Toward Legal Argument Instruction with Graph Grammars and Collaborative Filtering Techniques
This paper presents an approach for intelligent tutoring in the field of legal argumentation. In this approach, students study transcripts of US Supreme Court oral argument and create a graphical representation of argument flow as tests offered by attorneys being challenged by hypotheticals posed by Justices. The proposed system, which is based on the collaborative modeling framework Cool Modes, is capable of detecting three types of weaknesses in arguments; when it does, it presents the student with a self explanation prompt. This kind of feedback seems more appropriate than the “strong connective feedback” typically offered by model-tracing or constraint-based tutors. Structural and context weaknesses in arguments are handled by graph grammars, and the critical problem of detecting and dealing with content weaknesses in student contributions is addressed through a collaborative filtering approach, thereby avoiding the critical problem of natural language processing in legal argumentation. An early version of the system was pilot tested with two students.
KeywordsArgument Structure Argument Representation Intelligent Tutoring System Graph Grammar Legal Argumentation
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- 2.Ashley, K.: Modeling Legal Argument: Reasoning with Cases and Hypotheticals. MIT Press/Bradford Books, Cambridge (1990)Google Scholar
- 3.Bench-Capon, T., Leng, P., Staniford, G.: A computer supported environment for the teaching of legal argument. J. of Information, Law & Technology 3 (1998)Google Scholar
- 4.Carr, C.: Using Computer Supported Argument Visualization to Teach Legal Argumentation. In: Visualizing Argumentation, pp. 75–96. Springer, London (2003)Google Scholar
- 6.van Gelder, T.: Argument Mapping with Reason!Able. The American Philosophical Association Newsletter on Philosophy and Computers, 85–90 (2002)Google Scholar
- 7.Konstan, J., Riedl, J.: Collaborative Filtering: Supporting social navigation in large, crowded infospaces. In: Designing Information Spaces: The Social Navigation Approach, pp. 43–81. Springer, Berlin (2002)Google Scholar
- 8.Muntjewerff, J., Breuker, J.: Evaluating PROSA, a system to train solving legal cases. In: Proc. of AIED, pp. 278–285. IOS Press, Amsterdam (2001)Google Scholar
- 9.Paolocci, M., Suthers, D., Weiner, A.: Automated Advice-Giving Strategies for Scientific Inquiry. In: Proc. of ITS, pp. 372–381. Springer, Berlin (1996)Google Scholar
- 10.Pinkwart, N.: Collaborative Modeling in Graph Based Environments. dissertation.de Verlag, Berlin (2005)Google Scholar
- 13.Rissland, E.: Dimension-Based Analysis of Hypotheticals from Supreme Court Oral Argument. In: Proc. of AI & Law, pp. 111–120. ACM Press, New York (1989)Google Scholar
- 14.Schworm, S., Renkl, A.: Learning by solved example problems: Instructional explanations reduce selfexplanation activity. In: Proceedings of the 24th Annual Conference of the Cognitive Science Society, pp. 816–821. Lawrence Erlbaum, Mahwah (2002)Google Scholar
- 16.Toulmin, S.: The Uses of Argument. Cambridge University Press, Cambridge (1958)Google Scholar
- 18.Walton, D.: Legal Argumentation and Evidence. Penn State Press (2002)Google Scholar