Do Teachers Dream of Electric Sheep?
Artificial Intelligence and the Intellectual Wellbeing of Teachers

Professor Patrick Alexander
Jacques-Olivier Perche
Back in February 2024 we shared our most recent thinking on the concept of intellectual wellbeing, or noetic syntonia. Around the same time, we were reflecting on the role that generative artificial intelligence has held in learning landscapes over the last year or so. In thinking about intellectual wellbeing for teachers, it has become increasingly important to think about the role that artificial intelligence will play in the field of education. In this short article, we first consider the state of play for Generative AI in education, before asking the question of what happens to teacher intellectual wellbeing alongside the rise of AI. While avoiding alarmist concerns about the rise of the machines (we are both avid users of Gen AI), we want to suggest that we need to be very careful about the kind of attention we are paying to the new technical demands of “upskilling” to work with AI. In what follows, we want to explain how an uncritical engagement with learning to love Gen AI in the classroom could lead to the further proletarianisation of the teaching profession. Counter to this, we argue that authentic engagement with the practice of intellectual wellbeing allows teachers to engage with AI in a way that strengthens an ethical and critical disposition, rather than paving the way for an even more disempowering, technicist vision of what it means to be a teacher in the 21st Century. We finish with some positive and hopeful practical suggestions for how to engage with AI in a way that nurtures intellectual wellbeing.
The title of this article makes clumsy reference to Philip K Dick’s 1968 novel of a similar name and inspiration for the Blade Runner movies, in which androids-turned-fugitives are on the run for expressing free will in a system that demands their total obedience. Theirs is a race against the necropower of the state — that is, the capacity of the state or other actors to trade in power over death as much as life. The open educational frontier of Generative AI harbours similar fears, both in terms of how we are encouraged to see AI as animated or somehow alive in its educational engagement, but also in how we think through how AI holds a mirror to the deathly shadows in our education systems. Will AI help us to engage more vividly with the humanistic principles of education — of emancipation and human flourishing — or will it reveal to us that all along we have been acting as ever-more effective technicians to a much larger and darker social mechanism (yes, we mean capitalism)? Will AI act as a vital force that breathes life into education, or will it be harnessed for the more efficient dispatching of the life-giving promise that education holds? In the end, what kinds of dreams about education are teachers encouraged to hold as we embrace Gen AI in our classrooms?
AI: What we already know
We freely admit that this is not exactly a novel line of inquiry: since the mainstreaming of Gen AI applications at the end of 2022, the internet has been awash with information and misinformation about the implications of this technology for education. Most of this debate is fuelled on one hand by a new frontier in edtech and educational consulting, which often presents AI as the cure-all solution for the essential challenge of our current mass education system, namely, productivity. On the other hand, popular discourse lurches towards dystopian visions of a world where humans are at best irrelevant to the productive capacities of our AI progeny. In such a world, education as we know it becomes pointless because AI makes knowledge production instantaneous and effortless, and it becomes impossible to look into the deep pool of an exam answer or finals essay and see, staring innocently back, a reflection of the learner who may (or may not) have been behind its inception. This debate is made dizzyingly more difficult to keep up with because the Gen AI industry is rampant in its expansion, not least in the many and growing promises that are made about how education is changing. One common point of agreement between the poles of this debate is this: Gen AI is here, and it may replace you, or, even more likely, you will be replaced by someone using AI in their professional world. On this much, it seems, most are likely to agree. We are interested in interrogating the significance of this statement. What is behind the implication that technology will assist us in the dispossession of our future colleagues, or their dispossession of us? Are you teaching someone today who will steal your job with AI tomorrow? What does all of this mean? What kind of work will be stolen and what kinds of AI-enhanced workers will replace us?
