Can Simple Natural Language Generation Improve Intelligent Tutoring Systems?

Barbara Di Eugenio and Michael J. Trolio

One of our general goals is to answer the question of what is the "added value" of a Natural Language interaction for a learner that interacts with an ITS. To do so, we applied simple Natural Language Generation techniques to improve the feedback provided by intelligent tutoring systems built within the DIAG framework (Towne 1997a). We have evaluated the original version of the system and the enhanced one with a between subjects experiment. The results are mixed. Although differences rarely achieve statistical significance, there appears to be slight improvement on performance measures in the group interacting with the enhanced system; however, subjects using the original system have better recall of the actions they took.

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