Normal View Dyslexic View
Commentary
Democracy and discernment: the public purpose of the University in a world of ‘AI everywhere’
Abstract
We are entering an era of ‘AI everywhere’, in which Artificial Intelligence is not merely being utilised but integrated into countless aspects of human interaction and occupation. Such is the nature of this integration that AI is not only changing our behaviour but our capabilities. It is also exposing new societal vulnerabilities—particularly in our use of language and the way we access, accumulate, and process information. Communication and information are all-important for the exercise of power, including political power. To hold such power to account, it is imperative that human intelligence remains distinctive and discerning. The inculcation of neoliberalism and endemic thoughtlessness has compromised the capacity of the contemporary University to meet this challenge. This article argues that the need for the University to foster the freedom of human intelligence is not only a moral obligation but an urgent democratic necessity.
Keywords
artificial intelligencedemocracyhuman intelligenceneoliberalismthe UniversitythoughtlessnessDemocracy and discernment
We naturally associate democracy, to be sure, with freedom of action, but freedom of action without freed capacity of thought is only chaos.
John Dewey (1903: 193)
Democracy is not a natural condition. Discernment is not an innate skill. Where the two come together is in the human imagination. How the two are made possible is freedom of thought. John Dewey (1903: 204) describes the ‘efficacy and responsibility of freed intelligence’ as no less than the ‘spiritual basis of democracy’, that is, free intelligence gives democracy both its purpose and meaning. This is because, for Dewey, democracy is not simply a form of government but a social and moral ideal; it means nothing unless it is ‘chosen from within and freely followed from within’ (Dewey 1993: 62). Thus the quality of democracy depends on a particular type of freedom: the ability to act and think in the fullness of humanity, which is a social as much as an individual condition. In light of this, the present decay of democracy and our deteriorating capacity for discernment are intimately connected. The disease is becoming chronic because of our intellectual curtailment—of which unthinking use of Artificial Intelligence is both a symptom and a cause. The 21st-century University could yet provide the means of timely liberation.
As with a body, Émile Durkheim (1893) observed, the health of a democratic polity depends on the functioning of its essential organs and the systems by which they are connected. The University’s function in the body politic and its connection to other organs (such as the legislature, the judiciary, and the free press) may be less obvious or direct than others but it contributes to the strength of all of them. We could consider it as akin to an organ in the endocrine system in the body. The storage and release of hormones by the endocrine system can profoundly affect the condition of the body, from growth and metabolism to responses to stress or injury. In this analogy, we may see the University as offering something essential, in good measure, to the overall polity.
Considering the public purpose of the University requires understanding it both as an institution and as an idea. John Henry Newman’s elaboration of this idea centred upon the University as ‘a place of teaching of universal knowledge’ whose function is to educate ‘the intellect to reason well in all matters, to reach out towards truth, and to grasp it’ (2008: ix, 126). Although Newman (2008: xii) distinguished the University from the Academy (the latter being a place for discovery, ‘de nouvelles recherches à faire dans la carrière des sciences’), the present-day institution of higher education combines the two. As such a place of education and inquiry, for ‘collective learning and the development of knowledge’, it has ‘a special province’ within society (Said 2005: 27–8). Put very simply, the University provides the education of good citizens and the evidence for good policymaking. If it functions poorly in either regard, the strain subsequently placed upon other institutions in society becomes apparent over time: the body becomes weakened, impaired, and may even cause harm to itself.