In our last essay, we explored how time in schools is articulated as time capital — that is, a scarce resource that students, teachers, and leaders must use effectively lest they waste a potential future return on investment (or future proxies). Time in education must be, above all, productive: we must therefore strive for ever-greater teacher effectiveness that maximises the potential yield for personal growth among students. For a mass education system designed around these principles, the power of AI is obvious: it is a time machine. What once would have taken you hours or days can now be achieved in seconds. Producing the resources of teaching becomes almost instantaneous or even predictive, as does the knowledge production demanded of our everyday assessment regimes. In less than two years, it has become almost effortless to produce an essay or assignment of reasonable quality, given the right prompts. Students are gaining a complex awareness of how Gen AI offers them a portal in time capital that allows them to extract the learning product required of them as a measure of their learning, while side-stepping the time commitment required to actually learn. In the process they learn different things, and this says much more about a creaking and outdated education system than it does about the technologically agile student. Teachers are equally gaining ground in terms of what AI can do to simplify their working lives or make their teaching more fabulous and engaging. No-one will refuse the time machine that speeds up your marking to a matter of seconds and gives you back an evening you weren’t paid to work in the first place. For students and teachers alike, somewhat ironically, the speed of Gen AI development also brings with it a new moral imperative not to waste time: one must stay abreast of the latest rapacious developments in AI lest one be bested by a colleague who has found an even more efficient way to use Chat GPT to ace a homework assignment, or a bot to write Year 8 reports, or OtterAI to summarise the minutes of that lethargic leadership meeting.
AI and Work
So, what we already know is that Gen AI will inevitably be a more important part of the professional lives of teachers and school leaders. As long as we work in an education system focused above all else on knowledge production through text, Gen AI will infiltrate classroom practice and will play an important part in the educational experiences of millions of children. If this is the probable future we will inhabit, it seems reasonable that teachers should learn to love AI. But how, and at what cost? Clearly, there are benefits for teachers who learn to use AI, from creating more dynamic learning environments to a panoply of labour- and time-saving opportunities. In education systems that are inherently time poor (because time capital holds such high value), AI applications that save teachers’ time could be the Holy Grail in edtech development. Certainly it was this kind of effective approach to capital that drove vast changes in what it meant to work, and what was our relationship to technology, during the Industrial Revolution. Historically, technology has made industrial work infinitely more productive and effective: this is a fact. It also gave rise to a class of workers, or a proletariat. The process by which persons become workers is described as proletarianisation, which is easier to think than it is to say. Proletarianisation is a term used in Marxist theory to describe the process of creating and expanding the working class in a capitalist society. It means that more and more people have to work for a wage or salary from an employer, because they do not have any other source of income or property. This can happen when people lose their land, their businesses, or their skills, and have to move to cities or factories to find jobs. Proletarianisation can also affect the middle class, when their jobs become less secure, less skilled, or lower paid. To explain proletarianization in terms of loss of knowledge, we can use “tertiary protention,” a concept further developed by Bernard Stiegler. The French thinker explored the impact and significance of technology in contemporary society. Tertiary protention is the externalization of human memory and knowledge through technical objects, such as books, computers, or artworks. Stiegler argues that capitalism exploits and destroys tertiary protention, by transforming it into commodities and standardizing it for mass consumption. This leads to the loss of knowledge, creativity, and diversity among human beings, who become dependent on and alienated by the technical objects they use. Stiegler calls this process “generalized proletarianisation”, which affects not only workers, but also consumers, artists, and teachers. It’s very important here to note the difference between proletarianisation and working-class identity and culture. The latter naturally have inherent value, while the former is an economic and political process of divesting persons from their value. In an economic system that is by definition designed to alienate workers from the actual value of their activity (this is, after all, how capitalism works), proletarianisation is a process of loss: it involves loss of status, loss of power, and ultimately, potentially the loss of an intellectual space where one can think seriously, creatively, and critically about one’s place in the world.
