We describe a novel framework developed for transfer learning within reinforcement learning (RL) problems. Then we exhibit how this framework can be extended to intelligent tutoring systems (ITS). We compose an algorithm that automatically constructs a graphical representation based on the transfer framework. We evaluate this on a real-world ITS example and show that the model constructed by our approach performs better than previously published results. We propose that transfer learning is a useful and related area to explore for furthering intelligent tutoring systems.