The contribution of the University to healthy liberal democracy centres on the concept of human betterment: the advancement of humanity itself, not any one particular society or culture. After all, the noun ‘University’ derives from the word ‘universal’, meaning the unity of truth.1 On the premise that humankind can develop or improve through the application of knowledge and truth, the University evolved to become an important public institution in liberal democratic society. This is not to assume that the University is a product of liberal democracy,2 nor that one cannot exist without the other. But it is no coincidence that the spread of liberal democracy in the 19th-century West led to a new era for an organised system of public education. This was the period in which ecclesiastical and class interests were displaced by those of the state, and scientific values became paradigmatic of Western cognition and worldview. The British tradition of the civic university emerged in that context.3
There is a moral dimension to this public function. ‘Liberal democracy needs civility’, Edward Shils (1989: 455) argues, namely ‘a concern for the common good’. Widespread civility is essential to democracy; it means that people can be assured that their freedom is valued and respected by others in a sort of ‘confident mutual reliance’, as Philip Pettit (1999: 281) put it.4 This requires a flourishing civil society, within which universities are acting as ‘bearers of civility’ (Shils 1989: 455). For, as educational institutions, universities shape how citizens ‘encounter each other and exchange ideas and interests in the public sphere’ (Peterson 2019: 9). More specifically (and normatively), they can foster the two Aristotelian components of civility identified by Maria Silvia Vaccarezza and Michel Croce (2021): civic benevolence (that is, concern for the well-being of fellow citizens) and civil deliberation (which requires good judgement and capacity for serious inquiry).5 In the liberal tradition of John Henry Newman, Derek Bok (1982) claims that the educational role of universities gives them particular cause to consider their social obligations (10) and they must ‘constantly address moral issues and ethical responsibilities’ (299).6 Thus, and with an unwavering eye to the future,7 universities should advance knowledge and enable its translation into social action and public good.8
As Regenier Gagnier (2025: 8) put it in her discussion of the current crises of purpose in UK higher education: ‘universities are for making thoughtful people, better informed voters, and better societies’. If that function is compromised or curtailed, then not only can the University become a source of harm [as seen in totalitarian regimes (Wolin 1998)], its right to be seen as a source of public authority disappears. In the absence of the unique, potentially critical, authority of the University, a government can act more freely, ‘without fearing contradiction’ (Docherty 2011: 1). The public should be alarmed, therefore, when the University becomes less likely to contradict the powerful and less able to cultivate free thought. This is happening today. What we are increasingly substituting for free human thought is artificial, specifically Artificial Intelligence (AI). Democratic society is imperilled by an inability to distinguish between the two or, indeed, to cherish the difference.9 ‘AI everywhere’ is the deliberate and widespread integration of AI into products and applications. As one exponent put it:
the future of technology isn’t so much about more AI as it is about ubiquitous AI. We expect that, going forward, AI will become so fundamentally woven into the fabric of our lives that it’s everywhere, and so foundational that we stop noticing it (Raskovich 2024).
What ubiquitous AI risks diminishing is not the public purpose of the University per se but the ability of the University to fulfil it. Offering education for good citizens and evidence for good policymaking requires a ceaseless freeing of intelligence. And such liberation requires careful, perpetual thought; it sits uneasily with Artificial Intelligence.
Institutionalised thoughtlessness
How is it that human thought (as distinct from human opinion) has become so uncommon in liberal society? Tadeusz Gadacz (2018) identifies four causes of this condition of endemic thoughtlessness. First, there is a tyranny of mediocrity. The pap that constitutes much of the written, visual, and verbal outputs today relates in two ways to the digital age. First, in terms of the unfathomable scale and spread of information and opinion (including those generated by machine) that claim so much of our waking attention. Secondly, in the ways in which technology has affected social behaviour. Gadacz describes the contemporary technical civilisation as ‘a civilisation of facilitation’ which contributes to ‘intellectual idleness’. Mediocrity is thus not only predominant but rarely challenged. It is accepted because of ‘a lack of courage to think’ and persists because we are becoming less able to do so (Gadacz 2018: 253). I would go further and suggest that—in the digital age—words have become more important than their meaning or, specifically, what is intended by their use. Judgement of human agency is too-often limited to someone’s choice of words rather than on the context or purpose of that choice, sometimes leading to a debilitating cautiousness (as distinct from carefulness) in discourse. This does not make language more powerful; it diminishes it and risks making it one-dimensional. In that sense, it is not that AI machines’ language processing has equalled our capabilities, it is that we have chosen to depreciate our own use of language. Our wilful corrosion of linguistic capacity and possibilities makes it all the easier for the outputs of Artificial Intelligence large language models to pass as replacements for human thought.
The second cause of thoughtlessness today is the promotion of pragmatism: ‘thinking counts only to the extent to which it offers tangible effects’ (Gadacz 2018: 253). This limited view of thinking goes hand in hand with a limited view of teaching.10 Dewey (1903: 201) critiqued educational practices which saw ‘acquiring [knowledge] take the place of inquiring’. Today pragmatism manifests itself in the neoliberal University as education ‘for the real world’ and has seen learning in terms of the ‘accumulation and optimisation’ of knowledge (Lindsay 2014: 142). John Preston (2017) critiques the ‘competence-based education and training’ movement in higher education, in which employers can specify the skills they want graduates to have, which in turn shape what and how students are taught. He argues that this makes human learning redundant, and the very idea of pedagogy and knowledge disappear because ‘all that matters is outcome’ (Preston 2022: 74). If the educational purpose of the University is bound with the graduate marketplace,11 the research function of the University has become similarly characterised by neoliberal rationality. Oriented towards the ‘user’ and the market, ‘the primary criteria for determining the relevance of academic knowledge’—and even its value—is its utility and the potential to sell it for profit (Kronstad Felde et al. 2021: 10, Popp Berman 2012).12 Such valuation of knowledge and ‘compulsory commodification’ is another example of the neoliberal ‘rationality through which capitalism finally swallows humanity’ (Brown 2015: 44)—the very humanity the University is supposed to cultivate (Nussbaum 1997). No wonder Thomas Docherty (2018) castigates the leadership of the University for nothing less than a betrayal of society.
