Toward an intelligent tutoring system for teaching law students to argue with cases

This paper describes a research project to devise and test an intelligent, case-baaed tutorial program for teaching law students to argue with cases. In order to present pedagogically interesting lessons and develop a Student Model, we have designed memory structures such as Argument Contexts and a hierarchy of Issues in Case-Baaed Legal Reaaoning. Using logical expressions in the knowledge representation language Loom, we also explicitly represent case-based argument concepts such as a case’s being on point to a problem, more on point than another case, most on point of all the cases, a best case to cite, and a counterexample to another case. The program will be able to reason with the explicit concepts in selecting cases from a Case Library, assembling lessons and examples, analyzing student inputs, and in generating explanations and feedback. We hope to demonstrate empirically that, by providing law students a conceptual model of the criteria for selecting and describing precedents that would be useful in an argument, the tutorial program will help them to learn to select and apply cases more efficiently and to make more effective arguments.