Students and society in the 2020s. Three future ‘histories’ of education and technology

Oct 9, 2019 by

As social science fiction, this paper imagines three possible futures for education and technology. Among the most important technologies emerging today are data-aggregating technologies such as AI, affective computing, adaptive or predictive software, clouds and platforms. The paper is not, however, directed at specific technologies, but at indeterminate sociotechnical configurations. Set in 2040, it offers three ‘histories’ of the 2020s. Might students become (i) ‘smooth users’, improving themselves in the pursuit of frictionless efficiency within a post-democratic frame created by large corporations, (ii) ‘digital nomads’, seeking freedom, individualism and aesthetic joy as solopreneurs exploiting state regulations and algorithmic rules while stepping out of the state and deeply into the capitalist new economy, or (iii) participatory, democratic, ecological humans embedded in ‘collective agency’ that see institutions as spaces for exploring more equitable ways of living? The paper reflects on the future research and the political, educational and technological decisions which would make each of these three fictional future histories more or less likely.


This paper speculates three different futures into the coming decade. Starting from current policy on ‘education in a digital world’, we identify three directions in which the 2020s could unfold. Our focus lies less on specific emerging technologies, and more on the socio-economic-material embedding of technologies in future educational practices. The paper explores how this embedding configures the contours of what is thought to be a desirable future student-subject: What priorities will they have? How might they organise their lives? What kinds of experience will they want to have. In short, who might they want to ‘be’?

Current research imagines different kinds of student-subject that can, could or should be shaped by education and technology. One set of observers highlights the potential for learning analytics, artificial intelligence, adaptive learning, maker-centred learning and other interactive or data-driven technologies to increase equality of opportunity by fostering independent, flexible, reflective, team-working individuals who have developed grit, tenacity and a sense of self-empowerment (Clapp et al. 2017 Clapp, Edward P., Jessica Ross, Jennifer O. Ryan, and Shari Tishman. 2017. Maker-centered Learning: Empowering Young People to Shape Their Worlds. San Fransisco, CA: Jossey-Bass. [Google Scholar]; Hamilton et al. 2019 Hamilton, Ali, Donald Rubin, Michael Tarrant, and Mikkel Gleason. 2019. “Digital Storytelling as a Tool for Fostering Reflection.” Frontiers: The Interdisciplinary Journal of Study Abroad 31 (1): 5973. [Google Scholar]; Luckin et al. 2016 Luckin, Rose, Wayne Holmes, Mark Griffiths, and Laurie B. Forcier. 2016. Intelligence Unleashed. An Argument for AI in Education. [Google Scholar]; Shechtman et al. 2013 Shechtman, N., A. H. DeBarger, C. Dornsife, S. Rosier, and L. Yarnall. 2013. Promoting Grit, Tenacity and Perseverance: Critical Factors for Success in the 21st Century. Washington: U.S. Department of Education, Office of Educational Technology & Society. [Google Scholar]).

