The cover of a recent international report on artificial intelligence in education captures the crux of the problem schools face today: a conventional classroom, students seated in rows facing the front, while an AI system projects content from the traditional position of the teacher. The scene is bathed in a blue glow designed to communicate modernity, yet the pedagogical architecture remains unchanged: frontal organization, receptive students, transmission-centered teaching, and the familiar structure of the classroom. In other words, it’s all bullshit.

Yet, report after report, we see this beautiful, blue glow of bullshit. These images are important because of what it reveals about the institutional imagination now available to education systems. Rather than asking how school time, assessment, or educational authority might change, the image places technology inside an existing function. AI appears as a substitute or supplement for what already happens, not as an occasion to reconsider the design of education itself.

Artificial intelligence is not destroying education. It is showing, with increasing clarity, how much of education still rests on fragile practices: assignments that confuse textual production with thinking, assessments that reward repetition before judgment, policies that outsource trust to administrative procedure, and innovation discourse that rarely changes the lived experience of learning.

Over the past few years, this tension has stopped being abstract. In staff rooms, leadership meetings, faculty committees, ministries, accreditation offices, and board discussions, AI has moved from technological curiosity to daily institutional problem. Student work now appears with uncertain authorship. Academic integrity policies are being rewritten under pressure. Educational systems are trying to respond to technologies that change faster than the institutional frameworks meant to govern them.

The rapid expansion of generative systems creates understandable pressure on educational organizations. School leaders, faculty, institutional managers, and policymakers must respond to immediate problems: academic integrity, curriculum redesign, data governance, and unequal access. Yet when the discussion remains limited to adoption, control, or instrumental training, artificial intelligence is reduced to a matter of implementation. That displacement impoverishes the debate, because the deeper question is not only which tools institutions will use, but how those tools reorganize the conditions under which learning, teaching, assessment, and institutional governance take place.

In this context, international organizations, ministries of education, universities, schools, and technology companies have produced guidance documents, training programs, policy statements, and frameworks intended to bring order to the incorporation of artificial intelligence in education. Such work can be useful, but it does not by itself explain what is at stake. The discussion should not be reduced to tools. It must also address deeper questions about knowledge, authority, assessment, and institutional governance.

The history of educational technology shows that this pattern did not begin with artificial intelligence (Cuban, 1986; Tyack & Cuban, 1995). Textbooks, calculators, personal computers, the internet, and mobile phones have each been presented, at different moments, as threats or promises of transformation. In each case, urgency, resistance, regulation, and modernization followed.

Yet new technologies have often produced more visible changes in devices than in the deeper organization of teaching and learning. Tyack and Cuban described this phenomenon decades ago in their studies of school reform.

Schools have a remarkable capacity to absorb innovations without altering the routines and structures that organize institutional life.

This point is central to the present moment. Despite decades of technological innovation, many institutions continue to organize learning through rigid structures and compliance models. Even when platforms, interfaces, and policy vocabularies change, the basic forms through which time is managed, performance is defined, and authority is distributed often remain in place. Artificial intelligence makes this tension more visible because it alters the conditions of access, production, and circulation of knowledge, while many institutions attempt to respond from organizational models designed for another era.

The contemporary crisis in education cannot be explained only by technological or administrative deficits. It also reflects a loss of institutional purpose, an erosion of trust, a reduction of teacher and learner agency, and a difficulty imagining different ways to organize learning. Artificial intelligence did not create that crisis. It made it harder to ignore.

The illusion of innovation

Many current AI initiatives present themselves as educational transformation, even though their primary uses continue to automate existing practices: generating exercises, correcting texts, producing summaries, monitoring academic misconduct, and accelerating administrative tasks (Xia et al., 2024; Kizilcec et al., 2024). In such cases, innovation becomes defined mainly as the incorporation of tools, while the harder institutional questions remain largely untouched.

How does assessment change when generative systems can produce complex texts? How should faculty or teacher time be redistributed when administrative tasks can be automated? How is authority reorganized when knowledge circulates through distributed systems? These questions require a deeper institutional and political discussion than the production of usage guidelines.

