Perspectives in Technology
Whose technology is this - and who pays the price when it goes wrong?
Whose technology is this - and who pays the price when it goes wrong?
THE PROVOCATION
Why are you reading on this particular screen?
Why are you reading on this particular screen?
If you are at school it is almost certain that the device in front of you was paid for by your school - or that your school made a decision, at some point, that education here should happen partly through screens. The interactive boards in your classrooms probably cost several thousand euros each. Add the software licences, the learning management system, the attendance tracking platform, the assessment tools, etc: the total investment, across a typical school, runs to thousands of euros per student per year. Who decided to spend that money? Almost certainly not you. The budgets are controlled by teachers, school administrators, and governors - the people who sit at the other end of the technology. They decided that this technology would improve learning. And most importantly, the companies who sold it to the school agreed.
But there is nothing new about this agreement. Audrey Watters, in Teaching Machines (2021), traces the history of technology in education back to Sidney Pressey's mechanical testing device in the 1920s, B.F. Skinner's teaching machine in the 1950s, educational television in the 1960s, the language laboratory in the 1970s, computer-assisted instruction in the 1980s. Each generation of educators and tech companies believed they had finally found the technology that would personalise, accelerate, and transform learning. The promises made for your school's interactive boards are essentially the same promises made for the overhead projector. None of the previous transformations quite delivered what was promised.
Neil Selwyn, in Is Technology Good for Education? (2016), pursues that question further. His conclusion is not that technology is bad for education. It is that the assumption that technology is good for education has been accepted almost without examination - and that when you follow the money, the technology that gets built and bought tends to serve teachers, administrators, and vendors more reliably than it serves students. The management information system is not for students. The interactive whiteboard is primarily a teacher tool. The learning analytics platform that tracks your engagement and flags low-performers produces data that is useful to managers, not to learners. The market for educational technology has a specific customer, with a specific budget, and specific interests but that customer is not you.
But there is nothing new about this agreement. Audrey Watters, in Teaching Machines (2021), traces the history of technology in education back to Sidney Pressey's mechanical testing device in the 1920s, B.F. Skinner's teaching machine in the 1950s, educational television in the 1960s, the language laboratory in the 1970s, computer-assisted instruction in the 1980s. Each generation of educators and tech companies believed they had finally found the technology that would personalise, accelerate, and transform learning. The promises made for your school's interactive boards are essentially the same promises made for the overhead projector. None of the previous transformations quite delivered what was promised.
Neil Selwyn, in Is Technology Good for Education? (2016), pursues that question further. His conclusion is not that technology is bad for education. It is that the assumption that technology is good for education has been accepted almost without examination - and that when you follow the money, the technology that gets built and bought tends to serve teachers, administrators, and vendors more reliably than it serves students. The management information system is not for students. The interactive whiteboard is primarily a teacher tool. The learning analytics platform that tracks your engagement and flags low-performers produces data that is useful to managers, not to learners. The market for educational technology has a specific customer, with a specific budget, and specific interests but that customer is not you.
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Neil Selwyn highlights the rapid growth of private online education, especially in India, where companies like BYJU'S have expanded by serving students underserved by public education. He warns against focusing on futuristic “metaverse” fears while ignoring current harms such as surveillance, discriminatory exam software, and ineffective AI systems in schools. He argues that much edtech is overhyped, unreliable, and driven by profit rather than learning. Examples like Hawaii’s problematic Acellus programme show how poor-quality digital education can spread unchecked during crises.
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On the Scope page, Marx's argument was that technology is never neutral: the hand-mill gives you society with the feudal lord; the steam-mill, society with the industrial capitalist. The technology produces not only goods but the social relations that surround their production. The question Marx would ask of the learning management system is the same question he asked of the mill: what social relations does this technology produce? Who controls it, who works within it, and who profits from it? When a student sits at a school device running proprietary software, tracked by an analytics platform, producing data that belongs to the institution rather than to them, the social relations being produced are not difficult to identify. They are the social relations of managed labour. The technology made those relations more efficient, more legible, and harder to refuse.
