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Childhood marginalization is the result of complicated processes that appears difficult to address for policymakers worldwide. Neo-institutional theory enables studies of the complexity of educational organizations, showing how they evolve in responses to their contradictory surroundings and generate unintended social inequality. Three Nordic municipalities are currently participating in a project that focuses on the increasing polarization between exposed and privileged schools in urban areas and on the significance of institutional and organizational factors in their local welfare models in ensuring childhood equity. The project we report on combines data in contrasting urban school areas. After showing some examples of voices in the project, the authors discuss how municipal actors are informed about social problems in school organizations yet lack research-based tools to counteract social inequality in education. They theorize how collaboration between researchers and welfare providers can contribute to counteract social inequality. Lessons from the project indicate a need for further collaboration between different stakeholders that includes different voices to ensure that research on childhood inequality is relevant and has an impact.

Machine learning and other artificial intelligence (AI) technologies are predicted to play a transformative role in primary education, where these technologies for automation and personalization are now being introduced to classroom instruction. This article explores the rationales and practices by which machine learning and AI are emerging in schools. We report on ethnographic fieldwork in Sweden, where a machine learning teaching aid in mathematics, the "AI Engine," was tried out by 22 teachers and more than 250 primary education students. By adopting an Actor-Network Theory approach, the analysis focuses on the interactions within the network of heterogeneous actors bound by the AI Engine as an "obligatory passage point." The findings show how the actions and accounts emerging within the complex ecosystem of human actors compensate for the unexpected and undesirable algorithmic decisions of the AI Engine. We discuss expectations about AI in education, contradictions in how the AI Engine worked and uncertainties about how machine learning algorithms 'learn' and predict. These factors contribute to our understanding of the potential of automation and personalisation--a process that requires continued re-negotiations. The findings are presented in the form of a fictional play in two acts, an "ethnodrama." The ethnodrama highlights controversies in the use of AI in education, such as the lack of transparency in algorithmic decision-making--and how this can play out in real-life learning contexts. The findings of this study contribute to a better understanding of AI in primary education.