IMPROVING THE SCOPE OF INTELLIGENT TUTORING BY ADAPTING A CASE-BASED METHODOLOGY THROUGH A DISTRIBUTED ARCHITECTURE

Abstract

This paper describes an architecture for distributed case-based tutoring, called DICABTU, which provides an environment that facilitates cooperation among independent agents working together to provide highly individualized instruction. The fusion of these agents through a blackboard platform creates a distributed learning environment in which the most competent agents are called up to assist a student during a tutoring session. Following a curriculum derived from a node-based knowledge network, case-based reasoning is used to compose lessons at various levels of knowledge, to generate teaching materials, and to solve problems.