Codes of Educational Transformation: Interaction, Meaning, and Communication Strategies in the Age of Artificial Intelligence

This study critically examines the evolving role of Artificial Intelligence (AI) technologies in educational transformation, with a particular focus on interaction, meaning-making, and institutional communication strategies. Within the Resource-Based View (RBV) framework, AI is conceptualized as a strategic and intangible asset capable of restructuring the pedagogical, operational, and symbolic dimensions of education. Through a qualitative thematic analysis of 26 peer-reviewed journal sources published between 2019 and 2024, the study investigates how generative AI models, especially ChatGPT, mediate new forms of interaction, foster personalized learning, and transform the communication infrastructure of educational institutions. The analysis identifies three interconnected “transformation codes”: interactional (system-mediated interaction and personalization), meaningbased (ethical, cultural, and epistemological redefinitions), and communicational (strategic signaling and legitimacy construction). These codes highlight a shift from instructor-centered paradigms toward AI-supported ecosystems in which educational value is co-produced by human and algorithmic agents. The findings reveal the transformative potential of AI, as well as the inherent risks it poses, including concerns about academic integrity, digital inequality, and cultural homogenization. The study argues that sustainable AI integration requires not only technical competence but also ethically informed and culturally contextualized communication strategies. Thus, it proposes that AI functions as a catalyst for reimagining education as a communicational institution, contingent upon conditions of trust, equity, and strategic alignment.