Other observers critique these traits as encouraging young people to constantly monitor, evaluate and improve themselves, practices which reduce schooling to the preparation of young labour to be exploited by a capitalist economy (Means 2018 Means, Alexander J. 2018. “Platform Learning and On-demand Labor: Sociotechnical Projections on the Future of Education and Work.” Learning, Media and Technology 43 (3): 326338.[Taylor & Francis Online], [Web of Science ®] [Google Scholar]; Peters 2013 Peters, Michael A. 2013. Education, Science and Knowledge Capitalism: Creativity and the Promise of Openness. New York: Peter Lang.[Crossref] [Google Scholar]; Thompson and Cook 2016 Thompson, Greg, and Ian Cook. 2016. “The Logic of Data-sense: Thinking through Learning Personalisation.” Discourse: Studies in the Cultural Politics of Education 38 (5): 740754.[Taylor & Francis Online], [Web of Science ®] [Google Scholar]). Technology becomes, in this view, a tool for the constant surveillance/dataveillance of young people, hindering democratic decision-making, determining what counts as good schooling and distracting efforts from the structural transformations necessary to overcome entrenched socio-economic inequalities (Eynon and Huw 2018 Eynon, Rebecca, and Davies Huw. 2018. “Is Digital Upskilling the Next Generation our ‘Pipeline to Prosperity?’New Media and Society 20 (11): 39613979.[Crossref], [Web of Science ®] [Google Scholar]; Macgilchrist 2017a Macgilchrist, Felicitas. 2017a. “Backstaging the Teacher: On Learner-driven, School-driven and Data-driven Change in Educational Technology Discourse.” Culture-Society-Education 12 (2): 83103. [Google Scholar]; Williamson 2018 Williamson, Ben. 2018. Big Data and Education. London: Sage. [Google Scholar]). Yet other observers foreground the need for students to gain radically critical and political perspectives on technology, citizenship and resistance today (Caines 2017 Caines, Autumm. 2017. “#DigCiz. June 2017 is Here.” May 22. Accessed September 10, 2017. [Google Scholar]; Emejulu and McGregor 2019 Emejulu, Akwugo, and Callum McGregor. 2019. “Towards a Radical Digital Citizenship in Digital Education.” Critical Studies in Education 60 (1): 131147.[Taylor & Francis Online], [Web of Science ®] [Google Scholar]), and for scholarship, pedagogy and technology design to prioritise students’ potential to live in relations of care and community, intellectual privacy and safety (Bali 2017 Bali, Maha. 2017. “Against the 3A’s of EdTech: AI, Analytics, and Adaptive Technologies in Education.” November 29. Accessed December 20, 2017. [Google Scholar]; Doxtdator 2017 Doxtdator, Benjamin. 2017. “Maybe We’re Not Afraid: On Edtech’s Inability to Imagine the Future.” March 26. Accessed April 28, 2017. [Google Scholar]; Reich and Ito 2017 Reich, Justin, and Mizuko Ito. 2017. From Good Intentions to Real Outcomes: Equity by Design in Learning Technologies. Irvine, CA: Digital Media and Learning Research Hub. [Google Scholar]; Zeide 2017 Zeide, Elana. 2017. “The Structural Consequences of Big Data-Driven Education.” Big Data 5 (2): 164172.[Crossref], [PubMed], [Web of Science ®] [Google Scholar]).

This paper draws on recent ethnographic and conceptual work that emphasises the constitutive indeterminacy of technology in education. This work foregrounds ambivalences, tensions, dialectical relationships and lines of flight. It shows how potentially transformative technologies are domesticated by everyday practices and entrenched hierarchies; and how reforms oriented to growing the economy and shaping pliable workers sow the seeds of their own destruction by creating spaces conducive to collective action (Au 2018 Au, Wayne. 2018. A Marxist Education: Learning to Change the World. Chicago: Haymarket Books. [Google Scholar]; Selwyn et al. 2018 Selwyn, Neil, Selena Nemorin, Scott Bulfin, and Nicola F. Johnson. 2018. Everyday Schooling in the Digital Age: High School, High Tech? London: Routledge. [Google Scholar]; Sims 2017 Sims, Christo. 2017. Disruptive Fixation: School Reform and the Pitfalls of Techno-Idealism. Princeton, NJ: Princeton University Press.[Crossref] [Google Scholar]).

The transformative moments of practice can unfold in [at least] two different directions, they can be directed towards the re-enforcement of explicated rules, standards, and codified bodies of knowledge, or they can be geared towards the practical enactment of qualitatively new forms of interaction and experience that account for alternative knowledges, values, and aesthetic criteria. (Richter and Allert 2019 Richter, Christoph, and Heidrun Allert. 2019. Critical Incidents as a Participatory Research Approach for Transformative Cultural Practices. Singapore: UNITWIN – Second Yearbook on Arts Education Research for Cultural Diversity and Sustainable Development. [Google Scholar])

