Author: heducationist

Associate Professor - UK Higher Education

Illusions of usefulness and singing trees

heducationistLeave a comment

I am two weeks back from keynoting and conferencing in Switzerland. I have declined most overseas invites (sometimes with a tear in my eye) but this was a chance to work with someone I have known in the faculty development space for several years and whom I greatly admire, Dr Ruth Puhr. I had expected to find time to reflect as soon as I got back from this visit to Zurich for the Swiss Faculty Development Network (SFDN) conference but instead of the usual cheese and chocolate delicacies, I brought back flu and only now is my brain beginning to de-fug. Anyway, it was hosted at the impressive PHZH in the very clean (but still with a livelier side designed for those younger than me) city of Zurich. 

Padagogische Hochschule Zürich

I was invited to focus specifically on the ‘interesting’ (and challenging, difficult, complex) space academic developers find themselves in when it comes to AI so framed my keynote as ‘Promises, pitfalls and paradoxes’. The purpose of the day was to create a shared, evidence-informed space to think seriously about what AI means for faculty development, not as a technical problem to be solved but as a pedagogical, institutional, ethical and perhaps even existential challenge to be worked through collectively. The programme deliberately balanced framing and provocation with empirical and practice-based contributions. Across the day, sessions ranged from my provocations, the student perspective and through a range of experiments and studies of AI use in teaching and learning, to practical explorations of chatbots, institutional support structures, sustainability and emerging competency frameworks. The institutions represented ranged from large research intensives to small, specialist providers. All against a backdrop of Christmas markets, singing trees and the aroma of Gluhwein.

Taken together, the conference showed that the Swiss context is not so different from the UK. Grappling with lack of consensus, real tensions between a sense of urgency to adapt against a necessity for prudence and anxiety about the many complex implications.  Discussions centred on how AI is actually being used by students and staff (and where it is not), where assumptions do not always align with evidence, and why faculty development must find ways to acknowledge, work with and move beyond blind engagement narratives or refusal to engage towards more nuanced, literate and context-sensitive approaches.

I was particularly delighted to share the keynote platform with Julia Bogdan, whose perspective as co-President of the Swiss Student Union brought an essential counterpoint to institutional and academic viewpoints. The breadth of sessions that followed reinforced this aim, showing how questions of AI adoption cut across levels, roles and disciplines. 

The whole day was excellent but two sessions stood out in particular. The first, presented by Valentina Rossi and Marc Laperrouza from EFPL (the Swiss Federal Tech Institute) looked at teacher-curated chatbots and provided a solid illustration of core tensions. Evidence from a real course context showed, probably unsurprisingly,  that students reported the grounded/ curated chatbot as supportive for learning and more trustworthy than a generic chatbot, though still not as effective (or at least valued) as the conventional method used (teaching supported with annotated slides), and with only modest effects on interest. The study was a good reminder that curation (and who does the curation) matters, that student trust is shaped by pedagogical framing (so surfacing rationales for approaches is key), and that AI is not a substitute for teaching so much as a means to reconfigure or augment it. What is paramount though is that these students were given the opportunity to realise this as they worked through the module. 

The second session, presented by Adrian Holzer from Neuchatel University, reported findings from a controlled field study examining whether generative AI enhances or impairs active learning and outcomes in a data visualisation course. Undergraduate students completed a 90-minute task under three conditions: students working alone, students working with AI and AI working alone. While students with AI experienced a substantial reduction in perceived task difficulty, their assignment scores did not differ significantly from those of students without AI. By contrast and a reminder if needed that we need to keep talking about assessment design and validity, AI on its own outperformed both student groups. The most important finding for me and one that students need to be helped to understand was that students who perceived AI as most useful were, counterintuitively, those who performed worst, indicating an illusion of usefulness rather than genuine performance gains. High-performing students used AI strategically, giving clear context, iterating on outputs and treating AI as a reviewer or tutor. Lower-performing students struggled to articulate needs, issued vague commands and drifted into off-task interactions. The team concluded that AI’s educational value depends less on access and so much more on students’ AI literacy. 

