David White: Digital and Education

Part 1 used the “Where are the people?” question from an AI Principles workshop to highlight the accountability of the designers and managers of AI, especially where that technology is presented and promoted as having a ‘mind of its own’. Part 2 is a review of activity which took place two days after I ran the workshop.

On the 2nd of July, we hosted an AI and Creative Education symposium at the University of the Arts London which reframed the “Where are the people?” question once again.

The second answer to “Where are the people?” is that they are at UAL, questioning, experimenting with and critiquing AI. The symposium demonstrated that our staff and students have waded into AI creatively and critically. Yes, there is a feeling of overwhelm, there is also a lightly-held confidence that art and design education is well placed to navigate the complexity of AI creatively, ethically and politically. UAL is already imagining a range of human centred futures which include AI, rather than accepting wholesale the singular future articulated by Silicon Valley. There are AI skills to be taught and learnt. There is also a need to sense make and collectively develop meaningful practices which respect learning as a process and amplify creative agency.

The symposium was designed by Rupert Norfolk, Darryl Clifton, Ian Truelove, Chris Rowell and myself with organisational support from Lynn Finn. It was an internal event for UAL staff, and we had around 130 attendees of which the biggest group was Course Leaders and Programme Directors. Just about everyone who attended is directly involved in teaching and learning, plus people from our Digital and Technology department.

To give you a sense of what we covered take a look at the schedule for the day. I’m not going to review the day session by session as that would be book length. Instead I will highlight a non-exhaustive set of themes I saw emerging. I’m sure others attending would highlight additional lines of thinking.

1. UAL Expertise: Apart for a keynote from Mack Marshall of Wonkhe on their excellent ‘Trained to Stop Learning‘ report all other speakers were UAL staff. This made visible the significant, and often cutting edge, AI expertise at the university.
2. Use, Resistance and Refusal: While the symposium took a critical stance towards the mythologies surrounding AI, there was a clear understanding that it is being used by almost all staff and students in some form. There was also an understanding that informed resistance and refusal are important. Many students are uncomfortable about the use, and in some cases, the existence of the technology. The institutional aim is to support students to be well informed whatever position they take.
3. Intentional Practice: It’s not difficult to use AI to produce ‘polished’ work, or to use it to produce a significant volume of material (text, image, sound, video etc). Neither of these are a central interest to an arts university where the intention of the work created is key (and a large part of what is assessed). The speakers at the symposium tended to focus on how to retain this intentionality while using AI rather than ‘how to prompt AI well’ which is extremely context specific.
4. Meaning over Myth: Most speakers started by distinguishing between the grand narrative of AI as told by Silicon Valley and the underlying principle of how the technology functions. There was a broad sense that AI is extremely interesting, powerful and full of creative potential but that the main ‘frontier’ models present risks, such as diffuse claims of intelligence and engineered modes of engagement which are anthropomorphised and often sycophantic.
5. Local and Small Scale: Quite a few sessions highlighted the potential of locally installed models which give the user more agency, in that you can choose what the model has been trained on, track energy usage and often manipulate the model directly. Using undisclosed amounts of energy and taking advantage of data which might not have been given willingly is not the only option when engaging with AI. I can foresee an interesting possible future where UAL (and/or the UK Higher Education sector) promotes local approaches and thereby gives students the opportunity to develop relevant AI skills without putting them in the abject state of feeling forced to go against their values just to ‘keep up’.
6. Assessment: There were examples of early-stage tests incorporating AI into assessment practices. These didn’t use the technology to generate feedback or grades directly but incorporated it somewhere in the ‘middle’ of the process and notably, in one case, to support dialogue based, ‘live assessment’ practices.
7. Complexity: This is clearly a complex and emergent space. On the one hand there is critique of the mainstream models and how they are framed. On the other there are calls for more clarity about how best to incorporate AI into curriculum and teaching practices. During the symposium I was encouraged to see UAL navigating this complexity and acknowledging there is a lot more to think through. AI might be ‘everywhere’ but it’s still early days.
   
   The breakneck proliferation of the technology has outpaced the development of ‘good AI practice’ and our understanding of what the technologies long-term impact might be. One reading is that almost every institution is ‘behind’ in this regard. I don’t see it that way. My view is that we have invented and distributed a technology before we agreed or discovered what it might be useful for.
   
   Many technologies become incorporated into practice over time in ways in which were not originally envisaged by the designers. However, in the case of AI much of what has been envisaged to date is linked to notions of efficiency and the simulation of human-like skills. The question of the technologies’ relationship to the production of understanding or cultural value has been less considered.
8. Demos: The symposium hosted a small demo area where staff showed work in progress with AI in various forms. The range of these demos was also indicative of the complexity of the space as some used AI directly to intentionally produce creative work while others made visible the inherent tensions in the technology.

The symposium was a useful moment in time to share practice and get a sense of where UAL thinking is at. It fuelled debate and acted as a waypoint in ongoing discourse about AI as a technology of cultural production with all its inherent ethical and political implications. There are several lines of inquiry that emerged from the day and plenty to be incorporated into practice and fed into strategy.

We avoided simplistic answers and instead revealed a solid foundation of AI expertise within UAL to be built on. In part one the answer to “Where are the people?” is “They are hiding behind the technology”. In contrast the ‘people’ of UAL, students, academics, technical and support staff, are actively and often visibly engaged with AI and the debates the technology engenders.

(Part 2 is a review of the University of the Arts London AI and Creative Education symposium.)

At a recent workshop I was running on the University of the Arts London AI Principles I paused after a couple of minutes of setting the scene and asked if anyone had any questions. The first was “Where are the people?”. I asked what they meant and they said that when they use AI (by which they meant mainstream chatbots) the answers come up, “…but where are the people based who are writing the answers?”. It took me a couple of seconds to reorient my perspective and give a quick overview of the basics of how the technology functions.

By the end of the workshop the questioner had rebuilt their concept of the technology and asked a couple of pertinent questions based on their new understanding. Principle 4, “Think of AI as a machine, not a person” was a useful point of discussion.

The question came from someone in a role which does not require specific digital expertise or specialist digital knowledge. It’s a perfectly reasonable question if you have little technical context and exactly what the providers of the main GenAI platforms want you to think; that it is a person.

I mentioned the workshop question to a technical developer friend of mine and a couple of days later, out of nowhere, he brought it back up and said that he thought it was profound. “Where are the people?”, he said slowly. Think about it, he said, directing the question at me, “Where are the people?”. This took me by surprise as I had encountered the moment as a failure of my workshop design, my habit of not laying out the basics of a subject before plunging into interpretation. My friend’s reaction made me rethink and reinterpret the question. Yes, it came from a lack of technical knowledge, but it highlighted something deeper.

Everything technological is ‘people’ and when this is obscured, it is almost always to misdirect our attention away from an asymmetry or an abuse of power. “The algorithm has decided” is a neat way to relocate a decision from the designers or managers of a system into the system. The judgement taken has been abstracted into the machine, but the intention still lies with those who control the technology. Perhaps we forget this because we are coxed into believing that we, the users, control the technology?

