METHODOLOGICAL FOUNDATIONS OF GENERATIVE ARTIFICIAL INTELLIGENCE TRAINING FOR FUTURE COMPUTER SCIENCE TEACHERS

This article is devoted to the development and experimental verification of a scientific and methodological model for teaching generative artificial intelligence to future computer science teachers. The relevance of the research lies in the rapid integration of artificial intelligence technologies into educational systems and the growing need to develop digital competencies among future teachers. The study used a mixed-methods approach, including theoretical analysis, pedagogical experiments, surveys, observations, and statistical data processing. The proposed model is purposeful, includes substantive, technological, and evaluative components and is aimed at the systematic introduction of generative artificial intelligence tools into the educational process. The purpose of the research is to develop and scientifically substantiate a methodological model for teaching generative artificial intelligence to future computer science teachers. The study was conducted at the Khoja Ahmed Yasawi International Kazakh-Turkish University and the West Kazakhstan Innovative and Technological University, which 75 students attended. The level of students' artificial intelligence competence in the experimental group showed a significant increase compared to the control group, confirming the effectiveness of the proposed model. The results show that the introduction of generative artificial intelligence tools into the educational process has a positive impact on the development of professional, digital and creative competencies of future teachers. The proposed scientific and methodological model can serve as a practical basis for improving artificial intelligence education in universities.

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