This paper addresses the challenge of designing spoken dialogues that are of educational benefit within the context of an intelligent tutoring system, yet predictable enough to facilitate automatic speech recognition and subsequent processing. We introduce a design principle to meet this goal: construct short dialogues in which the desired student utterances are external evidence of performance or learning in the domain, and in which those target utterances can be expressed as a well-defined set. The key to this principle is to teach the human learner a process that maps inputs to responses. Pilot results in two domains – self-generated questions and morphology exercises – indicate that the approach is promising in terms of its habitability and the predictability of the utterances elicited. We describe the results and sketch a brief taxonomy classifying the elicited utterances according to whether they evidence student performance or learning, whether they are amenable to automatic processing, and whether they support or call into question the hypothesis that such dialogues can elicit spoken utterances that are both educational and predictable.