Learning by diagramming Supreme Court oral arguments

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. Using LARGO, students diagram argument transcripts in a special-purpose graphical language. LARGO provides feedback in the form of reflection questions. Keywords Argument models, argument diagrams, intelligent tutoring systems, hypothetical legal reasoning

• Type: Conference
• Year: 2007
• Where: ICAIL
• Authors: Kevin D. Ashley, Niels Pinkwart, Collin Lynch, Vincent Aleven
• Keywords: Artificial Intelligence, Hypotheticals, ICAIL 2007, Intelligent Tutoring Systems, Legal Reasoning