Using more-than-human approaches to study the politics of AI in the automating of decision making in education

This article explores how more-than-human research approaches can respond to the complexity of political and ethical questions of AI-mediated practices amidst complex transformations of education practice. Three shifts to research practices are explored: understanding AI-data systems based on the day-to-day experiences of workers, evoking conceptual and methodological re-envisioning to produce new forms of situated data, and considering how researchers and professionals can work closely together to document and critically analyse the performative relations between AI systems and workers. A participatory research project, which worked with education practitioners to study how they are learning to work with AI-automated and assisted decision-making systems, provides empirical data to help illustrate facets of this methodology. Insights include working in the hyphenated AI-human space, materializing digital-human relations differently, and unsettling and shifting the gaze.