Recent work on AI governance warns that the accelerated adoption of opaque systems can strengthen automation, surveillance, and nontransparent decision-making if effective public oversight is absent (Taeihagh, 2025; Williamson, Molnar, & Boninger, 2024). In these cases, technology does not necessarily open a discussion about the purpose of learning. It can instead reinforce mechanisms that were already under pressure before the rise of generative systems.

In Beyond the digital enclosure (Moravec, 2026a), I analyze a risk that exceeds the adoption of imperfect tools: the displacement of educational authority toward external systems that institutions cannot fully audit. When criteria for classification, prioritization, moderation, and updating move outside the control of educational communities, technological innovation can become a form of institutional dependency.

Questions for educational leaders

• Which AI platforms does the institution currently use?
• What student, faculty, or institutional data do those platforms collect?
• Who can audit the systems being used?
• Which educational decisions are shaped by external providers?
• What professional capacities do leadership and teaching teams need to develop?

Platforms, power, and educational sovereignty

The second problem concerns the role of major technology platforms in reorganizing education. Much of the AI infrastructure now entering schools, universities, training systems, and public agencies belongs to private companies that operate through closed development models, intensive data collection, and constant system updates. Educational institutions integrate tools whose internal mechanisms they cannot fully audit, modify, or supervise. This situation exceeds software adoption because it affects the conditions under which knowledge is produced, circulated, and validated.

Platforms do not merely provide services. They also organize interactions, prioritize content, register behavior, and shape forms of intellectual work. In educational settings, those decisions carry pedagogical significance because they influence which questions are asked, which sources appear relevant, which forms of production become suspect, and which forms of participation become normalized. The institution may retain formal educational responsibility, but part of its operational authority shifts toward systems designed and governed by external actors.

This phenomenon reflects broader debates about the concentration of technological power and the extraction of data. Critical research on digital platforms has shown how everyday activities can become sources of data, algorithmic influence, and institutional dependency (Benjamin, 2019; Couldry & Mejías, 2019; van Dijck, Poell, & de Waal, 2018).

Education participates in this dynamic when ordinary academic activities become part of private systems that educational institutions do not fully control.

Put plainly: the thinking, exploration, and intellectual production of students and educators can become commercial resources for external platforms.

The issue is not only privacy, although privacy is important. The central issue is governance. When private platforms control critical infrastructure for learning, institutions lose practical capacity to determine how their learning environments function. Updates follow corporate calendars, moderation criteria can change without local educational deliberation, and classification mechanisms often remain opaque to students, educators, and institutional leaders.

Under those conditions, institutions end up managing consequences they cannot always explain or correct.

In Beyond the digital enclosure (Moravec, 2026a), this dynamic is analyzed as a progressive loss of educational sovereignty: private platforms begin to define central aspects of institutional life that previously remained under local educational control. Dependency does not arise only through contracts or licenses. It also emerges through the everyday incorporation of metrics, interfaces, and decision criteria designed outside educational communities.

This issue matters across regions, but it does not appear in the same way everywhere. In high-income countries, institutional dependency may emerge through procurement, platform lock-in, analytics dashboards, assessment systems, and outsourcing of educational judgment. In lower- and middle-income countries, the problem often intersects with infrastructure inequality, donor agendas, language marginalization, and the importation of models designed elsewhere. In international education and development contexts, AI may deepen dependency when institutions adopt closed systems that organize knowledge, assessment, and interaction according to commercial or geopolitical priorities beyond local control.

The Global South faces particular vulnerabilities. Schools and universities may incorporate software produced in North America, Europe, or China while exposing student data, linguistic patterns, and traces of intellectual work to systems used to train external models. At the same time, institutions may import automated classification, assessment, and recommendation mechanisms built from cultural, linguistic, and economic assumptions that do not reflect local realities.

For this reason, the global debate should not remain limited to technical training or responsible use. It must also address educational sovereignty, audit capacity, democratic control of digital infrastructure, language justice, public accountability, and the institutional right to define educational purposes.

The exhaustion of performative leadership

Institutions often produce strategies, manuals, policy statements, and events about artificial intelligence to demonstrate responsiveness to technological change. Such activity performs understandable institutional functions: it organizes internal conversations, communicates public concern, and signals that the organization is keeping up.

