A methodology for building verified and validated intelligent educational agents through the integration of machine learning and knowledge acquisition
Harry N. Keeling, Gheorghe D. Tecuci · 1998
The acquisition of domain knowledge has traditionally been recognized as a major impediment to the development of knowledge-based systems. In particular, this “knowledge acquisition bottleneck” has significantly slowed the development of intelligent educational software where the domain experts are teachers with little or no experience with knowledge engineering. However, recently there has been a growing recognition of the potential benefits that can be derived from the application of advances in the AI-related fields of machine learning and knowledge acquisition. This dissertation research has addressed this problem by defining, implementing and evaluating a full life cycle methodology for building educational agents that can be used in classrooms. This innovative approach synergistically combines methods from machine learning and knowledge acquisition, concepts from intelligent tutoring research and advances in skill assessment from educational research. The result has been a methodology for building educational agents that are taught by educators in much the same way that one would teach a human apprentice. This comprehensive methodology includes a multi-dimensional evaluation phase and a customized software toolkit that facilitates its application. This methodology has been successfully applied to develop three educational agents that act as indirect communicational channels between the educator and the student. Applied in the areas of American history and statistics, these agents generate test questions and provide tutoring through hints and explanations. One agent has been used to enhance the functionality of an existing educational software package, while the other two have been used in a stand-alone mode. This research demonstrates solutions to the problems involved in building intelligent educational software and prescribes a new approach that draws from the fields of artificial intelligence and educational research.