The same ‘market-fundamentalist ideology’ advanced by the University (Docherty 2018) is also behind the third cause of thoughtlessness: measurability. People working at all levels and sectors in public universities have become accustomed to the saturation of rationality and ‘measurements of excellence’ (Brown 2015: 198, Maisuria & Helmes 2020: 59). The value of a university (and its faculty and students) is evaluated by metrics intended to compare contributions made towards domestic competitiveness. ‘The entire calculation is neoliberal’, argues Mats Benner (2023: 13), with the relative status of professionals ‘elevated and dismantled according to … performance’. The quality of academic work is thus primarily judged in terms of quantitative markers. Our scholarly, disciplinary, and institutional status is assessed by grant income, number of citations of published work, journal impact factors, PhD completion times, student satisfaction scores, retention rates, average graduate earnings … and how all these rank against those of our competitors (who inevitably include our colleagues and collaborators).
With the survival of a university depending on outcompeting others,13 institutional priorities have required greater bureaucratic control over academic work (Kronstad Felde et al. 2021: 26). The neoliberal alliance between those who hold state power and economic power erodes other forms of societal governance, including among the professions (Freidson 1994). The academic profession is no exception. In 1987, Burton Clark observed that an academically governed university was an increasing rarity. Today it largely exists in name only, keeping the trappings of academic titles whilst shedding the critical abilities they were intended to reward and signify. For the University has been no more resistant than the Hospital, Civil Service, or Prison to the neoliberal agenda. Alasdair MacIntyre (2010: 18–19) warned where this would lead:
the ability to think about ends, including the ends of the university, will be lost and with it the ability to engage in radical self-criticism, so that the leadership of those universities will become complacent in their wrongheadedness.
Rapidly, it has proven to be so. As is ubiquitous in public institutions today, time and attention in the University are predominantly devoted to planning and reporting. Few consider what we want to achieve in such practices, other than perhaps greater efficiency—itself assumed to be a handmaiden to profitability. And so Docherty (2011: 120) observes of university administration: ‘those who should be “leading” their organizations now “lead” by internalizing a logic, infiltrated from elsewhere, that has been neither debated nor discussed, nor even established’. This logic sees activities associated with critical knowledge replaced with practices that prioritise ‘the efficient and controlled management of information’ (Docherty 2011: 127). Bureaucratic control is impinging on freedom in the University.
All these maladies are excretions of the disease of mercantilism, in which value is ascribed in terms of a capacity to increase wealth—the final cause of thoughtlessness identified by Gadacz. As we have seen, if the University’s outputs are to be measured, the standards are set by the marketplace. The 2008 economic crisis ‘deepened the neo-liberalisation of the nature and structure of higher education’ (Maisuria & Helmes 2020: 15). Ironically, when the destructive greed of capitalist corporations was most exposed, their bailout by the state was followed by severe cutbacks in the funding of the institutions most able to facilitate alternative models of collective ‘wealth’, namely universities. Thus, the University became subject to neoliberalism’s ‘economization’ of political and civic life which, Wendy Brown (2015: 17) argues, has spread to hollow out contemporary liberal democracy, including:
the institutions and principles aimed at securing democracy, the cultures required to nourish it, the energies needed to animate it, and the citizens practicing, caring for or desiring it.
This is because the ‘social reproductive realm’ in neoliberalism is one in which ‘good citizens’ are those who ‘develop their human capital to make themselves more marketable in the world of work’ (Williams & Satgar 2021: 10). Accordingly, education is to be ‘fast, wide and effective’ because only ‘superficial knowledge’ is required, along with competences for operating in the labour market and credits clocked up to gain the degree and expand the CV (Gadacz 2018: 255–8). Similarly, processes of subsumption, exploitation, and commodification are rife in academic labour (Preston 2022). Academics and graduates are commodities; books, papers, and degrees are products (Standing 2010: 134).
