When UNESCO released AI and the Future of Education: Disruptions, Dilemmas and Directions (2025), it positioned the text as a “global commons” for dialogue on how artificial intelligence might reshape education. With contributions from researchers, educators, and policymakers worldwide, the volume foregrounds pressing questions of equity, care, governance, and the changing role of teachers in AI-augmented classrooms.
Yet if we read the report through the lens of autopoietic ecology, something striking emerges: the report itself is an autopoietic system. Its think pieces, ethical appeals, and policy frameworks are not external commentaries on education but operations that recursively regenerate UNESCO’s legitimacy as custodian of educational futures.
What Is Autopoietic Ecology, and Why Does It Matter?
Autopoietic ecology is a way of seeing systems not as fixed entities with stable essences, but as recursive processes that sustain themselves by regenerating their own conditions of existence. A school, for example, is not simply a building with rules and curricula; it is a network of operations—teaching, learning, assessment, administration—that persist only because they continually reproduce the distinctions that make them possible. This perspective matters for reading UNESCO’s report because it shifts our attention: rather than asking what AI is or what education should be, we ask how these systems co-regulate, how they sustain viability through perturbations, and how values like equity or care are enacted recursively rather than given once and for all. It is in this recursive, ecological reading that UNESCO’s text reveals its deeper significance: not a blueprint for controlling AI, but an ongoing enactment of educational futures.
From Essence to Operation
UNESCO speaks of “reclaiming education’s public purpose.” This phrasing implies that education has an essence that can be restored. Autopoietic ecology challenges this. Education does not have a fixed purpose—it sustains itself through recursive operations: curricula, classrooms, assessments, policies. Purpose is not a given but a distinction continually enacted. To speak of “reclaiming” is to perform a re-entry: the system observes itself as “losing purpose” precisely by generating the discourse that restores it.
For practitioners, this means that debates over AI should not aim to defend some imagined essence of education, but to observe how different practices regenerate or erode viable futures. For example, is a school’s use of generative AI reinforcing reliance on standardized testing, or is it opening new ways to cultivate reflective, relational learning?
Rethinking Temporality
The report narrates AI as a rupture, a historical break that unsettles education. From an autopoietic perspective, this linear temporality is misleading. AI does not rupture education from outside. It perturbs existing programs, which education systems reconfigure according to their own closure. Evolution, here, is not progress through history but structural transformation: systems alter their enabling conditions while sustaining their recursive continuity.
For teachers, this means we should view AI less as a one-off disruption and more as a long-term perturbation. How classrooms adapt depends not on the “nature” of AI but on the recursive ways schools integrate or resist it. The question is not whether AI will change education but how education reconfigures itself in response.
The Fantasy of Control
UNESCO emphasizes guidelines, guardrails, and governance frameworks. Yet education systems, like all social systems, are operationally closed. They cannot be controlled from the outside. Policy frameworks function as perturbations, not directives. Their efficacy lies not in commanding systems but in resonating with their internal logics. Governance, then, is less about mastery than modulation: learning to perturb systems in ways that remain viable.
In practice, this suggests that AI literacy campaigns, procurement policies, or ethical codes succeed only if they resonate with the daily operations of teachers and students. A top-down AI ban may fail if students already rely on generative tools for learning support, while a participatory code of practice co-developed with teachers may create lasting shifts.
Beyond Anthropocentrism
Though the report highlights “more-than-human” entanglements in some contributions, its framing remains largely anthropocentric: teachers and learners are centered, while AI appears as tool or threat. Autopoietic ecology suggests otherwise. AI systems are themselves recursive operators. They do not merely assist or disrupt education; they co-regulate with human, institutional, and communicative systems in an ongoing ecology of interaction.
Practically, this means educators should not only ask “How can AI serve us?” but also “How does AI reshape what we count as learning, assessment, or participation?” Generative AI tools can subtly shift classroom rhythms: for instance, when students lean on them for brainstorming, the teacher’s role in cultivating original thought shifts. Recognizing AI as a co-regulator, not a passive tool, invites more nuanced pedagogy.
Universals as Programs
UNESCO grounds its authority in universal values—equity, inclusion, dignity. Autopoietic ecology reframes these not as essences but as programs: stabilizing codes that make further operations possible. “Inclusion” is not an eternal principle but a recursive distinction. It persists because it is re-enacted in policy, pedagogy, and discourse, sustaining the very coherence of education as a global project.
For schools, this means equity initiatives must be judged by how they operate recursively. Does the introduction of AI tutoring tools genuinely expand access for underserved students, or does it deepen divides by privileging those with bandwidth and devices? Equity is not a claim but an ongoing practice of recursive renewal.
UNESCO as an Autopoietic System
Seen through this lens, the report is not only about AI and education. It is an instance of recursive communication:
- Operations: think pieces, frameworks, recommendations.
- Closure: reference to UNESCO’s own legitimacy as steward of education’s future.
- Perturbations: AI technologies, inequalities, geopolitical pressures.
- Programs: equity, inclusion, ethics, care.
- Evolution: reconfiguration of its enabling conditions under new challenges.
The volume does not so much map the future of AI in education as enact it—through distinctions that make UNESCO’s continued role intelligible.
Practical Implications for Generative AI in Education
- Assessment Reform
If generative AI can produce polished essays, then traditional assessments lose validity. The task is not to “ban” AI, but to reconfigure assessment practices toward relational, collaborative, and project-based learning that AI cannot easily replicate. - Teacher Professional Development
Teachers should be seen not as passive recipients of AI tools but as designers of AI-augmented pedagogy. Professional development must focus on helping educators modulate AI integration in ways consistent with their own pedagogical closure. - Student Agency and AI Literacy
AI literacy should move beyond technical skills to include reflexivity: helping students observe how their own learning is recursively shaped when they use AI. This cultivates responsibility, not just competence. - Equity-Oriented Deployment
Generative AI can either widen or narrow gaps. The critical question is: who has access, under what conditions, and how are distinctions of inclusion enacted? Local adaptation, multilingual development, and offline accessibility are vital. - Governance as Modulation
Policies should avoid the illusion of universal control. Instead, they should aim for resonance—developing flexible guidelines that align with local practices and invite iterative refinement.
Toward an Ecological Reading
If we shift from essence to operation, from rupture to recursion, from control to modulation, UNESCO’s report becomes less a plan for the future and more a demonstration of how global education systems reproduce themselves under conditions of perturbation. Its strength lies not in resolving dilemmas but in sustaining a communicative space where dilemmas remain thinkable.
For educators, this means embracing AI not as destiny but as perturbation: a force that will reshape their work only insofar as they recursively reconfigure their own practices. For policymakers, it means designing frameworks that perturb systems into viable transformations rather than imposing illusory control. And for all of us, it means recognizing that education—like any living system—persists not by reclaiming an essence, but by recursively enacting the conditions of its own viability.
Cambridge EdgeLab 
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