  We present a novel intelligent tutoring system which builds upon
well-established hypotheses in educational psychology and incorporates them
inside of a scalable software architecture. Specifically, we build upon the
known benefits of knowledge vocalization, parallel learning, and immediate
feedback in the context of student learning. We show that open-source data
combined with state-of-the-art techniques in deep learning and natural language
processing can apply the benefits of these three factors at scale, while still
operating at the granularity of individual student needs and recommendations.
Additionally, we allow teachers to retain full control of the outputs of the
algorithms, and provide student statistics to help better guide classroom
discussions towards topics that would benefit from more in-person review and
coverage. Our experiments and pilot programs show promising results, and cement
our hypothesis that the system is flexible enough to serve a wide variety of
purposes in both classroom and classroom-free settings.
