A digital twin of a university as a tool for predictive analysis and optimization of educational and scientific processes

Introduction. The level of development of the modern economy and the entire socio-economic sphere places increased demands on the effectiveness of universities. The issues of using digital twins in universities and other higher education institutions are considered to be rather poorly studied methodologically and have a low frequency of application in practice. This makes it important to conduct research in this area in order to develop digital twin prototypes aimed at solving a wide range of tasks to improve the strategic and operational efficiency of new generation universities.

Purpose. Development of a conceptual model of a digital twin in the contour of the university management system.

Methods. To address the research objectives, general scientific and empirical methods of economic research were used: economic observation, systems analysis, expert assessments, and a scenario approach. Research in the field of simulation modeling and digital twin construction served as the theoretical and methodological basis.

Results and conclusions. The author's conceptual model of a digital twin in the contour of the university management system is proposed, and a classification of the university's digital twin functionality is developed. The key features of the proposed concept are: the distribution of the digital twin's functionality by levels - strategic, operational and resource, in order to apply various simulation mechanisms in each component to achieve the best result.; the use of artificial intelligence tools to automate the management decision-making process; the possibility of automatic self-learning of simulation models as part of a digital twin.

The methodological proposals described in this article can be used for promising practical prototyping of the university's digital twin by: distributing the functionality of the digital twin at levels - strategic, operational and resource, in order to apply various simulation modeling mechanisms in each component to achieve the best result; using artificial intelligence tools to automate the management decision-making process.; the possibilities of automatic self-learning of simulation models as part of a digital twin and the availability of mechanisms for improving the entire university's digital twin system.

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