Towards Sharing Student Models Across Learning Systems

Modern AIED systems develop sophisticated and multidimensional models of students. However, what is learned about students in one system-their skills, behaviors, and affect-is not carried over to other systems that could benefit students by using the information, potentially reducing both the effectiveness and efficiency of these systems. This challenge has been cited by a number of researchers as one of the most important for the field of AIED. In this paper, we discuss existing progress towards resolving this challenge, break down five sub-challenges, and propose how to address the sub-challenges.