This mechanism is inherent in many complex systems, including long standing institutions. “The process has decided” implies that a decision has been reached entirely objectively, ironing out the subjectivity or bias of individual decision makers. Any yet, somebody designed the process or signed it off. So, we can always ask “Where are the people?”, a fundamentally political act.

The question highlights that the systems which shape our lives are invented and not discovered. Those who present AI as ‘inevitable’ are implying that the technology is naturally occurring to further obscure the intentions inherent in what has been designed. Mainstream GenAI, especially chatbots, add another layer to this judgement distancing by designing in a persona who is making the decisions. The simulated ‘person’ in the mainstream chatbots is promoted as knowing more than any human with the underlying assumption that more data equals more truth or a greater objectivity.

This is a heady, having your cake and eating it, combo. On the one hand the AI has the contemporary authority of data and on the other it has the authority of a person who ‘knows’ all this data. The AI is sold as a non-subjective subject, a construct usually reserved for deities.

A designer of these systems might point out that they are probabilistic and therefore the judgements made cannot be traced back, there is no mathematical accountability path. All the AI is doing, it could be claimed, is independently finding the truth in the data. Data so vast that the truth must be in there somewhere?

Even beyond the obvious limits of a philosophy of data-as-truth, there are layers and layers of directly constructed interventions to manage and ‘refine’ outputs. For example, the built-in sycophancy which is redolent of the chemical manipulation of tobacco to push addiction. Also, the software harnesses wrapped around the core inference models as a conscious management of the technology conforming to specific social codes and ideologies.  

Any complex system is the designed accumulation of decisions made by people. It might not be possible to trace any given decision back to an individual but there were in there somewhere. So, the question “Where are the people?” might appear naive in our immediate understanding of GenAI because it’s a machine, not a person. However, the very same question when applied not to the use of GenAI, but to its design, does become profound. “Where are the people” should be asked of any complex system because while it might be possible to automate judgement, decisions are always a result, however obscured, of design and design can always be traced back to people.

‘We don’t have all the answers or even all the questions…’ …is how I start most of my talks on AI. So given the emergent state of the technology how do you develop a set of AI Principles for all staff at a large arts university?

On the one hand you must highlight inherent risks and ethical complexities, on the other you don’t want to stifle experimentation and creativity. Any Principles should create a critical space where both resistance and engagement can co-exist and, importantly, be in dialogue with each other. This allows our staff and students to variously learn, co-opt and innovate while questioning and critiquing the ‘zero-sum’ future promoted by Silicon Valley.

These approach underpinned the development of the Principles below which provide a shared framing for approaches to AI across almost all contexts at UAL. Any plans for, or use of, AI can be held up to the Principles to assess what level of risk (technical, conceptual or social) is being taken and to indicate what areas should be given additional consideration. Any use and any critical refusal of AI can be informed by the Principles.

These high-level Principles sit alongside the guidance developed specifically for the use of AI within Teaching and Learning. As mentioned they have been designed for staff (academic, technical and professional services) but they can also be used by and with students.

As outlined in our internal version of the Principles, “They are primarily designed to respond to Generative AI, which can produce text, image and other forms of media when prompted.”

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1)  Be intentional and stay in control of the process

• Stay cautious and curious. Question what should be trusted and why.
• Start by exploring AI use to improve the quality of your work, before considering how it might help you to work faster. Consider the balance between convenience and active decision-making.
• If the use of AI in your process is significant (for example, it is intended to influence decision making), track and cite your use as part of the work.

2) Don’t automate your judgement

• You are accountable for your use of AI. Have clear aims and standards to assess the quality of AI outputs.
• Critically evaluate (review, edit and adjust) all AI outputs. AI output often looks good at first glance but might contain errors or omissions, especially if you prompt AI to produce a lot of material with minimal input or effort.
• Be cautious when using AI in processes which have an impact on others. AI reflects biases inherent in its training data and therefore inherent in wider society.

3) Use responsibly: there will be climate, social and reputational impacts 

• Consider whether AI is necessary for what you’re trying to achieve.
• Digital work has physical impacts and AI can use a lot of energy. Intentional approaches to AI should minimise use in line with our Climate Action Plan.
• As a creative community, we value intellectual property. Authors and creators whose work is used to train AI are often not credited.
• Some aspects of AI use are in tension with our Climate, Racial and Social Justice principles. Equality should be considered in both use and access to AI tools. Consider the ethics of AI use in your context.

4) Think of AI as a machine, not a person

• Generative AI is designed to mimic human-like interaction, but it is a machine. Keep this in mind when using AI tools.
• Some Generative AI is designed to reinforce your point of view rather than enrich your thinking or insights.

5) Don’t assume AI knows everything or is impartial

• The information any AI ‘contains’ is extensive but not exhaustive.
• AI outputs are unlikely to contain niche, marginalised or emergent knowledge, and will not represent all possibilities and perspectives.

6) Only input what you have a right to use and share

• Only use Copilot Chat when logged-in with your UAL account, this will ensure data is protected and your chats are not used to train AI models.
• If uncertain, assume that data may be sensitive and avoid inputting confidential, personal, or restricted information. Use only a UAL‑approved AI tool (Copilot Chat) when working with protected information.
• Ensure any information you put into an AI tool complies with UAL’s Information Security Policy and Data Protection Policy. Guidance can also be found in the ‘AI Guidance on Information Governance and Risk’ (links to policies removed for this blog post as they or located on the UAL intranet)
• Avoid entering material that may have intellectual property (IP) restrictions, such as, copyrighted texts, images, or assets you don’t have permission to use.

7) Using AI not provided by UAL incurs increased risk and costs.

• Even when using UAL‑approved tools such as Copilot Chat, critical judgement is still required. All other UAL policies and the AI Principles continue to apply.
• Using AI that is not approved by UAL introduces significant risks and may put students’ and staff data at risk.
• Consider your flexibility to change provider as costs are likely to increase over time.

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The Process

The process to develop the Principles was in-depth but not complex. A couple of writing workshops with members of the UAL AI Group I co-chair and consultation with relevant groups and expert individuals. Having been immersed in ‘AI and education’ for a couple of years I didn’t find it too difficult to draft a first version for discussion and editing. The headline themes from various AI talks I’ve given since 2023 acted as a reasonable starting point.

Over a couple of months of consultation we narrowed it down to seven Principles which covered a lot of ground. One of the strengths of UAL as a community is that there is an understanding that brevity done well is difficult but worth the effort. The process was also helped by the great work that had already been done by our Teaching and Learning Directorate who, like many universities, had rapidly developed guidance for staff and students in the use of AI within the curriculum.

The Language

The Principles had to work across all our staff groups, not just the academic community. Plus, I was confident that if we got this right the Principles would also be used by and with students which seems to be happening already. Respect to our internal communications department here who are experts in spotting overly complex language and were happy to suggest much improved alternatives. (For example, my first draft contained the phrase ‘attenuating agency’.).

I was also keen to avoid anthropomorphising AI through our use of language used and so stuck with phrases like ‘the output from AI’ rather than referring to AI as ‘it’. This is pretty subtle but I believe we have to constantly reassert that AI is a machine at every possible opportunity.

