Participation is a prevalent topic in many areas, and data-driven projects are no exception. While the term generally has positive connotations, ambiguities in participatory approaches between facilitators and participants are often noted. However, how facilitators can handle these ambiguities has been less studied. In this paper, we conduct a systematic literature review of participatory data-driven projects. We analyse 27 cases regarding their openness for participation and where participation most often occurs in the data life cycle. From our analysis, we describe three typical project structures of participatory data-driven projects, combining a focus on labour and resource participation and/or rule- and decision-making participation with the general set-up of the project as participatory-informed or participatory-at-core. From these combinations, different ambiguities arise. We discuss mitigations for these ambiguities through project policies and procedures for each type of project. Mitigating and clarifying ambiguities can support a more transparent and problem-oriented application of participatory processes in data-driven projects.

This article examines the impact of generative artificial intelligence (GAI) on higher education, emphasizing its effects in the broader educational contexts. As AI continues to reshape the landscape of teaching and learning, it is imperative for higher education institutions to adapt rapidly to equip graduates for the challenges of a progressively automated global workforce. However, a critical question emerges: will GAI lead to a more inclusive future of learning, or will it deepen existing divides and create a future where educational access and success are increasingly unequal? This study employs both theoretical and empirical approaches to explore the transformative potential of GAI. Drawing upon the literature on AI and education, we establish a framework that categorizes the essential knowledge and skills needed by graduates in the GAI era. This framework includes four key capability sets: AI ethics, AI literacy (focusing on human-replacement technologies), human–AI collaboration (emphasizing human augmentation), and human-distinctive capacities (highlighting unique human intelligence). Our empirical analysis involves scrutinizing GAI policy documents and the core curricula mandated for all graduates across leading Asian universities. Contrary to expectations of a uniform AI-driven educational transformation, our findings expose significant disparities in AI readiness and implementation among these institutions. These disparities, shaped by national and institutional specifics, are likely to exacerbate existing inequalities in educational outcomes, leading to divergent futures for individuals and universities alike in the age of GAI. Thus, this article not only maps the current landscape but also forecasts the widening educational gaps that GAI might engender.

In this editorial, we draw insights from a special collection of peer-reviewed papers investigating how new data sources and technology can enhance peace. The collection examines local and global practices that strive towards positive peace through the responsible use of frontier technologies. In particular, the articles of the collection illustrate how advanced techniques—including machine learning, network analysis, specialised text classifiers, and large-scale predictive analytics—can deepen our understanding of conflict dynamics by revealing subtle interdependencies and patterns. Others assess innovative approaches reinterpreting peace as a relational phenomenon. Collectively, they assess ethical, technical, and governance challenges while advocating balanced frameworks that ensure accountability alongside innovation. The collection offers a practical roadmap for integrating technical tools into peacebuilding to foster resilient societies and non-violent conflict transformations.

Global food security worsened during the COVID-19 pandemic. In Nigeria, food security indicators increased in the first months of the pandemic and then decreased slightly but never returned to their pre-pandemic levels. We assess if savings groups provided household coping mechanisms during COVID-19 in Nigeria by combining the in-person LSMS-ISA/GHS-2018/19 with four rounds of the Nigerian Longitudinal Phone Survey collected during the first year of the pandemic. A quasi-difference-in-differences analysis setup leveraging the panel nature of the data indicates that savings group membership reduces the likelihood of skipping a meal but finds no statistically significant effect on the likelihood of running out of food or eating fewer kinds of food. Given theoretical priors and other literature positing a relationship, we also implement an OLS regression analysis controlling for baseline values finding that having at least one female household member in a savings group is associated with a 5–15% reduction in the likelihood of reporting skipping meals, running out of food, and eating fewer kinds of food. This analysis is not able to establish causality, however, and may in fact overestimate the effects. Together, the results indicate that savings group membership is positively associated with food security after COVID-19, but the causal effect is statistically significant for only one of the three food security indicators. To conclude, considering the interest in savings groups and expectations of continued food security shocks, the importance of collecting better gender-disaggregated longitudinal household data combined with experimental designs and institutional data on savings groups.

The coupling of the disruptive processes of digitalization and the green transformation in a so-called “Twin Transformation” is already being considered a strategic step within the European Union and is discussed in the academic sphere. Strategically, this coupling is necessary and meaningful to realize synergies and to avoid counterproductive effects, such as rebound effects or lock-in effects, particularly given the time constraints imposed by climate change. The European data strategy not only calls for the establishment of various data spaces, such as the data space for the European Green New Deal, but also calls for the opening, integration, and utilization of European data for stakeholders from administration, business, and civil society. Considering this, it is argued that administrative informatics as a discipline could be integrated as an additional analytical perspective into the political science heuristic of the policy cycle. This integration offers substantial added value for analyzing and shaping the policy processes of the European Green transformation. Moreover, this heuristic approach enables the ex-ante prediction of changes in policymaking based on the theories, models, methods, and application areas of administrative informatics. Building on this premise, this article provides insights into the application of the proposed heuristic using the example of the European Green transformation. It analyzes the resulting implications for the analysis of policymaking considering an increasingly digitalized public administration.