But, if research is pursued principally as anything other than ‘as a means to the end of knowledge’, then the capitalist logic has consequences: we not only devalue the process of research but ‘clutter the ground’ with second-rate papers (Fletcher 1968: 6)—all of which are swept up in the machines’ mining of the digital fields of scholarship. We academics thus compound the problem. Students are overwhelmed by the sheer quantities of readily accessible information and suffocate ‘beneath an oppressive weight of disconnected trivia’ (Heft, quoted in Tierney 2016: 12). No wonder we are grateful for easy means of managing the mire. AI’s promise to trawl through the bottomless reams of information for us, and then to order it in some way is quite so alluring because we have produced too much at too cheap a cost to scholarly effort. Thus, Artificial Intelligence apparently offers easy solutions to the problems we have been complicit in creating as the consequences of thoughtlessness have taken hold. Without a clear sense of the distinctiveness or (non-commercial) value of human intelligence, the University appears broadly content to let it do so.
Artificial intelligence
An AI system is a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. Different AI systems vary in their levels of autonomy and adaptiveness after deployment.
A machine can be considered intelligent if it can pursue its goals under a range of conditions in a complex environment (Cristianini 2023). What is interesting for our purposes here is not so much the apparent cognitive powers of the machine, but how we as humans use the machine’s power and how that changes the way we behave. Despite knowing full well that the machine is not ‘thinking’ as we would understand thinking to be,14 we are beginning to treat it as if it were. The greatest risk lies in terms of what we subsequently stop doing—or being able to do—as AI takes the place of human cognition across a vast range of private and public activities, processes, and decision-making. Needless to say, there is nothing original in identifying risks in the expansion of AI capability and application. Laudable multidisciplinary efforts of researchers in information and communications technology, computer science, social science, and humanities have produced both practical and philosophical tools for ethical AI and AI ethics (e.g., Mökander et al. 2024; Leonelli 2025). What is of concern in this paper are the perils arising from human decisions and omissions in our embracing of AI.
All human interaction entails the exchange of information, and there is increasing capacity to capture and store it (though not without costs, see Adams 2024). Our desire to measure, control, and consume is leaving huge waste piles of data. Utilising data as capital is entirely logical in a capitalist system—but how to do so when there is ‘too much information, too little time’? AI enables such data to be converted into economic capital and socio-political resource. More mundanely, it offers ‘assistance’ to the complex data management tasks that so many professions and industries have come to routinely entail. Most AI systems that we are familiar with, and are increasingly dependent on, are discriminative models: that is, they can sort and categorise data. Few IT-using employees are untouched by the promise of AI to help them reach new heights of efficacy in managing and interpreting data, even as they add to the amount of it. As noted above, academia is far from an exception. We are turning to technology to address problems that arise from the ways we have allowed technology to change our behaviour and our understanding of what constitutes productive academic pursuit.
Traditional AI is based on what the machine has ‘seen’ before in terms of information or patterns. The process of machine learning is essentially repetitive guesswork, with the resulting answers being improved under human supervision and filtered through a probability calculation. One such version, large language models (LLMs), work by identifying patterns and relationships among words and sentences. They do this initially through an algorithm that converts language into tokens of data—be they words (or groups or parts thereof), punctuation, or spaces between them. Those tokens are converted into numerical form, which then can be mapped in such a way that patterns can be identified. This is how LLMs are able to list the main themes of a given subject in order of prevalence. By identifying patterns, they can also predict (on the grounds of probability) what is likely to come next. The machine’s capacity to scan and sweep from vast sets of data is producing a step-change in diagnostics, prediction, and problem-solving, notably in the fields in which the datasets are most demarcated and curated, such as medicine, computer science, and engineering. Sanja Bojanić describes such analytical models as ‘the algorithmic automation of the capacity to perceive’.15 But because they work only from what is measurable and already-known, AI perception is retarded by existing biases, imbalances, and voids in data (Golebiewski & Boyd 2019; Chua et al. 2025; Urman & Makhortykh 2025).
Debates of Analytical Philosophy around semantics and semiotics are highly pertinent (as John Dewey would have appreciated). While the language-based functions of AI may, for some purposes, be deliberately akin to that of a human, the process of generating and applying language is fundamentally different. We must not lose sight of the fact that language, images, and sounds have no meaning for Artificial Intelligence other than as tokens for computation. There is a fundamental difference between the mental lexicon of humans (organised around types) and the lexical knowledge of LLMs (organised around tokens)—the latter’s non-human-like lexicon inevitably leading to ‘non human-like generalizations’ (Hofmann et al. 2025: 8). What are words tokens of? How do they relate to reality? Natural Language Processing in AI may have enormous capabilities that will continue to expand—but all that exists for the machine are the signifiers, not the signified. In this regard, the output from an AI LLM may be quite accurately described as a manifestation of ‘thoughtlessness’.
