Simple Natural Language Generation and Intelligent Tutoring Systems

In this paper, we report on our approach to adding Natural Language Generation (NLG) capabilities to ITSs. Our choice has been to apply simple NLG techniques to improve the feedback provided by an existing ITS, specifically, one built within the DIAG framework (Towne 1997a). We evaluated the original version of the system and the enhanced one with a between subjects experiment. On the whole, the enhanced system is better than the original one, other than in helping subjects remember the actions they took. Current work includes exploiting more sophisticated NLG techniques but still without delving into full fledged text planning. We are also conducting a constrained data collection, in which students and tutors interact via the ITS. The system presents the human tutor with the facts the ITS deems relevant to answer the student’s question, and the tutor uses them as appropriate in the answer.