Deeper natural language processing for evaluating student answers in intelligent tutoring systems


This paper addresses the problem of evaluating students' answers in intelligent tutoring environments with mixed-initiative dialogue by modelling it as a textual entailment problem. The problem of meaning representation and inference is a pervasive challenge in any integrated intelligent system handling communication. For intelligent tutorial dialogue systems, we show that entailment cases can be detected at various dialog turns during a tutoring session. We report the performance of a lexico-syntactic approach on a set of entailment cases that were collected from a previous study we conducted with AutoTutor. Copyright © 2006, American Association for Artificial Intelligence ( All rights reserved.

Publication Title

Proceedings of the National Conference on Artificial Intelligence

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