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 (www.aaai.org). All rights reserved.
Proceedings of the National Conference on Artificial Intelligence
Rus, V., & Graesser, A. (2006). Deeper natural language processing for evaluating student answers in intelligent tutoring systems. Proceedings of the National Conference on Artificial Intelligence, 2, 1495-1500. Retrieved from https://digitalcommons.memphis.edu/facpubs/2679