  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.
