AI-First as a New Paradigm of Business Education in Ukraine: Transformation of Content, Teaching Methods and the Role of the Instructor

The relevance of the study is determined by the growing use of artificial intelligence in managerial activities and the resulting need to update the training of management professionals. The use of intelligent systems for data analysis, forecasting, modelling, and decision support requires a transition from the fragmented application of digital tools in the educational process to their systematic integration while maintaining human oversight, critical verification of results, and personal responsibility. The purpose of the study is to conceptualize AI-first as a systemic approach to management education and to develop an organizational and methodological model for its implementation in Ukrainian higher education institutions. The methodological framework comprises conceptual synthesis, comparative analysis, structural and functional modelling, and logical generalization of scientific, regulatory, and institutional provisions concerning the use of artificial intelligence in education. Fragmented, AIenhanced, and AI-first approaches are differentiated according to the extent to which technologies are integrated into learning outcomes, course content, teaching methods, assessment, and the institutional environment. The study substantiates the cross-cutting development of artificial intelligence competencies through foundational training, subject-specific applied integration, and interdisciplinary management projects. Directions for redesigning learning activities and assessment are identified, with an emphasis on problem formulation, verification of obtained results, their interpretation, argumentation, and responsibility for managerial decisions. The changing professional role of the instructor is outlined, and the need to combine educational transformation with institutional support is substantiated, including digital infrastructure, data governance, professional development of academic staff, quality assurance, allocation of responsibilities, and engagement with employers. A conceptual model is proposed, centred on developing future managers’ ability to make well-founded decisions in interaction with artificial intelligence while maintaining human oversight and responsibility.