Deeper natural language processing for evaluating student answers in intelligent tutoring systems
Vasile Rus, Arthur C. Graesser · 1999
This paper addresses the problem of evaluating stu-dents ’ answers in intelligent tutoring environments with mixed-initiative dialogue by modelling it as a textual entailment problem. The problem of meaning represen-tation and inference is a pervasive challenge in any inte-grated intelligent system handling communication. For intelligent tutorial dialogue systems, we show that en-tailment 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.