Natural Language Generation for Intelligent Tutoring Systems: a case study

Abstract

To investigate whether Natural Language feedback improves learning, we developed two different feedback generation engines, that we systematically evaluated in a three way comparison that included the original system as well. We found that the system which intuitively produces the best language does engender the most learning. Specifically, it appears that presenting feedback at a more abstract level is responsible for the improvement.