ENVIRONMENTSMODELING ADAPTIVE PEDAGOGICAL SYSTEMS IN DIGITAL LEARNING ENVIRONMENTS

Abstract

DOI: https://doi.org/10.26565/2074-8922-2026-86-30

Purpose. The purpose of the study is to design an integrated simulation-based management model for adaptive pedagogical systems that addresses the problems of uncertainty in digital learning systems and affect the effectiveness of managerial decision-making. The research is focused on bridging that gap between the pedagogical adaptivity and the strategic management of digital education systems.

Methods. The methodological approach is a combination of dynamic panel econometric model and simulation-based analysis. The empirical framework includes pedagogical adaptivity, resource allocation, strategic management quality and environmental uncertainty as important factors determining learning effectiveness. The model is estimated using a System Generalized Method of Moments approach to deal with the endogeneity and unobserved heterogeneity. A simulation component of stochastic modelling and Monte Carlo methods is implemented to consider alternative scenarios to evaluate performance of the system under different degrees of uncertainty. The analysis is done with the help of the panel data of four countries across the time period of 2020-2025, which provides the cross-country comparability and time movement.

Results. The results show that pedagogical adaptivity and strategic management have major positive implications on the effectiveness of learning in each of the cases studied. The interaction between the use of adaptive technologies and the quality of management produces a strong positive effect, implying that integrated approaches show superior outcomes. Environmental uncertainty has a negative effect on the system performance, but the effect is vanished by effective allocation of resources and the strategic planning. Simulation results show that adaptive optimization strategies improve learning effectiveness by about 20 - 25 percent relative to baseline scenarios. The findings also show that systems with stronger managerial frameworks are more resilient and better able to recover following external shocks, and that they recover more quickly.

Conclusions. The study validates the role of managerial logic in adaptive pedagogical systems, which should be used to increase the level of their efficiency and resilience in digital spaces. The proposed model can be used to offer a comprehensive analytical and predictive framework for optimizing educational decision-making under uncertainty. The findings add to the development of digital pedagogy through the introduction of a management-oriented perspective and have practical implications for policy makers and educational institutions. Future research should focus on expanding the model by taking into account nonlinear dynamics and more general datasets in order to enhance the generalizability of the model.