Such limitations are profound, yet even before we have fully realised the implications of an over-reliance of AI based on machine-learning, AI capabilities have expanded into processes of deep learning. These allow the machine itself to identify relevant features from unlabelled data and find patterns in that data without human supervision. Deep learning is behind the generative models of AI, which use algorithms to generate content in response to a prompt. Generative AI refers to a suite of models capable of generating new data, including in text, audio, and visual forms. Bojanić describes generative AI as ‘the algorithmic automation of the capacity to imagine’. This is already having enormous cultural and socio-political consequences, not least because it transforms how the world appears to us and how we communicate in it. Such incredible means of expanding creativity and communication bring risks that are unprecedented in their unpredictable impact, not least in the capacity to deceive and manipulate. These risks affect all aspects of human (intellectual) endeavour in which authenticity and integrity serve a valued purpose.
The challenge cannot be managed by regulation alone.16 Indeed, the capacity for misinformation and disinformation may already be on a scale that is beyond regulation (Bontridder & Poullet 2021). Nor can it be sufficiently addressed by equipping everyday users of AI with new skills of critical literacy or metaliteracy—as important as they are.17 In truth, the danger lies not only in our use of AI—or its impact on us—but more fundamentally in our habitual tendency to handle information in thoughtless ways. This is a profoundly civic challenge in which the University has a part to play.
Habituative thoughtlessness
The changing forms and means by which information is generated, disseminated, and received expose societal and democratic vulnerabilities.18 In this case, the weaknesses are worsened by the collective and individual effects of the endemic neoliberal condition of thoughtlessness outlined above. There are six in particular that affect our intellectual capacity as well as our social relationships. These are: superficiality (in which nothing is interrogated or considered in detail, and we make judgements based on scroll-throughs and soundbites); convenience (time seems the most precious commodity and we will err towards the easier, quicker option for any task); unoriginality (we are more inclined to ‘copy and paste’ or repost than to create or critique); functionality (if something ‘does the job’, we are unlikely to question how it does so or the implications of it doing so instead of us); competitiveness (the marketplace demands competition and conflict, even in terms of the strength of opinions); and predictability (bureaucratic control and the power of large corporations reduce the scope for unpredictability, and this is mistaken for reliability). All these habits are connected and most are ripe for replication by generative AI. Artificial Intelligence produces superficial, functional, predictable, and unoriginal outputs—for our convenience.
Such habituative thoughtlessness makes us more vulnerable to the potential democratic threats posed by the use of AI. Examples include the use of AI to curate our social media feeds, perform online content moderation, assist in hiring staff, or provide financial credit scores (Raso et al. 2018). Freedoms that ‘protect people’s right to think and express what they want’ and ‘protect the right to listen to the ideas and opinions of others’ can be radically curtailed by AI without our ever being aware (Gaumond & Régis 2023). Nowhere are these rights—to think, to listen, to be heard—more crucial than in the University. Such freedoms are important for all in the University, students as well as academics. They are necessary because they serve to foster and bolster another freedom: freedom of thought. It is this freedom that has been weakened in the neoliberal University (in varying degrees of subtlety), as discussed above. And there is a risk that if the University’s approach to Artificial Intelligence fails to value human intelligence as of civic and democratic import, it will hasten the demise of our capacity for free intelligence at precisely the time that we need it most.
The place for discernment
To clarify thought, to discredit the intrinsically meaningless words; and to define the use of others by precise analysis—to do this, strange as it may appear, might be a way of saving human lives
Simone Weil, ‘The Power of Words’ (1937)19
AI is not necessarily or inevitably a direct threat to the University and its purpose. What makes it dangerous is the disjunction between how we think of the University and what it is the University is actually doing—topped with a huge dose of complacency. There is a duty on all of us who value the University as an institution to choose to see this disconnectedness and to address it. This is not a professional indulgence but a social necessity. After all, ‘indifference to what is happening in one’s own university is a companion to indifference to what is happening in one’s own society’ (Shils 1989: 454). Recognising the public good of the University, Jaroslav Pelikan (1992: 168) went so far as to claim that the most fundamental duty a University has towards society is to conduct the work of self-reformation. As noted at the beginning of this essay, the University’s purpose is intimately connected to other institutions that are key to the healthy functioning of society. If the University is blighted, liberal society will moulder.20 The responsibility of academics, therefore, is a moral as well as a practical one.21
As Gadacz (2018) (after Bollnow) put it: the University needs to do more than teach us how to build a house—it needs to teach us how to inhabit it wisely with others. What must we do to inhabit society wisely? Alan Wolfe (1989: 208, emphasis added) urged social scientists to remind people of their role as ‘moral agents’ and that they should ‘work actively and deliberately at protecting what is social about themselves’. Such exhortations seem particularly profound in the digital and post-Covid age. AI agents are neither moral nor social. Their integration with human intelligence does not make them so either, but instead raises new questions about our own responsibility as moral and social beings.