Big idea 1 - Technology is built around an assumed user. That user is probably not you.
Every piece of technology is designed for someone. The design process requires assumptions about who will use it: their physical dimensions, their level of technical literacy, their language, their access to power and connectivity, their typical use case. These assumptions are usually not stated explicitly. They are embedded in the object.
As we saw in Lesson 4 of the Core Knowledge and the Knower section Caroline Criado Perez, in Invisible Women (2019), documents this pattern across an enormous range of technologies and institutions. The crash test dummy used in car safety testing was, for decades, modelled on an average male body. The result was that cars which passed safety tests for men were systematically less safe for women, whose different height, weight distribution, and seating position made them significantly more likely to be seriously injured in a collision that an equivalent male driver would survive. Medical research excluded women from trials for decades on the grounds that hormonal variation complicated the data - meaning that dosage recommendations and side effect profiles for a large range of drugs were derived entirely from male subjects and applied to everyone. Voice recognition systems trained predominantly on male voices had higher error rates for women and children. The people designing the research, the cars, and the software assumed that the 'default human' was male - and that assumption was invisible to them because it was so widely shared.
As we saw in Lesson 4 of the Core Knowledge and the Knower section Caroline Criado Perez, in Invisible Women (2019), documents this pattern across an enormous range of technologies and institutions. The crash test dummy used in car safety testing was, for decades, modelled on an average male body. The result was that cars which passed safety tests for men were systematically less safe for women, whose different height, weight distribution, and seating position made them significantly more likely to be seriously injured in a collision that an equivalent male driver would survive. Medical research excluded women from trials for decades on the grounds that hormonal variation complicated the data - meaning that dosage recommendations and side effect profiles for a large range of drugs were derived entirely from male subjects and applied to everyone. Voice recognition systems trained predominantly on male voices had higher error rates for women and children. The people designing the research, the cars, and the software assumed that the 'default human' was male - and that assumption was invisible to them because it was so widely shared.
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Caroline Criado Perez, Invisible Women
The crash test dummy, used in virtually every vehicle safety test since the 1970s, was designed on a male body. Female dummies were introduced eventually, but often as scaled-down male dummies rather than female bodies, failing to account for the different ways women's necks, spines, and seatbelt positioning interact with impact forces. Women are 47% more likely than men to be seriously injured in car crashes, even controlling for other factors. The data was there. The question was never asked, because the standard body, the body for which the world was designed, was male.
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The same logic applies to educational technology, and to technology generally. The default user of most software is assumed to be: literate in English, living in a high-bandwidth environment, in possession of a recent device, without significant visual or motor impairment, and culturally familiar with the interface conventions of Western consumer software. Every assumption that does not hold for a given student makes the technology work less well for them - not dramatically, just steadily, in ways that are individually small and collectively significant.
There is a deeper version of the same argument. Technology can only work with knowledge it can operationalise - knowledge that can be converted into data and quantified. This is a technical constraint, not a design choice. A learning platform can record whether a student selected the correct answer. It cannot record what the student understood, or how their thinking changed, or what connections they made that will only surface months later. They resist the form that digital processing requires - no one failed to think of measuring them. The effect, however, is the same as if the platform had decided they did not exist: what the technology cannot handle, it does not count. The constraint may be technical but the consequence is epistemic backwash.
Big idea 2 - You are not the customer. You are the product.
In a conventional market, the customer pays for a product and receives it. The relationship is clear. But a growing portion of the technology that students use every day does not work this way. The platform is free - or the school pays a licence - but the real commercial transaction is happening in a different direction: your behaviour, your preferences, your patterns of attention and response are being collected, processed, and sold.
Shoshana Zuboff, in The Age of Surveillance Capitalism (2019), argues that the dominant business model of the modern technology industry is not the sale of products but the extraction of behavioural data. Every search, every click, every pause before a scroll and every pattern of engagement produces data that can be used to predict and modify future behaviour. This data is enormously valuable to advertisers, to political campaigns, to anyone who wants to influence what people do. The users are the raw material, not the customers. Zuboff describes this as a form of extraction structurally similar to earlier forms of primitive accumulation: a resource is claimed from a commons and turned into private property. The resource, in this case, is human experience.
