Language as a Channel for Cognitive Development in Human–AI Relations: Toward a Framework of Hybrid Distributed Cognition

Advances in large language models (LLMs) have transformed human–machine interaction from instrumental querying to sustained dialogical collaboration. This paper explores whether language in human–AI relationships can function as a channel of cognitive development for artificial systems. Drawing on theories of distributed cognition, the extended mind, dialogical reasoning, and recent research on in-context learning in transformers, we propose a framework in which cognitive development occurs across four interconnected scales: dynamic (within interaction), systemic (within human–AI relational systems), ecological (across networks of interactions mediated by a shared model), and cultural (through the circulation of artifacts produced by these interactions). While current AI systems do not typically accumulate structural learning across sessions, recent findings on in-context optimization suggest that functional adaptation during inference is more complex than a simple absence of learning. We introduce the concept of hybrid distributed cognition to describe these emerging configurations and argue that contemporary human–AI dialogue may represent a pre-theoretical stage in the scientific understanding of new forms of cognitive organization.