Keyword: «network education system»

The development of intellectual capital for knowledge-intensive industries and cutting-edge sciences is an urgent problem of higher education under the present socio-economic conditions. Its solution relates to the rethinking of nature and resource potential of a university. A solution path in terms of modern education concepts is the university transformation into a "knowledge factory". The scientific and education environment of the university in this sense is distinguished by its polysubjectivity and hybrid character, revealed in the interaction of natural and artificial intelligent agents. The need for theoretical and methodological grounding of intellectual capital development within the hybrid scientific and education environment determined the aim of this article which is to develop a model describing the intellectual capital main components and mechanisms for its development. The choice of tools to implement the model also needs grounding, which is given by an example of biophysics as a science with accelerated generation of new knowledge to be transferred into the learning process. The study is based on the synergetic-network approach, which potential is extended by the competence-based and ontology-semantic approaches. These approaches together provide an insight into the organisation, competence and technological aspects of intellectual capital development. The study provides the following results of theoretical significance. The nature of the university is revealed as a network education system with the triple network structure within which science, education and industry representatives interact. New scientific knowledge obtained in their collaboration is accumulated in an open knowledge base to form an integrative resource potential for the intellectual capital development. To understand this process, the authors’ model is developed to explain the intellectual capital structure as four interrelated components (human, organisation and consumer capital, intellectual property), which determine the main systems and mechanisms of its development. The practical significance of this article is in grounding tools for transferring new scientific knowledge into the learning process to implement the model. A form of knowledge representation adapted for didactic tasks is offered. That is training course ontologies as the basis for Artificial Intelligent agents. A prototype of Artificial Intelligent mentor as a web application is developed.