Shoshana Zuboff, in The Age of Surveillance Capitalism (2019), argues that the dominant business model of the modern technology industry is not the sale of products but the extraction of behavioural data. Every search, every click, every pause before a scroll and every pattern of engagement produces data that can be used to predict and modify future behaviour. This data is enormously valuable to advertisers, to political campaigns, to anyone who wants to influence what people do. The users are the raw material, not the customers. Zuboff describes this as a form of extraction structurally similar to earlier forms of primitive accumulation: a resource is claimed from a commons and turned into private property. The resource, in this case, is human experience.
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Yanis Varoufakis on technofeudalism
The Institute of Art and Ideas Economist and former politician Yanis Varoufakis argues capitalism is being replaced by “technofeudalism,” where digital platforms like Google and Amazon dominate society through “command capital.” Unlike traditional capitalism, users unknowingly generate value through data and online activity, often without wages. He claims central bank money after the 2008 crisis replaced profits as capitalism’s driving force, while algorithms increasingly shape choices, weaken markets, concentrate power in tech elites, and undermine democracy, labour, and social equality.
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Yanis Varoufakis, in Techno-Feudalism (2023), extends this argument in a different direction. The great technology platforms are not simply companies operating in a market. They are something more like feudal lords: owners of a territory - the cloud - on which other people live and work. When you post on a social media platform, you are working on someone else's land. The content you produce, the attention you generate, the data you create: these enrich the platform, not you. You get access to the territory in return. Varoufakis argues that this is not a market relationship at all, because no competition is possible: you cannot choose to live on a different internet.
Yanis Varoufakis, in Techno-Feudalism (2023), extends this argument in a different direction. The great technology platforms are not simply companies operating in a market. They are something more like feudal lords: owners of a territory - the cloud - on which other people live and work. When you post on a social media platform, you are working on someone else's land. The content you produce, the attention you generate, the data you create: these enrich the platform, not you. You get access to the territory in return. Varoufakis argues that this is not a market relationship at all, because no competition is possible: you cannot choose to live on a different internet.
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In an educational context, this dynamic has a specific form. When students use a learning platform, their interactions produce data - which questions they answer correctly, which they skip, how long they spend on each section, how their performance changes over time. This data can be used to improve the platform. It can also be used to profile the student - to make inferences about ability, learning style, and predicted future performance that are packaged and sold, or used by the school in ways the student cannot see and may not have consented to. The student did not sign a data agreement. They used a platform their school required them to use. There are cookies on this website and by reading this page you agreed to hand over your data. The data was produced as a byproduct of education. Who owns it? Not me.
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The attention economy The attention economy is the competition for human attention as a commercial resource. Social media platforms are its most visible form: the features designed to maximise time-on-platform - infinite scroll, notification systems, variable reward schedules - are deliberate design choices. They are the product. You are the resource being sold to advertisers. The ethics of this is the Ethics page's question. The epistemic question is different: what kind of knowing does an attention economy produce, and what kind does it crowd out?" |
Big Idea 3 - Technology encodes assumptions about what counts as knowledge.
A map appears to be the most objective form of knowledge available: it shows what is there. It has no argument to make, no perspective to defend. It simply records. This appearance of neutrality is exactly what makes maps so powerful - and exactly what makes them worth examining. Every map is made from a position: from a particular place, using particular instruments, for particular purposes, in the service of particular interests. The Mercator projection (below left), standard in Western classrooms for centuries, represents Greenland as roughly the size of Africa. Africa is fourteen times larger. The distortion was a consequence of the mathematics of projecting a sphere onto a flat surface, but the choice of projection was not neutral - it placed Europe at the centre and at a scale that overstated its size relative to the global south.
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Colonial cartography claimed territory by recording it. To name a place on a map, in the conventions of European cartography, was an act of possession: the naming erased prior names, prior occupancy, prior knowledge systems. Terra nullius - the legal doctrine that land was empty and unclaimed if it was not cultivated and enclosed in the European manner - was enforced through maps that showed indigenous-occupied land as blank. Courts across the world have had to adjudicate between competing cartographic traditions: European legal maps against indigenous oral and practical knowledge of the same territory. The question of which form of knowing counts as evidence of ownership is political, with material consequences measured in land, sovereignty, and survival.