However, it can also create a wide gap between discourse and practice when documents do not change the resources, responsibilities, or working conditions required to transform educational experience.

In many cases, these guidelines function as an institutional release valve. They allow the organization to show that it has done something, while much of the emotional, ethical, and operational work moves to individual teachers and faculty members who must decide, often alone, how to assess, what to allow, what to prohibit, and how to interpret student use of generative tools.

Over recent decades, the education sector has produced an extensive institutional literature on technological innovation. Much of it shares a predictable vocabulary: teacher training, equity, ethical use, pilot programs, and personalized learning. These elements can have value, but they lose force when they are not connected to decisions about budget, professional time, assessment, infrastructure, data ownership, and participation of educators and students in technological governance.

The problem, then, is not the existence of frameworks or recommendations. The problem is confusing discursive production with institutional transformation. When policies only regulate individual behavior, responsibility shifts toward educators and students while the technological systems that generate new risks remain opaque. As a result, people working and learning inside institutions absorb costs that rarely appear in policy documents: redesigning assessments, reviewing suspect work, adapting to changing platforms, and responding to contradictory expectations.

Artificial intelligence did not create these tensions. It amplified preëxisting conditions and made them more visible because it introduced systems capable of intervening in writing, synthesis, classification, and initial evaluation. For that reason, adequate institutional policy cannot be limited to regulating user behavior. It must also examine the material, technical, and political conditions that organize how these systems function within educational life.

Beyond compliance

The deeper problem is not simply the incorporation of new tools. It is the development of institutional capacities for thinking educationally under conditions of accelerated change. Many organizations still respond to artificial intelligence through administrative categories designed to manage stability, control, and predictability.

Generative systems, however, raise questions that cannot be resolved only through technical regulation or bureaucratic compliance. They require institutions to reconsider what assessment means, how educational authority is distributed, which human capacities gain value, and which forms of educational experience deserve protection.

The discussion about artificial intelligence often oscillates between two limited positions: uncritical adoption and defensive rejection. Neither allows the deeper problem to be understood, because both keep attention fixed on the tool rather than on the institutional conditions that give it meaning. Recent debates on AI literacy suggest a different path: institutions must not only teach people to use generative systems, but also help them understand how those systems produce responses, which assumptions they carry, which risks they introduce, and how they affect the autonomy of educators and learners (Mills et al., 2024; Conrad & Kamperman, 2025).

The central question is not whether educational institutions will use artificial intelligence. The challenge is to define under what pedagogical, political, and organizational conditions these systems are incorporated, which forms of authority they displace, and which educational capacities they help develop.

Answering these questions requires institutions to revisit some of the assumptions that have organized much of contemporary schooling: transmission-centered teaching, standardized assessment, rigid administration of time, and separation between policy design, teaching, and learning. Many systems still operate through models conceived to manage stability, scarcity of information, and relatively predictable pathways. The expansion of algorithmic systems, automation, and distributed production of knowledge changes those conditions and makes many inherited institutional responses insufficient.

Assessment offers a clear example. When generative tools can produce coherent texts in seconds, mechanisms centered only on individual reproduction of information become insufficient.

Likewise, when access to knowledge no longer depends on memorization, the capacities education must value begin to change.

This shift requires institutions to reconsider:

• the distribution of authority in learning environments;
• the recognition of new forms of intellectual work;
• the development of judgment in information-saturated contexts;
• the place of creativity, collaboration, and interpretation in educational experience; and
• the protection of autonomous thought in increasingly automated systems.

From this perspective, educational leadership cannot be reduced to technological implementation. It must also include governance, institutional purpose, and collective capacity to imagine alternatives. For institutional leaders, this requires a style of leadership less centered on the administration of compliance and more oriented toward creating conditions for professional judgment, responsible experimentation, and pedagogical trust. Many current problems in education do not derive from technological deficits, but from organizational models that no longer respond adequately to contemporary conditions.

Artificial intelligence did not create that situation. Its importance lies in the way it amplifies and exposes many of education’s contradictions. For that reason, the debate should not remain limited to adapting to new tools. It must also open a deeper discussion about how learning should be organized, how trust can be built, and which forms of educational experience deserve to be sustained in a context shaped by automation and digital platforms.

