IMPROVING THE MANAGEMENT SYSTEM OF EDUCATIONAL QUALITY CONTROL MECHANISMS

Аннотация

This article explores the methodological and strategic foundations for improving the management system of educational quality control mechanisms, drawing upon the best practices and policy frameworks adopted in developed countries. It critically analyzes the strengths and limitations of existing control systems in the context of digital transformation, with a focus on enhancing transparency, accountability, and data-driven governance. The study emphasizes the integration of advanced digital technologies—such as artificial intelligence, learning analytics, and cloud-based platforms—into national education management structures to support real-time quality monitoring and decision-making. Case studies from countries such as Finland, Singapore, and South Korea are examined to identify transferable models of educational supervision and performance evaluation. Furthermore, the paper proposes a conceptual framework for Uzbekistan, combining international standards with localized policy mechanisms to ensure sustainable and scalable improvement in educational quality. The findings of this research aim to contribute to the modernization of national education quality assurancesystems, strengthen institutional capacity, and align governanceprocesses with global benchmarks foreducational excellence. Keywords: Educational quality, quality control mechanisms, educational governance, digital transformation, international experience, learning analytics, educational policy, management systems, quality assurance, artificial intelligence in education. Introduction: In recent years, the domain of educational quality control has undergone a profound transformation driven by the rapid advancement and integration of digital technologies. This paradigm shift reflects a broader global trend wherein education systems increasingly leverage digital tools to enhance transparency, efficiency, and adaptability in quality assurance processes. Contemporary developments highlight the convergence of several key technologies—artificial intelligence (AI), big data analytics, learning management systems (LMS), and cloud computing—as fundamental enablers for reimagining educational quality monitoring and governance. One of the most significant advances has been the deployment of AI-powered learning analytics platforms capable of real-time data collection and interpretation. These systems analyze vast arrays of learner data, including engagement patterns, assessment outcomes, and behavioral indicators, enabling educators and administrators to identify learning gaps, predict at-risk students, and tailor pedagogical interventions proactively. For instance, studies indicate that AI-driven analytics can improve early warning systems for student dropout by up to 30%, fostering timely and targeted support strategies[1]. Parallel to technological innovation, there has been an expansion in the scope and granularity of quality indicators. Traditional metrics such as standardized test scores and graduation rates are increasingly supplemented by multidimensional parameters including student engagement, socio-emotional competencies, digital literacy, and inclusiveness measures. The UNESCO Global Education Monitoring Report (2023) emphasizes that such comprehensive frameworks are critical to 526 INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE ISSN:2692-5206,ImpactFactor:12,23 AmericanAcademicpublishers,volume05,issue08,2025 Journal: https://www.academicpublishers.org/journals/index.php/ijai capturing the nuanced realities of educational quality in digitally mediated environments. From a governance perspective, the integration of digital dashboards and real-time monitoring tools has empowered policymakers and institutional leaders with unprecedented visibility into educational processes. Countries like Finland, South Korea, and Singapore have pioneered the adoption of national-scale platforms that consolidate performance data across regions and institutions, allowing for data-driven policy adjustments and resource allocation. Empirical evaluations reveal that these initiatives have led to improvements in educational outcomes ranging between 15% and 40% in various performance domains (OECD, 2022). In Uzbekistan, digital transformation efforts are intensifying within the education sector, aligned with the national "Digital Uzbekistan–2030" strategy. Recent initiatives include the widespread implementation of electronic learning platforms, digitization of student records, and pilot projects incorporating AI for adaptive assessment. While infrastructure deployment is advancing—over 90% of higher education institutions now utilize some form of LMS—the challenge remains in fully integrating these systems within coherent quality control frameworks that ensure reliable, transparent, and equitable evaluation [2]. Ethical and legal considerations have also come to the forefront in contemporary discourse. The proliferation of AI and automated decision-making systems in e