The review, published online on August 1, 2026, in ECNU Review of Education, was conducted by Luís Miguel Carvalho and Catarina Gonçalves of the Institute of Education, University of Lisbon. The authors examined 42 peer-reviewed articles published between 2005 and 2023, mapping a body of work known as the sociology of policy instruments in education. Their goal was twofold: to take stock of what this research tradition has revealed about contemporary education policy, and to identify where the field should go next. The result is both a synthesis of nearly two decades of scholarship and a framework for understanding how apparently mundane administrative devices carry profound political weight.

The intellectual core of the sociology of policy instruments is the claim that instruments are simultaneously technical and social devices. Legislation, contracts, assessments, benchmarks, indicators, funding mechanisms, and communication platforms all embody specific values and assumptions about the relationship between government, education professionals, learners, and society. A national testing regime, for instance, encodes beliefs about what knowledge matters, how learning should be demonstrated, and who is entitled to judge school quality. A funding contract encodes assumptions about incentives and accountability. Studying these embedded assumptions, the review argues, makes it possible to see how power operates through arrangements that are usually presented as merely practical.

The literature reviewed shows a clear center of gravity. Researchers have concentrated especially on performance-based instruments: the OECD’s PISA assessments, national large-scale assessments, school evaluation and self-evaluation schemes, indicators, standards, and accreditation procedures. This concentration, the authors suggest, reflects a broader transformation in how education systems are governed—a shift toward governance through comparison, data, and performance-oriented accountability. In such systems, the instrument is not an afterthought to policy; it is the medium through which policy is imagined, debated, and enforced.

The review also documents a second trend in the research: growing attention to European education governance, to global intergovernmental organizations, to private companies, and to intermediary actors who work between national policy and local practice. This shift mirrors changes in the policy landscape itself. As international assessments and transnational benchmarks gain influence, the actors who design, interpret, and circulate these tools—organizations, consultants, data providers, and broker institutions—acquire authority that traditional accounts of education policy rarely captured. Instrument-based research, the review shows, is particularly well suited to illuminating this redistribution of influence.

To organize the existing knowledge, Carvalho and Gonçalves distinguish three dimensions. The first concerns the institutional ideas embedded in instruments—the values, theories of schooling, and assumptions about governance that tools carry within them. The second concerns the trajectories instruments follow as they are designed, adopted, and used in practice. The third concerns the effects instruments generate once they are in circulation. Crucially, the review emphasizes, instruments do not simply travel unchanged from policymaking centers to schools. National traditions, political interests, professional identities, and local conditions can lead to adoption, opposition, hybridization, or the creation of entirely new tools. A similar assessment or indicator scheme may therefore take quite different forms in different systems, depending on the institutional soil in which it is planted.

From this synthesis, the authors distill six recurring effects of policy instruments. Instruments can legitimize certain actors and coalitions while marginalizing others. They can reorganize political spaces and timelines, changing who participates in decision-making and on what schedule. They can privilege certain forms of knowledge and expertise over others, elevating statistical and managerial competence in the process. They can gather actors around common issues, creating new arenas of cooperation and contestation. They can produce hybrid policies when several tools and governing logics interact within the same system. And they can generate unintended consequences that no designer anticipated.

The review offers concrete illustrations of these dynamics. Comparative assessments such as PISA may strengthen the authority of international organizations and structure national debate around rankings and league tables. Indicators designed to inform policy deliberation can harden into simplified rankings that obscure the complexity they were meant to capture. Performance tools introduced to encourage innovation may instead intensify supervision or standardize classroom practice. In each case, the effect flows not from malice or error but from the way the instrument itself organizes attention, incentives, and authority—a reminder that design choices are consequential even when they appear innocuous.

“Studies on policy instruments are well suited to address the complexity of education policy: the multiple policy scales as interdependent contexts, the variety of individual and collective actors involved, and the various governance patterns, ranging from state-oriented to market-oriented regimes, and from norm-based governance patterns to performance-oriented ones,” said Carvalho and Gonçalves. The methodological strength of this approach lies precisely in its capacity to connect levels: a single testing regime can be analyzed simultaneously as a national political project, a transnational data infrastructure, a professional constraint on teachers, and a market opportunity for private providers. Few other lenses in education research hold these scales together so naturally.

