Implementation of Artificial Intelligence Technologies in Education: Managerial Challenges

This study examines fundamental interdisciplinary issues and key managerial challenges associated with decision-making processes regarding the legalization and implementation of artificial intelligence (AI) technologies in educational settings. The research aims to assess potential effects, advantages, and risks of integrating large language model (LLM)-based AI technologies into educational processes at the discipline level. The authors propose an original theoretical framework utilizing Manuel DeLanda’s assemblage theory. This approach enables the incorporation of all actors into communication models regardless of their material substrates—a crucial consideration in contexts where communication becomes heterarchical and extends beyond human participants. Building on this theoretical foundation, novel methodological approaches have been developed to systematize professional teaching tasks in hybrid (phygital) environments incorporating AI technologies. The study includes analysis of AI functionality alignment with pedagogical requirements, development of AI effectiveness evaluation methodologies, demonstration of task transformation through a case study of course structure design, classification of enhanced LLM utilization approaches (industrial engineering, RAG, LoRA, multi-agent systems). The paper analyzes digital transformation processes in higher education driven by AI adoption, with particular emphasis on managerial considerations at various organizational levels. This research will benefit higher education administrators, researchers, and educators engaged in educational digitalization and institutional transformation.