This paper presents Entagogy, a posthumanist, systems-theoretical framework for AI-integrated learning that fills gaps left by pedagogy, andragogy, and heutagogy. Building on Luhmann’s structural coupling, Entagogy reframes interaction between the Human Cognitive System and the AI Semantic Subsystem as co-autopoietic, mutually adaptive processes within an entangled Zone of Proximal Development (e-ZPD). Its contributions are threefold: (i) a measurable Coupling Index and thresholds of adaptivity, latency, and governance that signal genuinely recursive, co-constructive learning; (ii) the Entagogy Stack, an integrative schema linking computational substrates, interface semantics, exogenous perturbations, and policy; and (iii) a methodological roadmap spanning scenario-based reasoning, learning-analytics trace ethnography, mixed-methods longitudinal inquiry, and comparative multimodal analysis. Addressing risks of digital inequality, bias propagation, and ethical oversight, Entagogy equips researchers, educators, and policymakers with actionable constructs, validation criteria, and equity-driven governance principles for developing inclusive, adaptive AI-enhanced learning environments.Keywords: Entagogy, AI-integrated learning, posthumanist education, systems theory, structural coupling, entangled Zone of Proximal Development (e-ZPD),
Alex H. S. Harris (Thu,) studied this question.