It is widely acknowledged, both in academic studies and the marketplace, that the most effective form of education is the professional human tutor. A major difference between human tutors and computer tutors is that only human tutors understand unconstrained natural language input. Recently, a few tutoring systems have been developed that carry on a natural language (NL) dialogue with students. Our research problem is to find ways to make NL-based tutoring systems more effective. Our basic approach is to derive new dialogue strategies from studies of human tutorial dialogues, incorporate them in an NL-based tutoring system, and determine if they make the tutoring system more effective. For instance, some studies are determining if learning increases when human tutors are constrained to follow certain strategies. In order to incorporate the new dialogue strategies into our existing text and spoken NL-based tutoring systems, two completely new modules are being developed. One new module will interpret student utterances using a large directed graph of propositions called an explanation network, which is halfway between the shallow and deep representations of knowledge that are currently used. The second new module uses machine learning to improve the selection of dialogue management strategies. The research is thus a multidisciplinary effort whose intellectual merit lies in new results in the cognitive psychology of human tutoring, in the technology of NL processing, and in the design of effective tutoring systems. Improved NL-based tutoring systems could have a broad impact on education and society.
