Chechen State University

The article discusses the economic aspects of the implementation and functioning of inclusive education in Russia. Based on the analysis of the regulatory framework, statistical data and cases of educational organizations, direct and indirect costs are assessed, financing models are examined, and the long-term socio-economic effects of inclusion are assessed. It is concluded that inclusive education, despite high initial costs, is an economically viable investment in human capital and social cohesion, but requires a transition from a cost-based to an investment approach in public financing.

Abstract. The relevance of this study is determined by the need to overcome structural imbalances in regional economies dependent on federal funding and to find effective mechanisms for transitioning from a budgetary redistribution model to a market-oriented production development model. The objective of the study is to identify paths and prospects for diversifying the Chechen Republic's economy based on a quantitative assessment of the sectoral structure and identifying priority areas for expanding the region's production base in the period 2022-2025. Research methods include a systemic and statistical analysis of gross regional product, employment, and investment indicators, calculation of the diversification coefficient using the Herfindahl-Hirschman formula and the structural coefficient of industries, as well as a matrix ranking of economic activities. The results showed that the formally high diversification coefficient (Kdv ≈ 0.886) is achieved primarily through budget-dependent sectors, while the share of manufacturing, information technology, and tourism in GRP remains critically low. The share of federal transfers exceeded 80% of budget revenues throughout the entire period. The practical significance of the study lies in the substantiation of four priority areas for diversification: the agro-industrial complex, the tourism and recreation sector, production-oriented small and medium-sized enterprises, and the digital economy. Their implementation will ensure a gradual increase in the financial self-sufficiency of the Chechen Republic.

This article addresses a problem that at first glance seems obvious, but upon closer examination reveals unexpected depth: how exactly should the changing behavior of digital consumers influence the design of e-commerce interfaces? The authors analyze the “Messy Middle” model of the nonlinear consumer journey, explore the phenomenon of attention deficit in the digital environment, and trace the consequences of the mobile shift for the mobile-first principle. The paper identifies and describes trends changing the face of the market: social commerce, subscription consumption models, and ecosystem competition. Particular attention is paid to Russian specifics–in particular, the fact that marketplaces account for over 70% of online orders in the country. The novelty of the paper lies in its attempt to establish a system of connections between behavioral shifts and specific, verifiable design requirements for user interfaces.

The article examines the fundamental role of human capital as a key factor in the successful development and implementation of artificial intelligence (AI) technologies. Based on the analysis of empirical research and theoretical concepts, it is shown that a high level of education and qualifications is a prerequisite for the diffusion of AI technologies, explaining up to a third of the differences in the pace of their implementation between countries and industries. At the same time, the deep paradox of the current moment is revealed: rapid automation generates redundancy of personnel in traditional roles, while the shortage of specialists with critical AI competencies reaches 40-60% or more. The article substantiates that bridging this gap requires not targeted measures, but a systemic transformation of approaches to personnel management – the transition from role models to skill models, large-scale retraining and redesign of workplaces in the logic of human-machine cooperation.

This article examines the use of artificial intelligence to improve the quality of management control in an organization. It is shown that intelligent algorithms enable a transition from fragmented performance monitoring to continuous monitoring, deviation forecasting, and anomaly detection in management data. The use of predictive analytics, machine learning, and automated information processing improves the efficiency of control, reduces the risk of management errors, and strengthens the validity of decisions. Particular attention is paid to implementation limitations: source data quality, model interpretability, cyber risks, and the need to maintain managerial expertise. Contemporary research also confirms the importance of AI for management accounting, internal control, and decision making.