The paper also draws on post-foundational theories of subjectivation which question the notion that individuals act autonomously with technological tools; instead we see individuals as subjectivated in social practices co-constitutively entangled with technology (Butler 1997 Butler, Judith. 1997. Excitable Speech: A Politics of the Performative. London: Routledge. [Google Scholar]; Richter and Allert 2017b Richter, Christoph, and Heidrun Allert. 2017b. “Poetische Spielzüge als Bildungsoption in einer Kultur der Digitalität.” In Digitalität und Selbst – Interdisziplinäre Perspektiven auf Subjektivierungs- und Bildungsprozesse, edited by H. Allert, M. Asmussen, and C. Richter, 237262. Bielefeld: Transcript.[Crossref] [Google Scholar]). The social practices enacted with technology invariably exceed developers’ expectations. Just one example is when self-optimising fitness-tracking apps, which use GPS data to mark progress, have been (mis)appropriated to create artwork using the GPS traces or to highlight the dangers of such data-tracking apps by revealing secret military locations.11 See, for instance, the GPS art on all notesFor each of the following three scenarios, written as if it were 2040, the paper engages in a kind of social science fiction to speculate on how technology will have been used in schools, and what this means for how future student-subjects will have been addressed in the future past of the 2020s. Each section first sketches sociotechnical developments over the course of the 2020s, second, identifies the roots of these developments in educational policy decisions in Germany and Europe of the 2010s, and third, explores the effects of these socio-political-technical developments on students as the 2020s progressed.

Scenario 1: smooth users, competent subjects

Artificial intelligence, learning analytics, predictive analytics, adaptive learning software, school management software, learning management systems (LMS), school clouds. No school was without these and other technologies branded as ‘superintelligent’ by the late 2020s. Tied into the aims of solving social problems and making the world a better place, Silicon Valley tech entrepreneurs and major edu-businesses successfully rebranded existing companies (which were no longer called ‘textbook publishers’, but instead ‘global learning platforms’) and founded new companies which produced software for learning, teaching and assessment. They developed platforms to integrate learning resources and track student data. Since they were seen as the only people with the ability to understand digital technologies, in particular data technologies, they were invited to take on central roles as advisors to national governments and local districts on educational futures.The competences prioritised in Germany remained consistent over the years: From the first national strategy for ‘Education in a Digital World’ in 2016, the goal was to prepare students to become efficient, capable, self-sufficient users of available digital technologies (KMK 2016 KMK (Kultusministerkonferenz). 2016. Strategie Bildung in der digitalen Welt. Accessed June 10, 2019. [Google Scholar]). Media literacy, including digital as well as information literacy, became an important feature in education and democratic participation, emphasising ‘the ability to understand, select, evaluate and use media as a leading purveyor and processor, if not producer, of information’ (UNESCO 2013 UNESCO. 2013. “UNESCO Global Media and Information Literacy Assessment Framework. Country Readiness and Competencies.” Paris. [Google Scholar], 29). Students were to ‘respond to’ the ‘challenges’ of the digital world, i.e., to become proficient users of hardware and software provided by others, and to react to changes being made to the world by other entities (politics, commercial actors). They were not encouraged to believe they could actively shape the digital world by understanding, programming, controlling, contextualising and critiquing digital technologies and processes. The German national strategy – and similar policy documents across Europe – emphasised the individual rather than the collective. In addition, seeing individuals as ‘having’, ‘developing’ and ‘exhibiting’ competences presupposed that competences were relatively static, decontextualised, autonomous abilities rather than situation-specific, relational performances, deeply embedded in power relations.

With this focus on individual abilities, a major report by technology advisers to governments in 2022 prioritised adaptive software and predictive education as the key measures to improve national educational systems: The goal throughout was to ‘optimise’ educational processes and help students to optimise themselves. Data science promised to enable educational technology to respond to learners’ needs and to their emotions (see Williamson 2018 Williamson, Ben. 2018. Big Data and Education. London: Sage. [Google Scholar]). Personalised learning pathways would motivate students and thus help to close the achievement gap. Affective computing and AI techniques would enhance learning by their ability to sense, interpret and interact with student’s feelings, moods and emotions (McStay 2018 Mcstay, Andrew. 2018. Emotional AI: The Rise of Empathic Media. London: Sage. [Google Scholar]). Satisfied and fully immersed learners, capable of using the newest technologies, would be an asset to innovation in the workplace, and spur economic efficiency and GDP growth. Technology-enhanced assessment tools would improve formative, summative and predictive test procedures. Learning management systems (LMS) would make school management, administration, teaching and learning more efficient.