For many institutions, this discussion does not begin with sweeping structural reform. It begins with everyday decisions about assessment, professional time, pedagogical trust, and technological governance. The capacity to respond critically to artificial intelligence will depend less on the speed of adoption than on the institutional capacity to sustain judgment, collective deliberation, and professional autonomy under conditions of accelerated change.

The most critical part is identifying which aspects of human learning should remain at the center of educational experience.

That discussion also runs through my book Build a Positive Rebellion: Create New Education Futures (Moravec, 2026b), where I explore how institutions can recover agency, trust, and collective imagination in response to educational models that no longer meet the conditions of the present.

Recommended readings

Benjamin, R. (2019). Race after technology: Abolitionist tools for the new Jim code. Polity.

Conrad, K., & Kamperman, S. (2025). Building critical AI literacy: An approach to generative AI. Thresholds in Education, 48(2), 142–158. https://eric.ed.gov/?id=EJ1483967

Couldry, N., & Mejías, U. A. (2019). The costs of connection: How data is colonizing human life and appropriating it for capitalism. Stanford University Press.

Kizilcec, R. F., Huber, E., Papanastasiou, E. C., Cram, A., Makridis, C. A., Smolansky, A., Zeivots, S., & Raduescu, C. (2024). Perceived impact of generative AI on assessments: Comparing educator and student perspectives in Australia, Cyprus, and the United States. Computers and Education: Artificial Intelligence, 7, 100269. https://doi.org/10.1016/j.caeai.2024.100269

Mills, K., Ruiz, P., Lee, K. W., Coenraad, M., Fusco, J., Roschelle, J., & Weisgrau, J. (2024). AI literacy: A framework to understand, evaluate, and use emerging technology. Digital Promise.

Moravec, J. W. (2026a). Beyond the digital enclosure: AI, coloniality, and the pursuit of educational sovereignty. Learning Futures and Emerging Technologies, 34(3). https://doi.org/10.1108/LFET-01-2026-0004

Moravec, J. W. (2026b). Build a positive rebellion: Create new education futures. Education Futures. https://positiverebellion.org/

Taeihagh, A. (2025). Governance of generative AI. Policy and Society, 44(1), 1–22. https://doi.org/10.1093/polsoc/puaf001

Tyack, D., & Cuban, L. (1995). Tinkering toward utopia: A century of public school reform. Harvard University Press.

van Dijck, J., Poell, T., & de Waal, M. (2018). The platform society: Public values in a connective world. Oxford University Press.

Williamson, B., Molnar, A., & Boninger, F. (2024). Time for a pause: Without effective public oversight, AI in schools will do more harm than good. National Education Policy Center. https://nepc.colorado.edu/publication/ai

Xia, Q., Weng, X., Ouyang, F., Lin, T. J., & Chiu, T. K. F. (2024). A scoping review on how generative artificial intelligence transforms assessment in higher education. International Journal of Educational Technology in Higher Education, 21(1), 40. https://doi.org/10.1186/s41239-024-00468-z

A new study in the U.S. on school phone bans gives us some food for thought.

Researchers examining the use of lockable phone pouches across U.S. schools found what many would expect at a surface level: phone use drops. But the downstream effects are harder to reconcile with the policy narrative. In the first year, disciplinary incidents increase and student well-being declines. Over time, those effects fade and even reverse. Academic outcomes, meanwhile, remain largely unchanged (Allcott et al., 2026).

It is tempting to read this as a simple story about adjustment. Take something away, people react, then they adapt. There is plenty of research like this in organization psychology, so there should not be much surprise. But we risk missing the more important question:

If removing phones produces disruption, what does that say about the dependency structure schools have allowed to develop?

Phones did not create distraction inside schools. They absorbed it. They filled time, mediated social interaction, and compensated for weak engagement. When they disappear, the system does not suddenly produce better learning conditions. It exposes the gaps that were already there.

The response to those gaps is revealing. Instead of redesigning learning, schools tighten control. This means more rules, more enforcement, more attention to behavior. The policy thus addresses the visible symptom, but not the underlying condition.

