- Author:
- Vincent A. W. M. M. Aleven
CATO is an intelligent learning environment designed to help beginning law students learn basic skills of making arguments with cases. Using CATO, students practice tasks of induction and analogical argumentation. They practice testing theories against a body of cases and making written arguments about a problem, comparing and contrasting it to past cases.
CATO's model addresses arguments in which two opponents analogize a problem to favorable cases, distinguish unfavorable cases, assess the significance of similarities and differences between cases in light of normative knowledge about the domain, and use that knowledge to organize multi-case arguments. CATO communicates the model to students by presenting dynamically-generated argumentation examples and by reifying argument structure based on the model. CATO also provides a case database and tools based on the model that help make students' tasks more manageable.
CATO was evaluated in the context of an actual legal writing course, in a study involving 30 first-year law students. We found that instruction with CATO leads to statistically significant improvement in students' basic argumentation skills, comparable to that achieved by an experienced legal writing instructor teaching groups of 4-10 students. However, on a more advanced legal writing assignment, meant to explore the frontier of the CATO instruction, students taught by the legal writing instructor had higher grades, suggesting a need for more integrated practice with the CATO model.
CATO contributes to AI research fields modeling educational techniques as well as case-based and legal reasoning. It is a novel result that students can learn basic argumentation skills by studying computer-generated examples. It means that an instructional system does not necessarily need to rely on a very sophisticated understanding of students' arguments, which would be a significant obstacle to developing such systems.
Also, CATO presents novel techniques for using background knowledge to support similarity assessment in case-based reasoning. Drawing on its background knowledge, CATO characterizes and re-characterizes cases in order to argue that two cases are similar or different. This is an important skill in the legal domain not previously modeled. CATO's arguments may help a user in assessing the similarity of cases in a more discriminatory way.
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