In spite of the fact that the relation between theory and practice is a foundation of scientific and technological development, the trend of increasing the gap between theory and practice accelerates in these years. The gap embraces a risk of distrust of science and technology. Ontological engineering as the content-oriented research is expected to contribute to the resolution of the gap. This paper presents the feasibility of organization of theoretical knowledge on ontological engineering and new-generation intelligent systems based on it through an application of ontological engineering in the area of learning/instruction support. This area also has the problem of the gap between theory and practice, and its resolution is strongly required. So far we proposed OMNIBUS ontology, which is a comprehensive ontology that covers different learning/instructional theories and paradigms, and SMARTIES, which is a theory-aware and standard-compliant authoring system for making learning/instructional scenarios based on OMNIBUS ontology. We believe the theory-awareness and standard-compliance bridge the gap between theory and practice because it links theories to practical use of standard technologies and enables practitioners to easily enjoy theoretical support while using standard technologies in practice. The following goals are set in order to achieve it; computers (1) understand a variety of learning/instructional theories based on the organization of them, (2) utilize the understanding for helping authors' learning/instructional scenario making and (3) make such theoretically sound scenarios interoperable within the framework of standard technologies. This paper suggests an ontological engineering solution to the achievement of these three goals. Although the evaluation is far from complete in terms of practical use, we believe that the results of this study address high-level technical challenges from the viewpoint of the current state of the art in the research area of artificial intelligence not only in education but also in general, and therefore we hope that constitute a substantial contribution for organization of theoretical knowledge in many other areas.
2009 JSAI (The Japanese Society for Artificial Intelligence)