With this 2022 landmark report, schools and colleges integrated new technology providing learning opportunities for students to become efficient, autonomous, team-working and creative problem solvers. Students engaged in citizen science projects, they worked independently with AI-driven technologies, collaborated in international teams, and used 3D graphics and virtual reality/augmented reality (VR/AR) to experience new dimensions of the curriculum content that they were expected to learn.

Critical voices noted that the discussion about ‘new’ educational media in the 2020s had historical precursors, which can be traced back over one hundred years. When, for instance, educational films were gradually incorporated into primary and secondary schools in most European countries and the USA from the 1920s, progressive educationalists were convinced that films could fulfil their aspirations to extend educational opportunities for all and to help reform the traditional system of education based on more democratic values (Bruch 2018 Bruch, Anne. 2018. “Educational Cinema in the Weimar Republic.” Educació i Història: revista d’història de l’educació 31: 113124. [Google Scholar]; Cuban 1986 Cuban, Larry. 1986. Teachers and Machines: The Classroom Use of Technology since 1920. New York: Teachers College Press. [Google Scholar]).

Topics from the 2020s such as equality, democracy, efficiency, skills, training, dynamic images and modernisation echo these debates of the 1920s (Kurig 2015 Kurig, Julia. 2015. Bildung für die technische Moderne. Pädagogische Technikdiskurse zwischen den 1920er und 1950er Jahren in Deutschland. Würzburg: Königshausen & Neumann. [Google Scholar]). Educational policymakers and film entrepreneurs in the 20th century had asserted that films were not only an efficient means of education, but that they responded to the immediate needs for more skilled and trained workers. They based their claims on the theory of suggestibility and the supposed impact that images had on the minds of students (Binet 1900 Binet, Alfred. 1900. La Suggestibilité. Paris: Schleicher. [Google Scholar]). They argued that films provided a more direct and immediate form of instruction over text-based methods or textbooks because they believed the mind learned through the accumulation and association of images. In this respect, they positioned themselves as modernisers, attempting to revitalise and to change pedagogical practices. However, though the proponents considered films to be pedagogic instruments which could help achieve the established educational goals of the time, they did not use them to transform educational goals, nor, for instance, as an avant-garde medium that could construct novel ways of seeing using film techniques as montage or the aesthetics of expressionism.

Similarly, in the 2020s, despite the new technology, the goals of formal education remained static. By 2025 large-scale studies demonstrated unequivocally that the use of new technologies had not fulfilled the promise of ameliorating socio-economic inequalities. Students’ self-efficacy decreased as they followed learning pathways determined by computational devices (Bali 2017 Bali, Maha. 2017. “Against the 3A’s of EdTech: AI, Analytics, and Adaptive Technologies in Education.” November 29. Accessed December 20, 2017. [Google Scholar]). The response was to roll out more new technology. Learners became increasingly invested in optimising and improving the self (since they were seen to be individually responsible for developing their competences and for closing the achievement gap, see Ladson-Billings 2006 Ladson-Billings, Gloria. 2006. “From the Achievement Gap to the Education Debt: Understanding Achievement in U.S. Schools.” Educational Researcher 35 (7): 312.[Crossref] [Google Scholar]). It became self-evident that being an agreeable, pleasant person who could independently solve problems, create novel ideas and find compromises without conflict, was the key to social and economic success.

