Kazakh-British Technical University

The accelerated adoption of artificial intelligence (AI) in higher education has intensified expectations regarding instructional quality and learning effectiveness, yet empirical evidence on its pedagogical value remains contextually contingent. This study investigates students' perceptions of AI-enabled teaching in Kazakhstan, with a specific focus on perceived instructional quality dimensions and discipline-related skill development. Grounded in contemporary AI-supported learning literature, a structured self-administered questionnaire was developed and administered using a stratified random sampling design across higher education institutions. Instrument reliability and construct validity were established through Cronbach's alpha and the Rasch Rating Scale Model using a pilot sample representing 10% of respondents. Power analysis indicated a minimum requirement of 1504 observations; 2700 valid responses were ultimately analyzed. Data were processed using Microsoft Excel and RStudio, and partial least squares structural equation modeling (PLS-SEM) was employed to test seven hypothesized relationships within the proposed framework. The measurement model exhibited satisfactory reliability and validity, while the structural model showed low multicollinearity, strong explanatory and predictive power, and acceptable fit indices (SRMR, NFI, GoF). The findings reveal that usability, engagement, content quality, and accessibility of AI-based instruction significantly enhance student satisfaction, while instructional quality strongly predicts perceived skill acquisition, particularly in problem-solving, conceptual understanding, and technological competence. Conversely, perceived gains in interpersonal and diagnostic skills were comparatively weaker, and feedback-related pathways were not statistically significant, indicating limitations in current AI feedback mechanisms. The study offers robust perception-driven empirical evidence on the pedagogical implications of AI-integrated instruction in Kazakhstan's higher education system and provides actionable insights for evidence-based instructional design and AI-enabled teaching policy. • Power analysis established a minimum sample of 1504; the study analyzed 2700 valid student responses from Kazakhstan. • A large, demographically diverse dataset enhances the robustness and generalizability of findings on AI-enabled teaching. • PLS-SEM results demonstrate that usability, engagement, content quality, and accessibility significantly drive student satisfaction. • AI-supported instruction strongly predicts problem-solving, conceptual, and technological skill development, while feedback-related effects remain limited. • The findings provide actionable insights for higher education leaders and policymakers to optimize AI-driven teaching and digital strategies.

This paper presents an experimental study of an RF-DBD plasma, establishing how variations in operational parameters affect its characteristics and discharge behavior. The study identified the optimal parameters for plasma ignition – including pressure, applied power, and gas flow rate – and quantified their influence on key plasma characteristics, namely electron temperature, electron density, and Hα emission intensity as an optical indicator of hydrogen-containing species. Emission spectroscopy was employed to characterize the argon and argon-methane plasmas, with particular attention paid to the hydrogen Hα line (656.2 nm) in the mixture. For argon plasma, the electron temperature increased from ~ 0.98 eV to ~ 1.08 eV as the pressure was raised from 0.5 to 1.0 Torr at a constant discharge power of 4 W. Under the same pressure and power conditions, the electron temperature for an argon-methane plasma was approximately ~ 1.2 eV. A further increase in both power and gas flow rate leads to a decrease in electron temperature. Concurrently, the electron density was found to be on the order of 1014– 1015 m− 3. Material properties were examined using scanning electron microscopy (SEM) for morphology and Raman spectroscopy for structural analysis and defect characterization. The deposited carbon-based materials – films on the RF electrode and particles on the reactor wall – exhibited an amorphous structure. These findings elucidate the fundamental relationship between plasma parameters and gas conversion efficiency, providing a valuable framework for the controlled hydrogen production for energy applications.