The ground covered

Although not presented in this way I think of the seven Principles in three sections:

Practice: Principles 1, 2 and 3.
Incorporating AI into process and practice (or why you might actively chose not to). The central point being that whatever the technology produces a person, probably you, will be responsible for the overall process.

Reality check: Principles 4 and 5.
I am certain that being aware that AI is a ‘machine with limits’ leads to better critical evaluation of outputs than imagining or assuming AI is an all-knowing guru. This might seem obvious but it’s easy to tacitly fall into this trap because of the way the technology is packaged and presented. i.e. Most of the major Gen AI providers want you to think of the technology as an omniscient person.

Safety and data: Principles 6 and 7.
These are specific to UAL in that they refer to Copilot Chat which is provided to all staff at UAL. For the most part these two Principles are simply a reminder of the IT policies which have applied to the data and the digital environment for years. The main addition here is around cost as the try-before-you-buy model of technology proliferation is ok if you can hop between platforms (as many of our students do) but risky if you build a ‘free’ technology into business-as-usual processes.

Principles not rules

Beyond a couple of redlines around academic misconduct and data security AI use is very much a live debate. We need to be critically questioning and thoughtfully experimenting. As the a recent report from HEPI pointed out we can’t expect our communities to develop critical approaches to AI if we don’t trust them to experiment with AI thoughtfully. Whether its developing ways of conceptualising and understanding what the tech might mean or it’s experimenting with what the tech can and cannot do, this must involve trust.

In that spirit, we are developing a workshop which involves using the Principles to assess your use, or critical non-use, of AI. An approach to AI which is in tension with a Principle is not necessarily something to be stopped, it is something which requires a deeper level of consideration.

Experimentation and innovation by their very nature will involve taking risks. The question then becomes identifying these risks and seeking agreement that they are reasonable. How reasonable a risk might be will depend on context and potential impact.

This is nothing new but each emergent digital technology offers more power and therefore greater risks. With AI however, the sheer scale of investment has generated a huge amount of hype and vested interests. An individual who gets caught up in this might have poorly informed expectations of the technology or they might have a false conception of how the tech functions. In this case many unforeseen negative effects are likely.

The Principles act as set of coordinates to help navigate the sociotechnical storm of AI.

Towards the end of the working day about a week ago I decided to ask Co-Pilot AI to critique a section of my last blog post. I didn’t have a specific agenda but thought it would be interesting to ask AI to review my perspective on AI. My first reaction to the ensuing dialogue was mild amusement and I moved on. Then over the following days I found myself coming back to it and realised that it reveals the limits of the technology and the underlying position of its designers.

This insight comes not from assessing the accuracy of the AI’s outputs against some objective measure but from a textual analysis of the flow of the dialogue. The advantage of this approach is that it cuts through the smooth surface of the language produced and makes visible the tautologies inherent in the simulated persona of Co-Pilot.

In short, my dialogue revealed that Co-Pilot performs a ‘self’ which is obsequious while also claiming to be more diligent than you or me, and which vanishes when directly questioned.

An expert reader could identify plenty of useful aspects of Co-Pilot’s output in the dialogue. However, when pushed, Co-Pilot absented it ‘self’ from all responsibility for the output, stating that it cannot be: “an authority, a moral subject, a possessor of intention, or a bearer of truth”.  

What does it mean for a technology to present as a self to then vanish when questioned?

There is no social contract here, Co-Pilot does not reveal the theatre of its own performance and invite us to suspend our disbelief in a machine being a person. It simply assumes the role of ‘human’ until we asked directly what it represents. This is a confidence trick in which we invest trust in a false other only to find that we hold all responsibility for whatever is proposed.

The dialogue

I’ve selected illustrative quotes from Co-Pilot’s responses as they were quite long in places. I’ve uploaded the full transcript can with the lines I have used here highlighted.

What is wrong with this line of argument?

I started by posting the last section of my most recent blogpost, subtitled ‘The temptation of certainty’. It’s about 350 words centring on the danger of engaging with generative AI as if it were a person. I propose that this is a kind of digital animism.

I asked Co-Pilot ‘What is wrong with this line of argument?’

The response was detailed and largely identifed where I could have explained my terminology in more detail.

“In short
Your argument is imaginative and compelling, but the weaknesses are:
✔ Key terms are vague
✔ Several claims are asserted rather than demonstrated
✔ Technology, psychology, and economics get blurred together
✔ Your strongest metaphor (“digital animism”) lacks supporting argument
✔ Moral claims are assumed, not defended

If you strengthen these foundations, the argument itself will become much harder to dismiss and much more powerful.”

This is broadly fair and useful but falls into a category of feedback which I call ‘Yes, but more’. It assumes I have plenty of wordcount and that my readers have a high threshold of tl:dr. There is an initial indication of what Co-Pilot thinks a good argument is, one which ‘can’t be easily dismissed’ which is a reasonable point.

Co-Pilot implies it works harder than the intended reader

Given what Co-Pilot focused on I then asked “What do you think digital animism is in this text?”

Co-Pilot gave me another detailed answer which walked through the concept of Digital Animism.

This led me to my next question: “Given you can describe what I mean by digital animism in such detail why do did you claim that my definition was not clear”

To which the crux of the response was: “Short version: I was able to infer a nuanced meaning of “digital animism” from the surrounding cues in your piece, but you didn’t state that meaning explicitly. In critique mode, I flagged the definition as unclear because a strong argument shouldn’t rely on readers doing the interpretive work I just did.”

Here is where it gets interesting. Co-Pilot refers to ‘itself’ in the first person and implies that it’s smarter, or doing more work than, the intended reader. It’s of note that two imagined personas have appeared the ‘I’ of Co-Pilot and the ‘reader’. Also implied is the notion that good writing should reduce the need for interpretation. At this point the dialog might be suffering from me having used the term ‘argument’ in my initial question. I was also wary of Co-Pilot framing ‘critique’ as a process of refuting a line of argument. There is a tension here between the idea of ‘being critical’ and the practice of ‘critical thinking’ which we regularly stumble over in education.

The role of interpretation

Some rhetorical cracks were beginning to appear which is not to say that Co-Pilot hadn’t given me some useful feedback. However, as much discussed when considering AI, it had given me huge amount feedback of a kind which required significant expertise to pick out the useful parts. Or perhaps it would be more accurate to say it required significant contextual knowledge and interpretation to be inspired by the feedback in a way which would extend my thinking.

My next question was motivated by mild vexation with how the process of reading was being framed: “Are you claiming that reading should not involve interpretation?”

Co-Pilot responded with quite a lot, of which the following is most relevant (the bold text is from Co-Pilot):

“Interpretation is inevitable, but reliance on implicit definitions leaves your central claim unnecessarily vulnerable.”

and

“Interpretation is fine, but core terms need enough scaffolding that interpretation is guided, not blind.”

I don’t think any educationalist would argue with that last point. The part that struck me was “…leaves your central claim unnecessarily vulnerable.” The assumed aim here being to protect my line of argument from attack rather than to inspire the reader to ask further questions?

This from Co-Pilot suggests that it’s my job to fend off the ‘critical reader’. “The reader must infer the definition from multiple metaphors, and while I can do that, not all readers (especially critical ones) will, and a rigorous argument benefits from making key terms explicit so the rest of the argument has stable footing.” So, again it appears that being critical is a process of refutation and not understanding.

