METHODOLOGY OF TEACHING THE «ARTIFICIAL INTELLIGENCE» SUBJECT TO FUTURE COMPUTER SCIENCE TEACHERS

Gulnazira Koshanova, А А Миндетбаева, Nuray Zhumabay

2026

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DOI 10.52269/skvc2622147
Data confidence 85 high
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Abstract

This article focuses on the theoretical and methodological foundations of teaching Artificial Intelligence (AI), including machine learning technologies, to future computer science teachers. The aim of the study is to present the results of experimental research on the effectiveness of a methodology for developing AI competencies among future computer science teachers through the use of the Jupyter Notebook platform and Python libraries for data analysis and visualization. The research objectives were to analyze national and international experience in AI education; to train future computer science teachers in machine learning using Python libraries such as NumPy and Pandas for data processing, analysis, and visualization within the interactive Jupyter Notebook environment; and to illustrate the learning process through practical examples. The study was conducted with students enrolled in the “Computer Science” educational program at Khoja Ahmed Yasawi International Kazakh-Turkish University. The participants demonstrated the acquisition of competencies, knowledge, and practical skills related to the course “Fundamentals of Artificial Intelligence.” The experimental work focused on teaching future computer science teachers methods of data analysis and visualization using Python libraries within the Jupyter Notebook platform. As part of the study, a survey was conducted to assess students’ needs for mastering Jupyter Notebook tools and Python libraries used in solving AI-related tasks. Based on the survey results, an educational module entitled “Jupyter Notebook and Python Libraries” was developed and integrated into the “Fundamentals of Artificial Intelligence” course. The findings demonstrated that the proposed educational module contributes to the development of sustainable skills in data analysis, processing, and visualization among future computer science teachers. Practical work with the NumPy and Pandas libraries enhanced participants’ professional competencies in programming and data analysis, thereby confirming the effectiveness of the proposed teaching methodology.