AI-SUPPORTED DIGITAL-DIDACTIC TECHNOLOGY FOR MONITORING AND DEVELOPING METHODOLOGICAL COMPETENCE OF FUTURE PHYSICS AND ASTRONOMY TEACHERS
Authors
Berdiyev Shokir Xudoynazarovich ()Files
Abstract
This article describes an AI-supported digital-didactic technology for developing and monitoring the methodological competence of future physics and astronomy teachers. The technology is based on the MethodEdu.uz adaptive digital learning platform described in the dissertation plan. The aim of the article is to define the functional roles, learning algorithm, AI support mechanisms, competence map, portfolio evidence, and assessment logic of the platform. The study uses a design-based methodological approach and treats the platform as a digital-didactic environment that connects electronic modules, interactive assignments, virtual laboratories, diagnostic testing, adaptive recommendations, teacher feedback, and responsible AI assistance. The results present a staged technology: initial diagnostics, competence profiling, integrated module completion, methodological task performance, AI and teacher feedback, competence map updating, portfolio accumulation, adaptive recommendation, and final diagnostics. The article also proposes a monitoring matrix based on tests, task completion, portfolio quality, AI-use history, teacher confirmation, and competence levels. The discussion emphasizes that AI should support methodological thinking but should not replace pedagogical judgment; final assessment must be confirmed by the teacher or expert. The article concludes that MethodEdu.uz can become a reliable digital-didactic mechanism for individualized, evidence-based, and reflective development of future physics and astronomy teachers' methodological competence.
References
1. Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.
2. Mayer, R. E. (2009). Multimedia Learning. Cambridge University Press.
3. Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017-1054.
4. Redecker, C. (2017). European Framework for the Digital Competence of Educators: DigCompEdu. Publications Office of the European Union.
5. Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15(2), 4-14.
6. UNESCO. (2023). Guidance for Generative AI in Education and Research. UNESCO.
7. Wiggins, G. (1998). Educative Assessment: Designing Assessments to Inform and Improve Student Performance. Jossey-Bass.
8. Chirchik State Pedagogical University. (2026). MethodEdu.uz adaptive digital learning platform: Technical specification with AI module. Internal project document.
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