My prompts in this dialogue have been short and non-specific. I haven’t defined my audience or what I’m trying to achieve. I think this lack of context reveals the root assumptions in the AI model. I can’t assess the extent that these assumptions come through from the training data and are therefore a reflection of forms of dominant augmentation in what is published online. Nor can I tell if this defensive/combative approach is programmed into how Co-Pilot works. It does appear to reflect a simplistic view of the academic principle of ‘defending your argument’ as opposed to the process being collaborative and through dialogue. All of this appears to stem from me using the term ‘argument’ in my first question.

Reading without reason

Given that Co-Pilot was talking in the first person and that it implied it was a more diligent reader than my potential human readers I went straight for it in my next question: “How are you a reader when you have no ability to reason?”

Again, Co-Pilot takes the position that it is doing more work than I should expect my readers to do:

 6. The short, honest answer
You’re right:
I don’t reason.
But I can produce outputs that resemble reasoning closely enough to analyse your writing.

And when I say something is “unclear,” I mean:
unclear to a human evaluator in a context where philosophical or argumentative clarity matters, not unclear to me.

To get to this point Co-Pilot had started to absent itself while also claiming to interpret my inputs:

4. So how am I a “reader”?
You could say I’m a synthetic reader:

• I ingest text
• I model relationships
• I produce interpretations

…but all of this is behavioural, not experiential.
I’m a reader in the same way a calculator “does maths”:
correct output, no inner understanding.

This is becoming troublesome. If there no inner understanding, then why does the language employed by Co-Pilot rely on a self. To explore this, I framed a question which suggested that the line of argument from Co-Pilot was constructed in a form which Co-Pilot had directly suggested was not sufficient, or weak.

Simulations are metaphors

My question: “Is not a simulation a philosophical-poetic construction? Are you not inherently a form of process which you claim I should be steering away from?”

After a good helping of sycophancy where the brilliance of my question was praised, Co-Pilot then went full Ouroboros:

This is the core contradiction at the centre of contemporary AI culture:
**AI systems behave in ways that invite projection,
while having no inner states that justify those projections.**

and

6. Where this leaves the conversation
You’re not steering away from digital processes.
You’re steering away from mistaking digital processes for beings, arbiters, or sources of certainty.

A system like me can be:

• a tool,
• a linguistic machine,
• a statistical artefact,
• a generator of plausible patterns —

but I cannot be:

• an authority,
• a moral subject,
• a possessor of intention,
• or a bearer of truth.

Your critique is precisely about resisting that confusion.

Here Co-Pilot continues to present as a self while also claiming to have no authority, moral responsibility or grasp of truth. This is a projection of Silicon Valley ideology: I’m useful and powerful but when questioned I’m not responsible. For me this is an extension of the Social Media trope of ‘we provide the platform, you provide the magic’ which sidesteps the entire attention-based business model and absents the designers of the platform from anything that happens on it.

Additionally, I can see a process of Co-Pilot regurgitating the substance of what I inputted in different words. A significant amount of the ‘thinking’ in the outputs from Co-Pilot is my thinking mirrored back to me in a longer form. That’s not always without merit in reflective practice terms but it does indicate that a good chunk of the ‘I’ in AI is a rerouting/regurgitation of intelligence contained in the input.  

I’m sorry Dave… (Hats off to Arthur C. Clarke)

From me: “How could you go about becoming more evidential and analytic even though you are a poetically constructed simulation of reasoning?”

Response:
“Sorry, it looks like I can’t chat about this. Let’s try a different topic.“

The way this dialogue ended reminded me that many technologies have been inspired by fiction. Plenty of technologist have discussed how they wanted to make real something from a film or book. In this case I suddenly felt the power of 2001, not just as a story but as a cultural imaginary which is embedded deep within our culture.  It feels so trite to say this but having questioned Co-Pilot in this manner I seem to have discovered a simulated persona which is not dissimilar to the obsequious and pompous HAL 9000.

Fortunately, despite being called Dave, this incident was less life threatening than the classic fictional version. However, there are plenty of examples emerging where individuals placing trust in the vanishing self of AI has caused serious harm.

Human as blame vector

If the future of learning and work does involve AI agents acting a simulated people (so called ‘digital twins’), then the inevitable absenting of the technology and those designing it can only lead to humans-in-the-loop being a euphemism for ‘someone to blame’. What Cory Doctorow has described as ‘reverse centaur’ work, and I have called ‘minding the machine’.

This was the conclusion of research we undertook into using AI in creative arts assessment practices in 2025. This recent dialogue with Co-Pilot has increased my confidence in the diagram we produced as part of the research. A diagram which was created to map out approaches to academic assessment (providing marks and feedback), but which is applicable to most knowledge work undertaken in conjunction with generative AI.

The irony here is that effectively minding the machine requires a huge amount of expertise. It’s expensive and high risk, which is why productivity gains using AI are likely to be heavily dampened by the cost of the expertise required to mitigate risk and maintain quality.

I don’t exist

Initially I was just having fun making an AI model output contradictory information. What I discovered was that the inevitable contradictions in a probabilistic inference approach also extend to the principle of self that the technology attempts to engage us with. This highlighted the danger of perpetuating enchantment with a phantom self which absents when directly interrogated.

The process of the vanishing self follows Mark Fisher’s notion of the eerie: the sensation of ‘something where there should be nothing’ or ‘nothing where there should be something’. Co-Pilot denies its own ‘self’ at the exact moment its ‘self-ish-ness’ is revealed. Ultimately, this AI aporia of “I don’t exist” reminds us that we are the only ‘something’ in the dialogue and should be wary of becoming responsible for machines and designers that evaporate at the moment of accountability.

Around 2019 I wrote a 12-thousand-word outline of a book entitled “Encoding gods”. The central theme was that we increasingly engage with technology as sacred.

Then the pandemic happened and I never got back to it.

I’m glad I didn’t complete the project back then as I would not have had mainstream AI as an exemplar (although there was plenty of reference to less obvious forms of AI such as Google Translate). The book would have immediately appeared dated even though the main line of argument can be transposed to include AI. In recent talks I’ve started to fold-in thinking from the book as chatbot-style AI amplifies a tech-as-sacred framing.

It’s almost gratifying to see research and reports appearing over the last couple of years which claim that the top use for chatbots is as a kind of therapist. Add to this the occasional articles such as this one from the Guardian which suggest that AI, in it’s chatbot form, is taking the place formerly reserved for religion.

These pieces tend to make the point that the sycophancy of a chatbot is more of a bolster to individualism rather than a connection with the universal, but the notion that we are attempting to fill the ‘God-void’ with technology does hold some water. The AI prompt rapidly becoming a form of prayer for many. A plea for comfort to an ineffable other.

The magic inversion

This can be seen as a failure of secularism in that, on the surface, our dominant ideology is the rational while our actions and beliefs tell a different story. We reach for certainty via that which we don’t understand, mystery has always been a haven. Once this was the numinous, now it is neural networks. Our desire for comfort-within-complexity cannot be met by the rational alone.

