is this skill a gem?

Note / Introduction: This post is an experiment testing Gemini’s newly released ‘Skills’ feature, specifically the ‘Write Like Me’ profile. To see how well an AI model can mirror my natural writing without relying on prior AI-assisted material, I grounded this skill using my doctoral thesis and a selection of blog posts written in 2022 or earlier. What follows below is the unedited text generated from a raw spoken transcript of my thoughts as I reflected on AI literacy, tool churn, and our ongoing work at UEL.

For those of us working in UK Higher Education, this week brought the quiet release of Gemini’s ‘Skills’ feature into our institutional accounts (arriving right around the 12th of October, if memory serves). As is usually the case with these updates, I have been spending a bit of time working my way around it to figure out what it actually offers beneath the vendor rebranding.

It is a constant challenge for anyone tasked with supporting colleagues and developing institutional AI literacy. The tools are continually marketed as disruptive, but for those of us trying to make sense of them and offer people practical, accessible ways in, the constant shifting of features makes it difficult to pin anything down. I am often reminded of the AI playgrounds I ran back in 2023 for colleagues who had never used ChatGPT. Watching people experiment for the first time and gradually realise the implications for assessment and student work was eye-opening for everyone involved.

My view has always been that the real utility of these tools lies in using them in a grounded, controlled fashion rather than for generic generation. That was why I found custom GPTs so useful: they allowed me to build detailed, focused prompts with embedded reference materials and share them with others so they could benefit from that work without needing to start from scratch. Grounded platforms like Gemini Notebook (formerly NotebookLM) serve a similar purpose, giving staff and students a protected space to interrogate source texts directly with verbatim verification and reframe materials for different educational contexts.

At UEL, we recently went through the process of securing specific permissions to enable Gems within our Google Gemini EDU accounts, including the ability to share them across the institution. The point of doing this was simple: not everyone is a compulsive fiddler like me, nor should they have to be. Most academic and professional services staff do not have the time to invest in working out what every new update means, but they still recognize the importance of getting under the skin of these tools to understand their practical application. Enabling shared Gems meant that those of us willing to experiment could build tailored tools and hand them directly to colleagues to support their day-to-day work.

I was just on the verge of presenting this shareable Gem workflow to our AI literacy working group when the announcement arrived that Gems are being phased out in favour of Skills. It feels like an inevitable cycle in ed-tech, reminiscent of previous shifts across other platforms, but it does make the task of building stable institutional habits feel a bit like aiming at a moving target.

Which brings me back to this current experiment. I decided to test the new ‘Skills’ setup by dictating my thoughts into a speech-to-text transcript and letting the ‘Write Like Me’ skill process the raw audio feed. Grounding the skill in my pre-2022 writing and doctoral thesis ensured that no prior AI-generated text was present in the training sample.

Coincidentally, I received an email yesterday from someone who described my writing and approach as ‘irreverent’. I actually took it as a compliment, though when I looked up the dictionary synonyms, I was amused to find words like ‘disrespectful’, ‘disdainful’, and ‘contemptuous’. I certainly have no desire to be disdainful or contemptuous, but I do not mind being called irreverent if it refers to a healthy skepticism towards administrative tech-churn and overhyped promises. Interestingly, the initial skill profile analysis picked up on that same tone before the email even arrived.

Whether that irreverence or my actual voice comes through in this generated draft is something I will leave to you to decide.

No I won’t: it’s a long way off I think. Just to note GPT Zero assumed it was ‘human written; polished by AI’ . I hope this isn’t what I sound like normally. I worry that it is clearer than I am and undoubtedly it really helps with organising things structurally so for functional things it has really potential utility but the unedited output above is definitely not something I’d be comfortable putting my name to without significant editing. Finally, I had to remove two short self-standing sentences common in (what I assume to be) AI generated posts on Linkedin. It was too uncomfortable. I am, though, going to use this as an attempt to iterate and improve it.

for reference; here’s the raw transcript.

“Okay, let’s try this. This is going to be a short blog post, and it’s basically to test the skills feature. So, for EDU users of Gemini, the skills function in the UK at least was released on the 12th of October, I think it was. Anyway, this week it was released. And I am still working my way around this. As has been the case frequently with so many of these AI tools that like to see themselves as disruptive, but for somebody like me who’s trying to make sense of it and offer people ways in, it’s really difficult to pin anything down because everything keeps changing. I remember back in 2023 running AI playgrounds for people who’d never touched ChatGPT or similar, and just watching as they were experimenting with things, aghast as they were realizing the implications for the ways in which their students were likely to work, and so on and so forth. For me, it’s the obvious areas of utility have always been in the ways in which you can use the tools in a much more grounded and controlled fashion. So, I was an enthusiastic user personally for customized GPTs and very much appreciated the ability to share those custom GPTs with others so that they might use my own efforts at detailed and focused prompts with built-in grounded material. It’s quite clear, probably for cost-based reasons, that the ability to do that was going to change. But I do like the way that you can effectively ground what is now called Google Notebook, formerly NotebookLM. And in EDU spaces, I think it’s really important that people, all stakeholders, that’s both students and staff, are sufficiently familiar with these tools and their utility and function. So, I was delighted when I found out that we were able here at UEL with a specific request and additional permission I would generate. [at this point it stopped transcribing and gave me a fail message. The next bit of recording didn’t work so when I started again, I’d lost my thread a bit]. Okay, I’m trying again, so it failed on the first attempt. Look, what I’m trying to do is get over the point that not everybody is a compulsive fiddler like me. Not everyone is willing to invest time in working out what all the very many changes mean. But a lot of people in positions like me recognize that it’s really important that we get under the skin of LLMs and their functions in order that we might better understand actual utility and application. This is absolutely fundamental. But also the ways in which sophisticated users with complex multi-tool workflows are going to be utilizing these tools to get the most out of them. And I’ve always thought for a long time that customization, whether it was custom GPTs or Claude projects or whatever it is or Copilot agents, were going to be a critical way forward. And agentic AI is something that we have limited ability to control. But I keep changing it and I think that’s largely due to cost reasons. And the reason I’m writing this is because I was all set to tell colleagues in our AI literacy working group that they had of course access to Gems in our Google Gemini EDU accounts, but that we’d enabled for some people sharing functionality. And I think it’s with the sharing that we really start seeing some of the real opportunities. What used to be called NotebookLM, now Gemini Notebook is a good example of how you can ground an LLM, interrogate the text or texts or sources that you include, have verbatim verification opportunities, and can reframe those same documents in any combination that you like into different formats from flashcards to infographics, short animated videos, and what have you. What’s key to me though is that that important distinction that students need to understand and academic colleagues and professional services colleagues need to understand between grounding the LLM in whatever way it happens to be or using a generic tool for free or whatever it is. So more protected spaces like Gemini through the approved system here at UEL, I think is a no-brainer, but it takes a while to encourage people to change their behaviors. And the point I’m trying to make is that I was going to present this thing on Gems and then I suddenly find out that they’re phasing this out in the way that which was inevitable, I suppose. OpenAI custom GPTs, which had such utility. What Google have done is they said, ‘Yeah, we’re getting rid of Gems, but we’re bringing in Skills.’ And literally this week it’s been released, and the first one that caught my eye was Write Like Me. For very obvious reasons, this is something that we need to be worried about, but it also is something that offers potential. So what I did, by way of experiment, is I uploaded my doctoral thesis and a selection of blog posts that I wrote in 2022 or earlier, just so that I could guarantee that there wasn’t any kind of sense of AI assistance creeping its way into any of those documents. And I thought, ‘Let’s give this a go.’ So I don’t know what it’s going to come with, but I am going to post verbatim, and I want this noted in the introduction, the both the final result and the transcript. And let’s have a look at its application of its analysis of my writing style. I got an email yesterday from someone who called me irreverent, and I like that as an adjective. And I thought, I wonder what they meant, and I looked it up, and it was great because the word itself I took as a compliment, but the synonyms that came up were disrespectful, disdainful, scornful, contemptuous, derisive. I don’t want to be any of those things, but I actually don’t think I mind being irreverent. And one of the things that the skill noted, not in so many words, but it noted my tendency towards what might be called irreverence. So let’s see if that comes through.”