Biodegradable polymer films based on poly(vinyl alcohol) (PVA), starch (S), and itaconic acid (IA) were fabricated via solution casting to assess their potential for sustainable packaging applications. FTIR confirmed esterification between the hydroxyl and carboxyl groups, indicating successful crosslinking. Mechanical tests revealed that the balanced PVA/S/IA (33.3/33.3/33.3 wt%) film presented the highest tensile strength (10.5 ± 0.8MPa) and elongation at break (38.9% ± 2.4%), whereas IA-rich films demonstrated increased viscosity (up to 3250 ± 110 cP) and improved thermal stability with decomposition temperatures ∼15°C-20°C higher than those of starch-rich films. Conversely, starch-rich films displayed faster biodegradation, with up to 67% mass loss in soil after 30 days, whereas the mass loss rates were 42% for balanced films and 28% for IA-rich formulations. All the films fragmented in water within 24h without complete dissolution due to the presence of a crosslinked three-dimensional network. These findings demonstrate that formulation ratios markedly influence mechanical performance, solubility behavior, and degradation kinetics. Overall, the tunability of PVA/S/IA films highlights their potential as biodegradable alternatives to petroleum-based single-use packaging materials.

EPDM is widely used as the polymer matrix for solid rocket motor (SRM) internal thermal protection because of its low density, chemical inertness, and ability to form carbonaceous residue. Practical performance is frequently limited by weak char integrity and barrier properties, char oxidation, mechanical stripping in gas-dynamic flow, and by the poor comparability of published results due to non-uniform test conditions and reporting. This review systematizes studies on 0D nanofillers in EPDM ablatives and harmonizes the key metrics, including linear and mass ablation rates (LAR, MAR), back-face temperature (Tback), and solid residue yield. The major 0D additives-nSiO2, nTiO2, nZnO, and carbon black (CB) are compared, and their dominant mechanisms are summarized: degradation-layer structuring, reduced gas permeability, thermo-oxidative stabilization, and effects on vulcanization. Several studies report larger improvements for hybrid systems, where CB enhances char cohesion and retention, while oxide nanoparticles improve barrier performance and resistance to oxidation. Finally, an application-oriented selection matrix is proposed that accounts for thermal protection efficiency, processability, agglomeration limits, and density penalties to support EPDM coating design and improve comparability.

Repeated sowing of winter cereals represents one of the adaptive dryland approaches to make more sustainable the rainfed agriculture activities in southern Kazakhstan. This study conducted a multi-year reconstruction of crop transitions using Sentinel-2 imagery for 2018–2025, based on the combined analysis of Normalized Difference Vegetation Index (NDVI) temporal profiles and the Plowed Land Index (PLI), enabling the creation of a field-level harmonized classification set. The transition “spring crop → winter crop” was used as a formal indicator of repeated winter sowing, from which annual repeat layers and an integrated metric, the R-index, were derived. The results revealed a pronounced spatial concentration of repeated sowing in foothill landscapes, where terrain heterogeneity and locally elevated moisture availability promote the recurrent return of winter cereals. Comparison of NDVI composites for the peak spring biomass period (1–20 May) showed a systematic decline in NDVI with increasing R-index, indicating the cumulative effect of repeated soil exploitation and the sensitivity of winter crops to climatic constraints. Precipitation analysis for 2017–2024 confirmed the strong influence of autumn moisture conditions on repetition phases, particularly in years with extreme rainfall anomalies. These findings demonstrate the importance of integrating multi-year satellite observations with climatic indicators for monitoring the resilience of agricultural systems. The identified patterns highlight the necessity of implementing nature-based solutions, including contour–strip land management and the development of protective shelterbelts, to enhance soil moisture retention and improve the stability of regional agricultural landscapes.

Silicon carbide (SiC) has emerged as a robust and tunable semiconductor for advanced photocatalytic applications. This review provides a comprehensive overview of recent progress in the development of SiC-based materials for environmental remediation and solar-driven hydrogen production. Key aspects discussed include morphological engineering, heterostructure design, doping strategies, and plasmonic enhancement. Emphasis is placed on structure-activity relationships, insights from density functional theory (DFT) and machine learning (ML) models, and synergistic effects in composite systems. This review concludes with a critical analysis of current challenges and future research directions, highlighting the potential of SiC implementation as a sustainable platform for next-generation photocatalytic technologies.