We explore the effectiveness of a simple algorithm for adaptively deciding whether to further decompose a step in a line of reasoning during tutorial dialogue. We compare two versions of a tutorial dialogue system, Rimac: one that always decomposes a step to its simplest sub-steps and one that adaptively decides to decompose a step based on a student's pre-test assessment. We hypothesize that students using the two versions of Rimac will learn similarly but that students who use the version that adaptively decomposes a step will learn more efficiently. Our initial results suggest support for our hypothesis but the sample size for the experiment is still small and we are continuing to collect more student interactions with the two versions of the system.