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We present a novel intelligent tutoring system which builds uponwell-established hypotheses in educational psychology and incorporates theminside of a scalable software architecture. Specifically, we build upon theknown benefits of knowledge vocalization, parallel learning, and immediatefeedback in the context of student learning. We show that open-source datacombined with state-of-the-art techniques in deep learning and natural languageprocessing can apply the benefits of these three factors at scale, while stilloperating at the granularity of individual student needs and recommendations.Additionally, we allow teachers to retain full control of the outputs of thealgorithms, and provide student statistics to help better guide classroomdiscussions towards topics that would benefit from more in-person review andcoverage. Our experiments and pilot programs show promising results, and cementour hypothesis that the system is flexible enough to serve a wide variety ofpurposes in both classroom and classroom-free settings.

A Scalable, Flexible Augmentation of the Student Education Process