Toward Legal Argument Instruction with Graph Grammars and Collaborative Filtering Techniques

  • Niels Pinkwart
  • Vincent Aleven
  • Kevin Ashley
  • Collin Lynch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4053)


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.


Argument Structure Argument Representation Intelligent Tutoring System Graph Grammar Legal Argumentation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Niels Pinkwart
    • 1
  • Vincent Aleven
    • 1
  • Kevin Ashley
    • 2
  • Collin Lynch
    • 3
  1. 1.HCI InstituteCarnegie Mellon UniversityPittsburghUSA
  2. 2.School of LawUniversity of PittsburghPittsburghUSA
  3. 3.Intelligent Systems ProgramUniversity of PittsburghPittsburghUSA

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