How a data-driven smart classroom can create new spaces of injustice: a case study of the Xiaoya AI Smart Classroom

Abstract

This essay examines how data-driven technologies reshape justice in everyday classrooms through a case study of the Xiaoya Smart Classroom. Drawing on data justice, decolonial critique, and the capability approach, it shows how constant capture of behaviour makes students hyper-visible while giving them little real choice about participation or appeal. The analysis traces how simple labels of attention and engagement compress diverse ways of learning into narrow categories, which can lead to both distributive and recognitional harms. It also explores the cultural scripts built into behavioural analytics and the risks of exporting one model of good learning across settings. In addition, the essay maps power at the publicprivate interface, where vendors influence rules and data uses that teachers and students cannot meaningfully contest. Finally, it considers the spatial effects of classroom hardware and seating, which can magnify uneven visibility and accuracy. The conclusion sets out conditions under which such systems might be justified: genuine opt-outs or low-tracking routes, published reasons and appeal pathways, third-party audit, and a shift from behavioural compliance to capability expansion as the primary measure of success. Together, these steps move data-driven education toward forms of governance that are fair, transparent, and freedom-enhancing.

Affiliated Institutions

• Yunnan Provincial Department of Education CN

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