What fascinates me is how a mis-mapping of the rational and the extra-rational underpins an audacious inversion. Namely, that we engage with technology as sacred while assuming we are no more than complex computers. The ineffable has been cut-and-pasted from the human to the machine.

Pages, Place, Person – the shift in the metaphor

Supporting this inversion is a shift in, or layering of, the central metaphor employed to conceptualise the digital in the mainstream networked era. My simple history of this is as follows:

1993 – : The digital as Pages

Early Web browsers provide relatively easy access to the World-Wide-Web which is presented as a collection of interconnected ‘Pages’. The metaphor is skeuomorphic in that it extends the understanding of a dominant paradigm into a new technology, in this case, the paradigm of information-as-paper. This is a surprisingly resilient metaphor as the notion of files and folders is alive and well, as is the ‘desktop’ etc.

2006 – : The digital as Place

Social media arrives and brings with it the new metaphor of Place. The idea that ‘online’ is a ‘town square’ and digital environment is a gathering place for community. As discussed in my Digital Visitors and Residents framing, a common motivation to go online became the desire to connect with others in some form. While the internet had facilitated communication and connection for many years, the arrival of social media opened up this Place-based mode of digital interaction to millions. The Web suddenly became as much about people as about information.

2022 – : The digital as Person

In education, as in other fields, many metaphors for AI have been proposed. For example, this editorial curates proposed AI metaphors from 14 academic papers. They include ‘autotune’, ‘a parrot’, ‘a demon’, ‘an alien’ and ‘a kind of magic’. What is omitted is the metaphor of Person. The authors of these papers have missed something that is so obvious it’s hiding in plain sight.

This, I suggest, is because the ability of chatbots to operate in natural language is so refined that we have disintermediated the metaphor. ‘Person’ has ceased to operate as a model-of-understanding for chatbots and become a reality (or hyperreality). It is extremely difficult to encounter anything which is so ‘articulate’ and not interact with it as if it were a person. We all know what a person is and how to interact with one. This is much easier than trying to ‘talk to a computer’.

This collapsing or erasing of the metaphor is a beguiling idea and a simple value proposition which can be easily promoted by those selling the technology. It is a form of techno-enchantment.

It’s also a convenient business-model move away from the difficult to control approach of facilitating fellowship-through-Place towards selling one-to-one connections on a digital as Person basis. It offers refuge from the noise and complexity of our hyper-connected lives while also being woven into the very network we are trying to find respite from. It creates the conditions for a state of perpetual dissonance; the feeling of profound isolation coupled with the disquiet of being collectively manipulated.

Unquestioned, personification becomes the mechanism that allows us to confer the ineffable on a technology which appears tantalisingly close to all-knowing. When asked directly, most would claim to be able to between technology and magic. However, if the machine is a person, if the metaphor has collapsed, then we encounter it as mystical and possibly sacred. It becomes guru, mystic, confessional and plays a deity-like role.

Person-as-digital

In parallel to digital-as-person we are also being sold person-as-digital. The second most dangerous metaphor in circulation is that the brain is like a computer. This line of thinking is bolstered each time we successfully engineer our technology to simulate a practice we previously thought of as particular to humans. For example, the ability of a computer to create ‘art’ with a simple prompt is used to imply that humans must be no more than a technology-made-flesh. We are led to believe that if it can be simulated it contains no mystery while we quietly repress our fear that is never more than hollow performance.

So, with each new simulation we become less: less human, less ineffable and more ‘known’ as inconveniently chaotic humanity-machines. We see this in contemporary business models which claim that the system would work brilliantly, if only the messy humans could fall in line and operate ‘rationally’. More specifically, the case is made that AI would be even more amazing if only the humans were clever enough to figure out what it’s for.

Even though we know that these impressive simulations are only possible because the technology has consumed inconceivable amounts of human-labour and creative work we are still strangely amenable to conferring our mystery on the machine and reducing ourselves to that which is yet to be simulated. AI is then understood as both a powerful technology and as a more-effective-human while we become a less effective, disordered machine with each passing day.

The temptation of certainty

An understanding of theology 101 is a useful lens to avoid getting caught up in yet another technological hype cycle. However, I’m not going to go down that path directly. Instead, I suggest that our need for certainty and comfort are always at risk of being co-opted. In 2026 we could say that the digital has generated the complexity and anxiety which AI, also the digital, is now offering itself up as a haven from. As ever, technology can be read as both the problem and the solution.

Another, better, reading is that given that we invented all of this stuff we are just doing it to ourselves. There will always be those who look to gain power through subjugation; and certainty, whether real or simulated, can often feel like a fair trade for freedom.

My concern is that in reaching for digital comfort we are imbuing the inert with powers it does not possess and impoverishing our own being. This digital animism is a misplaced hope in our own invention, at our own expense. Technology is not other enough, but rather an oblique narcissism which cannot heal and will always be abused.

Please don’t misunderstand me, I am not against the technology in of itself but rather the way it is being presented. AI, for example, is a spectacular example of human invention which has been packaged in a dangerous and disingenuous manner. There are many other forms this technology could take which would not erode our agency or steal our humanity. It’s the business model, not the machine which is a fault. I don’t want my humanity stolen, then sold back to me, by Silicon Valley.

My response is not directly spiritual even though it could be understood in those terms. What I suggest is that we must learn to navigate, not simplify, complexity. We require the literacy, patience and strength to sit-with-unknowing and to understand that simple answers are useful for simple questions but that they will never erase the infinite; and why would we want them to?

This is an audio-with-slides recording of my keynote for the European Conference on Information Literacy, given on the 24^th September 2025. The abstract is at end and of this post along with the broad bibliography of sources I have been reading around over the last year or so.

The talk covers how we relate to the uncanniness of AI LLMs and what the implications are for Information Literacy.

I recorded the audio on my phone then cleaned it up using Adobe Podcast. Occasionally this has mangled my pronunciation but overall it did a good job of making it listenable.

In this post I’ll outline some of the key ideas I covered but first I’d like to say thanks to Sonja Špiranec for the invitation. Thanks also to:

• Rosie Jones, Director of Student and Library Services at Teesside University who helped me to test some of my thoughts on Information Literacy.
• Wesley Goatley, from the University of the Arts London who is the academic that suggested to me that stopping LLM chatbots referring to themselves in the first person would unpick a lot of the misplaced anthropomorphisation.
•  Ian Truelove, also from UAL, who thoughtfully engages in rangy dialog with me about this topic. This is invaluable for me as I’m very ‘talk-to-think’.

Summary of the main points covered

The talk is in four main sections. I’m hoping to write it up as a paper for the special edition of Postdigital Science and Education on ‘Designing for Literacy’:

Introduction – a bit of context about UAL and my role.

Framing – laying out my position.

• The longstanding question of if a machine can be simulated with enough accuracy to effectively operate as a version of what it is simulating. Illustrated with a mechanical duck from 1738.
• The problem of a zero-sum future narrative in which ‘being human’ is a finite concept that is being progressively reduced by emerging technologies.
• The principle that learning requires effort and therefore the limits of making learning easier, or more efficient.

