The research, published on 19 February 2026 as volume 3, article 11 of the journal, rests on a structured literature review and conceptual analysis rather than new empirical data. Pesek draws on governance theory and on critical scholarship concerning digitalisation, platformisation, and datafication to build an analytical framework capable of describing what actually happens when AI enters schools. The core insight is that AI behaves as a systemic and transversal actor. Unlike a textbook or a learning management system, it does not sit neatly inside a single layer of the education system. It operates across the boundary between centralised regulation and decentralised educational practice, shaping decisions at the ministry level and in individual classrooms simultaneously, often without either policymakers or teachers fully noticing.

To make this complexity tractable, the paper introduces a conceptual distinction between three layers of educational AI. The first layer consists of foundational AI infrastructures, essentially the large AI models on which everything else depends. The second layer is AI content, the generated material that flows into lessons, assessments, and learning resources. The third layer comprises AI tutors, the interactive systems that increasingly mediate the relationship between learners and knowledge. Pesek argues that these three layers demand different governance responses, because they concentrate power, risk, and accountability in different places. Treating them as a single undifferentiated category of “AI tools,” he suggests, is one of the reasons existing policy frameworks have struggled to keep pace.

The analytical heart of the paper is a reconfigured hybrid governance model. Hybrid governance refers to arrangements in which authority is shared between state regulators, commercial platform providers, local institutions, and professional educators, rather than residing in any single actor. The study shows that AI has reconfigured these hybrids in ways that were not anticipated by earlier digital education policy. When a school adopts an AI tutoring system, for example, decisions about what counts as mastery, how feedback is phrased, and which learning pathways are offered are no longer made solely by curriculum authorities or teachers. They are partly encoded in models trained on data that reflect commercial priorities and statistical patterns far removed from any school’s local context. Governance, in other words, has been partially delegated to infrastructure.

From this analysis, Pesek identifies four key governance risks. The first is the homogenisation of learning processes, in which widely deployed AI systems push diverse classrooms toward similar rhythms, formats, and definitions of success, narrowing the space for pedagogical diversity. The second is intensified surveillance, as AI-mediated platforms continuously capture data on how students read, write, hesitate, and solve problems, extending the datafication of education into its most intimate moments. The third risk is blurred accountability: when an AI system produces a misleading explanation, an unfair assessment, or a harmful recommendation, it is often unclear whether responsibility lies with the developer, the platform, the school, or the teacher. The fourth risk is the erosion of student and teacher agency, as both groups find their decisions increasingly shaped by opaque recommendation engines rather than by professional judgement or learner choice.

These risks are not hypothetical. The paper situates them within a well-documented policy trajectory. International organisations such as the OECD and UNESCO have repeatedly highlighted both the promise and the governance gap of educational technology, from the OECD’s Digital Education Outlook 2023 to UNESCO’s 2021 guidance for policy-makers on AI and education. Critical scholars including Neil Selwyn and Ben Williamson have long warned that the digitalisation of schooling concentrates power in platform companies and reconfigures educational purposes around data and measurement. Pesek’s contribution is to connect these strands into a single governance argument: the problem is not merely that AI may be used badly, but that the structures through which education systems steer themselves are no longer adequate to the technology they now contain.

The proposed response is a reconfigured hybrid governance approach that differentiates responsibilities across system levels and across AI functions. At the level of foundational AI infrastructures, the paper points toward regulatory oversight of the models themselves, including transparency about how they are trained and evaluated. At the level of AI content, accountability mechanisms need to establish who is answerable when generated material is inaccurate, biased, or inappropriate for a given age group. At the level of AI tutors, governance must protect the integrity of the learner-teacher relationship and ensure that interactive systems support, rather than supplant, pedagogical relationships. Crucially, the framework insists that these responsibilities cannot all be discharged centrally; they must be distributed across national regulators, intermediate authorities, school leaders, and classroom professionals in a deliberate and explicit way.

The paper also advances concrete policy recommendations aimed at operationalising this approach. These include regulatory oversight tailored to the three-layer distinction between AI models, AI content, and AI tutors; accountability mechanisms that make responsibility traceable across the chain from developer to classroom; and explicit protection of educational purpose and professional autonomy, so that teachers retain the authority to interpret, adapt, and when necessary refuse AI-mediated recommendations. The underlying principle is that governance should safeguard democratic values and human-centred education while still allowing education systems to harness the genuine benefits of AI, such as personalised support and reduced administrative burden. The alternative, the paper implies, is a drift in which governance defaults to the incentives of platform providers rather than the purposes of public education.

What makes the study distinctive is its refusal to treat AI in education as a purely technical or ethical dilemma to be solved at the level of individual classrooms. By foregrounding governance as the central analytical and policy concern, it reframes the debate: the question is not only whether an AI tool helps students learn, but who holds power over the systems reshaping learning, who answers when those systems fail, and how the distribution of authority can be redesigned deliberately rather than left to market forces. For policy-makers, school leaders, and educators confronting the accelerating arrival of AI in education, the message is that the deepest challenges are structural. The technology has already reconfigured the architecture of educational governance; the task now is to reconfigure it back, with accountability, transparency, and human agency as explicit design goals rather than afterthoughts.

Subject of Research: Governance of artificial intelligence in education systems

Article Title: Rethinking Education Governance in the Age of AI

Article References: Pesek, I. (2026). Rethinking Education Governance in the Age of AI. Frontiers of Digital Education, 3(1), Article 11. https://doi.org/10.1007/s44366-026-0085-z

Image Credits: AI Generated

DOI: 10.1007/s44366-026-0085-z

Keywords: AI in education, education governance, hybrid governance, accountability, datafication, platformisation, AI tutors, surveillance, teacher agency, education policy, EdTech, governance risks