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GPS and satellite mapping are the current form of this argument. They appear more objective than colonial hand-drawn maps because they are produced by instruments rather than by people with obvious interests. But the satellite still looks down from a particular orbit, the imagery is still processed by particular algorithms, the coordinates are still referenced to a particular datum - and the result still treats the territory as unmapped until mapped by its own methods. The navigational knowledge encoded in Pacific Islander wayfinding traditions, the land management knowledge held in Aboriginal Australian songlines, the ecological knowledge embedded in Kimmerer's account of indigenous plant relationships in Braiding Sweetgrass: none of these appear on a GPS screen. They are in a form the technology cannot process - accuracy is beside the point. Big Idea 1 showed that technology works with the knowledge it can measure. Here, the same principle operates at the scale of entire knowledge systems. A map is never without perspective. The perspective it lacks is always someone else's.
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What counts as evidence? In a series of landmark legal cases across Australia, Canada, and New Zealand from the 1990s onwards, indigenous communities presented oral testimony, ceremonial knowledge, and traditional land-use practices as evidence of prior ownership in courts that were structured around written documentary evidence. The question the courts had to answer - what counts as evidence of knowing a place? - is a TOK question with direct material consequences. In some cases the oral testimony was accepted; in others it was not, on the grounds that it could not be corroborated by the kind of records the legal system recognised. The technology of cartography and the technology of legal documentation worked together to define which knowledge counted. That definition was not neutral. |
The same logic runs through educational technology. A learning platform defines what counts as evidence of knowing just as a map defines what counts as evidence of occupying territory. The student who learns through movement, builds understanding through making, or demonstrates knowledge through performance leaves little trace. The student who thinks in three dimensions, solves problems visually, or knows a forest because they grew up in it - who can read weather, identify species, and manage land in ways that took generations to accumulate - registers on a platform as a blank. So does the student who reasons well in conversation and builds understanding through argument.
The neuroscientist Hannah Critchlow argues that human cognition is fundamentally social - that the brain evolved not for solitary calculation but for thinking with others, and that collective intelligence consistently outperforms individual intelligence on the problems that matter most. Vygotsky - whom you encountered in Lesson 2 - made the structural version of this argument: learning happens in the space between what a student can do alone and what they can do with others, and it is in that space that the most significant cognitive development occurs. Both arguments point to the same blind spot in the platform model. A system that records individual responses to individual questions is not just missing some students. It is missing the dimension of cognition that both a neuroscientist and a developmental psychologist identify as central to how humans actually learn.
Gardner's argument from the Core Lesson 1 applies here with full force. If intelligence is plural, a system that measures only its propositional and linguistic forms is assessing the subset of knowledge that fits its own format and recording everything else as absence. That account travels: into reports, into records, into the data profile that follows the student forward.
The neuroscientist Hannah Critchlow argues that human cognition is fundamentally social - that the brain evolved not for solitary calculation but for thinking with others, and that collective intelligence consistently outperforms individual intelligence on the problems that matter most. Vygotsky - whom you encountered in Lesson 2 - made the structural version of this argument: learning happens in the space between what a student can do alone and what they can do with others, and it is in that space that the most significant cognitive development occurs. Both arguments point to the same blind spot in the platform model. A system that records individual responses to individual questions is not just missing some students. It is missing the dimension of cognition that both a neuroscientist and a developmental psychologist identify as central to how humans actually learn.
Gardner's argument from the Core Lesson 1 applies here with full force. If intelligence is plural, a system that measures only its propositional and linguistic forms is assessing the subset of knowledge that fits its own format and recording everything else as absence. That account travels: into reports, into records, into the data profile that follows the student forward.