This pattern reflects a deeper issue that predates phones, AI, or any specific technology:

Education does not have a technology problem. It has a model-of-learning problem.

For generations, schooling has been organized less around learning than around management. It groups students by age, moves them through fixed schedules, divides knowledge into discrete units, and evaluates performance through standardized outputs. These structures made sense in systems that needed to coördinate large numbers of people efficiently. They made schooling visible and easier to manage to administrators and policymakers.

But visibility is not the same as learning.

It is important to understand that distinction because the conditions that sustained the model no longer hold. Access to information is no longer scarce. Tools can assist, extend, or replace parts of human cognition, augmenting how we learn and “know” things. The boundary between individual and distributed thinking via machines has blurred.

Yet the structure of schooling remains largely intact.

That is why the last decade feels incoherent. Not long ago, schools could not get enough technology into classrooms. Devices promised access. Platforms promised personalization. “EdTech” became a stand-in for “innovation” because the system rarely asked what educational technology was supposed to change. Now the same system retreats from innovation back to the comfort of control, locking phones away and treating generative AI as a threat to academic integrity.

This may be rationalized as a system trying to restore control over conditions it no longer understands. When a tool fits the existing model, it is adopted. When it exposes the limits of the model, it is restricted.

AI brings this contradiction into sharp focus. Students and teachers are already using these tools, often faster than institutions can respond. The gap between policy and practice continues to widen (RAND Corporation, 2025). In an increasing number of contexts, AI is embedded in how work gets done.

However, the effects of AI are not uniform. Evidence suggests that AI can support learning when it is used to extend reasoning but undermine it when it replaces cognitive effort (Khalil & Er, 2025). The tool can produce both outcomes. The difference lies in how learning is designed. This creates a problem that the current system is not equipped to handle.

For decades, education has relied on static outputs as proxies for learning. Essays, exams, and assignments were treated as evidence of individual cognition. AI breaks the assumptions we’ve long held about the assessment system. It can produce outputs that look like learning without revealing to what extent thinking occurred.

If a student can generate a high-quality response with minimal effort, the issue should not be centered around academic dishonesty. It is that the approach we have relied on no longer carries the meaning we once assigned to it.

Researchers might describe AI and assessment as a “wicked problem,” not because it is complicated, but because it cannot be resolved within the current structure (Corbin et al., 2025). Efforts to preserve integrity through detection and surveillance attempt to restore visibility, but they do not address the underlying shift.

The system responds in the only way it knows how. It increases control. We see this in phone bans, AI detection tools, proctoring systems, and expansive policy statements about appropriate use. These measures of response to anxiety or paranoia create the appearance of order by reëstablishing the old regime, but they do not resolve the contradiction.

Decades ago, we faced a similar crisis with calculators. Calculators were eventually integrated because their function was limited and well-defined. They did not challenge the structure of assessment itself.

AI does and today’s challenges are thus wildly different. It operates across the full arc of thinking, from generating ideas to refining arguments. It does not simply assist with tasks. It reshapes them.

This is why the current moment feels unstable. The system is trying to apply old rules to a new form of cognition. As Albert Einstein is (mis)attributed to having said, “insanity is doing the same thing over and over again and expecting different results.”

And, in this case, the result is predictable: Control substitutes for design, policy substitutes for strategy, and restriction substitutes for equity. The gap between school and the world widens.

The way out does not lie in more restrictive policies or more enthusiastic adoption. It requires a different model of learning.

Assessment must move beyond static outputs toward evaluating process, reasoning, and judgment. Students need to demonstrate how they think, not just what they produce. Classroom practice must shift from controlling inputs to designing activity. The relevant question is not whether a student used AI, but what kind of thinking the task required and how the tool shaped that thinking.

Governance must move from prohibition to shared norms. Students and teachers need clarity about when and how tools are used, grounded in purpose rather than enforcement. Teacher preparation must also change. Educators cannot be expected to integrate tools effectively without frameworks that reflect how those tools alter cognition.

None of this represents a return to a previous version of education. The idea that the system can simply go back to promoting personal and community growth ignores the fact that it has never been structurally organized around those goals at scale.

That is the work ahead.

