A Scalable, Flexible Augmentation of the Student Education Process
Bhairav Mehta, Adithya Ramanathan

TL;DR
This paper introduces a scalable, flexible intelligent tutoring system that leverages deep learning and NLP to personalize student learning, incorporate educational psychology principles, and support teachers with detailed analytics.
Contribution
The paper presents a novel educational platform integrating psychological hypotheses with scalable AI techniques, allowing personalized, real-time student support and teacher oversight.
Findings
Promising results from experiments and pilot programs.
System effectively personalizes learning at scale.
Supports diverse classroom and remote settings.
Abstract
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…
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Taxonomy
TopicsTopic Modeling · Advanced Text Analysis Techniques · Intelligent Tutoring Systems and Adaptive Learning