Summer of not love

I just had a really nice email from a high school student in South Africa saying that he’d read one of my blog posts and it had inspired him in his own school-based research. He was chancing his arm a bit and wondering whether he could interview me. But, he appears to be someone rather like I was when I was his age, and left it to the last minute and wanted to interview me today! I offered to speak to him on Monday, but that was a little bit too late for him. Nevertheless, what it did was make me think: my God, I haven’t written anything on my blog for months. So I had a look and the last entry was in May. How is that even possible? I know we had a hot summer but it feels now like it lasted about 5 minutes.

Anyway, it made me think, firstly, why am I not writing on my blog? And on that question I refer you to my calendar, my grey hair and the anguished look behind my eyes. But it also made me think about where everything is going. Because even though I don’t really have that much time to get my thoughts onto paper, doing so actually really helps me. It helps me cohere ideas and learn for myself exactly what it is that I’m thinking, particularly when it’s messy and messy is a word that captures not only the World but a lot of HE right now.

I might not be writing much (beyond completing a couple of chapters and doing a few reviews; nearly forgot about those) but I  do nevertheless still read a lot of controversial posts, especially on LinkedIn where expatriates of Twitter now seem to have gone to argue and share pictures of their dinners (or is that just my algorithm?). It’s especially grim in the AI and education space. The digs and the jibes from all ‘sides’, the over exhuberant ‘influencer’ language effusiveness from others. The horror at our imminent destruction. The bafflement that we’re not taking it seriously enough, or not using it well enough. Everybody’s going to be stupid within the next five years! No, everybody will be able to do super-intelligent things within the next five years with all their free time! No, actually AI will kill us all!  All of this kind of stuff. Actually it gets to me quite a bit.  Some of it is certainly ill-informed. But even when it isn’t ill-informed, it is very often incredibly bile-filled and increasingly acrimonious. Like a mirror to the polarised political car crash unfolding across the world we just don’t seem to have preserved or value reasoned debate.

I say this all the time but we have to be a little bit more nuanced, particularly from the perspective of those of us working in academia. This is unlike any phenomenon that has come before. And I increasingly find myself wanting to find a language for talking about it. A language of compromise, perhaps. A language of mutual respect. A language that enables us to take some of the heat out of the vitriolic, the dismissive, the scornful, the abusive. The shouting prevents people from allowing themselves the space to ask questions that they worry might be perceived as stupid. It makes people pick a side. And, while here I am picking the fence/ shelf/ tightrope (select preferred metaphor), I’m increasingly convinced that picking sides is one of the least useful things we can do.

A lot and not very much has happened since I last posted. One thing I think has genuinely shifted is that the big tech companies have recognised the necessity of, at least superficially, challenging the very real threat of overdependence, cognitive dependency or excessive cognitive offloading. And cognitive offloading is an interesting example of precisely the problem I’m talking about. As many people with superior intellects to mine have pointed out, we have been cognitively offloading in all sorts of ways, and with all sorts of tools, since the dawn of civilisation. Whether it was chipping symbols into rocks to keep tabs on stock or to confirm contracts, all the way through to everything we have done in the digital era and continue to do now. Cognitive offloading isn’t inherently a bad thing; it’s a thing humans do. It is the way in which you do it, when you do it and the tools with which you do it that are the issue. And even that relatively simple discrimination, that simple distinction, seems sometimes to have been lost on people who are very, very intelligent themselves and very well educated and should know bloomin’ better.

Yes, the big tech companies are obviously doing their best to muscle in on the learning space. There has been a noticeable reframing of AI away from the idea of the glorified search engine, which it never was anyway, and the quick-answer machine, towards the idea of the learning machine. Something that can supposedly support cognitive development rather than replace it. Whether that is actually having much impact on student behaviour is another matter.I suspect it is having limited, perhaps minimal, impact so far. All the evidence I see seems to suggest that sophisticated workflows are still very much in the minority. Relatively simplistic uses and applications of free versions of tools remain the norm. And that, I think, is where some of the work now lies. Can we properly help students understand where the affordances lie and where the issues lie?

We have some brilliant colleagues here at UEL doing fantastic work around defining, or helping define for students, what it means to be an AI critic. And the more we can do of that, the better. But we also increasingly need to acknowledge that there are a lot of students who are either vocally taking a stand against AI or, if not exactly taking a stand, are reluctant in their compliance when asked to use AI tools. And often quite cynical and sneery about it.

My own daughter is 15 and talks about AI slop, AI garbage, AI propaganda and AI misinformation. Its rep is poor amongst the youth, I think it is fair to say. That matters because while all of this is going on, AI itself is changing. Big tech is steering towards learning utilities framings – from beyond that idea of a quick-answer machine towards something that can supposedly help develop synaptic firing and the cognitive development. At the same time, we have the increasingly agentic functions of new tools, the vibe-coding potential, the creating-things potential and everything else that is emerging.