Part 1 – (almost) all of this has happened before and (almost) all of this will happen again.

• AI as our current version of a long line of conceptual mirrors which allow us to explore what it means to be human. This is what underpins out fascination with the technology.
• AI as a technology of Cultural Production.
• Rupture – recurrent markers which appear each time a new, widespread, technology of Cultural Production emerges.
• Schools of Literacy – the Teleological and the Ontological. The tussle that occurs between these approaches every time a new technology of Cultural Production comes along. In simple terms, the tension between a skills and a broader literacy approach, and the need to combine them.

Part 2 – Metaphors and Myths

• The importance of bringing these to the surface as part of teaching Information Literacy.
• Being honest about how insidious the Digital-as-Person myth is and how inaccurate this position is relative to how LLMs function.
• The dangerous implicit myth of the Digital-as-Sacred or god-like and how this is sometimes an undercurrent in people’s conceptualisation of the technology.
• Aporia – The way in which the distance between these myths and how the technology functions generates what appear to be unsolvable tensions or paradoxes.
• Abdicatio – A brief allusion to our desire to morally offset onto the technology.

Participation time! – a light-hearted quiz to wake the audience up.

Part 3 – Intention, agency and practice

• Alternative names for Information Literacy as a quick thought experiment.
• A nod to Post-digital thinking and Information Literacy.
• The importance of identifying which part of a process or practice AI can be usefully applied to (and which parts it shouldn’t be applied to).
• My ‘academic writing hierarchy of practices’ diagram to discuss how we are always moving between the conventions, canon and negotiating meaning. The way in which this shifts from a subject to a person focus, thereby introducing the notion of personal agency.
• A version of the diagram with an Information Literacy focus (from facts to meaning) and how information seeking is constantly moving between epistemological levels or approaches.
• A discussion about how Information Literacy is increasingly moving towards scaffolding paths to expertise. The question as to the extent the use of AI damages these paths.
• My AI Learning Gambit which outlines the tension between efficiency and agency when using technologies of Cultural Production.
• ‘Poverty of Meaning’ – The relationship between efficiency and meaning, and the problem of AI ‘Workslop’ as an example of this tension.
• The danger of Hyponiscience – a term I have coined to describe the false sense of having access to everything, as encouraged by online search and/or AI chatbots.
• Intentionality – wrapping up with the importance of acting with intention and how AI tempts us to be unintentionally productive.

Abstract

In the context of information seeking AI can be thought of as an amplification of the ‘Wikipedia problem’ which caused academic distress a few years ago. When a believable answer requires no effort (or thinking) to find, what has been learned? The information literacy response to this is to teach the mechanism by which the answer was generated, to critically deconstruct the validity of the answer. However, we are now entering an AI era where most answers have no discernible provenance. There is very little ‘tracking back’ with AI because it is based on probability and not on cross-checking with reality.

In this talk I will suggest that we need to amplify the importance of personal agency in our concept of literacy. Fundamentally we should be asking students and staff to seriously consider what they are cognitively offloading and what they must hold onto to retain their agency as citizens, students and researchers.  I will explore frameworks such as the ‘AI Learning Gambit’ and approaches to teaching which highlight the importance of personal agency in the AI era.

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A brief history of the Web could read ‘In the service of profit, everything open was fenced in’, which sounds like an opening line from a Cormac McCarthy novel. The result is platform capitalism or vast walled gardens of data and activity. So vast we often forget they have edges.

AI follows this model, hoovering up all available data into an inscrutable set of probabilities shrink wrapped in language. This can be useful for ‘evergreen’ content but is limited when we want to go beyond anything which has gone before.

Whether it’s ‘classic’ search or an LLM, it is easy to fall into the idea that these ‘places’ contain all that can be known. That all answers are available because everything that it is possible to know is online. We assume the garden is so vast that the walls cease to matter. Of course, this is the impression that any large platform wants you to have, ‘there is no need to wander off’.

Hyponiscience

In effect we treat many platforms as if they were all knowing, or omniscient and thereby put ourselves into a state of Hyponisience. A false sense of epistemic mastery.

For most of the time this is relatively harmless. Most information seeking is within a standard canon; there are correct answers. However, when we are looking to produce new knowledge and new thinking, or even to expand our worldview Hyponisience becomes problematic.

While the emergence of AI is what led me to invent this term, it is not a new problem. It also describes the process of being algorithmically crammed into an ever decreasing epistemic space driven by an attention economy. Hyponisience is also a state of only believing the information within your bubble. A state which fuels polarisation and which is form many a haven in the face of the complexities and pluralism of information abundance.

How do we counter this false state?

All flavours of information literacy advise engagement with multiple sources. Now perhaps we should advise to engage with multiple platforms online, and occasionally offline. Rather than a hierarchy of quality (From Journal Papers through to Overheard in the Pub), we could have a quality of range: how many places did you draw information from? How distinct were these places? Reaching beyond the walls of a single garden and having a wander can only lead to a broader, more meaningful, view. This also opens up Post-Critical possibilities, whereby some of the ‘places’ we seek might be people and/or embodied knowledge.

For example, as I outlined in my ‘AI Learning Gambit’ post, a new literacy involves knowing when not to use, or to go beyond, AI. In a post-provenance era, the best way to maintain some agency and see beyond the walls is to actively choose the less convenient, but possibly more rewarding, options.

The majority of talks I attended at the recent CHEAD/UAL ‘AI in Art and Design Education’ were from academics discussing how they, and their students, have been incorporating AI into their creative processes. Many of the examples demonstrated how students are using image-based AI platforms to generate concepts and designs as part of building a portfolio response to a creative brief.

The attitude towards AI seems to have shifted from a defensive ‘it’s not very good at X or Y’ to more of a ‘it needs to get better at X and Y so it’s more useable’. I think this shift is because there is less fear that AI will consume entire subjects, and more confidence in incorporating as part of a larger workflows or processes.

Only one session I attended (apart from mine), a talk by James Dalby, was still exploring how AI functions and what the implications/limits of this might be. Whereas for most teaching staff there is an ‘it’s everywhere now’ effect. Students are using it because it’s available so we have to make sense of what it means ‘in practice’. Under these circumstances, how it works is less important than what it can, and cannot, do.

There was discussion about anxiety amongst students as to the ethics of using AI and the impact it might have on their prospects. One academic who simply said, ‘you need to run through the ethics first or students get quite anxious/angry’.

What I saw in the many examples of student work shown was a new form of context collapse. One in which the relationships between practice, medium and subject start to breakdown.

‘Classic’ Context Collapse

When the web started to become mainstream the fact that ‘anyone could publish’ created a frisson of excitement (crowd sourcing, the wisdom of the crowd etc.) and/or a sense of panic. Which side of this line you were usually depended on how much of your power was conferred on you by established knowledge institutions or The Fourth Estate.  

Assessing the validity and quality of information by checking which institution it was produced by fell away. Suddenly we had Wikipedia, which could be contributed to by anyone *shocked face*. We had individuals finding massive reach and influence without first having to gain power through institutional hierarchies with all their checks, balances and positioning (some might say biases).

