METHODOLOGY OF USING ARTIFICIAL INTELLIGENCE TOOLS (CHATBOTS, ADAPTIVE SYSTEMS, GENERATIVE MODELS) IN TEACHING PEDAGOGICAL DISCIPLINES

Abstract

This article examines the methodology of using artificial intelligence tools - specifically chatbots, adaptive learning systems, and generative AI models - in teaching pedagogical disciplines at higher educational institutions. The rapid development of AI technologies has created both unprecedented opportunities and significant challenges for teacher education. While AI tools are already transforming many sectors of education, their systematic integration into the teaching of pedagogy, didactics, educational psychology, and teaching methodology remains theoretically underdeveloped and practically inconsistent. The purpose of the article is to develop a methodological framework for the purposeful, pedagogically grounded, and ethically responsible use of AI tools in pedagogical disciplines. The research draws on international scholarly literature on AI in education, learning analytics, and human-computer interaction, as well as the works of Uzbek scholars who have studied educational technology, innovative teaching methods, and the modernization of pedagogical preparation. The article classifies AI tools into three functional categories - conversational AI (chatbots and large language models), adaptive learning systems, and generative AI for content creation - and analyses the specific methodological possibilities and limitations of each category in the context of pedagogical disciplines. The main result is a five-component methodological model that defines the goals, content, methods, tools, and assessment approaches for integrating AI into pedagogical courses. The model is organized around the principle that AI should serve as a cognitive partner that enhances pedagogical thinking rather than as a substitute that replaces it. The study concludes that the effective use of AI in teaching pedagogical disciplines requires not only technical literacy but also a new form of methodological competence that includes the ability to design AI-enhanced learning activities, to evaluate AI-generated content critically, and to maintain the primacy of human pedagogical judgement in all educational decisions.