Funded by the European Commission and coordinated by Radboud University, the cornerstone project NatureRestoreR: guiding nature restoration through integrated ecological modelling was officially launched on 1 September 2026. The project carries grant number 101287178 and will be implemented over a four-year period, concluding on 1 September 2030. The primary objective of NatureRestoreR is to provide publicly available ecological modelling tools and guidelines. These resources are intended to enable national and regional authorities to design restoration plans based on a scientifically sound understanding of how species, habitats, and ecosystem services respond to restoration measures in different places and under evolving climate dynamics.

A critical challenge in guiding ecosystem restoration efforts is that there is no single approach to restoring nature. Species and habitats may respond differently to a given restoration measure depending on local environmental conditions. Furthermore, climate change can affect the long-term outcomes of restoration efforts. Consequently, authorities need robust scientific evidence to understand how species and ecosystems are likely to respond to restoration under specific circumstances. They also need to identify where interventions can create synergies or trade-offs. NatureRestoreR seeks to address this challenge by convening existing data, scientific knowledge, and modelling approaches to develop an innovative suite of adaptive and easy-to-use ecological modelling tools.

These modelling tools will enable evidence-based prospective assessment of restoration measures. Specifically, they will model the potential impacts on species’ habitats, the extent and condition of habitats protected under the EU Nature Directives, and the provision of ecosystem services. By providing these insights, the project aims to help decision-makers make more informed choices about where and how to allocate restoration resources. This approach is designed to ensure that restoration efforts are not only ecologically sound but also practically feasible for national and regional authorities.

The development of these modelling tools will be a collaborative effort, co-developed and tested through a co-creation process involving researchers and implementation partners. The project will use information from the initial and final revised National Restoration Plans of five EU Member States: Austria, Italy, France, Estonia, and the Netherlands. This collaborative approach will enable the project partners to assess how these plans contribute to the objective of the EU Biodiversity Strategy and the Nature Restoration Regulation. By working with these specific national plans, the project ensures that the tools are relevant to real-world policy contexts and can be adapted to different national circumstances.

Reflecting on the project’s long-term vision, Aafke Schipper, a member of the coordination team at Radboud University, emphasized the importance of the partnership structure. Schipper noted that NatureRestoreR relies on a unique co-creation partnership between ecological researchers and national authorities responsible for or involved in the implementation of the Nature Restoration Regulation. Through this partnership, the project ensures that it meets the needs of decision-makers and practitioners who are implementing and monitoring the regulation. This focus on user needs is central to the project’s design, aiming to create tools that are not only scientifically rigorous but also practical for those on the ground.

NatureRestoreR’s transdisciplinary partnership brings together ecological modelling experts and national agencies with deep knowledge of the EU Nature Directives and National Restoration Plans. Working together, these partners aim to strengthen the relation between ecological knowledge and the decisions shaping Europe’s landscape. The project recognizes that effective restoration requires a close link between scientific understanding and policy implementation. By integrating these two areas, NatureRestoreR hopes to create a more cohesive approach to ecosystem restoration across the continent. This integration is seen as essential for achieving the ambitious targets set by the EU.

Throughout the project, NatureRestoreR will engage with researchers, practitioners, policymakers, and other stakeholders interested in advancing effective, evidence-informed ecosystem restoration across Europe. This broad engagement is intended to ensure that the project’s outputs are widely accessible and useful to a diverse range of actors involved in nature restoration. The project team encourages stakeholders to follow NatureRestoreR on LinkedIn to stay up to date with the project’s latest news, activities, and results. This ongoing communication is part of the project’s strategy to build a community of practice around evidence-based restoration.

The launch of NatureRestoreR represents a significant step forward in the effort to make nature restoration smarter and more effective in Europe. By providing the tools and evidence needed to design and refine restoration plans, the project aims to support the implementation of the EU Nature Restoration Regulation. The focus on co-creation and transdisciplinary collaboration is expected to ensure that the project’s outputs are relevant and useful to those responsible for implementing restoration measures. As the project progresses over the next four years, it will provide valuable insights into how ecological modelling can be used to guide restoration efforts in a changing climate. The ultimate goal is to contribute to the restoration of Europe’s ecosystems and the achievement of the EU’s biodiversity targets.

Subject of Research: Environmental Policy and Ecology

Article Title: NatureRestoreR launches to support Europe in making nature restoration smarter and more effective

Article References: NatureRestoreR launches to support Europe in making nature restoration smarter and more effective. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: Nature Restoration, EU Policy, Ecological Modelling, Biodiversity, Climate Change, Radboud University, European Commission, NatureRestoreR, launches, support, Europe, making

The research team, led by education researcher Susanne de Mooij, examined the development of numeracy skills among 7,885 primary school pupils during the period between 2014 and 2017, when adaptive learning systems were first being introduced in Dutch classrooms. The design was observational and comparative: classes that used an adaptive learning system for their mathematics instruction were matched against classes that taught numeracy through conventional means, without such software. The researchers then followed the mathematical growth of both groups across multiple school years, asking a deceptively simple question. Do children who practice arithmetic with software that continuously adjusts to their ability level actually progress faster than children who do not?

