Toward supporting hypothesis formation and testing in an interpretive domain

Authors:
Vincent Aleven;Kevin Ashley
Affiliations:
Human-Computer Interaction Institute, Carnegie Mellon University;Intelligent Systems Program, Learning Research and Development Center, University of Pittsburgh
Venue:
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Year:
2005

Citing 2
Cited 0

Teaching Case-Based Argumentation Concepts Using Dialectic Arguments vs. Didactic Explanations

ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Using background knowledge in case-based legal reasoning: a computational model and an intelligent learning environment

Artificial Intelligence - Special issue on AI and law

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Abstract

The research field of AI & Education has long been interested in cognitive processes in which students formulate and test hypotheses by considering them in light of specific cases. However, few if any of the systems that have been built target domains which are ill-structured and in which determining whether a hypothesized rule and proposed outcome are consistent with past decisions is a matter of interpretation, rather than deductive inference. The goals of our project are to (1) develop an AI model of hypothesis formation and testing in an interpretive domain, US Supreme Court oral arguments and (2) to use it in an intelligent tutoring system to guide law students in learning that process. As a first step toward these goals we will conduct an experiment to evaluate whether self-explanation prompts facilitate learning by studying argument transcripts.