The review is candid about its limits and the gaps it exposes. The evidence base remains strongly centered on Europe, leaving large regions of the world underrepresented in the literature. Researchers have also paid less attention to combinations of multiple instruments, even though real education systems almost always deploy tools in bundles rather than in isolation. The authors call for a broader geo-cultural range of studies, clearer theoretical foundations, and more systematic explanations of why the same instrument follows different trajectories in different settings. Answering these calls, they suggest, would turn a promising research perspective into a genuinely comparative science of educational governance.

For policymakers and education leaders, the findings carry a practical caution: choosing a policy instrument is itself a political decision. A test, an indicator, a contract, or a digital platform may affect not only whether a policy achieves its stated goals, but also what goals appear legitimate in the first place, who gains authority over schools, and which realities remain visible or become obscured by the data collected. A ministry that selects a new evaluation framework is, in effect, redrawing the map of who may speak credibly about education. Recognizing this does not mean abandoning instruments—governance without tools is impossible—but it does mean treating their selection and design as matters requiring the same scrutiny as policy content itself.

For researchers, the review provides a map of a developing field and a framework for examining the complex links between technical devices, institutional change, and power in education. The sociology of policy instruments, as synthesized by Carvalho and Gonçalves, invites scholars to ask deceptively simple questions about the tools that surround schooling: What ideas do they carry? Whose interests do they serve? How do they travel, mutate, and combine? And what worlds do they make possible—or foreclose? As education systems worldwide continue to embrace data, comparison, and performance, those questions grow only more urgent, and the instruments themselves grow only more consequential.

Subject of Research: The role of policy instruments in shaping ideas, actors, and governance in education policy

Article Title: Policy instruments are never neutral — they shape ideas, actors, and change

Article References: Policy instruments are never neutral — they shape ideas, actors, and change. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: education policy, policy instruments, scoping review, PISA, accountability, governance, indicators, school evaluation, Europeanization, international organizations, performance governance, sociology of education

The core problem the study addresses is what educational researchers have long called the difference between first-order and second-order change. First-order changes are visible and easy to count: a school buys tablets, installs interactive whiteboards, or secures a fast internet connection. Second-order changes are far harder to see because they concern beliefs, practices, and the deep structure of teaching and learning itself. When a school moves from digitizing its existing lessons to genuinely reorganizing learning around personalized, competence-based trajectories, it has undergone a second-order transformation. Traditional indicators, built for the infrastructure era, simply cannot register that shift, which is why a school can score highly on equipment measures while its classrooms remain pedagogically unchanged.

Uvarov’s framework, which he calls the school digital renewal process, describes this evolution as a progression of stages. In the earliest stages, digital technology is grafted onto traditional teaching: the same lectures, the same textbooks, and the same assessments, now delivered through screens. At intermediate stages, schools begin to adapt their content and routines, blending online and face-to-face work and experimenting with new formats. Only at the most advanced stages does the transformation become structural, with learning organized around individual competence development, flexible pacing, and the kind of personalized mastery approaches that researchers have pursued since Benjamin Bloom famously posed his two sigma problem in 1984, asking why classroom instruction could not be made as effective as one-to-one tutoring. Bloom’s revised taxonomy of educational objectives, later formalized by Anderson and Krathwohl, supplies the conceptual ladder the new study climbs: from remembering and understanding at the bottom to analyzing, evaluating, and creating at the top.

The methodological innovation at the heart of the paper is deceptively simple to state and radical in its implications. Instead of surveying teachers, interviewing principals, or auditing technology stocks, the approach gathers its evidence from publicly available digital resources that schools themselves publish: websites, course catalogs, shared repositories, project descriptions, and other artifacts of their everyday digital activity. Artificial intelligence tools, including large language models of the ChatGPT class, are then used to analyze this material at scale, classifying it and mapping it against the stages of the renewal framework. What a school says about itself in public, in other words, becomes a measurable trace of how far its transformation has actually progressed.

