# Using expert tutor knowledge to design a Self-Improving intelligent tutoring system

## Abstract

What knowledge does an intelligent tutoring system need to learn from its experiences with students and improve its tutoring, and what are the necessary learning mechanisms? I address these in discussing (1) SIFT, a Self-Improving Fractions Tutor and (2) my study of an expert tutor on whose knowledge SIFT is based. SIFT is a production system with a tutor and a learning module which learns from its interactions with the students who use it. The *students* who use it are models of problem solvers, and the input transcripts are simulations of interactions. After augmenting its knowledge, SIFT evaluates its modifications and updates its rule probabilities using the Dempster-Shafer theory of evidence—a domain-independent modification. Thus its choice of which rule to fire is determined by the empirical effects of the changes it makes to its tutorial knowledge.

## Keywords

Intelligent Tutoring System Student Modeling Rule Probability Human Tutor Compound Problem## Preview

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