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Social media harms girls' mental health
CBC News: The National During the pandemic, Kelly D noticed her daughter spending far more time online, where fitness and food-related content was constantly pushed to her phone. The teen developed anorexia, leading to repeated hospital stays that disrupted her education and isolated her socially. A new UNESCO report shows girls are more negatively affected by social media, especially by body-image content amplified by algorithms. Studies found teenage girls experience greater mental health strain than boys, with many saying Instagram worsened body insecurities.
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Jonathan Haidt, in The Anxious Generation (2024), documents what happened to young people's mental health between 2010 and 2015 - a period that coincides closely with the mass adoption of smartphones and social media. Rates of anxiety, depression, and self-harm rose sharply across the English-speaking world. But the rise was not uniform. Girls were significantly more affected than boys. The sharpest rises were in countries with the highest smartphone penetration. Among girls, the effects were concentrated in the social platforms most built around public appearance and social comparison. The technology deployed its effects unevenly - a specific set of social pressures on a specific population at a specific developmental moment, with consequences that were severe and measurable. The costs were borne by teenage girls, not by the people who built the platforms.
Bringing it together
Whose technology is this?
The Scope page established that technology is not neutral and that every generation struggles to see what its own technology is doing from inside. The Perspectives page has been asking whose technology this actually is - and the answer that emerges across the three big ideas is more specific than it might first appear.
The technology in your school was designed for, sold to, and evaluated by the people who run the school - with you as the end user rather than the primary beneficiary. The knowledge it measures reflects the assumptions of the people who built it, and what it cannot operationalise it does not count (Big Idea 1). The activity you produce on it enriches someone who is not you (Big Idea 2). And the costs of that technology fall unevenly - on girls more than boys, on students without the right home conditions, and on the communities whose knowledge systems the technology renders invisible by working only with what it can already process (Big Idea 3). The map that treats uncharted land as empty and the platform that treats unmeasurable knowledge as if it does not exist are the same kind of instrument.
All three things can be true: that a technology produces real benefits, that those benefits are unevenly distributed, and that the narrative of universal benefit serves the interests of those who sell it. The Perspectives question is: good for whom, at whose cost, and on whose terms? The two pages that follow are built on this foundation. Methods and Tools asks what kind of knowledge digital technology produces - and what kind it structurally cannot reach. Ethics asks what we owe each other given what we now know about how technology shapes attention, distributes inequality, and operates beyond any individual's control..
The Scope page established that technology is not neutral and that every generation struggles to see what its own technology is doing from inside. The Perspectives page has been asking whose technology this actually is - and the answer that emerges across the three big ideas is more specific than it might first appear.
The technology in your school was designed for, sold to, and evaluated by the people who run the school - with you as the end user rather than the primary beneficiary. The knowledge it measures reflects the assumptions of the people who built it, and what it cannot operationalise it does not count (Big Idea 1). The activity you produce on it enriches someone who is not you (Big Idea 2). And the costs of that technology fall unevenly - on girls more than boys, on students without the right home conditions, and on the communities whose knowledge systems the technology renders invisible by working only with what it can already process (Big Idea 3). The map that treats uncharted land as empty and the platform that treats unmeasurable knowledge as if it does not exist are the same kind of instrument.
All three things can be true: that a technology produces real benefits, that those benefits are unevenly distributed, and that the narrative of universal benefit serves the interests of those who sell it. The Perspectives question is: good for whom, at whose cost, and on whose terms? The two pages that follow are built on this foundation. Methods and Tools asks what kind of knowledge digital technology produces - and what kind it structurally cannot reach. Ethics asks what we owe each other given what we now know about how technology shapes attention, distributes inequality, and operates beyond any individual's control..
Questions, assessments, films and other stuff.
Questions to think about
These are questions for discussion, reflection, and your TOK journal. They do not have single correct answers, they have better and worse arguments.
These are questions for discussion, reflection, and your TOK journal. They do not have single correct answers, they have better and worse arguments.
- Selwyn argues that the technology adopted by schools tends to serve the interests of teachers and administrators more reliably than those of students. Is this a problem with educational technology specifically, or with how institutions make decisions about technology generally? What would a student-centred approach to educational technology look like?