It’s  particularly complex for those people who have still refused to really engage with or touch these technologies in any meaningful way, or who abandoned them early on because they thought, ‘Yeah, this isn’t for me’, or ‘this is killing the planet’, or for any of the other perfectly legitimate reasons people have for resisting. Meanwhile, people in roles like mine are trying to work out how we can best get people up to speed, whether they be staff or students, whilst continuing to acknowledge the very many valid reasons why people might push back, resist or reject. And, increasingly, I think all of this has to connect to the bigger questions about higher education. It’s still all about:

What is education for?

What is assessment for?

What is it that we are actually trying to judge?

What is it that we need to teach?

What is it that we want to evaluate?

And how well do our current structures, pedagogies and assessment designs actually enable us to do those things? Which is perhaps the funny thing about looking back at what I was writing in May. The technology has moved on an the tools are faster and more capable. The rhetoric has become louder and with that the polarisation is greater. The arguments about dependency, cognition, authenticity, assessment and human capability have become more urgent. But the questions underneath it all haven’t really changed. What are we trying to achieve through education, and which bits of that should technology help us with?

And perhaps, somewhere between the evangelists telling us that everything has changed and the uber sceptics telling us that everything is being destroyed, there is still some space for the rest of us to work that out, without shouting at each other like hecklers and snarly, stick wielding pensioners at a reform rally…. sorry, conference.

Customising LLMs: a case of cognitive seduction?

Over a year ago now, in an article for the AI in Education course, Trevor Baxter and I explored what was then still an emerging shift in the way generative AI was being used in HE: from general-purpose large language models towards greater visibility and awareness of tools that could be customised, grounded and shaped by users. The process is still ongoing and slow and there is, I think, a disparity in both awareness and use between students and their teachers (i.e teachers are less aware and doing it less).  At the time, much of the public conversation around AI focused on generic chatbot interactions. Early adopters and compulsive fiddlers like me and a bunch of people I have got to know these last few years from a range of institutions had been fiddling around with Custom GPTs in ChatGPT, Projects in Claude, and on its release in the UK, I think towards end of 2024, Google’s NotebookLM, which changed the user interface and pushed RAG to the forefront of capability awareness. Domain-specific AI assistants grounded in selected documents, tailored instructions and contextual memory addressed to an extent hallucination and trust issues which meant that if you were able to push the many many other issues to one side (!) (and increasingly it seems that students are definitely finding a way to do this), it completely changed the previously typical ‘short prompt in; long bit of superficial but supremely self-assured content out’ interaction. Put even more simply- generic chats are often poor quality but using (no code, non specialist) techniques to ‘ground’ the AI can lead to much improved outcomes. In that article, we discussed how these developments might address some of the persistent frustrations surrounding AI systems beyond hallucinations and generic outputs arguing that this might be the trigger towards more effective use in educational workflows. 

At the end of the article, we asked participants three broad questions:

  • What are your thoughts about the customisation potential of these tools?
  • Have you produced or used customised versions of generative AI models?
  • If not, what do you think the primary gains might be, and what might we lose if we use them too much or unthinkingly?

The response from the learning community was-and continues to be- really intersting. Across more than 400 comments over the last year or so, participants rarely settled into simplistic ‘AI is amazing’ or ‘AI is terrible’ positions which is good given the narratives of AI that persist in both regular and social media. Instead, the discussion revealed something consistently more nuanced- that I’d probably characterise as cautious (granted also sometimes exuberant) optimism mixed with genuine (granted sometimes staring-eyed) concern. One of the clearest themes was that participants saw grounding and customisation as a major improvement over generic AI systems and my own view is that this does mean we need to ensure that every stakeholder has to have at least a broad understanding of the implications of this. Many course participants described a sense of relief at the possibility of more focused, context-aware outputs:

“It feels less like asking a random stranger on the internet and more like consulting an assistant who has actually read the material.”

Another participant nailed the thing that worries me a lot about how far our students are prepared to go along with AI outputs uncritically: 

“The accuracy is exciting, but also dangerous because people may trust it too much precisely because it sounds more grounded.”

No aspect of the discussion generated more visceral reaction than NotebookLM’s podcast feature and while they have introduced more tools since which I think are genuinely valuable in terms of reformatting information, it is still the tool that is most likely to drop jaws of those unfamiliar (even though the default American accent and tone are often cited as reasons to not use it in those outside the US context). Participants used words like “astounding”, “uncanny”, “disturbing”, “impressive”, “surreal” and “frightening”. The realism of the generated dialogue appeared to unsettle people as much as it impressed them. It made me think how we are firmly in an era where the uncanny valley effect has seeped into audio media. 

Screenshot from Notebook LM interactive ‘call in’ podcast feature.

One participant said:

“I forgot after thirty seconds that the voices were artificial.”

But another noted the commonly expressed feeling:

“It sounds polished and convincing, but also oddly empty, like listening to people perform understanding.”

I think that last point is really critical going forward. Many participants were not simply evaluating technical quality because, either explicitly or implicitly, they were addressing issues of trust and deeper connections with content. It’s useful I think to acknowledge, as I did in my previous post reflecting on another forum discussion, that all those contributing had made the decision to follow and actively participate in these discussions- this suggests a strong predisposition to think though I imagine, in open forums, to also be influenced by others in those same threads.  Trust was explicitly raised in terms of the implications for the relationships between students and their teachers and, incorrect or misinformation. If synthetic conversations become indistinguishable from human ones, what happens to assumptions around expertise, testimony and authenticity?

Increasingly the discussion revealed educators who had been experimenting with customised AI systems in interesting and quite sophisticated ways: building assessment-support GPTs, lesson-planning assistants, revision tools and discipline-specific tutoring systems. Others discussed using NotebookLM or Claude Projects to synthesise literature, organise ideas or support research coding.

“I uploaded policy documents, module guides and marking criteria into a custom GPT and immediately got more useful outputs than from standard ChatGPT.”

another offered a more cautious perspective:

“It’s brilliant at producing structure and starting points, but weak at nuance and disciplinary judgement.”

Perhaps my tendency to be optimistic is clouding my view here but participants were not by and large seeming to treat AI as a replacement for expertise- though would they admit that if they were? (unlikely I know). Many explicitly framed it as a collaborative or augmentative tool that could remove repetitive labour leaving them, the (presumably!) humans responsible for interpretation, evaluation and decision-making.