The phone ban study does not show that extreme control measures works. It shows that removal produces disruption and adaptation without addressing learning itself. AI does not create a new problem. It makes the existing one visible.

The system can continue to oscillate between adoption and restriction, or it can confront the harder question:

What does learning look like when tools are part of thinking?

Policymakers and school systems architects need to figure this out now.

References

Allcott, H., Baron, E. J., Dee, T., Duckworth, A. L., Gentzkow, M., & Jacob, B. (2026). The effects of school phone bans: National evidence from lockable pouches (NBER Working Paper No. 35132). National Bureau of Economic Research.

Corbin, L., Bearman, M., Boud, D., & Dawson, P. (2025). The wicked problem of AI and assessment. Assessment & Evaluation in Higher Education.

Khalil, M., & Er, E. (2025). Will AI transform education? Evidence from student use of generative AI. Studies in Higher Education.

RAND Corporation. (2025). AI use in schools is quickly increasing but guidance lags.

Educators face a growing sense of concern about artificial intelligence. New tools enter classrooms faster than policies can adapt. National authorities release guidance documents that outline principles for safe or ethical use. Universities add statements to their syllabi about transparency and good conduct. Companies offer free training modules that show teachers how to integrate their platforms into lesson plans. These efforts have value, yet they sit on the surface of a deeper problem. Most decisions that shape how AI works occur far from the people who will live with the consequences.

When we talk about “using” AI in education, we often miss the larger reality shaping its arrival. The tools entering classrooms are not simple instruments. They come from a small group of firms that set the terms of access, define the rules of interaction, and shape how knowledge circulates. The rapid spread of these systems is guided, but not by educators. It reflects the priorities of companies that benefit when institutions adopt their platforms as default infrastructure. Educators may receive training and safety modules, yet they do not participate in the decisions that govern model behavior. This distance creates a form of dependency that weakens professional judgment and shifts control away from the public sphere.

Much of the current dialog treats AI as if it belongs in the same category as calculators or mobile phones. This analogy serves Big Tech well. It frames adoption as inevitable and harmless, and it encourages educators to focus on classroom management rather than on the structural forces shaping the tools themselves. It also masks the fact that earlier technologies did not carry embedded economic or political interests. AI does. When we rely on analogies that flatten these differences, we make it easier for companies to define the terms under which education will engage with them.

I explore that gap here. My argument is that education cannot meet its responsibilities if it approaches AI only as a question of classroom practice and control. The deeper issues lie in governance, institutional capacity, and the long history of how schools respond to new technologies. To make this case, I draw on examples from GEFRI, Manifesto 25, and Goblinly.

An abundance of frameworks with an absence of agency

AI enters education through a constant stream of frameworks and guidelines. They outline principles for transparency and safety, and they promise to help teachers navigate risk. Many come from credible institutions, yet they share an important feature: they focus on the behavior of educators and students, not on the systems that shape their choices. As a result, these frameworks normalize Big Tech’s presence. They train educators to adapt to platforms rather than question the power behind them.

Frameworks alone cannot compensate for the structural reality that teachers and institutions do not control the systems they are told to “use responsibly.” Ethical guidelines emphasize individual behavior rather than the priorities of the organizations that build and deploy AI. They describe responsible practice as something achieved through vigilance, documentation, and consistent checking of outputs, while the systems themselves remain outside institutional control.

This emphasis on user behavior signals a deeper problem. When responsibility sits with the user but power sits with the developer, the framework becomes a moral contract without a mechanism for shared governance. Educators follow rules they did not create. They navigate constraints they did not choose. They bear the professional risk associated with systems that operate beyond their view.

In earlier technological cycles, schools regulated the tools they allowed into classrooms and set the terms of their use. With AI, the flow reverses. The tools regulate the schools. Algorithms determine what students see. Content filters shape what they can ask. Recommendation engines encourage particular forms of inquiry. These mechanisms operate quietly, yet they redefine the boundaries of instruction. The shift from institutional authority to platform authority introduces a structural imbalance that many systems are not equipped to manage.

