Studies have highlighted how EdTech may be reconfiguring pedagogical and social relationships. For example, the use of dashboards influences how teachers understand their students and the students see themselves (Jarke & Macgilchrist, 2021); the use of EdTech platforms can encode expectations of what a learner should be and how they should act (Decuypere, 2019) and Google Classroom can shape the role of teachers (Perrotta et al., 2021). Concurrently, research shows that schools with less resources may tend to resort to more automated versions of EdTech (Zeide, 2017) which may have implications for learning and teaching relations (Saltman, 2016). This presentation adds to this emerging area. Drawing on in-depth data from ethnographic research in three secondary schools in England, which takes a relational socio-technical approach, this paper focuses on the ways in which the increasing use of digital technologies in schools is changing student-teacher relations, and the implications this has for educational and social equity. We combine the findings from participatory classroom observation (40 classes per school), interviews with students and teachers (40 per school), futures workshops with students (2 per school) and “socio-technical audits” of key EdTech platforms (Gleason & Heath, 2021). We focus on three themes and tensions in our data that raise questions for pedagogic relations: distraction and opportunity, (dis)trust and validity, and surveillance and communication. We show how the underlying logics – i.e. the design choices and pedagogical assumptions embedded within EdTech - come together with the varied structural and cultural conditions that students and teachers encounter in each school and how these have varied implications for educational equity. We show how the “hidden curriculum” along with the potential biases of EdTech, can shape teacher agency, how students think about themselves, their relationships to others, and the expectations society has for them (Biesta, 2016); and demonstrate how this has implications for the reproduction and reconfiguration of inequity. Viewing the future as a process of emergence from current school practices (Facer, 2013), our findings highlight the significant inequities in schools in England, and how the current EdTech on offer can often be inadequate. Although the implications of EdTech are never straightforward, we argue that stakeholders should be demanding and reimagining “better” EdTech, that fits with broader educational purposes (Biesta, 2016) and are “explicitly designed to address issues of equity” (Facer & Selwyn, 2021:143) to support pedagogical relations that enable positive social change.

Biesta, G. (2016). Beyond Learning: Democratic Education for a Human Future. Taylor & Francis. Decuypere, M. (2019). Researching educational apps: ecologies, technologies, subjectivities and learning regimes. Learning, Media and Technology, 44(4), 414-429. Facer, K. & Selwyn, N. (2021). Digital Technology and the Futures of Education: Towards ‘Non-Stupid’ Optimism. The Futures of Education initiative UNESCO. Facer, K. (2013). The problem of the future and the possibilities of the present in education research. International Journal of Educational Research, 61, 135-143. Gleason, B., & Heath, M. K. (2021). Injustice embedded in Google Classroom and Google Meet: A techno-ethical audit of remote educational technologies. Italian Journal of Educational Technology, 29(2), 26-41. Jarke, J. & Macgilchrist, F. (2021). Dashboard stories: How narratives told by predictive analytics reconfigure roles, risk and sociality in education. Big Data & Society, 8(1). Perrotta, C. Gulson, K., Williamson, B. & Witzenberger, K. (2021). Automation, APIs and the distributed labour of platform pedagogies in Google Classroom, Critical Studies in Education, 62:1, 97-113 Saltman, K. J. (2016). Corporate Schooling Meets Corporate Media: Standards, Testing, and Technophilia. Review of Education, Pedagogy, and Cultural Studies 38(2): 105–23. Zeide, E. (2017). The structural consequences of big data-driven education. Big Data, 5(2):164-172.