Other common gains included:

  • faster synthesis of complex material
  • support for revision and study
  • Change in the way interactions worked- eg clear indication of source of summary output
  • increased accessibility
  • multilingual support
  • reduced administrative workload
  • support for neurodivergent learners
  • improved contextualisation of AI outputs

“For the first time, I felt AI was pointing me back towards sources rather than away from them.”

“If AI can help with the labour of organisation and sorting, perhaps humans can spend more time actually thinking.”

At the same time, concerns about cognitive offloading cropped up a lot and increasingly so. I maintain that cognitive offloading is not necessarily a bad thing though it connotes that way currently. Writing can be a form of cognitive offloading after all. My most important offload I do at least twice a day (now, frequently, via dictation function in an LLM I have to admit) is the production of to-do lists. Nevertheless, inappropriate cognitive offloading is something we need to spend more time discussing I think.  Participants repeatedly questioned what might happen if learners increasingly bypassed difficult but important cognitive processes such as reading, synthesis, uncertainty and reflection.

“The danger is not that students will cheat. It is that they may stop wrestling with ideas.”

“Convenience is seductive. The risk is that we slowly outsource the struggle that learning depends on.”

And it is that seduction that I think could be a useful lens to reflect on our behaviours. One thing I noted was that a lot of folk were feeling  (like I have for a long time) that there is another cognitive label we have to confront: cognitive dissonance. Many were excited and uneasy at the same time. They argued that there is genuine potential for accessibility, efficiency (I am still a BIG sceptic about this, noting how I am busier – but likely more productive than ever) and learning support, while also insisting that critical thinking, disciplinary expertise and human judgement must remain central – even though they see patterns of loss of centrality as the treacherous AI terrain is navigated awkwardly and unevenly, often without the expert stewardship (for either teacher or student) that is critical. In the same way, though, that I think we need a nuanced perspective on ‘offloading’ I likewise think we need an increasingly nuanced one in terms of the seductive power of customisation. Seduction to me connotes being led astray but people are and will continue to be seduced by things and a lot of pleasure will undoubtedly follow too. Seduction need not be always negatively connoted- if we are drawn to the Siren call of customisation we need to go into experiementation with our eyes and ears open (so avoid Odysseus’ beeswax solution), be aware of the promise of potential new ways of seeing and doing and learning whilst understanding that there’s a real danger of falling too far under a spell.

‘Good’ English & the AI thing

I was once told by a fellow English teacher (back in the day) that it was funny I was teaching English. I asked why. ‘Well you’re all ‘wiv’ and ‘froo,” she said, highlighting my non standard accent. After that I meekly tried to speak ‘better’ for ages, especially in company of the other English teachers. It knocked my sideways but over time I fought back in small ways. Not enough though. Conversations and a couple of articles I read on WonkHE this week have given me a chance to think about how AI is offerring leverage to change and a critical lens on what we value in so called academic English.

I have just come from a meeting with Kelly Webb-Davies, whose thinking (nicely exemplified here ‘I wrote this- or did I?’ and here: ‘On the toxicity of assuming writing is thinking’ ) and ongoing work at Oxford I have long found a perfect prod to the hornets’ nest that is my brain. What Kelly consistently does so well, and what came through again very strongly in our discussion, is a refusal to accept binary, nuance-less framing of AI as either an existential threat or panacea to drudgery. Instead, Kelly’s focus in our conversation was towards much harder and more important questions: what do we think writing is for (anywhere but especially in academia where student writing is the means by which they are evaluated not the the thing they are being judged on), why we value it so highly in assessment, and where might our assumptions about writing, cognition and academic legitimacy be misplaced. 

Kelly’s work is particularly powerful in contrasting privileged and/or colonial thinking about ‘correct’ and ‘acceptable’ academic writing with translanguaging, our idiolects, cultural and linguistic histories and neurodiversity. Kelly problematises assumptions about writing as a technology that suggest writing is a natural proxy for thinking or even THE way of doing thinking (can you do thinking?). In addition, writing has always evolved alongside other technologies, from inscription to print to word processors, and yet higher education continues to treat a narrow, highly codified form of written English as if it were a timeless measure of intellectual engagement. Thinking manifests in multiple ways, many of which are poorly served by conventional academic writing. AI is able to bridge idiolect to ‘accepted’ language in standard forms (see how I keep using inverted commas?) translating between ways of thinking and the forms the academy currently recognises. This then raises questions about how far we might use AI to compromise in this way or whether we might leverage this truly disruptive phenomenon to challenge yet more of our fundamental beliefs about what is and is not acceptable as well as how we validate learning. Received wisdom may not be that wise at all; a point I tried to make in my previous post. I’m really looking forward to reading and hearing more about Kelly’s ideas a for Voice First Written Assessment (VFWA) which is a really practical and thoughtful approach that values the way people think, speak and write, putting that at the heart of assessments (while enabling valid assessment; everyone’s a winner!)

My meeting coincided with publication of a piece from Jim Dickinson earlier this week. Dickinson does not deny the risks of AI, nor does he minimise the evidence that poorly designed uses of AI can undermine learning. The folk (me included) writing or talking about AI often feel obligated to front load caveats and boundaries (I understand this; I don’t want to talk about that… while thinking:  ‘please don’t derail this session by insisting we talk about ‘y’) though Dickinson does pretty good job of weaving a LOT of things in! He very helpfully brings together ‘famous’ – perhaps even notorious- studies from a growing body of research that shows, when taken collectively, how uncritical, convenience-driven AI use can hollow out motivation, attention and the owning of ideas and learning itself but can also be a boon to ‘productive struggle’ and valuable scaffold (if used advisedly).  What emerged was that the problem is not helpful when framed as AI as this broad, abstract threat. The ongoing research is critical but that shouldn’t legitimise stalling interventions, exploration and confrontation of the harsh realities of the extensive, actual, often unsupported use of AI in research and writing (as production) workflows. The way learning and assessment continue to be designed around immature understandings constrained by tradition and conservatism (my reading, not his words) gives a sense that what we thought we were doing were frail even before ChatGPT. The same tool that produces passivity in one design can deepen judgement and persistence in another. Potential and frailty coexist, and the difference is pedagogical intent, design and scaffolding.