This imbalance appears in national readiness assessments. GEFRI, for example, distinguishes infrastructure from innovation, human capital, governance, and equity. Many countries rank high on access to devices and connectivity but score lower on governance, which signals limited capacity to steer and regulate technology rather than merely adopt it. The result is a pattern in which education systems look prepared because they possess the technical means to use new tools, yet lack the institutional depth needed to guide them toward public goals.

Frameworks help educators use AI within those systems. They do not grant influence over how AI evolves. This distinction matters because education is not a neutral environment. It is a public institution charged with supporting human flourishing, civic life, and broad participation in knowledge. If educators only receive instructions about how to act within someone else’s design, the profession loses its claim to shape how learning technologies serve society.

AI as the latest chapter in a long history of control

AI feels new, but the surrounding issues are familiar. Schools have long struggled with new technologies. Calculators were seen as shortcuts that would impair reasoning. Mobile phones were framed as distractions that would destroy concentration. Even printed books have, at various times, been governed by lists of approved titles or removed from shelves. In each case, institutions responded with a mix of fear, restriction, and eventual accommodation.

The pattern reveals something important. Schools tend to approach technology as a matter of control rather than understanding. The first instinct is to regulate rather than ask what kind of cognitive or social shift the technology represents. This dynamic repeats now with AI. Many policies emphasize detection of misconduct, prohibition of certain uses, or close monitoring of student behavior. These responses address immediate concerns, yet they rarely engage with the underlying shifts in epistemology, authorship, and authority that AI brings.

This tendency toward control rather than inquiry keeps education tethered to past structures. Manifesto 25 argues that mainstream education systems respond to global uncertainty with tighter discipline, standardized expectations, and rigid compliance cultures—seeking stability through fear, compliance, and control.

AI heightens this tension. Schools fear cheating, misinformation, and the erosion of trust. At the same time, companies market AI as a solution to labor shortages, administrative burden, and student disengagement. Educators feel pressure from both sides: contain AI to preserve integrity, adopt AI to improve efficiency. Neither impulse addresses the deeper question of what it means to teach and learn in an environment shaped by powerful, opaque systems.

Education requires structure. The challenge is to notice when control becomes a substitute for understanding. AI compels a more reflective approach. It asks educators to examine why they repeat restrictive patterns, what fears those patterns express, and what possibilities they obscure.

The illusion of “using” technology

We often assume that teachers use technology. We say that educators “use” learning management systems, “use” digital assessments, and now “use” AI tools to plan lessons or evaluate student work. This language implies human agency. It frames the teacher as the operator and the tool as the instrument.

But the reality is more complex. Platforms guide behavior in ways that feel natural yet are deeply structured. Default settings influence what teachers notice. Recommendation engines shape what students encounter. Safety layers define the boundaries of acceptable knowledge. Analytics dashboards determine which forms of evidence appear meaningful. Teachers operate these systems, but the system’s architecture steers their choices.

This is where colonization becomes visible. Big Tech’s influence enters through those same defaults, settings, and design decisions that limit educator agency while giving the impression of control. The interface encourages particular actions. The safety layer narrows what counts as legitimate inquiry. The analytics dashboard frames progress in specific terms. Long before anyone makes a decision, the platform has already shaped the conditions of practice and performance.

Douglas Rushkoff captures this dynamic in his call to “program or be programmed.” Programming in his sense refers not only to writing code but to understanding how systems behave and how they shape human action. Without that understanding, users adapt to systems rather than shape them.

Educational AI now trains its users in subtle ways. “Ethical use” modules instruct teachers on how to act. They outline responsibilities and risks but rarely explain how the platform manages data, sets boundaries, or interprets ethical principles. The burden shifts downward. Teachers become enforcers of student conduct, even though they cannot see inside the systems that define the rules. Students, in turn, learn to trust outputs because the interface presents them as stable and fluent.

Some tools challenge this orientation. Goblinly, for example, treats AI as an object of examination rather than a source of truth. Students receive outputs from playful personas that blend insight with error. Their task is not to accept the answer but to inspect it. They build skill by noticing patterns of reasoning, moments of overconfidence, and the subtle misalignments that often hide in fluent prose. The platform trains skepticism and deeper literacy over blind acceptance of machine outputs.

