Is a Dialogue-Based Tutoring System that Emulates Helpful Co-constructed Relations During Human Tutoring Effective?

We present an initial field evaluation of Rimac, a natural-language tutoring system which implements decision rules that simulate the highly interactive nature of human tutoring. We compared this rule-driven version of the tutor with a non-rule-driven control in high school physics classes. Although students learned from both versions of the system, the experimental group outperformed the control group. A particularly interesting finding is that the experimental version was especially beneficial for female students.