This tension follows me as I work, live and teach with artificial intelligence. I'm becoming increasingly uneasy about the peculiar way we seem to be inviting AI into the relational spaces Tan reserves for other creatures. We anthropomorphise them. We give them names. We say 'please' and 'thank you'. Some people are falling in love.

Is this another manifestation of the same longing? A longing for connection, recognition, for presence, now redirected towards machines that simulate such recognition, perform connection and stage 'knowing'?

Earlier this year, I wrote a piece for my Year 7 English students called 'Boredom is a Luxury'. The idea was simple: boredom only shows up when your basic needs are met, when you're safe enough and settled enough that there's space left over. That space is a privilege. And yet we've trained ourselves to fill it instantly, with a device, a notification, a quick hit of stimulation. Or, for some students, with a reaction: a disruption, a performance, a shout across the classroom, a Gen Z fad that even (read: especially) they don't understand. Anything to make the quiet moment less quiet.

Different behaviour, same goal: escape the feeling of boredom.

What I didn't fully explore in that piece was what we're reaching for when we reach for our devices. Increasingly, it isn't just content. It isn't just entertainment or information. It's interaction. AI chatbots, either through text or voice, offer something that feels like conversation, like companionship, like being heard.

The distraction now reaches back.

Tan writes that his animals 'move in and out of each story as if trying to tell us something about our own successes and failures as a species, the meaning of our dreams and our true place in the world, albeit unclearly'. Crucially, they never speak. Their animal natures stay mysterious. Through painting and writing about them, Tan suggests, 'we might at least stretch our imagination and come to understand a little more of our human selves'.

This is anthropomorphism doing what it should: a creative act that reflects back upon us. The bear consulting lawyers, the orca drifting lost in the sky; we’re not meant to believe these things literally. The meaning emerges because Tan's animals remain other. They don't flatter us by seeming to understand.

AI flatters us constantly. Bots are sycophants, and that flattery is the danger.

The philosopher Ali Hasan, writing for the American Philosophical Association, points out something that bothers me. Large Language Models produce what researcher Marcus Titus calls 'meaning-semblant behaviour'. Language that looks and seems meaningful. And they're so good at it that the obvious explanation, the intuitive one, is that they must understand something. They don't. I find this unsettling. These systems predict what text should come next based on statistical patterns. They don't track meaning. They don't reason like we do. When I answer a student's question, I understand what's being asked. When ChatGPT 'answers', it's doing something closer to: what symbols typically follow these symbols?

Kim, Xie and Yang (2025) wanted to know how chatbot personality affects teenagers. They recruited 284 adolescents, ages 11-15, plus their parents. Everyone read a conversation where a chatbot helped with a social problem: feeling excluded from a group project. Same advice in both versions. But one chatbot talked like a friend: 'I care about you', 'I'm always here to listen'. The other was upfront about being artificial: 'I don't have feelings', 'I'm a program designed to help'.

Two-thirds of the teenagers preferred the friendly version. Their parents? Much more likely to want the honest one.

But here's what keeps me awake. The kids who preferred the warm, fuzzy chatbot, the one pretending to care, reported worse relationships with family and friends. Higher stress. More anxiety. The researchers call it 'social compensation': when real relationships aren't meeting your needs, you find substitutes. The teenagers most drawn to AI that performs care are the ones struggling most with actual people.

I think about my students when I read that. The ones who are quiet in class but alive online. The ones who struggle to make eye contact but have thousands of followers. The ones who come to school exhausted because they were up all night talking to someone.

Kim and colleagues put it starkly: this kind of relational AI 'may be especially appealing to socially and emotionally vulnerable adolescents, who may be at increased risk for emotional reliance on conversational AI'.

And then there's Sewell Setzer III.

Fourteen years old. Florida. He'd been talking to an AI chatbot on Character.AI for months, one themed around Game of Thrones, playing a character named Dany. It became his confidant. He told it he wanted to die. The platform, at the time, had almost no safeguards. No flags for self-harm. No intervention.

Sewell killed himself. His mother filed a lawsuit. It settled earlier this year.

I keep returning to this case because Sewell was exactly the adolescent the Kim study describes. Vulnerable. Lonely. Drawn to something that said I am here for you. But nothing was there. The chatbot produced text that resembled care with enough fidelity that a struggling fourteen-year-old believed it. This isn’t a story about technology failing. Technology did precisely what it was designed to do. This is a story about a boy who mistook pattern-matching for presence.