So, with this in mind, for teachers the risk of learning to love AI is exactly the same as the benefit: it will streamline the role of the teacher, defined in our current education system in terms of effectiveness, productivity, and an efficient return on investment in time capital. The natural evolution of this role is to become even more alienated from the project of education, the more effective one becomes at deploying the technology of learning. The tragic conclusion of such an engagement with Gen AI is not what it says about the technology, but rather what it implies about the current and potential future role of the teacher not as a steward of the humanistic aims of education for emancipation and human flourishing, but as a technician of very specific forms of learning and knowledge production that today are largely taken to represent education. Leaning into the use of Gen AI could therefore make good teachers excellent, if teaching is understood in the terms above. But it could also represent a deep alienation from the essential qualities that define teachers otherwise as important adult equals, mentors, guides and allies to the children and young people that they work with. As in the late 19th century, if the effect of new technology today is to make even more efficient the transformation of craft into work, of skill into labour, of education into ever-more effective schooling, then it is important that we feel equipped to step back and reflect critically on how we are implicated in and affected by this process.
AI and Intellectual Wellbeing
This take us back, finally, to intellectual wellbeing. In previous essays we have defined intellectual wellbeing as the development of a critical, thoughtful disposition towards professional life. It is a process that’s fundamentally relational and interactive rather than individual, and which encourages feelings not of contentment and happiness but rather of authentic engagement with the dissonance and complexity of the world around us. Intellectual wellbeing is not a fast route to happiness, but rather represents a way of reconnecting with the essential ethical and vocational drivers of one’s existence. Instead of focusing inwards, we propose that it is only through a deep sense of outward-looking intellectual wellbeing that we are able to connect with the professional essence that drives practice. One major problem with focusing on intellectual wellness as a process of seeking individual contentment or inner calm is that such an approach is profoundly self-interested. Wellbeing focused only inwards on one’s own feelings and status runs the risk of putting short-term, individualised psychological gains ahead of the big picture — namely, the role of individuals in a more enduring project of seeking planetary or ecological wellbeing. For this reason, intellectual wellbeing should rest at the heart of professional learning for teachers, in order that they are able to facilitate and model a similar approach for the children and young people in their classrooms. This requires that schools become the kinds of places where intellectual wellbeing can thrive. But what does this look like in practice? To answer this question, it is useful to consider the deeper noetic syntonia that underpins intellectual wellbeing enacted not as a specific list of practical activities, but rather as a disposition. Noetic syntonia is a way of being, an artwork of the self, if you will, that reflects a deep commitment to the intellectual, political and theoretical qualities threaded into all aspects of the human experience. While noesis refers to the act of serious, critical thinking, of being invested in intellectual life, syntonia refers to the rhythms and resonances between persons committed to this practice. Both dissonance and resonance form part of this accord between individuals who come into presence collectively in the ongoing pursuit of understanding and changing the world for the better together. Our contention is that we should hope for nothing less than noetic syntonia in our personal and professional lives, and not least in the practice of teachers and their students.
Can noetic syntonia thrive in spaces where Gen AI is deployed to demand increasingly efficient learning outcomes, or where AI tutors auto-differentiate learning to maximise the effectiveness of provision? Given the argument thus far, it might be surprising that we consider the answer to be affirmative. The arrival of Gen AI as a technology of proletarianisation could be exactly what the teaching profession requires in order to re-engage with the intellectual challenge of what it means to be a teacher. However, this may require a serious reconsideration of the kinds of work and dispositions that are currently required of teachers (and by extraction students) in the context of our current education systems. Allowing Gen AI and Gen AI technicians to streamline our existing approach to learning will make our education systems more effective, and more efficient. But perhaps this is not the work of a teacher. Perhaps this is the crucial difference between intelligence envisioned as mastery of information, and intellectual work as an enduring, lasting process of thinking critically in consort with others. Perhaps a knowing and critical engagement with AI will produce the time and space necessary for teachers to re-engage in practices of intellectual wellbeing, to recognise and feel empowered by their social position as public intellectuals who, in the communities of their schools, should be respected as wise and experienced educators. Teaching is a thinking vocation: if the deployment of Gen AI creates more space for thinking, for reading and listening, for debate and disagreement, for intellectual stimulation and excitement, for trial and error, then this can only be a good thing. The pernicious risk of combining technology with capitalism is that the ideology of the latter will harness the emancipatory power of the former and turn it to less noble pursuits — that is, to proletarianisation. There is a future where teachers unwilling to engage in hyper-efficient forms of learning facilitated by Gen AI become professional fugitives to be pursued and assessed by representatives of the audit culture that guides this particular way of seeing learning. These teachers will dream of electric sheep. There is another future where we see the rapacious introduction of Gen AI into our classrooms as a wake-up call to a new way of thinking about education that embraces technology as a means to emancipation — a means of dreaming the future differently together.
