Suppose one is given a description of a system, together with an observation of the system's behaviour which conflicts with the way the system is meant to behave. The diagnostic problem is to determine those components of the system which, when assumed to be functioning abnormally, will explain the discrepancy between the observed and correct system behaviour. We propose a general theory for this problem. The theory requires only that the system be described in a suitable logic. Moreover, there are many such suitable logics, e.g. first-order, temporal, dynamic, etc. As a result, the theory accommodates diagnostic reasoning in a wide variety of practical settings, including digital and analogue circuits, medicine, and database updates. The theory leads to an algorithm for computing all diagnoses, and to various results concerning principles of measurement for discriminating among competing diagnoses. Finally, the theory reveals close connections between diagnostic reasoning and nonmonotonic reasoning.

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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 co...

After identifying essential student modeling issues and machine learning approaches, this paper examines how machine learning techniques have been used to automate the construction of student models as well as the background knowledge necessary for student modeling. In the process, the paper sheds light on the difficulty, suitability and potential of using machine learning for student modeling processes, and, to a lesser extent, the potential of using student modeling techniques in machine learning.

Knowledge about the rationale for a design---how and why a device is designed as it is---can be valuable, but is difficult to capture in reusable form. This paper presents a view of design knowledge capture and the use of design knowledge for design rationale. We define design rationales as explanations in response to questions about the design. These explanations are generated from knowledge of artifacts and design activities. We characterize design activity in terms of observable changes to design descriptions, and present a theory of design knowledge in the form of an ontology of concepts about design descriptions and operations on them. The theory unifies artifact description and decision-making views of design. Based on the theory, we characterize different methods of acquiring design knowledge and design rationale in the context of integrated design support environments. We then analyze in depth two design knowledge capture techniques: a semiformal representation tool and a model...

CONTENTS Page ACKNOWLEDGEMENT . . . . . . . . . . . . . . . . . . . . iii LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . vi CHAPTER I. INTRODUCTION . . . . . . . . . . . . . . . . 1 1.1 An Overview . . . . . . . . . . . . . . . 1 1.2 Evolution of Computer-Based Instruction at Rush . . . . . . . . . . . . . . . . . 3 1.3 Goals of the Thesis . . . . . . . . . . . 4 1.4 Organization of the Thesis . . . . . . . . 6 II. THE BACKGROUND . . . . . . . . . . . . . . . 9 2.1 Qualitative Reasoning . . . . . . . . . . 9 2.2 Subject Area . . . . . . . . . . . . . . 10 2.3 Organization . . . . . . . . . . . . . . 12 2.4 System Constraints . . . . . . . . . . . 14 2.5 Multiple Simultaneous Inputs . . . . . . . 15 III. ORGANIZATION OF CIRCSIM-TUTOR . . . . . . . . 18 3.1 Intelligent Tutoring Systems . . . . . . . 18 3.2 Domain Expertise . . . . . . . . . . . . 23 3.3 Input-Understander . . . . . . . . . . . 26 3.4 Student Modeler . . . . . . . . . . . . . 27 3.5 Instructional Planner . . . .

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CIRCSIM-Tutor is a computer tutor designed to carry out a natural language dialogue with a medical student. Its domain is the baroreceptor reflex, the part of the cardiovascular system that is responsible for maintaining a constant blood pressure. CIRCSIM-Tutor’s interaction with students is modeled after the tutoring behavior of two experienced human tutors. The effectiveness of CIRCSIM-Tutor has been tested by 50 first-year medical students. Using a pretest/post-test paradigm we have demonstrated that significant learning occurs during a 1 hr interaction with the program. Students were also surveyed and indicated considerable satisfaction with the program.

This paper presents a survey of the issues and previous research in Intelligent ComputerAided Instruction (ICAI). Unlike previous surveys of ICAI, which have been organized around the seminal ICAI systems, this one concentrates on the contribution of each effort to our understanding of the various components of ICAI systems. Along the way, comparisons are made between ICAI and CAI, although a CAI straw man is used due to the focus of the paper. Various learning scenarios are discussed, including computer coaches, gaming environments, mixed initiative dialog, Socratic tutors, articulate experts, interactive simulation, and discovery learning. Various forms of knowledge representation are discussed along with relevant issues and examples. Several techniques for student modeling and diagnosis are presented, as are their respective advantages and disadvantages. Pedagogical knowledge, its role in ICAI, and several examples are highlighted. The evolution of discourse management techniques fo...