The crisis is not artificial intelligence; It is institutional imagination

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–in press), 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–in press), 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(2). 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