New Potentials for Data-Driven Intelligent Tutoring System Development and Optimization

Such data can be used to help advance our understanding of student learning and enable more intelligent, interactive, engaging, and effective education. In this article, we discuss the status and prospects of this new and powerful opportunity for datadriven development and optimization of educational technologies, focusing on intelligent tutoring systems. We provide examples of use of a variety of techniques to develop or optimize the select, evaluate, suggest, and update functions of intelligent tutors, including probabilistic grammar learning, rule induction, Markov decision process, classification, and integrations of symbolic search and statistical inference. AI methods have advanced considerably since those early days, and so have intelligent tutoring systems. Today, intelligent tutoring systems are in widespread use in K-12 schools and colleges and are enhancing the student learning experience (for example, Graesser et al. [2005]; Mitrovic [2003]; VanLehn [2006]).