The University is the place where we can not only raise such questions but, together, find means of addressing them. To explain why, I return to the theme of thoughtlessness. We noted the poisonous habits it has given birth to: superficiality; convenience; unoriginality; functionality; competitiveness; predictability. It is time now to consider the remedy. If it is the ‘spiritual basis of democracy’ that we are seeking to nurture, after Dewey’s (1903: 204) call, so it is not inappropriate that we turn to a quality typically associated with religious practice. Our thoughtlessness makes us susceptible to deceit and has diminished our capacity to judge well. A counter to this could be something akin to discipline of mind combined with superior intuition, namely discernment (Orsy 2020). ‘Discernment’ is a word used in English translations of the Bible for two common Greek verbs. One, diakrinein (διακρίνειν), describes the act of distinguishing things that are difficult to distinguish. This involves separating one thing from another, in order to look back and forth between them, and then to come to a judgement. The other, dokimazein (δοκιμασία) is a procedure of examining and testing before recognising something to be genuine or worthy. It denotes a process of careful and precise validation. Thus we may understand discernment as a careful process that separates, weighs up, tests, and scrutinises before approving—a demanding process. Discernment entails consideration of what already exists but which is not fully recognised. With discernment, we are able to conceive something that is not objectively visible without it. There is no procedure to guarantee getting discernment right. In theological terms, discernment entails faith. For our purposes, the process of discernment similarly comes beyond the capacity of individual minds.22
You will observe that I am not proposing an easy-fix, practical solution to the AI challenge to the purpose or idea of the University. There cannot be one. On the contrary, if we accept that discernment is necessary, we need to be willing to work and to wait for it. To help create conditions for discernment, I would advocate a recalibration of what is valued and enabled in the context of the University. And so I set out six conditions for discernment that contrast with the six habits of thoughtlessness. The conditions for discernment may be summarised as: depth (going beyond the surface to engage with the roots of a matter); creativity (a willingness to do things differently, to engage the imaginative capacities of our brains); future-orientation (the courage to think ahead and to consider the consequences of our actions and decisions); diversity (of views, experiences and cultures, encouraging dissent over conformity);23 togetherness (discernment cannot come through individual labour but in relationship and communication with others); and patience (discernment can be laborious and it demands time). Interestingly, Iris Murdoch defined ‘knowledge’ itself in similar terms:
a refined and honest perception of what is really the case, a patient and just discernment and exploration of what confronts one which is the result not simply of opening one’s eye but of a certainly perfectly familiar kind of moral discipline (cited in Andic 1993: 147).
There is thus a physicality, a social nature and temporal depth—as well as intellectual endeavour—behind discernment that is already uncommon with the instantaneously generated information we have come to mistake for intelligence or knowledge. The University is uniquely placed to enable the capacity for discernment in modern society as is necessary to protect democracy in a world of ‘AI everywhere’.
Fostering the conditions for discernment requires one thing above all else: freed intelligence, the ‘emancipation of the mind’ (Dewey 1903: 193). Wolfe (1989: 233) argues that democracy requires citizens’ capacity for agency to make rules, as well as to follow them. Such agency requires autonomy and freedom (not least to think and to learn). Such values are so ‘central to the mission of the institution’, Bok (1982: 301) claimed, that ‘no university can possibly be “neutral” about its commitments to academic freedom and autonomy’. Some of those most aware of the corrosive power of authoritarian regimes cherish the freedom enjoyed within the University above all else. For such reasons, Edward Said (2005: 36) described joining the academic world as being, in ideal terms, ‘to enter a ceaseless quest for knowledge and freedom’.
It is very apt, therefore, that Thomas Docherty (2011: viii) describes his undergraduate experience as one of ‘enfranchisement’. Similarly, Martha Nussbaum (1997: 19) views freedom of thought as ‘an essential tool of civic freedom’. She thus claims that the University ‘must produce citizens who have the Socratic capacity to reason about their beliefs’ in order to:
foster a democracy that is reflective and deliberative, rather than simply a marketplace of competing interest groups, a democracy that genuinely takes thought for the common good.
And if the University is to teach and model these qualities of deliberation, it must enable students and scholars to come together (Schaffer & Longo 2019). As Newman noted, the learned assemble to ‘adjust together’ the claims and connections between their subjects. In so doing, they ‘learn to respect, to consult, to aid each other’.24 A change in thinking is a positive sign and this happens best through engagement with challenges facing humanity25 and among diverse communities, enabling conversations across lines of fragmentation (Tierney 2021). Learning, especially in the University, should be a process of transformation, not transfer. Acquisition of information is not transformative; knowledge should be.
