Learning by diagramming Supreme Court oral arguments

Authors:
Kevin Ashley;Niels Pinkwart;Collin Lynch;Vincent Aleven
Affiliations:
University of Pittsburgh, Pittsburgh, PA;Clausthal University of Technology, Germany;University of Pittsburgh, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA
Venue:
Proceedings of the 11th international conference on Artificial intelligence and law
Year:
2007

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Abstract

This paper describes an intelligent tutoring system, LARGO, that helps students learn skills of legal reasoning with hypotheticals by analyzing oral arguments before the US Supreme Court. The skills involve proposing a rule-like test for deciding a case, posing hypotheticals to challenge the rule, and responding by analogizing or distinguishing the hypotheticals and/or modifying the proposed test. Students diagram arguments in a special-purpose graphical language and receive feedback in the form of reflection questions.