In another piece this week on WonkHE, Rex McKenzie, makes an observation that will I am sure cause debate and consternation but it’s a fundamental one. McKenzie’s comparison between university expectations and journal publishing practices is pretty stark. He shows that while students in many (most? all?) institutions are policed for using AI to shape language, structure and expression, the professional academic world (as manifested in journal policies on AI use) has largely accepted AI-assisted writing, provided accountability and disclosure remain with the human author. This contrast exposes how much of what we currently assess is not intellectual substance but adherence to a particular linguistic performance. Across academic publishing trust appears to be growing on the assumption that if writing is honed it is ok because the research and ideas are owned by the authors. This then raises a really uncomfortable question about permitted AI use (not least in traffic light systems) where ideation is often seen as fine but the use of AI to support writing is taboo. Have we got it arse about face? (I used that phrasing as a less than subtle way to signal that no AI is choosing my metaphors). 

Rex McKenzie’s thinking converges strongly with Kelly’s argument. The insistence that “writing is thinking” is not an innocent pedagogical claim; it is historically and culturally situated. It privileges those already fluent in dominant academic registers and marginalises others, whether through class, language background, neurotype or other trait or characteristic. Treating one form of English as the sole legitimate evidence of cognitive engagement risks mistaking conformity for rigour. AI does not create this problem, but (finally!) it makes it harder to ignore. Talking with Kelly and reading these articles and thinking about the ‘problem’ of AI I arrive at what will be for many a really uncomfortable conclusion. It’s been said before but it’s worth saying again through a lens of challenge to the hegemony of standard forms of expression and writing: The AI threat is not primarily about cheating, efficiency or technological disruption. It’s a threat to convention; to conservatism; to tradition; to imagined halcyon times. Let us re-articulate and argue about what we value, what we recognise and what we are willing to redesign. If we continue to treat writing as both the locus and the pre-eminent proof of thinking, we will remain trapped in defensive and incoherent policy positions. If, instead, we take seriously questions of cognitive engagement, judgement and inclusion, then AI catalyses (and can even enable) long-overdue honesty about assessment, pedagogy and realities of how systems continue to marginalise. 

Kelly will be speaking at a compassionate assessment event on 5th March – you can also see my contribution to the compassionate assessment resources via QAA pages here.

‘I can tell when it’s been written by AI’

Warning: the first two paragraphs might feel a bit like wading through treacle but I think what follows is useful and is probably necessary context to the activity linked at the end!

LLMs generate text using sophisticated prediction/ probability models, and whilst I am no expert (so if you want proper, technical accounts please do go to an actual expert!) I think it useful to hone in on three concepts that help explain how their outputs feel and read: temperature, perplexity and burstiness. Temperature sets how adventurous the word-by-word choices are: low values produce steady, highly predictable prose; high values (this is on a 0-1 scale) invite surprise and variation (supposedly more ‘creativity’ and certainly more hallucination). Perplexity measures how hard it is to predict the next word overall, and burstiness captures how unevenly those surprises cluster, like the mix of long and short sentences in some human writing, and maybe even a smattering of stretched metaphor and whimsy. Most early (I say early making it sound like mediaeval times but we’re talking 2-3 years ago!) AI writing felt ‘flat’ or ‘bland’ and therefore more detectable to human readers because default temperatures were conservative and burstiness was low.

I imagine most ChatGPT (other tools are available) users do not think much about such things given these are not visible choices in the main user interface. Funnily enough, I do recall these were options in the tools that were publicly available and pre-dated GPT 3.5 (the BIG release in November ’22). Like a lot of things skilled use can impact (so a user might specify a style or tone in the prompt). Also, with money comes better options so, for example,  Pro account custom GPTs can have precise built in customisations. I also note that few seem to use the personalisation options that override some of the things that many folk find irritating in LLM outputs (Mine states for example that it should use British English as default, never use em dashes and use ‘no mark up’ as default). I should also note that some tools still allow for temperature manipulation in the main user interface (Google Gemini AI Studio for example) or when using the API (ChatGPT). Google AI Studio also has a ‘top P’ setting allowing users to specify the extent to which word choices are predictable or not.  These things can drive you to distraction so it’s probably no wonder that most right-thinking, time poor people have no time for experimental tweaking of this nature. But as models have evolved, developers have embedded dynamic temperature controls and other tuning methods that automatically vary these qualities. The result is that the claim ‘I can tell when it’s AI’ may be true of inexpert, unmodified outputs from free tools but so much harder from more sophisticated use and paid for tools. Interestingly, the same appears true for AI detectors. The early detectors’ reliance on low-temperature signatures now need revisiting too for those not already convinced of their vincibility.  

Evolutionary and embedded changes therefore have a humanising effect on LLM outputs. Modern systems can weave in natural fluctuations of rhythm and unexpected word choices, erasing much of the familiar ChatGPT blandness. Skilled (some would say ‘cynical’) users, whether through careful prompting or bypassing text through paraphrasers and ‘humanisers’,  can amplify this further. Early popular detectors such as GPTZero (at my work we are clear colleagues should NEVER be uploading student work to such platforms btw) leaned heavily on perplexity and burstiness patterns to spot machine-generated work, but this is increasingly a losing battle. Detector developers are responding with more complex model-based classifiers and watermarking ideas, yet the arms race remains uneven: every generation of LLMs makes it easier to sidestep statistical fingerprints and harder to prove authorship with certainty.

For fun I ran this article through GPT Zero….Phew!

It is also worth reflecting on what kinds of writing we value. My own style, for instance, happily mixes a smorgasbord of metaphors in a dizzying (or maybe its nauseating) cocktail of overlong sentences, excessive comma use and dated cultural references (ooh, and sprinkles in frequent parentheses too). Others might genuinely prefer the neat, low-temperature clarity an AI can produce. And some humans write with such regularity that a detector might wrongly flag them as synthetic. I understand that these traits may often reflect the writing of neurodivergent or multi-lingual students.

To explore this phenomenon and your own thinking further, please try this short activity. I used my own text as a starting point and generated (in Perplexity) five AI variants of varying temperatures. The activity was built in Claude. The idea is it reveals your own preferred ‘perplexity and burstiness combo’ and might prompt a fresh look at your writing preferences and the blurred boundaries between human and machine style. The temperature degree is revealed when you make your selection. Please try it out and let know how I might improve it (or whether I should chuck it out the window i.e. DefenestrAIt it)

Obviously, as my job is to encourage thinking and reflection about what this means for those teaching, those studying and broadly the institution they work or study in, I’ll finish with a few questions to stimulate reflection or discussion:

In teaching: Do you think you can detect AI writing? How might you respond when you suspect AI use but cannot prove it with certainty? What happens to the teacher-student relationship when detection becomes guesswork rather than evidence?

For assignment design: Could you shift towards process-focused assessment or tasks requiring personal experience, local knowledge or novel data? What kinds of writing assignments become more meaningful when AI can handle the routine ones? Has that actually changed in your discipline or not?

