Knowledge representation for an intelligent tutoring system based on a multilevel causal model

Abstract

An explicit representation of knowledge is central for an Intelligent Tutoring System (ITS). In order for a system to acquire the necessary flexibility, its knowledge representation framework should distinguish between several types of knowledge and structure them in layers. Here, we present a method for representing domain knowledge for an ITS, using hierarchical knowledge structures and a multilevel causal model of the domain. The successive levels of this causal model increase in complexity to more closely approximate a complete domain model. The resulting knowledge structures have the flexibility that is needed to invoke a sophisticated instructional session.