- Watters shows that the promise of personalised learning through technology has been made since the 1920s and has not been kept. Does the repetition of an unfulfilled promise change how we should evaluate the current version of the same promise? Or could it simply be that the technology was not yet good enough until now?
- Criado Perez shows that the "default human" in design is male - and that this assumption has caused measurable harm. What is the "default student" assumed by the educational technology you use? What characteristics does that student have - and which of your characteristics does the assumption exclude?
- Zuboff argues that surveillance capitalism extracts behavioural data from users without genuine consent. Is it possible to consent to data collection when using a service is required by your school, your employer, or the practical requirements of social participation? What would genuine consent look like?
- Varoufakis argues that the major technology platforms are more like feudal lords than market competitors, because their territory cannot be avoided. Is this a useful analogy? What does it illuminate - and what does it miss?
- Haidt's research shows that social media affected girls more severely than boys, and some countries more than others. What does the uneven distribution of mental health effects tell us about the claim that social media "affects everyone equally"?
- If a learning platform assumes students have quiet space, reliable internet, and uninterrupted time at home, and many students do not have these things, has the platform created inequality or revealed it? Does the distinction matter?
- Every map encodes the perspective of whoever made it. Does this mean that maps cannot give us genuine knowledge of the world - or only that they give us a particular kind of knowledge from a particular position? Is there a difference between "knowledge from a perspective" and "knowledge that is distorted"?
- Colonial maps rendered indigenous land as empty or unnamed, and courts used those maps as evidence of ownership. Does this mean that the technology of cartography caused the dispossession, or that it was used as a tool by people who would have found other tools? Does the answer change the moral analysis?
- If a court accepts written documentary evidence of land ownership but not oral traditional knowledge, it is making an epistemological claim: that one form of knowing is more reliable than another. Is that claim justified - and who gets to decide?
- If Hannah Critchlow is right that human cognition is fundamentally social, what would an educational technology designed around collective intelligence look like? And would it be possible to build one that did not simultaneously undermine the social conditions it depends on?
Exhibition connections
See more exhibition ideas and previous student work here
It is never too early to start to think about your TOK Exhibition, the ideas in this lesson connect strongly to three of the 35 prompts. Start noticing objects in the world around you that speak to these questions.
Prompt #12: Is bias inevitable in the production of knowledge?
Suggested object: a crash test dummy, a voice recognition system, a medical textbook with male-only research data, or any technology whose design reflects a specific set of assumptions about its user. Criado Perez's argument is that bias in knowledge production is not inevitable in principle but is systematic in practice when the people producing knowledge share the same unexamined assumptions about who knowledge is for. The exhibition argument needs to show specifically how the assumed user shapes what the technology can and cannot know - and what knowledge is missing as a result. The strongest essays will address the second part of the prompt: not just that bias exists, but whether it can be eliminated, and what it would take.
Prompt #14: Does some knowledge belong only to particular communities of knowers?
Suggested object: a GPS device, a satellite map, an indigenous navigation tool (Pacific wayfinding stick chart, Australian Aboriginal songline map, Inuit snow gauge), or any technology that encodes a particular community's knowledge of space, time, or environment. The argument runs in two directions. First: some knowledge is produced by particular communities and is not simply "knowledge in general" - it is shaped by practice, place, and transmission. Second: when that community's knowledge is displaced by a global technological standard, the question is not just what was lost but whether the displacement was acknowledged at all. The exhibition argument asks whether the GPS treats the territory as "unmapped" before it arrived - and what it means to overwrite one knowledge system with another.
Prompt #22: What role do experts play in influencing our consumption or acquisition of knowledge?
Suggested object: an educational technology product, a learning analytics dashboard, a "personalised learning" platform, or any expert-designed system that mediates between a learner and what they learn. Selwyn's argument is that educational technology is designed by experts - engineers, educational researchers, vendors - whose expertise is real but whose interests are not identical to those of students. The exhibition argument asks what happens when expert knowledge about learning is embodied in a platform that students are required to use: who defines what counts as learning, who measures it, and who benefits from the measurement. The strongest essays will engage with whether expert mediation is avoidable, or whether all education involves someone else deciding what is worth knowing.