For your students: How can understanding these technical concepts help students use AI tools more thoughtfully rather than simply trying to avoid detection? What might students learn about their own writing voice through activities that reveal their personal perplexity and burstiness patterns? What is it about AI outputs that students who use them value and what is it that so many teachers disdain?

For your institution: Should institutions invest in detection tools given this technological arms race, or focus resources elsewhere? How might academic integrity policies need updating as reliable detection becomes less feasible?

For equity: Are students with access to sophisticated prompting techniques or ‘humanising’ tools gaining unfair advantages? How do we ensure that AI developments don’t widen existing educational inequalities? Who might we be inadvertently discriminating against with blanket bans or no use policies?

For the bigger picture: What kinds of human writing and thinking do we most want to cultivate in an age when machines can produce increasingly convincing text? How do we help students develop authentic voice and critical thinking skills that remain distinctly valuable?

When you know the answer to the last question, let me know.

Essays & AI: collective reflections on the manifesto one year on

Its roughly a year since we (Claire Gordon and I plus a collective of academics from King’s & LSE) published the Manifesto for the Essay in the Age of AI. Despite improvements in the tech AND often pretty compelling evidence and arguments for the reduction of take home, long form writing in summative assessments, I STILL maintain the essay has a role as I did this time last year. On one of the pages of the AI in Education short course authored by colleagues at King’s from the Institute of Psychiatry, Psychology & Neuroscience (Brenda Williams) and Faculty of Dentistry, Oral & Craniofacial Sciences (Pinsuda Srisontisuk and Isabel Miletich) they detail patterns of student AI usage. They end with a suggestion that participants take a structured approach to analysing the Manifesto and the outcome is around 150 responses (to date) offerring a broad range of thoughts and ideas from educators working across disciplines and educational levels across the world. This was the forum prompt:

Is the essay dead?

The manifesto above argues that this is not the case, but many believe that long form writing is no longer a reliable way to assess students. What do you think?

Although contributors come from diverse contexts, some shared patterns and tensions really stand out which I share below. I finish with a wee bit of my own flag waving (seems to be a popular pastime recently).

Sentiment balance

The overwhelming sentiment is broad agreement and reformist.

  • Most participants explicitly reject the idea that “the essay is dead”. They value essays for nurturing critical thinking, argumentation, independence and the ability to sustain a coherent structure.
  • A minority voice expresses stronger doubts, usually linked to practical issues (e.g. heavy marking loads, students’ shrinking reading stamina, or the ease of AI-generated text) and call for greater diversification of assessment.
  • There is also a strand of cautious pragmatism: many see the need for significant redesign of both teaching and assessment to remain relevant and credible.

In short, the mood is hopeful and constructive rather than nostalgic or doom ‘n’ gloom. The essay is not to be discarded but has to be re-imagined.

Here are a couple of sample responses:

Not quite dead, no. I think of essays as a ‘thinking tool’ – it’s a difficult cognitive task, but a worthwhile one. I think, as mentioned in the study, an evolution towards ‘process orientated’ assessment could be the saviour of the essay. Perhaps a movement away from the product (an essay itself) being the sole provider of a summative grade is what’s needed. Thinking of coursework, planning, supervisor meetings and a reflective journal on how their understanding developed over the process of researching, synthesising, planning, writing and redrafting could be included. (JF)

In their current form, many take-home essay assessments no long reliably measure a students’ learning, nor mirror the skills students need for the workplace (as has arguably always been the case for many subjects). I wonder if students may increasingly struggle to see the value of writing essays too. However, I do value the thought processes that go into crafting long form writing. I think if essays are thoughtfully redesigned and include an element of choice for the learner, perhaps with the need to draw on some in-house case study or locally significant issue, then essays are not necessarily dead.(AM)

The neat dodge to this question is to suggest the essay will be like the ship of Theseus. It will remain but every component in it will be made of different materials 🙂 (EP)

Key themes emerging from the comments

1. Process over product
A strikingly common thread is the shift from valuing the final script to valuing the journey of thought and writing. Contributors repeatedly advocate staged submissions, reflective journals, prompts disclosure, oral defences or supervised drafting. This aligns directly with the manifesto’s calls to redefine essay purposes and embed critical reflection (points 3 and 4).

2. Productive integration of AI
Few respondents argue for banning AI (obviously the responses are skewed towards those willing to undertake an AI in Education short course in the first place!). Instead, many echo the manifesto’s seventh and eighth points on integration and equity. Suggestions include:

  • require students to document prompts and edits,
  • use AI to generate counter-arguments or critique drafts,
  • support second-language writers or neurodivergent students with AI grammar or audio aids,
  • design tasks tied to personal data, lab results or workplace contexts that AI cannot easily fabricate.

A persistent caution is that without clear guidance, AI may encourage superficial engagement or plagiarism. Transparent ground rules and explicit teaching of critical AI literacy are seen as essential.

3. Expanding forms and contexts
Many contributors support the manifesto’s second point on diverse forms of written work. They propose hybrid assessments such as essays combined with oral presentations, podcasts, infographics or portfolios. Others emphasise discipline-specific needs: scientific reporting, medical case notes, or creative writing, each with distinct conventions and AI implications.

4. Equity, access and institutional support
There is strong agreement that AI’s benefits and risks are unevenly distributed. Participants highlight the need for:

  • institutional investment in staff development and student training,
  • clarity on acceptable AI use across programmes,
  • assessment designs that do not disadvantage those with limited technological access.

5. Rethinking academic integrity
Several comments resonate with the manifesto’s call to revisit definitions of cheating and originality. Rather than policing AI, some suggest designing assessments that render unauthorised use unhelpful or irrelevant, while foregrounding honesty and reflection.

What this means for the manifesto

The forum feedback affirms the manifesto’s central claim that the essay remains a vital, adaptable form, but it also pushes its agenda in useful directions.

  • Greater emphasis on process-based assessment. While the manifesto highlights process and reflection, practitioners want even stronger endorsement of multi-stage, scaffolded approaches and/ or dialogic or presentational components as the cornerstone of future essay design.
  • Operational guidance for AI use. Educators call for more than principles: they need models of prompt documentation, supervised writing practices and examples of AI-resistant or AI-enhanced tasks.
  • Disciplinary specificity. The manifesto could further acknowledge the wide variance in how essays function, from lab reports to creative pieces and provide pathways for each. Of course we, like everyone are subject to a major impediment…
  • Workload and resourcing. Several voices stress that meaningful change requires institutional support and realistic marking expectations; without these, even the best principles risk remaining aspirational. This for me is likely the biggest impediment, not least because of the ongoing, multi layered crises HE is confronted with just now.

