  We present an adaptive learning Intelligent Tutoring System, which uses
model-based reinforcement learning in the form of contextual bandits to assign
learning activities to students. The model is trained on the trajectories of
thousands of students in order to maximize their exercise completion rates and
continues to learn online, automatically adjusting itself to new activities. A
randomized controlled trial with students shows that our model leads to
superior completion rates and significantly improved student engagement when
compared to other approaches. Our approach is fully-automated unlocking new
opportunities for learning experience personalization.