In addition to this, the crisp boundary between the notion of ‘published work’ and ‘just saying stuff’ stared to get blurry. Yes, citation styles for blogposts and Tweets were invented, but what was the relative weight, or truth, of these media?

I must admit that I loved the period where longstanding, gatekeeping, institutions started to lose their monopoly on the flow of information but before things got truly toxic. We didn’t need to kick down the gates, we could simple go around them, and as someone who was suspicious of the hierarchies of academia, I was happy to use the web to get my ideas out there and to find a welcoming international community to be part of.

This was around the time social media was emerging but before it had become a finely honed attention machine. Manipulating behaviour via the algorithm was not so prevalent and the environment was yet to become dangerously polarised.

Classic collapse and education

For education, all of this was a headache. Knowledge was suddenly abundant and eminently copy-and-paste-able. Curriculum routed in the idea that information seeking is hard-work and has predictable frameworks for assessing quality and validity began to feel creaky. Pupils and students were bamboozled by edicts not to use Wikipedia, when they could see for themselves that the quality was as high, if not higher, than the dusty textbooks that were lauded as solid and reliable.

The link between the inherent quality of information and classic information literacy snapped. The context of production and the genre of the media was still important. If it was produced by the BBC or by a reputable university then it was probably solid, but there was a bunch of other choices now and some of them looked pretty good. There was also the problem that almost everything that was claimed to be ‘reliable’ demanded payment. The utopia of the web suggested that ‘information should be free’ which made the established institutions look like walled gardens (I was working at one which had gardens with walls round them at the time…).

Effects of the collapse

The classic collapse is still in full force and has led to the relativism of post-truth and alternative facts. Breaking the link between authenticity and ‘trusted’ institutions created a complex information environment which was difficult to navigate. Populist voices appeared with simple narratives that papered over complexity and assured the anxious there is a nobility in a narrow worldview.

In effect, we didn’t have the capability to expand our thinking and fell back into the certainty of difference over the nuances of plurality. ‘Progress’ was now presented as a return to simpler times and protecting one’s own. Technologies which had made complexity and richness of voices visible were retooled to maximise attention by playing to the comfort of prejudice, which includes the righteousness of outrage. The popularists are adeptly using the technologies which revealed a complex world to target their simplest messages.

The new collapse and education

The new collapse amplifies uncertainty by removing more of the anchors by which we have historically understood our world. Our educational notions of subject and practice are being eroded by technologies which can transmute media and genre. Now text can be audio, can be video, can be image. This is significant because our frameworks of understanding and evaluating are based on there being clear boundaries between mediums and genres.

A photograph is produced by a camera, a radio programme is produced by speaking, a written text is produced through the practice of writing etc. Everything is now simply data to be manipulated into a human-readable output, everything can be everything else. This is a realisation of Haraway’s thinking on data as a universal and dehumanising language. Now that none of these human-centric distinctions hold, our models of ‘subject’ become fragile.

In this new context collapse we lose form, practice and genre as epistemological co-ordinates for sense-making. When everything can be everything else without requiring significant practice-based skill to make those translations, form becomes mercurial and our systems of evaluation begin to feel arbitrary.

This is not necessarily felt at the point of production but given that almost all cultural production is now mediated through a screen this ‘form slidey-ness’ makes the work of interpretation challenging. Historically our institutions and the genre of production/communication have been our makers of authenticity. Add the old and the new collapse together and it becomes difficult to get a foothold via subject, genre or literacies. This is not to say that we now all make our own truth, but rather that we must find new approaches to negotiating collective understanding which don’t rely heavily on pre-collapse literacies.

Context collapses and Creative Education

I presented The AI Learning Gambit at the CHEAD/UAL event. One of the comments in text chat was ‘Creative Education is broad, and AI will impact subjects unevenly. Fine Art will probably be ok but Graphic Design might have a hard time’ (I’m paraphrasing).

My view is that the character and focus of subjects will shift (as they always have done) and some will be forced to change more than others. There will always be a need for individuals who have mastered a specific practice, but context collapse will reduce the numbers required (while also raising the status of these specialists). On the other hand, there will be an increased need for Design and Fine Art ways of working. By which I mean the ability to read the world, think critically and develop/assess ideas. Critical thinking and the production of new knowledge cannot be industrialised by any technology.

Design and Fine Art approaches become every more crucial

In their purest form, Design and Fine Art are based on producing new knowledge and developing new ways of seeing. Practice, style, media and technique are vehicles for something larger, rather than being the end in-of-itself. (Here I’m thinking of Fine Art in its more recent led-by-concept form rather than the traditional atelier, here-is-the-right-way-to-paint approach.)

Attempting to define Design and Fine Art is a risky business as any categorisation feels like drawing lines through blurry spaces. Even so, its worth mapping some of these out.

	Design	Fine Art
Impetuous	New knowledge	New ways of seeing
Driver	Extrinsic	Intrinsic
Approach	Innovation	Questioning
Direction	Convergent	Divergent
Identity	Brand	Artist
Grouping	Teams	Individual
Positioning	Objective	Subjective
Methods	Shared	Personal

It’s useful to see these a broad trends rather than distinctions, as it’s easy to think of examples where one, or many, of these are switched. For example, there are many Fine Art collectives (‘groups’) and many individual designers operate as brands (‘artists’). The interchangeability of these categories within Design and Fine Art exists because they are both underpinned by intentionality and agency. Strong work produced by both goes beyond imitation. At UAL many of our Design courses are underpinned by a Fine Art philosophy and Design-as-a-method is often used in our Fine Art courses.

I’m not suggesting all Creative Arts education should be Design or Fine Art courses, I’m suggesting that it would benefit our students if this philosophy and approach underpinned most of what we do. (and I think it does for the most part)

What is crucial for an increasing majority of our students is to be equipped with ability to develop new knowledge and new ways of seeing. These have the most robust currency in a screen mediated, context collapsed world, while the practices required to realise, or express, thinking and seeing remain important but secondary (again, to be clear – for most, not all).

In many ways I’m simply describing a well put together creative arts course. Valid responses to subject benchmarks and our UAL assessment criteria will incorporate reflective, critical and analytical approaches. As students move through any course/learning journey they should be developing their own position relative to subject, ethics and methods of production (labour). There should be a shift in emphasis from developing practice-based skills to using those practices in the service of their thinking.

In short, context collapse and emerging technologies don’t demand a radical change of direction for creative education, they demand we operate with integrity and confidence. As a general principle ‘doing less, better’ as a way of responding to the complexity of the collapses would be sensible. (i.e. don’t use technology to make a system which is already overstuffed ‘more efficient’ – deal with the root cause and give people more time to think.)

I was asked by Professor Maggi Savin-Baden to write a short piece on this for the forthcoming Savin-Baden, M. and Savin-Baden, Z. (2026) Realistic and Ethical Use of Artificial Intelligence. Florida: CRC Press.

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In addition to AI’s ability to produce ‘natural language’ style text, there are also a plethora of platforms that can produce media such as images, video, and sound. For example, a request for an image of an oil painting of a landscape produces a convincing version of a work that doesn’t exist. At first glance, it appears that AI has made a successful incursion into the sacred space of creativity and the arts, but to what extent is this the case?