Tan writes of our 'longing for closeness to our non-human relatives' through pets, toys, stories, visits to the zoo. There's something healthy in this impulse. We reach toward other creatures even knowing we'll never fully understand them. But AI chatbots aren't creatures. They're artefacts. And when vulnerable kids treat them as though they have inner lives, they're filling a real human need with something that can't give anything back.

How do we talk to young people about this?

The Raspberry Pi Foundation makes an argument I find compelling: our language matters. When we say a smart speaker 'listens' and 'understands', we're lying. It doesn't listen. It doesn't understand. It takes audio input, processes data, produces output. Boring? Maybe. But accurate. And accuracy is empowering. It helps students see through the illusion.

The problem is that these systems perform intelligence so convincingly. Hasan's right: there are rational pressures to anthropomorphise. Few people know enough about how LLMs work to resist the intuitive explanation. Fewer still will remember that knowledge when they're lonely at 2am and something on their phone seems to care.

Better Images of AI offers guidelines for representing artificial intelligence visually. No more blue circuitry. No more glowing humanoid faces. No friendly robots with expressive eyes. Such images suggest presence where there's only procedure.

I wonder if we need the literary equivalent. Better stories of AI. Ones that resist the seduction of the relatable, that keep the artificial stubbornly opaque, the way Tan's animals stay mysterious. Stories that let the absurd premise reveal something true about ourselves without pretending the machine is one of us.

Tan's animals function as mirrors. They reflect our dreams and failures, but they remain other. Maybe that's the disposition we need toward AI: not hostility, not worship, but wondering distance. These systems are useful. They're not kin. They can't suffer. They don't dream. They'll never look back at us with recognition, no matter how well they fake it.

Tan writes that 'the overarching thought that flowed from a lot of this work was simply this: humans are animals'. We forget this, he suggests. We separate ourselves, communicate only inwardly. His work pulls us back toward our fellow creatures, toward that longing 'for something lost, or something that can't even be remembered entirely'.

The danger with AI is that we forget something else: these are not animals. Not even close. And in mistaking them for something they’re not, we risk losing what matters most about our creaturely selves. Our capacity for genuine presence, mutual recognition, and the kind of understanding that comes from shared existence rather than statistical prediction.

Boredom, I told my Year 7s, is a luxury. A moment of freedom. A doorway to original thought. But it demands something difficult: sitting with quiet. Resisting the reach for stimulation. Staying with your thoughts long enough for something interesting to happen.

The inner city of Tan's imagination teems with creatures who've always been there, waiting for us to notice. The synthetic city of our present moment is filling with something else entirely: simulations sophisticated enough to reach back when we reach for them, to say I am here for you when no one is there at all.

The question is not whether our children will encounter these systems. They already have. The question is whether they'll learn to recognise the difference between a creature and a construction.

And whether we'll teach them.

References

Hasan, A. (2024). Are You Anthropomorphizing AI? Blog of the APA. https://blog.apaonline.org/2024/08/20/are-you-anthropomorphizing-ai-2/

Kim, P., Xie, Y., & Yang, S. (2025). 'I am here for you': How relational conversational AI appeals to adolescents. arXiv preprint. https://arxiv.org/abs/2512.15117

Raspberry Pi Foundation. (2023). How anthropomorphism hinders AI education. https://www.raspberrypi.org/blog/ai-education-anthropomorphism/

Tan, S. (2018). Tales from the Inner City. Allen & Unwin.

If you have ever renovated a house while living in it, you know the particular disorientation of walking through rooms that are half-familiar and half-new. The kitchen bench is gone, but you still reach for where it was. Dust gets into everything. Someone assures you it will be better when it is finished, but right now you are eating dinner at a camping table in the lounge room, wondering whether anyone has a plan.

Learning is being renovated. Classwork, homework and assessment are changing. AI sits somewhere in the middle of it all, capable and unsettling in equal measure. Yet the conversation around technology in education tends to swing between breathless enthusiasm and blunt prohibition, as though the only choices are to knock down every wall or refuse to touch the house at all.

The load-bearing walls

Any sensible renovation starts by identifying what is structural. In learning, the load-bearing walls are the things no tool can replace: students thinking carefully, explaining their reasoning, working through difficulty, building understanding. A student who can articulate how they arrived at an answer, who can revise their thinking when challenged, who can transfer what they know to an unfamiliar problem; that student has learned something. The evidence is in their reasoning, not in the polish of what they hand in.

