Convergence of Optimization Methods and Big Data in Cognitive Training for AI Specialists
Abstract
Introduction. The exponential growth in the volume and dimensionality of data in the digital economy has led to a significant gap between the practical requirements of the AI industry and the content of traditional educational programs in optimization. This study aims to overcome this imbalance by developing an innovative approach to the cognitive training of specialists, based on the convergence of optimization methods and big data technologies.
Materials and Methods. The work is based on a systematic approach to designing the "Optimization Methods" teaching and learning kit for the field of study 09.03.02 "Information Systems and Technologies" (profile "Artificial Intelligence Systems and Technologies"). The methodological foundation of the research consists of the Wiggins and McTighe's Backward Design model, which defines the strategy for designing an educational trajectory based on target professional competencies; Sweller's Cognitive Load Theory, which optimizes the structure of educational material for the effective assimilation of complex mathematical concepts; and Kirkpatrick's four-level model, which provides a comprehensive assessment of educational outcomes.
Results. An educational complex was developed, implementing the principle of convergence through three integrative modules: theoretical-cognitive, practice-oriented, and instrumental-technological. The core of the program is built on the principle of cognitive continuity—from classical deterministic algorithms through modern stochastic and adaptive methods (SGD, Adam, RMSProp) to metaheuristic algorithms (genetic algorithms, swarm intelligence, simulated annealing). The practical component of the course includes the implementation of algorithms in Python using the NumPy and SciPy libraries, forming a basis for the subsequent mastery of machine learning frameworks.
Discussion and Conclusion. The implementation of the developed educational complex contributes to the formation of a holistic cognitive understanding of optimization methodology and develops the systemic professional thinking necessary for designing intelligent systems. The validity of the proposed approach is confirmed by a comprehensive assessment procedure based on the Kirkpatrick model, which allows tracking results at various levels—from the assimilation of educational material to long-term professional effects. The program is scheduled for pilot testing in the 2026-2027 academic year.
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