DEVELOPMENT OF AN ADAPTIVE CORPORATE EDUCATIONAL INFORMATION SYSTEM USING MACHINE LEARNING METHODS

DOI: 10.31673/2412-4338.2025.048922

Abstract

The article considers the problem of ensuring the functional stability of adaptive corporate educational information systems in conditions of dynamic loads, changes in user behavior and increasing requirements for information security. It is shown that traditional approaches to managing educational platforms do not provide the necessary level of stability and adaptability, which necessitates the implementation of complex information technologies focused on monitoring and decision-making in real time.

An information technology for ensuring the functional stability of an educational information system is proposed, developed on the basis of a mathematical model of functional stability. The technology is based on the integration of methods of statistical analysis, machine learning, optimization of information flows and resources, as well as models of risk assessment and information security. This allows for the detection of anomalies, prediction of changes in the system and automatic adjustment of individual educational trajectories of users.

The effectiveness of the proposed information technology is confirmed by the modeling results, which demonstrate an increase in the level of functional stability of the educational information system, a reduction in the impact of disturbances, and an improvement in the stability of its operation over time. The results obtained can be used in the design and modernization of corporate educational platforms with increased requirements for adaptability, reliability, and security.

The results obtained confirm the feasibility of using the proposed information technology to ensure the functional stability of adaptive corporate educational information systems and create a basis for further research aimed at increasing the efficiency of adaptive management of educational processes in real time.

Keywords: resilience, adaptation, educational system, information technology, monitoring, machine learning, optimization, risk, information security.