Overall, the conversation demonstrates an appetite for renewal rather than retreat to sole reliance on in-person exams though this remains still a common call. I stand with the consensus view that the essay (and other long form writing) is not in terminal decline but in the midst of a necessary transformation. What we need to see is this: Educators alert to the affordances and limitations of AI, conversations happenning between students and those that support them in discipline and with academic skills and students writing assessments that are AI-literate. As we find our way to the other side of this transititional space we are in, deluged by inappropriate use and assessments too slow in changing, eventually the writing will (again) be genuinely engaging, students will see value in finding their own voices and we’ll move closer to consensus on some new ways of producing as legitimate. When I read posts on social media advocating wholesale shift to exams (irrespective of other competing damages this may connote and in apparent ignorance of the many ways cheating happens in invigilated in person exams) or ‘writing is pointless’ pieces I am struck by the usually implicit but sometimes overt assumption that writing is ONLY valuable as evidence of learning. Too rarely are formative/ developmental aspects rolled into the arguments alongside a failure to connect to persuasive (in this and wider for learning arguments) rationales for reconsidering the impact on grades on how students approach wiritng. And, finally, even if 80% of students did want the easiest route to a polished essay, I’m not abandoning the 20% that appreciate the skills development, the desirable difficulties and will to DO and BE as well as show what they KNOW. Too many of the current narratives advocate not only thowing the baby out with the bathwater but then refuse to feed the baby because, you know, the bathwater was dirty. Unpick THAT strangled metaphor if you can.

Transparent AI workflows

I just posted this on innovation and I thought the way I got to that from an idea that popped into my head while listening to a podcast when walking to the station might be interesting for a few reasons. 1. Because we talk a lot about transparency in AI use…well the link below and then the final post reveals the stages I went through (other than the comprehensive final edit I did in MS Word). 2. I think it shows a lot about the increasing complexity in the nature of authorship. 3. It shows that where AI augments writing, how challenging it is to capture the nature of use and actually ‘be’ transparent because writing has suddenly become somehting I can do on the move and 4. will challenge many to consider the legitimacy and quality of writing when produced in this way. I should also note that this post, I did in the regular way of typing direxctly into the WordPress author window without (not sure why) built in spellechecker.

Here is the full transcript of the audio conversation followed by (at the draft stage) additional text based prompts I did while strap hanging on the tube. The final edit I did this afternoon on my laptop.

Image: https://www.pexels.com/@chris-f-38966/

The Manus from U.N.C.L.E.

‘Deploying AI agents’ sounds so hi tech and futuristic to (non Comp-Sci) me whilst weirdly also resonating of classic 60s and 70s TV shows I loved as a kid. I have been fiddling for a while on the blurred boundaries between LLMs and Agents, notably with Claude, but what appealed when I first saw Manus was the execution of outputs seemingly beyond what Claude can manage. Funnily enough it looks quite a bit like Claude but it seems it is actually a multi-tool agent. I pretty much concur with the conclusion from the MIT Tech review:

While it occasionally lacks understanding of what it’s being asked to do, makes incorrect assumptions, or cuts corners to expedite tasks, it explains its reasoning clearly, is remarkably adaptable, and can improve substantially when provided with detailed instructions or feedback. Ultimately, it’s promising but not perfect.

Caiwei Chen

Anyway, I finally got in, having been on the Manus waitlist for a while. Developed by Chinese startup Monica, it is an autonomous AI agent capable of executing complex online tasks without ongoing human input and created something of a buzz. TL:DR: This is the initial output from first prompt to web-based execution. The selection and categorisation need honing but this in my view is an impressive output. The second version after addition of a follow up prompt.

Longer version:

I wanted to see what I could get from a single prompt so decided to see if it could build a shareable, searchable web page that curates short how-to videos (under five minutes) by higher education educators demonstrating uses of Generative AI. I began by requesting Manus to collect and cluster videos showing how AI is applied in teaching, assessment, feedback, and research (Natural Language Prompt). Manus responded immediately by creating a structured project directory and initiating web searches to identify relevant video content, starting with collections from institutions like Notre Dame and Harvard (which it didn’t get beyond in the first iteration).

Once videos were sourced, Manus automatically filtered them to ensure they were under five minutes in length (but failed to note that one wasn’t a video and others linked to same page where they were already curated!) and produced by educators in the higher education sector (this it did get right). It then categorised them by thematic area (Teaching, Assessment & Feedback, AI Literacy, and Research but the categrosations were not always spot on) while also tagging institutional affiliations (mostly OK). The tagging and filtering work pretty well but the actual sourcing is very limited in that first iteration. A metadata database was created to support an interactive interface, allowing users to search videos by presenter, title, or description and filter by category or institution. Manus built this interface and deployed the collection to a live web page: https://cjubrvkx.manus.space. It took about 15 minutes. Iteration is by natural language prompting for fine tuning.

The initial prompt from me and the start of the executaiton of the processes

After launch, I expanded the scope by asking Manus to include contributions from additional individuals and institutions- I threw in my old buddy Dr. Gerhard Kristandl (Dr GeeKay) who I know has a YouTube channel, I added me- Martin Compton (without any additional info) and it found relevant content from each of us. The other three potential souces I threw in reaped no reward though. This is the reasoning:

I searched for content from Tadhg Blommerde, University of Sydney, and Monash University as well, but most of their videos exceeded our 5-minute duration criteria. I’ve documented all findings in the data directory for reference.

Manus resumed its asynchronous task execution, searching for and integrating further video content into the existing framework enabling me to publish a second version . So the output works, I would need much more thoughtful prompting to get it how I want it and, tbh, there are better ways of curating YouTube content but the principle is what interested me and the output is remarkable given the stages and processes it went through.

You can watch a replay of the agent in action here. Not as much fun as watching Napoleon Solo and Illya Kuryakin combat THRUSH (I know, I know).

But how? And why even? Practical examples of ways assessments have been modified

Modifying or changing assessment ‘because of AI’ always feels like it feeds ‘us and them’ narratives of a forthcoming apocalypse (already predicted) and couches the change as necessary only because of this insidious, awful thing that no-one wants except men in leather chairs who stroke white cats.

It is of course MUCH more complex than that and much of the desired change has been promoted by folk with a progressive, reform, equity, inclusion eye who do (or immerse themselves in) scholarship of HE pedagogy and assessment practices.