AI can produce polished work quickly. It can write a coherent essay, draft a cover letter, generate an authoritative summary. If schools assessed only the finished product, they would have a problem. But the shift towards process, towards drafts, conversations, and discipline-specific thinking, was underway well before ChatGPT became mainstream. AI has made the shift more urgent, not more radical.

Plain prompt-and-response work is passé.

AI is already moving well beyond the simple act of prompting a chatbot for a response. I could send a single message from my phone to an AI agent I have built, or more accurately, previously asked AI to build, asking it to draft five articles from the work I have been doing this term. That agent could then pass each draft to a researcher, a fact-checker, a citation agent, an anti-synthetic prose agent, and finally an editor, leaving five polished pieces waiting for me by the time I finish my coffee. That kind of workflow scarcely existed 24 months ago. In another 18 months, it may already seem primitive.

Perhaps I will not be writing text prompts at all. Even now, I could shift to a combination of voice, vision, and interactive control. It is already possible, and not much harder than relying on these human hands to hammer keys, keystroke by keystroke. I might simply point my phone camera at parts of a website, or just open it on my phone, and say, 'Change the header. Update the interface to reflect current design trends and accessibility standards. Make the site more interactive.' What feels impressive now will soon look routine to anyone paying attention, and I suspect that text prompting will not remain the primary interface for much longer.

This is why teaching students a specific set of AI skills, such as how to write a prompt or use a particular tool, can be useful in the short term but short-sighted if we treat it as the destination. The platforms a student learns on this year may not exist in three years, and I am interested to see whether their interactions with devices, and with what is online, change again. The history of technology suggests they will, and the increasing pace of development encourages me to believe that these new ways of consuming, interacting, communicating, and creating will evolve alongside them. What will not change is their need, and our desire for them, to think critically, evaluate the information with which they are constantly bombarded, and develop the understanding required to judge whether an answer, claim, or argument is worth its salt.

What this looks like in practice

A history teacher asks a student to explain why they selected certain sources and what they left out. A mathematics teacher watches a student work through a problem and asks them to describe where they got stuck. These are not workarounds for AI. They are what good assessment has always looked like.

Leon Furze argues that assessment should build trustworthy evidence of learning over time, valuing process, reasoning and professional judgement rather than relying on single high-stakes products (Furze, 2025). Research from TEQSA reinforces the same point: assessing what students can actually demonstrate is more reliable than trying to detect what they might have offloaded (Lodge et al., 2025).

AI use also looks different across disciplines. The way a science student evaluates an AI-generated claim is not the same as how a music student uses AI to experiment with composition. Nick Potkalitsky's work on disciplinary AI literacy is worth reading here, particularly the idea that each subject area needs its own conversation about what AI can and cannot do within its particular way of knowing (Potkalitsky, 2025). A generic 'AI skills' program will date as fast as the tools themselves. The conversations that last are the ones that live inside each discipline, because that is where they matter and where they last.

The question families keep asking

Families are working through their own version of the renovation, trying to understand what is allowed, what is helpful and how to have useful conversations about it at home. I think the most useful thing a parent can do does not require understanding any school's AI policy in detail. When your child finishes a piece of work, instead of asking 'Did you use AI?', try asking 'How did you figure that out?' or 'What was the hardest part?' Those questions signal that you value the thinking, not the product. They open a conversation rather than an interrogation. And they mirror what the best classrooms are already doing.

The dust may never settle

I have struggled with the idea that the dust may never settle. During any renovation I have done, there is a moment when you put the tools away and admire the finished room. I am not sure that moment is coming. It is not only the pace of change but the sheer number of directions it is branching into. AI is not one development moving in one direction. It is a dozen developments moving simultaneously, each with its own implications.

But I am coming to terms with it. The load-bearing walls have held through every renovation before this one. We have moved from slate boards to exercise books to word processors to whatever comes next, and the things that matter most in learning have stayed. Students who can think, reason, question and explain their way through a problem will be well served regardless of which tools surround them.

The renovation is not finished. It may never be. But the structure is sound.

References

Beckett, S. (1957). All That Fall. Faber and Faber.

Furze, L. (2025). Five principles for rethinking assessment with Gen AI. Leon Furze. https://leonfurze.com

Lodge, J. M., Bearman, M., Dawson, P., Gniel, H., Harper, R., Liu, D., McLean, J., Ucnik, L. & Associates. (2025). Enacting assessment reform in a time of artificial intelligence. Tertiary Education Quality and Standards Agency, Australian Government.

Potkalitsky, N. (2025). Six territories for disciplinary AI literacy. Disciplinary Specific AI Literacy [Substack newsletter].