Conclusion
If centred upon a quest for knowledge, the University ‘becomes a place where ideas powerful enough to transform the world are elicited, developed, tested and diffused’ (Fletcher 1968: 8). Change as a result of learning and knowledge is what enables us to be agents of history, as well as moral and social actors, as only humans can be.26 If we continue to view AI primarily in terms of the relative inadequacy, if not impending redundancy, of human intelligence,we are refusing to think—and the battle is lost already. The journey of discernment is before us and we know what we must nurture to achieve it: depth, creativity, orientation to the future, diversity, togetherness, and patience. These require a conscious rejection of the habits of thoughtlessness. Such habits curtail and diminish human thought and thus make our efforts all too easily replicable—and thus replaceable—by Artificial Intelligence. They should have no place in the University. This institution, with its privileges of autonomy and authority, holds the key to liberating human intelligence for the new challenges ahead. We must begin by seeing freedom of thought as a necessary companion to freedom of action. Dewey was surely right. Without the illumination of discernment, we face ever-darker chaos in which democracy ebbs. In the gloaming, the University can choose to be a tool of servitude—in which Artificial Intelligence is indeed ‘fundamentally woven into the fabric of our lives’ (Raskovich 2024)—or the bearer of civility. The means to the former will be pragmatic, computable, and cost-effective. To defend democratic civilisation and advance humanity will require more care, freer thought, and unbound imagination.
Acknowledgements
This paper was originally delivered as an Inaugural Lecture in the School of Social Sciences, Education and Social Work in Queen’s University Belfast on 18 October 2024. It was made possible by an ERSTE Stiftung/Institut für die Wissenschaften vom Menschen (IWM) Europe’s Futures Fellowship (2023/2024) and the intellectual resources provided by the IWM and the Europe’s Futures Initiative. I am indebted to Professors Sanja Bojanić, Thomas Docherty, David Livingstone FBA, and Stephen Williams for reading and generously responding to earlier versions of this paper, which has been further improved by the informed and constructive recommendations of the three anonymous reviewers. Any errors or misconceptions remain wholly my own.
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1
Relatedly, to consider the University as a coherent idea and singular institution as part of the body politic is not to deny the complexity of the contemporary university, with its diverse disciplines, directorates, funding sources, modes of education, types of research, etc.
2
The origins of what could be considered the principles and practices of liberal and humanist education lie in the 8th-century Islamic and Arab world (Makdisi, cited in Said 2005: 27).
3
By way of illustration, Queen’s University Belfast was founded in 1845 as one of four ‘Queen’s colleges’ established in Ireland during the Victorian era to offer places of learning for those excluded by the Catholic Church’s strictures on communicants attending the post-Reformation Trinity College Dublin.
4
Pettit (1999) makes this case in his arguments for ‘civilizing the Republic’, that is, making the laws, forms, and controls of the state a civil reality (supported by civic norms) and thus more effective.
5
Vaccarezza & Croce (2021) describe the traits of civil deliberation, arguing that it needs to be fostered to counter the ‘post-truth condition’ in the contemporary digital polis.
6
While this vision of educational purpose may lend itself most obviously to the social sciences and humanities, it is important that this is not confined to such disciplines; this is in part because graduates of all disciplines and degrees are being educated to participate in society and to contribute to democracy (as Dewey would have it). The obligations relating to civility etc. are attached to education itself.
7
Research should go beyond the limits of knowledge as it currently exists. Education should extend knowledge and thus expand possibilities. So Docherty (2011: 49) describes higher education as a battle ‘for the future ways in which we will occupy and relate to each other and to our environment’.
8
‘Our world is full of evils and deficiencies, and we need action if we want to overcome them, but action is not a goal in itself: it has to be enlightened and guided by thought and knowledge. The task of the university is to train thinking and to convey knowledge but not to act’ (Kristeller 1988: 132–3, emphasis added).
9
This paper focuses on the threat to democracy arising from collective thoughtlessness, of which risks posed by Artificial Intelligence are a corollary. In this sense, it is to be read as a complement, rather than alternative, to studies which focus directly on the topic of AI and democracy. For example, Bruce Schneier and Nathan Sanders (2025) see democracy as an information system which AI has the potential to either enhance or corrode. My arguments are not made in ignorance of the possible benefits of AI in this area, but from a broader understanding of liberal democracy and what a healthy democracy requires.
10
11
This leads to a situation in which university guidance on AI use in teaching can state: ‘assessment that validates skills that are unneeded or unimportant is of little value in itself. This is particularly important in a world where AI will replace certain job functions’ (Queen’s University Belfast, quoted in Hayward 2024). Such guidance makes no attempt to consider the interests behind, grounds for, makers of, or consequences arising from decisions that deem some skills ‘unneeded or unimportant’.
