Kenneth D. Forbus, Peter B. Whalley
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.