<![CDATA[This article introduces Entagogy, a posthumanist, systems-theoretical framework for AI-integrated learning that addresses conceptual gaps left by traditional paradigms such as pedagogy, andragogy, and heutagogy. Drawing on Luhmann’s theory of structural coupling, Entagogy reconceptualises the interaction between the Human Cognitive System (HCS) and the AI Semantic Subsystem (AISS) as co-autopoietic, mutually adaptive, and structurally coupled processes occurring within an entangled Zone of Proximal Development (e-ZPD). Entagogy’s novel contributions include (i) the introduction of a measurable Coupling Index and clearly defined mechanical thresholds of adaptivity, latency responsiveness, and governance permeability, that determine when genuinely recursive and co-constructive learning emerges; (ii) the elaboration of the Entagogy Stack, an integrative schema connecting computational substrates, interface semantics, exogenous perturbations, and institutional policy; and (iii) a methodological roadmap structured around four analytical lenses: scenario-based reasoning, learning-analytics trace ethnography, longitudinal mixed-methods inquiry, and comparative multimodal analysis. The article explicitly addresses limitations, including systemic risks associated with digital inequality, bias propagation, and ethical oversight. Ultimately, Entagogy equips researchers, educators, and policymakers with actionable theoretical constructs, robust validation criteria, and equity-driven governance recommendations, guiding the development of ethically grounded, adaptive, and inclusive AI-enhanced learning environments.]]>
Copyrights © 2026