This illustrates a shift in how we should perceive AI in education. When learners treat AI as something to interrogate, they become less susceptible to its illusion of authority. They develop practices of questioning, cross-checking, and resisting the superficial coherence of machine-generated text. These practices carry beyond the classroom. They form part of a broader public literacy needed in a world where AI will become a routine generator of content and claims.

Readiness without sovereignty

Policymakers often advocate for AI “readiness.” They invest in broadband, devices, and large digital platforms. They treat access as the primary barrier to participation in a global technological economy. In this framing, AI appears as another tool nations must deploy to remain competitive.

Yet readiness is not the same as control. A system can have robust infrastructure and still depend on external technologies that it cannot influence. It can adopt advanced platforms while absorbing assumptions, values, and governance structures that originate elsewhere. The surface looks modern, but the capacity to guide technology in the public interest remains limited.

These gaps do not amount to a formal loss of sovereignty, but they create conditions in which outside actors gain quiet influence. When public systems rely on technologies they cannot meaningfully evaluate or modify, they become subject to the priorities and update cycles of the firms that supply them. Dependence becomes the channel through which colonization emerges.

Big Tech exploits these conditions. When nations depend on external AI infrastructure, companies become unaccountable partners in the governance of public education. Their models shape how students search, write, and ask questions. Their terms of service determine what data flow out of classrooms. Their product decisions influence entire curricula. This is colonization through dependency: not imposed by force, but accepted through reliance on private systems as public infrastructure.

The challenge is not to reject external tools. Global collaboration and shared innovation matter. The issue is how to balance adoption with the capacity to steer. Education systems need the ability to question, negotiate, and demand transparency. They need institutional memory and professional expertise to assess new systems before they become entrenched. Without this grounding, AI adoption may accelerate dependence rather than strengthen public institutions.

Misplaced responsibility bears a social cost

When responsibility for ethical AI use is placed almost entirely on individuals, the burden becomes unsustainable. Educators are asked to monitor student conduct, ensure compliance, manage privacy risks, and check AI-generated text for accuracy. They do this in environments where they have limited control over the systems themselves.

This imbalance produces several effects. Teachers experience rising anxiety about misconduct and misplaced trust. Students receive mixed signals about what counts as legitimate learning. Institutions invest time writing detailed AI policies, yet these documents often shift the risk rather than address its causes. When something goes wrong, individuals feel compelled to justify their choices even when the platform design shaped those choices.

The pattern resembles a larger condition: systems offload responsibility onto individuals while maintaining structures that resist transformation. Provided an illusion of control, people feel accountable for outcomes they did not design and can never fully influence. This dynamic undermines agency. It frames education as a site of risk management and compliance rather than a space for shared inquiry and purposeful development.

This pattern mirrors a classic colonial logic. Responsibility flows downward while authority flows upward. Teachers are asked to safeguard integrity and manage risk, even though they cannot influence the systems that generate those risks. Big Tech remains insulated from accountability. Its platforms set the conditions under which misconduct becomes possible, yet educators and students bear the consequences.

A more balanced approach would align responsibility with actual influence. Educators and students should participate in decisions about which tools to adopt, how they are evaluated, and what safeguards are required. Institutions should demand clear information from vendors about data practices, model behavior, and documented limitations. Policymakers should create mechanisms for independent audits and community oversight. These measures shift responsibility upward into governance rather than downward into individual compliance.

Rebalancing the relationship between education and technology

If education continues to accept a passive role in the expansion of AI, it risks ceding its remaining autonomy to systems built for corporate priorities rather than public ones. Colonization in this context arrives through quiet consent. It emerges through defaults, narratives of inevitability, and the steady normalization of external control. Educators may develop more frameworks, but they will be forever trying to catch up with changes and will miss opportunities to address the deeper imbalance between the institutions that design AI and the institutions expected to absorb it. In such a space, control measures multiply at the expense of agency and enabling self-efficacy to learn.

The work ahead is institutional, cultural, and political, in addition to requiring technical change. It involves building environments where teachers and students are not only rule-followers but co-designers of their technological landscape. It involves claiming time, space, and authority for reflection. And it requires the courage to question systems that promise efficiency but weaken autonomy. This work begins when educators refuse to let AI define the terms of its integration and instead place education’s values at the center of the conversation.