USE OF ARTIFICIAL INTELLIGENCE MODELS IN EDUCATIONAL DATA MANAGEMENT TO SUPPORT PEDAGOGICAL DECISIONS

Abstract

The relevance of the topic is determined by the growing role of analytical systems in modern education and the need to combine forecasting accuracy with the efficiency and stability of algorithms. The paper compares three configurations – ML-Base, DL-Advanced and Hybrid-Assist – using the integral educational analytics effectiveness index (IEAEI), which combines indicators of accuracy (Acc), stability (Stab), processing time (Time) and interpretability (Interpret). The methodology included normalisation of results, non-parametric hypothesis testing (Kruskal–Wallis and Mann–Whitney criteria), analysis of changes in weight coefficients (ΔW-index) and assessment of correlations between platform dynamics and forecast stability (Spearman's coefficient). To test the transferability of the findings, a design with three contexts – Ukraine, Azerbaijan and Poland – was used, which made it possible to compare the behaviour of models in countries with transition economies and in European educational environments. The results showed that the hybrid configuration provides the best balance between stability and accuracy (IEOA > 0.84; cross-country: Ukraine – 0.821; Azerbaijan – 0.818; Poland – 0.824) with a relatively fast adaptation time; DL-Advanced achieves higher maximum accuracy, especially under conditions of more complete and consistent data (approaching Hybrid-Assist in the Polish subsample), but requires more time for convergence; ML-Base has the shortest response time in all three contexts, but is inferior in terms of predictive quality. The scientific novelty lies in the generalisation of the patterns of interaction between the algorithm type, platform characteristics and pedagogical practice requirements in an international context, which allows for the justified integration of intelligent models into educational analytics. Prospects for further research include expanding the set of evaluation metrics with indicators of cognitive convenience for users and adapting the methodology to cloud and mobile educational solutions, taking into account cross-country data variability.