Instituting socio-technical education futures: encounters with/through technical democracy, data justice, and imaginaries

Abstract

A (re)composition of encountered problems and possibilitiesArtificial intelligence, automation, algorithms, and datafication are increasingly instituted across educational systems and decision-making, including generative AI, student monitoring, exam grading, data analytics.The public release of ChatGPT raised stark issues of corporate infrastructuring, the politics and scientization of automation, conflicts over ethics, and the possibility of how things could be 'designed otherwise' (Williamson, Macgilchrist, and Potter 2023).The global reach of exam surveillance systems during the COVID-19 pandemic provoked questions about commercial provider roles, the hidden labour of automated systems, alongside the vulnerabilities of remote studying (Selwyn et al. 2021).A grades standardisation algorithm to calculate proxy grades for cancelled examinations sparked a range of unintended consequences, protests, and public trust issues (Kelly 2021).School openings and closings throughout the crisis increasingly relied upon the politicised release and reception of cross-sectoral data analytics, while attendant COVID-19 surveillance systems escalated the powers of both government and non-government entities (Mahase 2021; Oster 2021).These socio-technical controversies highlight the multiscalar power relations shaping present and future education possibilities (Macgilchrist, Potter, and Williamson 2021).A range of conceptual and methodological constraints are provoked by these shifting power relations between diverse constituents: students, educators, communities, technology corporations, professional bodies, cross-sectoral policymakers, plus all levels of government.Methods can enact worlds, Lury (2020) argues, and attention to (re)composing how knowledge continues with, and transforms through, societal institutions is vitally needed.There are multiple issues in education that arise from socio-technical controversies related to the use of algorithms.The first issue comes from what we know when socio-technical systems are used elsewhere.Algorithmic systems are used in high stakes areas such as policing and hiring.Problems in this use include over policing of people of colour using facial recognition technologies, and reinforcement of gender bias in automated selection in hiring practices (Noble 2018).In these areas, it has been shown that complex socio-technical systems are poorly understood by both users and those affected by the systems (Algorithm Watch 2019).Similarly, there are concerns about the capacity of educators, administrators and policy makers to understand the limitations and presuppositions of using complex socio-technical algorithmic systems in decision-making (European Commission 2021;Southgate et al. 2020).The second issue is that with the increased use of algorithmic systems, along with the identification of risks (European Commission 2021), has come awareness that ethical issues like fairness and bias are seriously underdeveloped in education (Sahlgren 2021).Additionally, with a wide range of expertise involved in developing and applying socio-technical systems, there is growing contestation over what counts as relevant

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