This is where the technical work of the study becomes concrete. Drawing on empirical data from international schools, the research demonstrates that the automated analysis of digital footprints can detect indicators of second-order change that conventional instruments miss. The publicly available resources of a school that has genuinely embraced personalized, competence-based learning look different from those of a school that has merely digitized its old practices: the tasks described require different levels of cognitive processing, the organization of learning is framed differently, and the language of the institution itself shifts. By anchoring the classification in Bloom’s revised taxonomy, the method gains a principled yardstick for judging whether the learning activities a school describes sit at the lower or upper rungs of the cognitive ladder, and whether the school’s stated practices match the deep pedagogical restructuring that the age of AI demands.

The significance of avoiding teacher surveys is easy to underestimate. Surveys are expensive, slow, and subject to well-documented biases: teachers may report what they believe is expected of them, may not recognize how far their practice departs from their self-image, or may simply be too burdened to answer carefully. Resistance to change, which researchers from Larry Cuban to Michael Fullan have documented across decades of reform efforts, further complicates self-reporting, because the very people whose transformation is being measured are asked to describe it themselves. By contrast, digital footprints are produced continuously, as a by-product of ordinary school activity, and can be re-analyzed whenever needed without imposing any additional workload on school staff. The approach is therefore not only cheaper but potentially more honest, capturing what schools actually do and display rather than what respondents recall or claim.

Scalability is the second major advantage. National governments and international organizations, from the OECD to UNESCO, have invested heavily in monitoring digital education, producing index reports and assessment toolkits that compare countries and systems. Yet these efforts overwhelmingly rely on aggregated self-reports and infrastructure statistics, which tell policymakers how many computers schools have but not whether learning has changed. An indicator system built on automated AI analysis of public digital resources could, in principle, track thousands of schools at a fraction of the current cost, producing comparable longitudinal data on the later and most important stages of digital renewal. For education systems struggling to justify enormous technology budgets, that kind of evidence would be transformative: it would show not whether schools have bought the tools, but whether the tools have changed the learning.

The timing of the study is not accidental. The arrival of generative AI has thrown the traditional school model into sharp relief. Students already use large language models for homework, research, and writing, as surveys such as the Digital Education Council’s global student survey have documented, and schools are scrambling to respond. Platforms like Summit Learning have demonstrated that personalized mastery ecosystems, organized around individual learning trajectories and competency-based progression, are technically feasible at scale, even as they have exposed the organizational and cultural difficulties of implementing them. Uvarov argues that in this environment the schools that matter to monitor are not those still acquiring equipment but those attempting the deepest form of renewal, and it is precisely those advanced stages for which existing indicators are least adequate.

The framework also reframes what educational scenarios mean for the AI era. If the goal of schooling shifts from transmitting a fixed canon of content to cultivating competencies that learners can apply and extend throughout their lives, then the indicators of success must shift as well. A school’s published digital presence, analyzed through the lens of cognitive complexity and learning organization, offers a window into that deeper reality. The study’s demonstration that this window can be opened automatically, using tools that are now widely available, suggests a future in which the monitoring of educational transformation becomes as continuous and data-rich as the monitoring of public health or the economy.

There are, of course, challenges the research does not claim to have solved. Digital footprints can be curated, and the gap between what a school displays and what happens in its classrooms will never be zero. The interpretation of public resources requires careful calibration to avoid penalizing schools with different levels of web presence or different cultural norms about self-presentation. Ethical questions about automated analysis of institutional data, even publicly available data, will need continued attention. But the study establishes something that has been missing from the field: proof of feasibility. It shows that the second-order changes, the pedagogical transformations that genuinely define digital renewal, can be detected without questionnaires, at scale, using the AI tools that themselves are driving the transformation. For a discipline that has spent forty years measuring the easy things because the hard things were unmeasurable, that is a genuine breakthrough, and one that policymakers, school leaders, and researchers monitoring the global wave of AI-era educational reform will be watching closely.

Subject of Research: Developing AI-based indicators to monitor the school digital renewal process and deep pedagogical transformation

Article Title: Developing Indicators for School Digital Renewal in the Age of AI

Article References: Developing Indicators for School Digital Renewal in the Age of AI. (n.d.). https://doi.org/10.1007/s44366-026-0080-4

Image Credits: AI Generated

DOI: 10.1007/s44366-026-0080-4

Keywords: school digital renewal, AI in education, digital transformation indicators, Bloom's revised taxonomy, personalized learning, ChatGPT, digital footprints, educational monitoring, second-order change, competence-based learning, Developing, Indicators