THEORETICAL MODEL FOR THE FORMATION OF PROFESSIONAL COMPETENCE IN THE FIELD OF ARTIFICIAL INTELLIGENCE OF FUTURE COMPUTER SCIENCE TEACHERS

Abstract

The rapid development of artificial intelligence technologies and their growing integration into education have significantly transformed both teaching practices and curricular content. AI-based tools are increasingly applied to adaptive learning environments, learning analytics, automated assessment, and pedagogical decision support. As a result, concepts such as AI literacy and AI competence of educators have emerged, emphasizing the need for informed, responsible, and pedagogically grounded use of intelligent technologies. The gradual incorporation of artificial intelligence topics into school computer science curricula, as well as the introduction of AI-related courses, has highlighted the need for a systematic approach to preparing future computer science teachers for professional activity in this field. The aim of this article is to provide a theoretical justification for the formation of professional competence in artificial intelligence among future computer science teachers and to propose a structural and logical model of this process. The study is theoretical in nature and employs comparative analysis, systematization, generalization, and conceptual modeling methods. The theoretical framework is based on publications indexed in the Scopus database, analytical reports of international organizations, and relevant national research. The study clarifies the conceptual distinctions between AI literacy of a teacher, AI competence of a teacher, and professional competence of a computer science teacher in the field of artificial intelligence. The professional AI competence of future computer science teachers is defined as an integrative professional quality comprising cognitive, technological, methodological, ethical, and reflective components. A theoretical model grounded in the “input–process–output” logic is proposed to describe the formation of this competence. The results of the study can be applied in teacher education programs and in further research focused on developing methodologies for fostering AI-related professional competence in computer science educators.