The technology at the heart of the study is conceptually elegant. An adaptive learning system automatically calibrates the difficulty of each exercise to the individual pupil working through it. When a child answers correctly and consistently, the system serves up more challenging problems, pushing the child forward along their own learning curve. When a child begins to struggle, the software responds by offering simpler tasks, allowing the pupil to consolidate foundational skills before moving on. In theory, this creates a continuously optimized learning trajectory for every child in a class, something that a single teacher managing thirty pupils simultaneously can only approximate with the greatest effort.

The results showed that, on average, pupils who used the adaptive system displayed more positive growth in their mathematics performance over the years of the study compared with pupils who learned without it. In cognitive terms, the feared scenario simply did not materialize. This matters because a persistent worry has shadowed digital learning tools since their arrival: that screen-based practice is somehow less effective than working with paper and pencil, and that children exposed to it learn less deeply. De Mooij and her colleagues found no support for that fear in the domain of arithmetic. If anything, the digital environment produced a small but consistent advantage in how quickly children’s numeracy skills developed.

It is precisely the longitudinal character of the study that gives this finding its weight. According to De Mooij, most previous research into adaptive learning systems has either measured only short-term effects or been conducted on much smaller samples, leaving educators and policymakers to make decisions about expensive technology on the basis of limited evidence. Adaptive learning platforms have now been in use for almost fifteen years, and today they operate in more than half of all Dutch primary schools. That widespread adoption, she notes, has run well ahead of the scientific evaluation of the tools, a situation this study begins to correct by tracking skill development over years rather than weeks.

The researchers are careful, however, to resist any temptation toward triumphalism. The effects they measured are small, and De Mooij stresses that the tool should be understood as producing a generally positive trend rather than a dramatic transformation. There is no miracle cure here, no software that will suddenly close achievement gaps on its own. What the data do reveal is a pattern of modest, reliable benefit, the kind of incremental improvement that, multiplied across thousands of classrooms and hundreds of hours of practice, can meaningfully shape how a generation of children acquires foundational mathematical skills.

Perhaps the most socially significant finding concerns where those benefits concentrate. The positive effect of the adaptive technology was particularly evident in large schools and in vulnerable schools serving many pupils from less advantaged socio-economic backgrounds. De Mooij offers a plausible mechanism: in such settings, there is often greater variation between pupils in their starting levels and learning paces. A conventional whole-class lesson must aim somewhere near the middle of that distribution, leaving the weakest and strongest learners underserved. Adaptive technology, by contrast, can tailor instruction to each individual child’s needs, and the wider the spread of abilities in a classroom, the more valuable that tailoring becomes. In this sense, the technology appears to function as an equity instrument, delivering its largest gains precisely where educational disadvantage is most concentrated.

This framing leads directly to the study’s central message about the role of the teacher. The researchers describe the adaptive learning tool as something that supports teachers rather than replacing them, and they are explicit about why. A single teacher cannot adapt lesson content to thirty pupils at the same time, but with a tool of this kind, pupils’ mathematics skills can be improved while the teacher’s workload is simultaneously reduced. The software absorbs the mechanical burden of individualizing practice, freeing the professional to do the things that only a professional can do. In classrooms under pressure, that dual benefit, better outcomes and less administrative strain, may prove to be the technology’s most persuasive selling point.

Crucially, the researchers insist that the teacher always remains in control of the learning process. It is the teacher who draws up the lesson plan, who decides what pupils learn, and who oversees everything that happens in the classroom. The adaptive system, for all its algorithmic sophistication, is fed solely by the data stored within it, whereas a teacher has access to vastly richer information: a child’s mood on a difficult morning, the social dynamics of the room, the moment when a frustrated pupil needs encouragement rather than another exercise. The algorithm sees answers; the teacher sees the child. That asymmetry, the researchers suggest, is not a temporary limitation of current technology but a structural feature of what teaching actually is.

The study arrives at a moment when schools around the world are being urged, sometimes aggressively, to adopt AI-driven educational products, often on the strength of marketing claims rather than longitudinal evidence. Against that backdrop, the Dutch findings offer a template for how the technology should be evaluated: over years, at scale, with comparison groups, and with honest acknowledgment that the measured effects are small. The picture that emerges is neither utopian nor dismissive. Adaptive learning tools, deployed under the guidance of skilled teachers, can help children learn arithmetic a little faster, and they can help most in the schools that need help the most. That is a genuinely useful result, and it comes with a built-in safeguard: the evidence itself reminds us that the decisive variable in the classroom is still the human being who plans the lessons, reads the room, and decides, every day, what learning should look like.

Subject of Research: Long-term effects of AI-supported adaptive learning systems on arithmetic skills in primary school pupils

Article Title: Learning maths goes (slightly) better with AI, but teacher plays a key role

Article References: Learning maths goes (slightly) better with AI, but teacher plays a key role. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: adaptive learning, artificial intelligence, primary education, mathematics, arithmetic, numeracy, educational technology, Radboud University, longitudinal study, teachers, Learning and Instruction, Netherlands