See more exhibition ideas and previous student work here
It is never too early to start to think about your TOK Exhibition, the ideas in this lesson connect strongly to three of the 35 prompts. Start noticing objects in the world around you that speak to these questions.
Prompt #12: Is bias inevitable in the production of knowledge?
Suggested object: a crash test dummy, a voice recognition system, a medical textbook with male-only research data, or any technology whose design reflects a specific set of assumptions about its user. Criado Perez's argument is that bias in knowledge production is not inevitable in principle but is systematic in practice when the people producing knowledge share the same unexamined assumptions about who knowledge is for. The exhibition argument needs to show specifically how the assumed user shapes what the technology can and cannot know - and what knowledge is missing as a result. The strongest essays will address the second part of the prompt: not just that bias exists, but whether it can be eliminated, and what it would take.
Prompt #14: Does some knowledge belong only to particular communities of knowers?
Suggested object: a GPS device, a satellite map, an indigenous navigation tool (Pacific wayfinding stick chart, Australian Aboriginal songline map, Inuit snow gauge), or any technology that encodes a particular community's knowledge of space, time, or environment. The argument runs in two directions. First: some knowledge is produced by particular communities and is not simply "knowledge in general" - it is shaped by practice, place, and transmission. Second: when that community's knowledge is displaced by a global technological standard, the question is not just what was lost but whether the displacement was acknowledged at all. The exhibition argument asks whether the GPS treats the territory as "unmapped" before it arrived - and what it means to overwrite one knowledge system with another.
Prompt #22: What role do experts play in influencing our consumption or acquisition of knowledge?
Suggested object: an educational technology product, a learning analytics dashboard, a "personalised learning" platform, or any expert-designed system that mediates between a learner and what they learn. Selwyn's argument is that educational technology is designed by experts - engineers, educational researchers, vendors - whose expertise is real but whose interests are not identical to those of students. The exhibition argument asks what happens when expert knowledge about learning is embodied in a platform that students are required to use: who defines what counts as learning, who measures it, and who benefits from the measurement. The strongest essays will engage with whether expert mediation is avoidable, or whether all education involves someone else deciding what is worth knowing.
Films
For more see my 10 films for the TOK journey page.
For more see my 10 films for the TOK journey page.
🎬 WATCH - Black Mirror: Nosedive (Series 3, Episode 1, 2016)
Remarkable that this was made in 2016. A near-future society in which every social interaction is rated on a five-star scale and your average score determines your access to housing, travel, and employment. The episode is a satire of social credit systems and the attention economy: every interaction is performed for the rating rather than for its own sake, and the result is a world of relentless mutual surveillance in which authentic connection becomes impossible. For TOK: the rating system does not just record social behaviour - it produces a specific kind of social behaviour. The technology and the social reality it creates are inseparable. Ask what knowledge the rating system produces about people - and what it makes impossible to know. My students can access the film here
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🎬 WATCH - Coded Bias (Shalini Kantayya, 2020)
A documentary following MIT researcher Joy Buolamwini, who discovered that the facial recognition software she was using failed to detect her dark-skinned face until she put on a white mask. Her subsequent research showed that commercial facial recognition systems had significantly higher error rates for darker-skinned faces and for women. The film traces the deployment of these systems in law enforcement, schools, and public spaces, and the almost total absence of regulatory oversight. The Criado Perez argument, documented in real time: the default user embedded in a technology, the harm that assumption causes, and the question of who is responsible when a system fails the people it was never built for. My students can access the film here
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Further reading
Books - all in the library
📚 READ - Neil Selwyn, Is Technology Good for Education? (2016) - The most direct treatment of the argument in the provocation. Selwyn surveys the evidence base for edtech claims, examines who the real customers of educational technology are, and makes the case for a more sceptical engagement with the assumption that technology improves learning. Short and accessible.
📚 READ - Neil Selwyn, Education and Technology: Key Issues and Debates (second edition) - A more comprehensive treatment across the full range of debates: access, equity, pedagogy, assessment, and the politics of edtech. Chapter 2 (on technology and educational change) and Chapter 4 (on learning) are most relevant to this page.