Across the day, I was struck by the value of experimentation and sharing practice with others, particularly where this involved calculated risk rather than certainty.  One of the sessions came with multiple apologies for the smallness of its scale but I do not think we should be apologising for such things- we are all learning and sharing experience and findings is critical. A key lesson was the reminder that experience and application can be profound teachers, often in unexpected ways and that, increasingly, we (faculty developers) find ourselves with the opportunity to talk good teaching, learning and assessment because of AI…and that’s something we are actually happy about all things considered!

A second set of reflections concerned scale and difference. While there was a strong sense of common issues and shared opportunities across institutions, these did not remove the need to engage seriously with contextual differences. What felt encouraging was a growing consensus around broad strategies for approach, even where local enactments necessarily diverge. This suggests a maturing subfield of faculty development, one that is moving towards broadly shared principles that acknowledge widely conflicting perspectives and don’t assume or attempt to impose uniform solutions.

Aside from all this, was the moment my keynote was disturbed by a deep throated, guttural scream that resonated across the lecture hall. I wondered out loud whether we should inviestigate the apparent catastrophe but I was re-assured it was the Padagogische Schule’s voice coaching class. I bet they don’t do THAT at the IoE.

So much ornate wood in the lecture halls!

Giant humanoids and automata

heducationistLeave a comment

Today I had the honour of delivering a lecture as part of the Associate of King’s College (AKC) series. The AKC has its origins in the earliest days of King’s, almost two centuries ago, and continues as a cross-disciplinary programme and this year there are around 5,000 participants, including staff. It was a busy room (most of the 5000 watch online though), and I felt very aware of the history behind the series while speaking. This post is not intended to summarise the entire lecture but is a quick reflection based on a question posed by one the students after the lecture.

The lecture itself, titled Rethinking Human Learning in the Age of AI, was structured as a journey through time and across cultures. I wanted to draw attention to the long history of machines, automata and tools designed to work alongside us or, at times, to imitate us. Alongside this, I wanted to acknowledge current concerns about cognitive offloading, over-reliance on AI, and the anxiety that students (and others) may be outsourcing thinking itself. Rather than focus solely on the present moment, I wanted to show that many of these concerns are not new. They have deep roots in myth, invention and cultural imagination.

I began by considering why humans have been drawn to making machines that act or look like us. First, Talos, the bronze giant forged by Hephaestus to guard Crete. Talos, always on, 24/7 sentinel, ever watchful, apparently sentient, yet bound to servitude. Despite his scale, he was defeated by Jason (see how around 6 mins in the video below). The question I raised was: why build a giant in human form to defend an island when there might have been other, more efficient forms of defence? And what are the hidden consequences of a defender shaped like a human? And do we not actually feel sympathy for Talos when he dies?

The second example was the story of Yan Shi, artificer to King Mu around 3000 years ago, who constructed a singing and dancing automaton. The figure was so lifelike that it provoked admiration, but when it began to flirt with women in the court, the king’s admiration turned to fear and fury. Yan Shi had to dismantle it to reveal its workings and save his own life. The story anticipates Masahiro Mori’s ‘Uncanny Valley’ effect. The discomfort arises not simply from human likeness, but from behaviour that unsettles what we assume about intention, autonomy and control.

The third example was the tradition of the Karakuri puppets dating from 17th century Japan, whose fluid, human-like movements still evoke fascination. As with the Bunraku puppets (life size theatrical puppets), we know they are not real, yet we are drawn to the artistry and precision. There is both enchantment and a kind of deception. The craftsmanship invites admiration, but it also encourages us to question what lies beneath the surface.

With all these examples I suggested that our enchantment with lifelike machines can be both captivating and disarming. In each case, the machine is inspired by human design and each in its own way astounds and captivates. But Talos, despite his size, had a single point of vulnerability. Yan Shi’s automaton ultimately profoundly disturbed its audience. The Karakuri mechnaical dolls delight and amaze in the main but like Talos and Yan Shi’s automaton challenge us to ‘look under the hood’ to see both how they work and to uncover frailties.  My point, when I eventually go to it, was that natural language exchanges and fluent outputs of some modern AI tools can similarly enchant us and lead us to assume capabilities it does not have. We need to, within our own capabilities, look under  the hood.

