Tutorial dialogue systems often simulate tactics used by experienced human tutors such as restating students' dialogue input. We investigated whether the amount of tutor restatement that supports student inference interacts with students' incoming knowledge level in predicting how much students learn from a system. We found that students with lower incoming knowledge benefit more from an increased level of these types of restatement while students with higher incoming knowledge benefit more from a decreased level of such restatements.