developing an Intelligent Tutoring System for Personalized Skill Development Using Reinforcement Learning

Abstract

The one-size-fits-all model of traditional education is increasingly inadequate for addressing diverse learner needs. Intelligent Tutoring Systems (ITS) offer a solution but are often limited by static, hand-crafted pedagogical rules that cannot optimize long-term learning trajectories. This paper presents the design, implementation, and empirical validation of RL-Tutor, a novel ITS that leverages Deep Reinforcement Learning (RL) to provide dynamic, personalized instruction. RL-Tutor integrates a Deep Knowledge Tracing model based on a Dynamic Key-Value Memory Network (DKVMN) to maintain a rich, continuous representation of the student's knowledge state. This state serves as the input to a Proximal Policy Optimization (PPO) agent, which functions as the pedagogical module, selecting optimal actions from a hierarchical space including problem selection, hint provision, and instructional review. A critical contribution is the formulation of a multi-faceted reward function that balances immediate performance, learning efficiency, and long-term knowledge retention. Due to the sample inefficiency of RL, the agent was first trained in a high-fidelity simulated environment with a population of 10,000 synthetic students. The trained system was then evaluated against a rule-based tutor and a static tutor in a between-subjects human study (N=90) in the domain of introductory Python programming. Results show that RL-Tutor led to significantly higher normalized learning gains (0.72 vs. 0.58 for rule-based and 0.45 for static, p < 0.01) and better retention in a one-week delayed post-test. Analysis of the learned policy revealed emergent, pedagogically sound strategies such as adaptive hinting and implicit spaced repetition. This work establishes that RL can autonomously discover complex, effective teaching policies that are tailored to individual learners and outperform traditional ITS architectures.