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. The rules were derived using the discourse relations of Rhetorical Structure Theory that correlated with learning in a corpus of human tutorial dialogues. 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.