MODEL FOR FORECASTING EDUCATIONAL PATHWAYS OF SCHOOL GRADUATES BASED ON DEMOGRAPHIC DATA AND BIG DATA ANALYTICS METHODS

This study focuses on the development of a forecasting model for the distribution of school graduates across educational pathways based on demographic and socio-economic factors in the Republic of Kazakhstan. The relevance of the research is driven by the need for a scientific understanding of how graduates are allocated among different forms of further education under conditions of demographic change and transformation of the education system. The study is based on official statistical data for the period 1992– 2025, including indicators of birth rates, living standards, and the distribution of graduates across educational pathways (higher education, vocational education, studying abroad, and alternative forms of activity). To identify relationships between demographic and socio-economic variables and the choice of educational pathway, correlation analysis and analysis of variance (ANOVA) were applied.

The results reveal statistically significant relationships between birth rates, population income levels, and the distribution of graduates across different educational directions. It is established that demographic fluctuations determine the volume of graduates, while socio-economic factors shape the structure of their educational choices. The proposed forecasting model enables the assessment of future demand for the education system and supports the development of evidence-based policy decisions in the field of education.

1. Kusherbaev K.E. Prioritetnye napravleniya razvitiya obrazovaniya i nauki na blizhaishii period / K.E. Kusherbaev, Ministr Obrazovaniya i nauki RK. Iz vystupleniya na rasshirennom zasedanii Kollegii Ministerstva.// Aktual'nye problemy vysshei shkoly. – 2000 g. (In Russian).

2. Satubaldin S. Biznes-obrazovanie v Respublike Kazakhstan: «Problemy i strategicheskie orientiry na XXI vek» / S. Satubaldin, ispolnitel'nyi direktor KIMEHP pri Prezidente Respubliki Kazakhstan, akademik, doktor ehkonomicheskikh nauk, professor // Aktual'nye problemy vysshei shkoly. – 2000 g. (In Russian).

3. Kusherbaev K.E. Strategiya razvitiya vysshego obrazovaniya v Respublike Kazakhstan / K.E. Kusherbaev, A.K. Akhmetov, A.E. Abylkasymova – Almaty: Izd.-vo «Bіlіm», 1998. – 232 s. (In Russian).

4. Prikaz Ministerstva obrazovaniya i nauki RK «Ob utverzhdenii Pravil o poryadke planirovaniya raskhodov na podgotovku kadrov s vysshim professional'nym obrazovaniem i raskhodov na obuchenie odnogo studenta». № 681 ot 18 sentyabrya 2002. – Astana. (In Russian).

5. Prikaz Ministerstva obrazovaniya i nauki RK «Ob utverzhdenii finansovykh normativov po vysshemu obrazovaniyu». № 467 ot 13 iyunya 2002. – Astana. (In Russian).

6. Zakonodatel'stvo ob obrazovanii v Respublike Kazakhstan. – Almaty: YURIST, 2005. – 212 s. (In Russian).

7. Aksel'rod V.I. Modelirovanie i realizatsiya sistemy dopolnitel'nogo kraevedcheskogo obrazovaniya uchashchikhsya / V.I. Aksel'rod // Dopolnitel'noe obrazovanie. – 1999. – № 2. – S. 9- 12. (In Russian).

8. Aksenova L.I. Model' kompleksnogo obrazovatel'nogo uchrezhdeniya / L.I. Aksenova //Defektologiya. – 2002. – № 4. – S.15-23. (In Russian).

9. Kundysheva E.S. Matematicheskoe modelirovanie v ehkonomike: Uchebnoe posobie dlya vuzov / Pod red. B.A. Suslakova. – 2-e izd., pererab. i dop. – M.: Dashkov i K, 2005. – 352 s. (In Russian).

10. Bondarevskaya E.V. Tsennostno-smyslovye orientiry i strategicheskie napravleniya razvitiya sel'skoi shkoly Opytno-ehksperimental'naya rabota po modelirovaniyu obrazovat. prostranstva v sel. shkole / E.V. Bondarevskaya // Pedagogika. – 2002. – № 5. – S. 52-64. (In Russian).

11. Artificial intelligence in education: A systematic review / S. Wang et al // Expert Systems with Applications. – 2024. (In English).

12. Cheah Y.H. Integrating generative artificial intelligence in K-12 education: practices and barriers / Y.H. Cheah, J. Lu, J. Kim // Computers and Education: Artificial Intelligence. – 2025. (In English).

13. Filiz O. Teachers and AI: Factors influencing AI integration in education / O. Filiz, M.H. Kaya, T. Adiguzel // Education and Information Technologies. – 2025. (In English).

14. Maheshwari G. Factors influencing students’ intention to adopt AI tools in higher education / G. Maheshwari // Education and Information Technologies. – 2023. (In English).

15. Hugerth M.W. Profiling students’ AI-use behavior in higher education / M.W. Hugerth // Computers and Education Open. – 2025. (In English).

16. Józsa G. School-level and demographic differences in AI use in education / G. Józsa // Education Sciences. – 2026. (In English).

17. Factors influencing students’ adoption of AI technologies in higher education / M.S. Rosli et al // IEEE Access. – 2026. (In English).