Anyway, a colleague suggested that we should have a collection of ideas about practical ways assessments could be modified to either make them more AI ‘robust’ or at least ‘AI aware’ or ‘ AI inclusive’ (I’m hesitant to say ‘resitant’ of course). Whilst colleagues across King’s have been sharing and experimenting it is probably true to say that there is not a single point of reference. We are in King’s Academy working on remedying this as part of the wider push to support TASK (transforming assessment for students at King’s) and growing AI literacy but first I wanted to curate a few examples from elsewhere to offer a point of reference for me and to share with colleagues in the very near future. I’ve gone for diversity from things I have previously book marked. Other than that, they are here only to offer points of discussion, inspiration, provocation or comparison!

Before I start I should remind KIng’s colleagues of our own guidance and the assessment principles therein, note that with collleagues at LSE, UCL and Southampton I am working on some guidance on the use of AI to assist with marking (forthcoming and controversial). Some of the College Teaching Fund projects looked at assessment and This AI Assessment Scale from Perkins et al. (2024) has a lot of traction in the sector too and is not so dissimilar from the King’s 4 levels of use approach. It’s amazing how 2023 can feel a bit dated in terms of resources these days but this document form the QAA is still relevant and applicable and sets out broader, sector level approarpriate principles. In summary:

  • Institutions should review and reimagine assessment strategies, reducing assessment volume to create space for activities like developing AI literacy, a critical future graduate attribute.
  • Promote authentic and synoptic assessments, enabling students to apply integrated knowledge practically, often in workplace-related settings, potentially incorporating generative AI.
  • Move away from traditional, handwritten, invigilated exams towards innovative approaches like digital exams, observed discipline-specific assessments or oral examinations
  • Design coursework explicitly integrating generative AI, encouraging ethical use, reflection, and hybrid submissions clearly acknowledging AI-generated content.
  • Follow guiding principles ensuring assessments are sustainable, inclusive, aligned to learning outcomes, and effectively demonstrate relevant competencies, including appropriate AI usage.

I’m also increasingly referring to the two lane approach being adopted by Sydney which leans heavily into similar principles. Context is different to UK of course but I have a feeling we will find ourselves moving much closer to the broad approach here. It feels radical but perhaps no more radical than what many, if not most, unis did in Covid.

Finally, the examples

Example 1. UCL Medical Sciences BSc.

  • Evaluation of coursework assessments to determine susceptibility to generative AI and potential integration of AI tools.
  • Redesign of assessments to explicitly incorporate evaluation of ChatGPT-generated outputs, enhancing critical evaluation skills and understanding of AI limitations.
  • Integration of generative AI within module curricula and teaching practices, providing formative feedback opportunities.
  • Collection of student perspectives and experiences through questionnaires and focus groups on AI usage in learning and assessments.
  • Shift towards rethinking traditional assessment formats (MCQs, SAQs, essays) due to AI’s impact, encouraging ongoing pedagogical innovation discussions.

Example 2 – Cardiff University Immunology Wars

  • Gamification: Complex immunology concepts taught through a Star Wars-inspired, game-based approach.
  • AI-driven game design: ChatGPT 4.0 used to structure game scenarios, resources, and dynamic challenges.
  • Visual resources with AI: DALLE-3 employed to create engaging imagery for learning materials.
  • Iterative AI prompting: An innovative method using progressive ChatGPT interactions to refine complex game elements.
  • Practical, collaborative learning: Students collaboratively trade resources to combat diseases, supported by iterative testing and refinement of the game.

Example 3 Traffic lights University Winsconsin Green Bay

The traffic light system they are implementing is reflected in these three sample assessments:

  1. Red light – prohibited
  2. Yellow light – limited use
  3. Green Light – AI embedded into the task

Example 4 Imperial Business School MBA group work

  • Integration of AI: The original essay task was redesigned to explicitly require students to use an LLM, typically ChatGPT.
  • The change: Individual component of wider collaborative task. Students submit both the AI-generated output (250 words) and a critical evaluation of that output (250 words) on what is unique about a business proposal.
  • Critical Engagement Emphasis: The new task explicitly focuses on students’ critical analysis of AI capabilities and limitations concerning their business idea.
  • Reflective Skill Development: Students prompted to reflect on, critique, and consider improvements or extensions of AI-generated content, enhancing their evaluative and adaptive skills.

3 for 1! Example 5 – Harvard

Create a fictional character and interview them

World building for creative writing

Historical journey

More to follow…

Also note:

Manifesto for the essay

Related article (Compton & Gordon, 2024)
 
Also see: (Syska, 2025)We tried to kill the essay

Bots with character

This is a swift intro to character AI *(note 1)- a tool that is available to use for free currently (on a freemium model). My daughter showed it to me some months ago. It appears as a novelty app but is used (as I understand it) beyond entertainment for creative activity, gaming, role playing and even emotional support. For me it is the potential to test ideas that many have about bot potential for learning that is most interesting. By shifting focus away from ‘generating essays’ it is possible to see the appeal of natural language exchanges to augment learning in a novel medium. While I can think of dozens of use cases based on the way I currently (for example) use YouTube to help me to learn how to unblock a washing machine I imagine that is a continuum that goes all the way up to teacher replacement.*(note 2) Character AI is built on large language model, employs ‘reinforcement’ (learning as coversations continue) and provides an easy to learn interface (basically typing stuff in boxes) that allows you to ground the bot with ease in a wysiwyg interface.

As I see it, it offers three significant modifications in the default interface to standard (free) LLMs. 1. You can create characters and define their knowledge and ‘personality’ traits by having space to ground the bot behaviour through customisation. 2. You can have voice exchanges by ‘calling’ the character. 3. Most importantly, it shifts the technology back to interaction and away from lengthy generation (though they can still go on a bit if you don’t bake succinctness in!) What interests me most is the potential to use tools like this to augment learning, add some novelty and provide reinforcement opportunity through text or voice based exchanges. I have experimented with creating some academic architypes for my students to converse with. This one is a compassionate pedagogue, this one is keen on AI for teaching and learning, this one a real AI sceptic, this one deeply worried about academic integrity. They each have a back story, defined university role and expertise. I tried to get people to test arguments and counter arguments and to work through difficult academic encounters. It’s had mixed reviews so far: Some love it; some REALLY do not like it at all!

How do/ could you use a tool like this?

Note 1. This video in no way connotes promotion or recommendation (by me or by my employer) of this software. Never upload data you are not comfortable sharing and never upload your own or other’s personal data.

Note 2: I am not a proponent of this! There may be people who think this is the panacea to chronic educational underfunding though so beware.