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


  • Kenneth R. Koedinger Carnegie Mellon University
  • Emma Brunskill Carnegie Mellon University
  • Ryan S.J.d. Baker Columbia University
  • Elizabeth A. McLaughlin Carnegie Mellon University
  • John Stamper Carnegie Mellon University



Increasing widespread use of educational technologies is producing vast amounts of data. 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 data-driven 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.

Author Biography

Ryan S.J.d. Baker, Columbia University

Teacher's College




How to Cite

Koedinger, K. R., Brunskill, E., Baker, R. S., McLaughlin, E. A., & Stamper, J. (2013). New Potentials for Data-Driven Intelligent Tutoring System Development and Optimization. AI Magazine, 34(3), 27-41.