Increasing AI Project Effectiveness with Reusable Code Frameworks: A Case Study Using IUCBRF / 000

Steven Bogaerts and David Leake, Indiana University

Instructors’ ability to assign artificial intelligence programming projects is limited by the time the projects may require. This problem is often exacerbated by the need for students to develop significant system infrastructure, requiring them to spend time addressing issues which may be orthogonal to the AI course’s core pedagogical goals. This paper argues that such problems can be alleviated by basing coding assignments on paradigm-specific frameworks, collections of reusable code designed to be extended and applied to a variety of specific problems. In addition, frameworks can provide a basis for further student research or application of projects to real-world domains, providing additional motivation. This paper illustrates the application of a framework-based approach to teaching case-based reasoning (CBR), introducing the Indiana University Case-Based Reasoning Framework (IUCBRF), discussing its design, and presenting sample exercises that take advantage of the framework’s characteristics.


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