The goal of this study is to work toward developing more effective feedback-targeting systems for tutoring software, to improve user performance. In this study, researchers built a tutoring software system that uses Natural Language Processing (NLP) to provide adaptive feedback to students in an online learning task.

• The researcher's system models the interaction between a student's natural language response and the software, which interprets students' responses and provides the best feedback action. This framework allows for the optimization of a reward signal.
• Researchers showed that natural-language based targeting policies were able to choose optimal feedback from fewer exercises compared to multiple-choice based policies. The natural-language policies were effective even when tested on novel interactions.