IJIET 2025 Vol.15(10): 2086-2094
doi: 10.18178/ijiet.2025.15.10.2407
doi: 10.18178/ijiet.2025.15.10.2407
Enhancing Master’s-Level STEM Education through AI-Driven IoT Projects: A Kazakhstan Experiment
Meruert Serik1, Kymbat Tleuzhanova1, and Symbat Nurgaliyeva2,*
1. Department of Computer Science, L.N. Gumilyov Eurasian National University, Astana, Kazakhstan
2. Department Сomputer Engeneering, Astana IT University, Astana, Kazakhstan
Email: serik_meruerts@mail.ru (M.S.); bazylkhan_kymbat@mail.ru (K.T.); symbat.nurgaliyeva@astanait.edu.kz (S.N.)
*Corresponding author
2. Department Сomputer Engeneering, Astana IT University, Astana, Kazakhstan
Email: serik_meruerts@mail.ru (M.S.); bazylkhan_kymbat@mail.ru (K.T.); symbat.nurgaliyeva@astanait.edu.kz (S.N.)
*Corresponding author
Manuscript received January 27, 2025; revised March 6, 2025; accepted May 8, 2025; published October 14, 2025
Abstract—In developing contexts such as Kazakhstan, Science, Technology, Engineering and Math (STEM) education at the master’s level continues to prioritize theoretical instruction, while offering insufficient exposure to the practical integration of emerging technologies. This study was conducted in response to the increasing demand for experiential, hands-on education in Artificial Intelligence (AI) and the Internet of Things (IoT), which are essential for preparing students for the digital economy. The research examines the integration of emerging technologies into master-level STEM education through an AI- and IoT-based system that merges Machine Learning (ML) and IoT, supported by AI and Deep Learning (DL), and delivered via Project-Based Learning (PBL). A mixed-method design was employed, including pre-test and post-test assessments and project-based evaluations to measure student motivation, content knowledge, and practical skills. The study involved 167 master’s students from two universities, divided into an experimental group utilizing AI-IoT projects and a control group following a traditional lecture-based curriculum. Statistical analysis using Pearson’s Chi-square test revealed significant improvements in motivation (χ² = 14.95), conceptual understanding (χ² = 10.50), and practical competencies (χ² = 12.92) in the experimental group (p < 0.05). These findings confirm the effectiveness of AI-supported PBL in enhancing STEM education outcomes.
Keywords—Artificial Intelligence (AI) in education, machine learning, internet of things, Science, Technology, Engineering and Math (STEM) education, project-based learning
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
Keywords—Artificial Intelligence (AI) in education, machine learning, internet of things, Science, Technology, Engineering and Math (STEM) education, project-based learning
Cite: Meruert Serik, Kymbat Tleuzhanova , and Symbat Nurgaliyeva, "Enhancing Master’s-Level STEM Education through AI-Driven IoT Projects: A Kazakhstan Experiment," International Journal of Information and Education Technology, vol. 15, no. 10, pp. 2086-2094, 2025.
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).