Personalized pedagogies, efficient organisational management, or precision policy develop-ment: Artificial Intelligence (AI) is progressively becoming involved in supplementing, sup-porting, or substituting decision-making processes in education. In the wake of this intensify-ing uptake, this thesis seeks to understand how AI is making a difference in how education comes to be understood, lived, and governed. To make sense of and follow the involvement of AI in educational decision-making processes, it is described across this thesis as automated education governance. In its extended meaning, this form of governance refers to a socio-material practice which comes to be understood through the notion of ‘method assemblage’ – a theoretical framework describing how methods – such as AI – and notions of reality rein-force and sustain one another through the crafting of presence. Across the method assemblage of automated education governance crafting of presence with AI becomes a pursuit of closure that enacts reality as being definite and singular. The thesis opens with three contextualising chapters: Governing locates the major themes of AI, education policy, automation, and digital education governance. Assembling positions AI as part of a wider method assemblage that shapes understandings of students, institutions, learning and education. The thesis then follows a diffractive metaphor outlined in chapter three and offers five cuts that expose the transformative impacts of AI: Automating investi-gates the convergence of notions of learning between humans and machines; Configuring is a multi-sited event ethnography that seeks to understand how students, machines and data are figured together for AI to work; Imagining explores socio-technical imaginaries of automation at a large Australian university; Translating is grounded in interviews and problematises how educational data exists in multiplicity but becomes enacted in singularity; and Intervening is an exercise in speculative computation that automates learning by building a device that can write and grade its own assignments. Reading across these investigations reveals that AI makes a difference in governing education because its hinterland affords certainty and therefore the possibility to make every educational event knowable and manageable prior to its emergence. Certainty, however, is not achieved through advances in computing machinery, but rather wider socio-material practices of the method assemblage working to redefine education into a series of step-by-step procedures and computational categories.