📚 READ - Audrey Watters, Teaching Machines: The History of Personalized Learning (MIT Press, 2021) - A detailed history of the claim that technology can personalise education, from Pressey's testing machines in the 1920s to Skinner's teaching machines in the 1950s to the adaptive learning platforms of today. The historical arc is the argument: the same promises, made to each generation, by different vendors.
📚 READ - Audrey Watters, The Monsters of Education Technology (2014) - A shorter and more polemical collection of essays. The opening essay on the history of educational technology hype is a useful companion to the provocation on this page.
📚 READ - Caroline Criado Perez, Invisible Women: Exposing Data Bias in a World Designed for Men (2019) - in the library Politics section. The crash test dummy chapter and the medical research chapter are the most directly relevant to Big Idea 1. The voice recognition and smartphone grip chapters illustrate the same argument with different cases.
📚 READ - Shoshana Zuboff, The Age of Surveillance Capitalism (2019) - Part 1 (chapters 1-3) establishes the core argument about behavioural data as raw material. Dense but important. The introduction is a good entry point.
📚 READ - Yanis Varoufakis, Techno-Feudalism: What Killed Capitalism (2023) - in the library (Technology and Social Science sections). Chapter 3 (Cloud Capital) is where the feudal analogy is developed most directly. Accessible and polemical.
📚 READ - Jonathan Haidt, The Anxious Generation (2024) - in the library (Education and Psychology section). Chapter 1 establishes the empirical case for the mental health crisis; Chapter 6 focuses on why social media harms girls more than boys; Chapter 11 covers policy responses including phone-free schools.
📚 READ - Hannah Critchlow, Joined-Up Thinking: The Science of Collective Intelligence (2022) - the neuroscientific case that human cognition is fundamentally social and that collective intelligence consistently outperforms individual intelligence. Directly relevant to the argument about what learning platforms cannot measure.
📚 READ - Neil Selwyn, Education and Technology: Key Issues and Debates (second edition) - A more comprehensive treatment across the full range of debates: access, equity, pedagogy, assessment, and the politics of edtech. Chapter 2 (on technology and educational change) and Chapter 4 (on learning) are most relevant to this page.
📚 READ - Audrey Watters, Teaching Machines: The History of Personalized Learning (MIT Press, 2021) - A detailed history of the claim that technology can personalise education, from Pressey's testing machines in the 1920s to Skinner's teaching machines in the 1950s to the adaptive learning platforms of today. The historical arc is the argument: the same promises, made to each generation, by different vendors.
📚 READ - Audrey Watters, The Monsters of Education Technology (2014) - A shorter and more polemical collection of essays. The opening essay on the history of educational technology hype is a useful companion to the provocation on this page.
📚 READ - Caroline Criado Perez, Invisible Women: Exposing Data Bias in a World Designed for Men (2019) - in the library Politics section. The crash test dummy chapter and the medical research chapter are the most directly relevant to Big Idea 1. The voice recognition and smartphone grip chapters illustrate the same argument with different cases.
📚 READ - Shoshana Zuboff, The Age of Surveillance Capitalism (2019) - Part 1 (chapters 1-3) establishes the core argument about behavioural data as raw material. Dense but important. The introduction is a good entry point.
📚 READ - Yanis Varoufakis, Techno-Feudalism: What Killed Capitalism (2023) - in the library (Technology and Social Science sections). Chapter 3 (Cloud Capital) is where the feudal analogy is developed most directly. Accessible and polemical.
📚 READ - Jonathan Haidt, The Anxious Generation (2024) - in the library (Education and Psychology section). Chapter 1 establishes the empirical case for the mental health crisis; Chapter 6 focuses on why social media harms girls more than boys; Chapter 11 covers policy responses including phone-free schools.
📚 READ - Hannah Critchlow, Joined-Up Thinking: The Science of Collective Intelligence (2022) - the neuroscientific case that human cognition is fundamentally social and that collective intelligence consistently outperforms individual intelligence. Directly relevant to the argument about what learning platforms cannot measure.