Bo‘lajak o‘qituvchilarning sun’iy intellekt vositalari yordamida adaptiv o‘quv jarayonlarini loyihalash kompetentligining tuzilmasi, mezonlari va darajalari
Litsenziya: cc-by
Annotatsiya
The relevance of this research is driven by the rapid integration of generative artificial intelligence (AI) tools into educational practice and the absence of a scientifically validated framework for future teachers' competence in designing adaptive learning processes with these tools. The purpose of this article is to theoretically substantiate and empirically test a four-component framework for AI-assisted adaptive learning design competence and to develop a system of its criteria, indicators, and proficiency levels. The methodology integrates systemic, activity-based, student-centered, and competency-based approaches; methods included a questionnaire (based on T.I. Ilyina's methodology), expert evaluation, cognitive testing, and rubric-based assessment. The pedagogical experiment involved 132 student-teachers (EG = 66, CG = 66) at two universities in Uzbekistan. The results demonstrated that the proposed framework—comprising motivational-axiological, cognitive, organizational-activity, and personal components—provides a comprehensive and operationalized description of the phenomenon under study. At the conclusion of the formative experiment, a significant improvement was observed across all four components (p < 0.001; d > 2.9), confirming the construct validity of the model. Practical significance: the developed diagnostic tools (MSA-2024, CBT-2024, AAR-2024, PAS-2024) can be applied within higher pedagogical education to monitor the development of AI competence among future teachers.