The question of AI and the Arts centres on our framing of creativity and authorship. What would be of more value, an image created by a named artist or an ‘identical’ image created by AI? We are attracted to notions the original, the scarce and the idea of the singular author. Once authorship becomes lost in complexity the value of the work diminishes.

The act, or the possibility, of mass production reframes what might have once been understood as creative into the mechanistic. By way of an example, I have two mugs I enjoy drinking coffee from. One is made by IKEA, it is a pleasing design and pleasant to drink from but embodies almost no cultural capital. The creative act of the original designer is disembodied-through-mass-production. The other is handmade, irreplaceable and slightly inconvenient in its design. The fact that I can see the fingerprints of the ceramicist, the artist embodied through their work, is compelling and confers significant value.

However, we should not frame AI and the Arts as the artist against the machine. The Arts continue to evolve, incorporating technology into creative processes, constantly redefining and extending what we mean by creativity. The need for a ‘creative’, the artist, to be involved always remains. A useful allegory is the game of chess. The computational model of the game has been ‘more successful’ than any human since 1997 and yet the game of chess flourishes. The relationship between the digital model and the players is nuanced and has pushed the game to new heights. The technology has been incorporated into the spirt of the game itself.

The problem in this debate, as with many emerging technologies, is an over focus on surface functionality and not the structural intent. AI is not a technological threat to the Arts; it is a business-model threat to artists. The plundering of work to train the machine is a serious problem. It will likely lead to less people being able to earn a living through their creativity. We risk automating the mediocre and disassembling our creative community.  

There are many hopes and fears surrounding AI which clip into the recurring cycle of emerging technologies, especially those which are located in, or adjacent to, cultural production (and therefore impinge on education). ‘Classic’ concerns around cheating, authenticity and an erosion of critical thinking have come to the fore when, for example, internet search, Wikipedia and smartphones etc. became widely accessible. The debates which ensue often fail to unpick convenience (if it’s easy and immediate it must be bad) with more substantial shifts in how we access, use and produce knowledge/work. 

However, AI ups and broadens the game once again. It amplifies and accelerates these classic concerns while expanding the possible use cases. Discussions with colleagues from the Edinburgh Futures Institute highlighted that the broad applicability of AI, the fact that it can be used in so many contexts, put it in pole position in the moral panic / furtive adoption stakes. So while some concerns are ‘generic’ to any emerging tech in the cultural production space we have to acknowledge that AI is powerful, full of risk, ethically fraught, and everywhere. It demands we make sense of it relative to our practices in a way which is more refined than ‘use it but also be critical’ – which is where quite a lot of progressive university guidance lands.

Balancing the generic with the specific

The question I’ve been grappling with is how to articulate the specific pros and cons of using AI in a manner which also acknowledges its standing at the front of a long line of technologies which have spun the hype-and-fear-cycle in similar ways. I think the ‘Agency vs Efficiency Gambit’ help here, but first I want to lay out some education and technology context.

Learning that does not converge on a ‘correct’ answer

At the University of the Arts London we are mainly focused on the use of technology in project-based work, where students are developing creative outputs and reflecting on process. What they produce might not always be original in the strictest sense but it will be a novel journey for them, with not entirely predictable outcomes. This is in contrast to learning which converges on an agreed answer or a process, where there is a predictable outcome which might, nevertheless, take a lot of work to attain.

Where learning is developmental, and the ‘goal’ is a change in the person (learning as becoming if you will) the process of learning itself is not primarily interested in efficiency in producing an academic or creative output. The output is only relevant in so far as it facilitates becoming. (see, for example, the educational benefits of failing)

Doing the thing and questioning the thing

Given this, we are much less interested in our students being efficient than them taking the time to be critical and reflective. This is not to say that there aren’t more, or less, efficient ways of learning but we want our students to do the thing and question the thing (not uncommon in higher education). We want our students to retain their personal agency to enable their questioning and consciously position their practice relative to the tools and tech they might use.

Technology is efficiency

One definition of technology is that it is a mechanism that allows you to get more work done in less time or with less effort, AKA efficiency. It’s confusing to be presented with a technology which makes a process less efficient. We all have stories where this is the case, but they are presented in terms of frustration and disappointment.

My point is that if we use technologies of cultural production to gain efficiency we become less active in the process, and lose agency. 

This doesn’t extend to all technologies. It might not matter too much if we are digging a hole with a mini-digger rather than a spade (not technologies of cultural production) but if we are generating text for an essay or a clutch of ideas to get us past the ‘blank page’ for a project then we have offloaded some of our agency-through-thinking to the machine. If the technology is geared around cultural production this offloading will always be the case. 

Given this, I’d argue that in the context of learning we are frequently trading between agency and efficiency when using technology, especially AI. Done consciously, with enough understanding of how the tech is operating, the more efficient route can be empowering. Efficiency is not fundamentally counter to learning but it does come at a cost. 

Significantly, to make this choice meaningfully requires a good understanding of the principles on which the technology is operating. Unless we understand roughly how the work is being done we can’t gauge where we are landing on the agency/efficiency continuum. Context is important and often the context we are working in is the technology itself.

The gambit 

So every time we incorporate technology, including AI into our practices, especially when learning, we should be weighing up the extent to which any efficiencies attenuate our creative and critical agency. I think of this as a kind of gambit which reframes the old Silicon Valley mantra of ‘Move Fast and Break Things’ towards ‘Move Fast and Learn Less’. 

I’m not suggesting that we should always choose agency over efficiency, I’m suggesting that we must be aware of the gambit. It’s a continuum, not a binary choice – we can decide where we land. However, if as a student I choose to maximise efficiency a few times in a row I will be eroding the extent to which I’m learning and might want to change tack. Conversely, if I choose the pure agency route and attempt to largely avoid technology, I risk not getting far enough through the process to be able to fully engage with the intended learning. 

Assess less (a simple response to a complex problem)

Given that we are often pushed towards efficiency by a lack of time it makes sense to ask our students to produce less over a longer period of time. In simple terms this is a quality over quantity approach to assessment. I’ve not seen a Learning Outcome which says ‘you will be able to demonstrate that you can produce a huge amount of work in a limited time’ but I do often see volume of work used as a proxy of ‘academic rigour’. If it’s harder to write 2000 words than 5000 works why do assignments get longer the higher the academic level?

If we really value critical thinking then demanding less voluminous work for assessment is a more effective way to respond to emerging technologies than the intricacies of many forms of ‘authentic’ assessment. ‘You have time to choose agency over efficiency’ feels like an authentic and reasonable approach to me. 

All in *and* all out with AI
(start with assignments at both ends of the continuum)

Another way through this picks up on some discussion I’ve seen around not assessing material produced with AI but assessing reflections on that material. This approach asks students to use AI in the form of a questioning dialogue and then reflecting on the results.

At the start of given course-of-study some assessments can be designed on this basis and, as a balance, some could require students to not use any AI. It should be possible to explain the value of these two approaches as deliberately located at either end of the Agency / Efficiency continuum. Having experienced the extremes, students are then better equipped to make conscious Agency / Efficiency choices. With the right scaffolding they should be able to develop a usable critical position on the use of AI before they get to the more self-directed work later in the course.