Personalized Education at Scale

TL;DR

This paper discusses leveraging emerging AI technologies like reinforcement learning and natural language processing to personalize education at scale, aiming to improve student outcomes and address inequities.

Contribution

It proposes a framework for using advanced machine learning techniques to tailor educational content to individual students at large scale.

Findings

Potential for massive improvements in student outcomes

Use of RL and NLP for personalized learning

Addressing educational inequity through technology

Abstract

Tailoring the presentation of information to the needs of individual students leads to massive gains in student outcomes~\cite{bloom19842}. This finding is likely due to the fact that different students learn differently, perhaps as a result of variation in ability, interest or other factors~\cite{schiefele1992interest}. Adapting presentations to the educational needs of an individual has traditionally been the domain of experts, making it expensive and logistically challenging to do at scale, and also leading to inequity in educational outcomes. Increased course sizes and large MOOC enrollments provide an unprecedented access to student data. We propose that emerging technologies in reinforcement learning (RL), as well as semi-supervised learning, natural language processing, and computer vision are critical to leveraging this data to provide personalized education at scale.