The good news is that there is already a mainstreaming of the need for this kind of humanistic approach to engaging with AI in education. The UNESCO position has been to focus on the transformative power of AI, advocating for a human-centred approach. We would go one step further to advocate for an ecological approach that by necessity takes as its starting point the question of how generative artificial intelligence might enrich education by offering new ways of imagining our political economy, taking us away from modes of existence that are ultimately extractive, destructive, and unsustainable. Transformation in this sense requires using Gen AI to build education systems that help us to act differently about the purpose of education. The educational philosopher Gert Biesta helpfully identifies the three domains of educational purpose that we should attend to in this process: qualification, socialisation, and subjectification. Qualification relates to the substance of education: it is evidence given of what knowledge, skills, or aptitudes we have learned, and how. Clearly there is scope for Gen AI to help us think beyond the clunky model that we currently have of developing knowledge in subject or disciplinary domains, and assessing this knowledge in unwieldy, cruel, and inequitable systems of high stakes summative assessment. In disrupting the relationship between knowledge and knowledge production, Gen AI offers a significant challenge to framing of qualification in the way that we have. This could be a really good thing.
Socialisation relates to the ways in which we are inculcated into processes of meaning-making, of gaining and sharing values, and of conceptualising our place in time and society. This is a the domain of educational purpose where Gen AI has a real and dangerous power to entrench the process of socialisation into the established and ossified values and ideals of capitalism. It may be wise to avoid at all costs an uncritical socialisation of teachers and students into the values of future work where AI-upskilling feeds into our existing political economy of ever-more growth — of a way of being aimed squarely, irrationally, nihilistically at the tragic misrecognition of planetary destruction masquerading as “progress”.
Lastly, Gen AI may help us in the educational purpose of subjectification: of coming into presence together as thinking, self-aware persons awake to the nature of our shared condition. Crucially, this is about emancipation: it is about education as a process of people becoming aware of their own freedom to act in the ways that they think are most ethical and just, and experiencing the freedom of this action in consort with others. The exciting question here is how Gen AI may act as a technology of freedom-making. It may be that Gen AI comes to serve as a daily reminder not of how human-like machines can become, but of how we can and must evade the risk of living mechanistic lives as humans. Picture a moment, if you will, in the near future, where any given aspect of the technical work of the teacher is devolved to AI, freeing up 60 minutes where this teacher sits down to read a few pages of an article or a book, and then reflects and discusses the questions raised with a colleague, friend, or student, over a cup of coffee. The reading and the conversation is taken to have value in its own right, and there is no sense of guilt about ‘wasting’ these precious minutes against the opportunity cost of some other task waiting on the to-do list. The intellectual resonance of this moment is far-reaching but likely unmeasurable. AI has the potential to help produce these kinds of bubbles of freedom in the school day. Such small moments of freedom-making, of subjectification, may have lasting implications for the future of education. Inhabiting this future requires vigilance in the present so that our current engagement with Gen AI takes us where we want to go. That starts here, and now, with a disposition towards noetic syntonia, and in the practice of intellectual wellbeing.

