Using background knowledge in case-based legal reasoning: A computational model and an intelligent learning environment

AbstractResearchers in the field of AI and Law have developed a number of computational models of the arguments that skilled attorneys make based on past cases. However, these models have not accounted for the ways that attorneys use middle-level normative background knowledge (1) to organize multi-case arguments, (2) to reason about the significance of differences between cases, and (3) to assess the relevance of precedent cases to a given problem situation. We present a novel model, that accounts for these argumentation phenomena. An evaluation study showed that arguments about the significance of distinctions based on this model help predict the outcome of cases in the area of trade secrets law, confirming the quality of these arguments. The model forms the basis of an intelligent learning environment called CATO, which was designed to help beginning law students acquire basic argumentation skills. CATO uses the model for a number of purposes, including the dynamic generation of argumentation examples. In a second evaluation study, carried out in the context of an actual legal writing course, we compared instruction with CATO against the best traditional legal writing instruction. The results indicate that CATO's example-based instructional approach is effective in teaching basic argumentation skills. However, a more “integrated” approach appears to be needed if students are to achieve better transfer of these skills to more complex contexts. CATO's argumentation model and instructional environment are a contribution to the research fields of AI and Law, Case-Based Reasoning, and AI and Education