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<p>Over the past twenty years, automated tutoring systems have gained an increasing recognition as a prominent area of Artificial Intelligence (AI). During this period, Intelligent Computer Aided Instruction (ICAI) systems have been developed using a variety of AI techniques to enhance the learning process. The core AI issue in designing these systems concerns knowledge representation. A review of current AI literature shows that there are numerous, distinctly different knowledge representation schemes, and most conventional programming environments do not readily support all of these representation schemes. This thesis proposes that tutoring systems are best designed in a programming environment that supports multiple, integrated knowledge representation schemes. Such an environment allows the designer to select and employ, with ease, the most natural knowledge representation scheme for each type of knowledge in the tutoring system. In this thesis we describe the components of a generalized ICAI system; discuss the various types of knowledge and knowledge representation schemes; and review the knowledge representation schemes used in several noted ICAI systems. In addition, we describe two prototype ICAI systems (Map Reading Tutor and Pilot Emergency Procedure Tutor) which we designed and developed in a specific programming environment that supports multiple, integrated knowledge representation schemes.
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<br>Subjects: Computer science; Artificial intelligence; Computer-assisted instruction; intelligent computer aided instruction; knowledge representation</div>
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<dl><dd>Ragsdale, Daniel Joseph</dd>
<dd>Tidd, John Patrick</dd></dl></div>
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