In this contribution we use the lens of nudging, which is design-based techniques that predict and ‘architect’ user choices, to ask not only what it means to sense, think, act and learn in, but also as a data-driven, automated and predictive learning environment. Originating from psychologically informed behavioral economics in public governance (John 2018), nudge principles have increasingly been deployed in the digital realm. Hereby, ongoing ‘real-time’ algorithmic data analysis predicts users´ habits, preferences and interests, and progressively personalizes their informational choice context in the direction preferred by the ‘choice architect’ (e.g. google searches), described as hypernudge (Yeung 2017). Building on material derived from former studies we did on apps and platforms (Decuypere 2019, Hartong 2020, Decuypere et al. 2021), joined with a case study of an organization developing nudging technologies for education in the UK (the nesta Behavioral Insights Group), we argue that digital educational practices are in the process of developing their own types of nudges, that extend and at the same time significantly transform nudge and hyper-nudge: edunudge. The general underlying assumption of edunudge-based instructional design (e.g. adaptive, gamified tutoring) is that students can be (exclusively) known through considering their present data activities on digital interfaces, while the data points also serve as ‘actionable intelligence’ to predict and regulate future learner behavior. In doing so, such forms of regulatory and predictive governance are heavily foreclosing potential futures or exploratory activities by design (Fourcade/Gordon, 2020), thus powerfully transforming how students and teachers come to experience, ‘education’, ‘learning’, and ‘teaching’ (a process we designate as nudgification). Finally, with the ongoing optimization of edunudge designs, pedagogy is increasingly displaced from being centered around what it is to sense, think and act as a student or teacher within and through a teacher-student relationship to being centered around algorithms and machines. Thus, it becomes a matter of finding design-based ways to put machines into the position of the learner and learners into the position of predictable machines: in automated learner environments, it are not so much students, but increasingly algorithms and machines that are ‘genuinely’ learning and experimenting in order to make sense of the world – learning understood here in the pedagogical terms of ‘dwelling comfortably with contingent events and uncertainties’ (Amoore 2020; Reich 2020). We conclude this paper by arguing that it is particularly because of this pedagogical inversion that edunudge urgently needs close scrutiny and active resistance.

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