12
An early study supported by the British Academy on the future impact of AI on work wanted to see that ‘technology-enabled change is harnessed to help improve productivity’, viewing the contribution of social scientists as being to ensure the ‘productivity dividend is shared across society’, to avoid widening existing inequalities (Crafts et al. 2018: 5). While laudable on its own terms, this may reflect a neoliberal understanding of society in terms of markets and competitiveness. The ‘AI and the Future of Work’ project of University College London, supported by the British Academy, has a similar focus on ‘enabling and upskilling the workforce to take full advantage of AI’ (https://www.thebritishacademy.ac.uk/projects/ai-and-the-future-of-work/).
13
This is in accordance with the logic of neoliberal necessitarianism, which denies that there are any alternative ways of surviving (Munck 2003; Hay 2004; Docherty 2015).
14
Twenty-first century advances in machine learning have attempted to directly address what Hubert Dreyfus (1965) identified as the fundamental human forms of information processing: fringe consciousness, essence/accident discrimination, and ambiguity tolerance. But unique qualities of human thinking remain, including the capacity for empathy and moral intuition, drawing upon information/experiences that cannot be measured or replicated, and an awareness of the nuanced and sometimes-unpredictable effects of our decisions on others.
15
Quotations from Bojanić are from personal correspondence with the author, 21 July 2024.
16
This is recognised in the existence of such initiatives as BRAID (Bridging responsible AI divides, co-funded by the Arts and Humanities Research Council) which seeks to contribute to a ‘responsible AI ecosystem’ by integrating arts, humanities, and social science research within it (see: https://braiduk.org/).
17
Critical Artificial Intelligence literacy—‘the capacity to analyze, critique, and transform AI’s critical implications’—is a conscientious effort to counter the ‘disruptive potential’ of new generative AI systems, including by equipping learners with the tools to ‘comprehend and resist AI-driven oppression’ (Veldhuis et al. 2025: 13). Metaliteracy is a framework that combines ‘metacognitive awareness, collaborative learning, and socially responsible engagement in the digital ecosystem’ (Adamakis & Rachiotis 2025: 6, Mackey & Jacobson 2014).
18
Larry Diamond (2019: 20) draws a comparison with social media which was ‘once hailed as a great force for human empowerment and liberation’ and has now come to ‘be regarded as a major threat to democratic stability and human freedom’. This threat is in no small part compounded by the business models which have driven the often-unchecked power and expansion of social media (Vaidhyanathan 2022; van Dijck 2013)—something it has in common with the companies behind AI development (Adams 2024; Weill et al. 2025).
19
Included in Miles (2005: 242).
20
Sir Eric Ashby, cited in Lucas (1996: 93), argued that the external structures of the University may change but what matters most is that, ‘the thin stream of excellence on which the intellectual health of the nation ultimately depends is not contaminated’.
21
Alan Wolfe (1989) viewed the social sciences as providing the operative moral philosophies of modern societies. What is most interesting about his argument is that he argued not for the defence of what is but forthe ambition of what might be. He argued passionately for the ‘recovery of sociology and its moral tradition’ as a necessary step towards enabling modern liberal democracies ‘to do a better job of managing their discontents’ (190).
22
Ladislas Orsy’s (2020) description of discernment as arising from a process of building and liberation within a community is also apt for the University.
23
Docherty (2011: 124) sees dissent in the University as ‘the language that shapes possible futures and keeps them open’. This is in the tradition of the European humanists, recognising that disputation and argument are necessary elements of the learning environment (Said 2005: 28). It relates to Dewey’s (1903) claim that freed intelligence flourishes most when the education system is democratised in practice, that is, when teachers can shape as well as follow.
24
Quoted in Lanford (2019).
25
Addressing the educational function of the University in the digital age, Yehuda Elkana and Hannes Klöpper (2016: 7) argue: ‘A concerned citizen must be conscious of the major problems that confront humanity today, must be aware of the limitations of our existing intellectual tools in coping with these problems and must develop some experience in thinking about what is needed in order to overcome these limitations.’
26
‘The extent that we can differ from that which has been handed to us’, as Hannah Arendt says (quoted in Docherty 2011: 66), enables us to ‘define ourselves as human subjects’.
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DOI: 10.5871/jba/014.a04
Published on: 23 March 2026
Volume: 14
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Hayward, K. (2026), ‘Democracy and discernment: the public purpose of the University in a world of “AI everywhere”’, Journal of the British Academy, 14(1): a04 https://doi.org/10.5871/jba/014.a04Crossref
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