Radkevych, Olexandr (orcid.org/0000-0002-2648-5726) (2026) Artificial intelligence as a tool for analytical support of quality assurance systems based on international and national assessments . Publishing House of University of Technology, Katowice, Poland, pp. 72-79. ISBN 978-83-68422-17-7
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Abstract
The study substantiates the conceptual foundations for utilising artificial intelligence (AI) to provide analytical support for educational quality assurance systems, a task of critical importance amidst global digitalisation and escalating demands for institutional efficiency. It explores the potential of neural networks and Data Mining techniques for the in-depth processing of results from international (PISA, TIMSS) and national monitoring assessments, such as Ukraine’s External Independent Evaluation (ZNO) and National Multi-Subject Test (NMT). Findings indicate that modern AI toolsets enable not only the automated detection of latent correlations but also the prediction of academic risks and the scenario-based modelling of educational reform impacts using Big Data. Particular emphasis is placed on the transition from static statistics to dynamic predictive analytics, which provides stakeholders with an objective basis for strategic planning and managerial decision-making. The study concludes that the integration of intelligent systems facilitates personalised learning and optimises managerial processes, thereby enhancing the overall competitiveness of national education on the global stage.
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