Metasystem Transitions in Education: Human-AI Interactions as Catalysts for Computational Thinking

Abstract

The paper explores how human–AI interactions, particularly with generative artificial intelligence (Gen-AI), serve as catalysts for developing computational thinking (CT) as a creative meta-competence. Grounded in a theoretical framework that synthesises Vygotsky’s mediated learning, Lotman’s Semiosphere, and Turchin’s Metasystem Transition theory, the study conceptualises education as a process of collaborative meaning-making within human–machine teams. Two case studies conducted in Russian middle schools provide empirical grounding: one involving scaffolded Gen-AI use in project-based learning, and the other involving unstructured Gen-AI use during programming tasks. Results indicate that structured, reflective engagement with Gen-AI enhances students’ CT skills and supports self-regulation, while not scaffolded use yields mixed outcomes. The findings suggest that Gen-AI can function as a semiotic and cognitive scaffold when meaningfully integrated into educational activities. The study contributes to the understanding of computational thinking as a form of cultural and cognitive mediation, and positions Gen-AI as a driver of metasystem transitions in education.