Application of artificial intelligence technologies for optimizing process management based on metadata processing statistics in an electronic document management system in an educational organization

Document automation is a key element of digital transformation, especially in educational institutions, where the volume of documents includes academic and administrative materials. Companies typically choose between managing the document lifecycle and automating business processes, where documents play a supporting role. In the educational sector, the second approach is more relevant, as it helps reduce approval times and improve data management efficiency. ECM systems have already become an integral part of university infrastructure, managing curricula, student applications, research publications, and financial reports. However, challenges such as semantic ambiguity, lack of standardization, and document duplication require new solutions. The integration of AI into ECM systems opens up opportunities for automatic classification, analysis of unstructured data, and optimization of approval processes. Nevertheless, the adoption of AI in education is progressing slowly because of technical and organizational barriers. The article investigates document automation in universities using the example of the student withdrawal process. Key approval stages are analyzed, bottlenecks are identified, and solutions are proposed. Special attention is given to the use of machine learning for metadata analysis, which enables the prediction of document processing routes and optimization of time expenditures. The aim of the article is to propose a document management efficiency model that considers both time and quality parameters. The model identifies «risk points,» suggests improvement measures, and ensures effective document management. The research results can be used to enhance ECM systems in universities, particularly during periods of high workload.

1. Development of a predictive analytics model for financial revenues from educational activities based on the digital footprint of students / D.N. Frantassov, A.V. Balanovskaya, E.G. Repina, E.V. Voronina // Vestnik of Samara State University of Economics. 2022. No. 5 (211). Pp. 52–59. doi:10.46554/1993-0453-2022-5-211-52-59. EDN EBRFXT.

2. Balanovskaya A.V., Frantasov D.N., Gorbunova O.A. Directions for the development of educational or-ganizations in the context of digital transformation in the science and higher education sector // Izvestiya of Baikal State University. 2022. Vol. 32, No. 2. Pp. 423–431. doi:10.17150/2500-2759.2022.32(2).423-431. EDN HWTALU.

3. Frantasov D.N., Balanovskaya A.V. Digital maturity as the basis for strategic development and digital transformation of educational organizations // Vestnik of Samara State University of Economics. 2022. No. 2 (208). Pp. 57–64. doi:10.46554/1993-0453-2022-2-208-57-64. EDN JGTLHN.

4. Logachev M.S. Data storage structure of an automated system for monitoring the quality of the edu-cational process // Bulletin of the Moscow State University of Printing named after Ivan Fedorov. 2016. No. 1. Pp. 79–81. EDN WBWHAH.

5. Frantasov D.N., Balanovskaya A.V., Prokaeva A.E. The role of digital services in the digital transfor-mation of higher education organizations // Economics and Entrepreneurship. 2022. No. 2 (139). Pp. 1043–1046. doi:10.34925/EIP.2022.139.2.205. EDN ERJWIN.

6. Istratova E.E., Dostovalov D.N. Development of a data mining subsystem for the Citeck electronic doc-ument management system // Data Analysis and Processing Systems. 2021. No. 3 (83). Pp. 115–128. doi:10.17212/2782-2001-2021-3-115-128. EDN BFQAMJ.

7. Varshavsky P.R., Ar Kar Myo, Shunkevich D.V. Application of classification and clustering methods to improve the efficiency of case-based systems // Software Products and Systems. 2017. No. 4. Pp. 625–631. EDN YMUFCT.

8. Grinko D.O. Application of data mining methods in electronic document management processes // Bulletin of the Peoples' Friendship University of Russia. Series: Engineering Research. 2014. No. 3. Pp. 61–69. EDN SMUFSV.

9. Tipikin V.V., Skibina M.A. Data mining in the electronic document management system // Automation of Control Processes. 2020. No. 1 (59). Pp. 65–74. doi:10.35752/1991-2927-2020-1-5-65-74. EDN NEUEMT.

10. Zlatkovskaya E.M. Electronic document management in educational organizations: structural analy-sis // Theory and Practice of Project Education. 2020. No. 3 (15). Pp. 58–64. EDN FMNEZP.