Identifying Relevant Prior Explanations

When human tutors engage in dialogue, they freely exploit all aspects of the mutually known context, including the previous discourse. Utterances that do not draw on previous discourse seem awkward, unnatural, or even incoherent. Previous discourse must be taken into account in order to relate new information effectively to recently conveyed material , and to avoid repeating old material that would distract the student from what is new. Producing a system that displays such behavior involves finding an efficient way to identify which previous explanations (if any) are relevant to the current explanation task. Thus, we axe implementing a system that uses a case-based reasoning approach to identify previous situations and explanations that could potentially affect the explanation being constructed. We have identified heuris-tics for constructing explanations that exploit this information in ways similar to what we have observed in human-human tutorial dialogues.