There was little public discussion of how global corporations were encoding highly ideologically valanced norms and standards into the software and infrastructures underlying education, and thereby subtly shaping what is valued as ‘good education’ (Jarke and Breiter 2019 Jarke, Juliane, and Andreas Breiter. 2019. “Special Issue: Datafication of Education.” Learning, Media and Technology 44 (1).[Taylor & Francis Online], [Web of Science ®] [Google Scholar]). Nor was there widespread concern about the fundamental relations of ‘rentier’-‘tenant’ which began to dominate society through the overreliance on products from commercial entities. In this world of ‘rentism’ (Frase 2016 Frase, Peter. 2016. Four Futures. Life After Capitalism. London: Verso. [Google Scholar]), the elites are those individuals and organisations that extract profit from renting out patterns to which they own the intellectual property rights through, e.g., patents or copyright. The main source of profit is no longer control over physical entities (factories, hardware, cables, chips) but control over ‘patterns’ (algorithms, blueprints, software and other kinds of information that produce and reproduce our world): ‘In order to maintain control over the economy, the rich increasingly need to control that information, and not just physical objects’ (Frase 2016 Frase, Peter. 2016. Four Futures. Life After Capitalism. London: Verso. [Google Scholar], 71).

By 2026, to meet the policy expectation that they assist students in becoming optimised technology users, and given the lack of publicly funded open source alternatives, educational institutions relied almost exclusively on intellectual property owned by commercial entities, from Microsoft and Google to commercial LMS and proprietary software for languages, STEM subjects, coding and robotics. Indeed, the centralising tendencies of platform capitalism led a small number of (transnational) corporations who built the fundamental technological infrastructures of education to become key players (Srnicek 2017 Srnicek, Nick. 2017. Platform Capitalism. London: Polity. [Google Scholar]). These platforms had the power to aggregate and analyse user data, and to prefigure classroom practices – and thereby societal values and forms of subjectivation – through the software and content they made available. User interfaces circulated an aesthetic of smoothness and predictability that lulled users into agreeable, smoothed personas.

Throughout the 2020s, activists and scholars pointed out opportunities for citizens to shape the digital world, to critique the power differentials between technology corporations and citizens, and to protest against the expropriation of common public resources through platform services (Noble 2018 Noble, Safiya Umoja. 2018. Algorithms of Oppression: How Search Engines Reinforce Racism. New York: NYU Press.[Crossref] [Google Scholar]; Stalder 2015 Stalder, Felix. 2015. Kultur der Digitalität. Berlin: Suhrkamp. [Google Scholar], 277). However, these debates had little effect on mainstream educational practices. Ethnographic research demonstrated that despite the hopes funnelled into technologies as key to ‘disrupting’ entrenched socio-economic inequalities and widening participation, traditional hierarchies and exclusions re-emerged (Sims 2017 Sims, Christo. 2017. Disruptive Fixation: School Reform and the Pitfalls of Techno-Idealism. Princeton, NJ: Princeton University Press.[Crossref] [Google Scholar]). In response, and in a genuine attempt to rectify these inequalities, major public-private partnership efforts were made in 2027 to build newer, more transparent, more participatory technologies. However, since these efforts retained the focus on individualised competences and individualised participation, most funding was funnelled into building new schools with high-tech AI, robotics, etc. equipment rather than initiating major economic transformations; socio-economic equality was still not achieved (Duane 2018 Duane, Daniel. 2018. “Learning the Hard Way.” Wired, 70–79. [Google Scholar]). The societal process of smoothing continued, fuelled by the aesthetics of the predictable, responsive, pleasurable interfaces of everyday life. At the same time, the ‘post-democratic’ move, in which technology companies began to make significant educational decisions outwith the purview of public, democratic contestation (Stalder 2015 Stalder, Felix. 2015. Kultur der Digitalität. Berlin: Suhrkamp. [Google Scholar]), continued to gain strength throughout the 2020s, with the former Google educational data scientist, Stephania Seerobbe, appointed the EU Commissioner of Education in 2029.

Source: Students and society in the 2020s. Three future ‘histories’ of education and technology: Learning, Media and Technology: Vol 0, No 0

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