Using qualitative physics to build articulate software for thermodynamics education

Kenneth D Forbus*, Peter B. Whalley

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

Abstract

One of the original motivations for research in qualitative physics was the development of intelligent tutoring systems and learning environments for physical domains and complex systems. This paper demonstrates how a synergistic combination of qualitative physics and other AI techniques can be used to create an intelligent learning environment for students learning to analyze and design thermodynamic cycles. Pedagogically this problem is important because thermodynamic cycles express the key properties of systems which interconvert work and heat, such as power plants, propulsion systems, refrigerators, and heat pumps, and the study of thermodynamic cycles occupies a major portion of an engineering student's training in thermodynamics. This paper describes CyclePad, a fully implemented learning environment which captures a substantial fraction of a thermodynamics textbook's knowledge and is designed to scaffold students who are learning the principles of such cycles. We analyze the combination of ideas that made CyclePad possible, comment on some lessons learned about the utility of various techniques, and describe our plans for classroom experimentation.

Original languageEnglish (US)
Title of host publicationProceedings of the National Conference on Artificial Intelligence
PublisherAAAI
Pages1175-1182
Number of pages8
Volume2
StatePublished - Dec 1 1994
EventProceedings of the 12th National Conference on Artificial Intelligence. Part 1 (of 2) - Seattle, WA, USA
Duration: Jul 31 1994Aug 4 1994

Other

OtherProceedings of the 12th National Conference on Artificial Intelligence. Part 1 (of 2)
CitySeattle, WA, USA
Period7/31/948/4/94

ASJC Scopus subject areas

  • Software

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