EDUCATIONAL ROBOTICS AND SMART LEARNING IN PRIMARY SCHOOLS OF KAZAKHSTAN: A SCIENTIFIC-METHODOLOGICAL FRAMEWORK FOR QUALITY EDUCATION, INNOVATION AND DIGITAL INCLUSION

Educational robotics has become one of the fastest-growing directions in smart learning. However, primary schools in emerging economies still lack a scientifically grounded methodological framework that links classroom practice to the national goals of quality education, innovation, and digital inclusion. This study examines how educational robotics can be taught to pupils in Grades 1–4 within a smart-learning environment in Kazakhstan, and whether the country's smart-learning infrastructure and public education investment are associated with long-run human capital outcomes. The study employs a mixed design: a four-year author-developed curriculum tested in a quasi-experiment with 30 first-grade pupils (experimental and control groups, Kohs Block Design, Raven's Coloured Progressive Matrices, Horizon Line and Gloves tests), and secondary annual time-series data for Kazakhstan for 2006–2025 analyzed with ARDL bounds testing, error-correction, Johansen, Toda–Yamamoto causality, VAR impulse responses, variance decomposition, mediation and 2SLS estimators. The results show that the experimental group reached a higher level of creative intellectual activity than the control group (68.75% versus 26.7% at the third level; Mann–Whitney p = 0.014). The bounds F-statistic of 17.35 confirms cointegration; a one percentage-point rise in Internet use is associated with a 0.57% long-run increase in the gross tertiary enrolment ratio (p = 0.014), the Digital Kazakhstan programme adds a 28.7% level shift, and 45.3% of any disequilibrium is corrected within a year. In contrast, education spending shows no significant long-run effect. Internet-use shocks explain 39.7% of the ten-year forecast error variance in enrolment. The findings suggest that smart-learning infrastructure, rather than spending volume alone, underpins the human-capital pipeline that primary-school robotics is designed to feed, consistent with SDG 4 (quality education) and SDG 9 (industry, innovation and infrastructure). JEL Classification Codes: I21, I25, I28, O33, O38, C22.

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