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Interest in artificial intelligence (AI) language models has grown considerably following the release of 'generative pre-trained transformer' (GPT). Framing AI as an extractive technology, this article details how GPT harnesses human labour and sensemaking at two stages: (1) during training when the algorithm 'learns' biased communicative patterns extracted from the Internet and (2) during usage when humans write alongside the AI. This second phase is framed critically as a form of unequal 'affective labour' where the AI imposes narrow and biased conditions for the interaction to unfold, and then exploits the resulting affective turbulence to sustain its simulation of autonomous performance. Empirically, this article draws on an in-depth case study where a human engaged with an AI writing tool, while the researchers recorded the interactions and collected qualitative data about perceptions, frictions and emotions. [ABSTRACT FROM AUTHOR]

In Applied AI, or 'machine learning', methods such as neural networks are used to train computers to perform tasks without human intervention. In this article, we question the applicability of these methods to education. In particular, we consider a case of recent attempts from data scientists to add AI elements to a handful of online learning environments, such as Khan Academy and the ASSISTments intelligent tutoring system. Drawing on Science and Technology Studies (STS), we provide a detailed examination of the scholarly work carried out by several data scientists around the use of 'deep learning' to predict aspects of educational performance. This approach draws attention to relations between various (problematic) units of analysis: flawed data, partially incomprehensible computational methods, narrow forms of educational' knowledge baked into the online environments, and a reductionist discourse of data science with evident economic ramifications. These relations can be framed ethnographically as a 'controversy' that casts doubts on AI as an objective scientific endeavour, whilst illuminating the confusions, the disagreements and the economic interests that surround its implementations. [ABSTRACT FROM AUTHOR]

In this analytical essay, part of Harvard Educational Review's symposium on Platform Studies in Education, Ben Williamson, Kalervo N. Gulson, Carlo Perrotta, and Kevin Witzenberger argue that global technology companies have begun acting as governance organizations in education. Their analysis focuses on the global technology company Amazon, which has begun penetrating education through a connective architecture of digital infrastructure and platform services. Looking at Amazon technical documentation and publicly available materials, the authors identify and examine five interlocking governance operations and their effects: inscribing commercial business models on the education sector, habituating educational users to Amazon technologies, creating new interfaces with educational institutions, platforming third-party education providers on the cloud, and seeking market dominance over provision and control of key information infrastructures of education. In showing how Amazon is potentially developing infrastructural dominance in the education sector as part of its transformation into a statelike corporation with significant social, technical, economic, and political power to govern and control state and public services, this article highlights the broader implications of increasing technological governance in education. [ABSTRACT FROM AUTHOR]