I went on from there… you can see the whole journey reflected in this which I presented as an advance organiser (try to ignore the flags, they spoil it a bit):

After the lecture, a student asked what I thought was an excellent question: why are modern robots, especially those that intersect with novel AI tools, and representations of them in popular culture so frequently humanoid? Why, given that the most effective machines are those designed for highly specific functions, are we drawn to building robots in our own image? We talked about how a Roomba vacuum cleaner is far more practical than a 5-foot humanoid robot pushing around a standard vacuum but, still, in the popular imagination the latter is the imagined domestic help of the future a la Rosie from the Jetsons.

Industrial robots in car plants are arms, not bodies, because arms are what are required to achieve the task. So why does the humanoid form persist so strongly in imagination and development?

I replied, almost instinctively, that one of the reasons we return to the humanoid shape is a lack of imagination. Even among the highly skilled, it is difficult to escape the pull of the human form. We continue to project ourselves onto our technologies, even when the task at hand requires something entirely different. This is not to say that humanoid robots are always misguided. Sometimes there is a clear functional rationale. But in many cases the fascination with the human shape seems to outweigh the practical benefits. We see this in videos of humanoid robots attempting to play football, really really badly, yet we persist.

I mentioned an example I saw recently at the New Scientist Festival: a robotic elderly human head, connected to a large language model, with articulated features. It was being trialled in care homes. I found this compelling because it was not the typical youthful, idealised (and so typically female – which raises other disturbing assumptions I have to say) robot form that popular technology tends to prioritise. It was designed because there was a specific need to support human interaction where human presence was limited. It was based on their own research evidence that residents responded better to a human face than to a screen or disembodied voice. It did not need a body to fulfil that role but it needed the head. Problem> design > research > testing > honing. It contrasts dramatically with the ‘let’s make a robot that takes us into the uncanny valley and out the other side!’

What do you think?

Incidentally, I do not know why I included the word automata in my lecture as I failed (as I always do ) to say it properly.

Comet limits restless legs

heducationistLeave a comment

I’m one of those people whose knee is constantly jiggling. Especially when I am sat in ‘receive’ mode in a meeting or something. To reduce the jiggling I fiddle with things and the thing I have been fiddling with will be familiar to anyone who likes to see what all the fuss is about with new tech. I’m always asking myself- novelty or utility? (I had my fingers burnt with interactive whiteboards and have been cautious ever since). You may be interested in the output of Perplexity’s ‘Comet’- the browser based AI agent, the outcomes of which are littering LinkedIn right now- or the video below which is a conversation between me and one of my AI avatars… if not either of these I’d stop reading now tbh.

In the image below is a link to what I instructed using a simple prompt: “display this video in a window with some explanatory text about what it is and then have a self-marking multi choice quiz below it.” [youtube link]

It is a small web application that displays a YouTube video, provides some explanatory text, and then offers a self-marking multiple choice quiz beneath it.

Click on the image to see the artefact and try the quiz

The process was straightforward but illuminating. The agent prepared an interactive webpage with three generated files (index.html, style.css, and app.js) and then assembled them into a functioning app. It automatically embedded the YouTube video correctly (but needed an additional prompt when it did not initially display), added explanatory text about the focus of the video (AI in education at King’s College London), and then generated an eight-question multiple choice quiz based on the transcript.

The quiz has self-marking functionality, with immediate feedback, score tracking and final results. The design is clean and the layout works in my view. The questions cover key points from the transcript: principles, the presenter’s role, policy considerations and recommendations for upskilling. The potential applications are pretty obvious I think. Next step would be to look at likely accessibility issues (a quick check highlights a number of heading and formatting issues), finding a better solution for hosting and then the extent to which fine tuning the questions for level is do-able with ease. But given I only needed to tweak one for this example, even that basic functionality suggests this will be of use.

The real novelty here is the browser but also the execution. I have tried a few side-by-side experiments with Claude and in each the fine tuning needed for a satisfactory output was less here. The one failed experiment so far is converting all my saved links to a searchable / filterable dashboard. The dashboard looks good but I think there were too many links and it kept failing to make all the links active. Where tools like notebook LM are offerring a counter UX to text in; reams out LLMs of the ChatGPT variety, this offers a closer-to-seamless agent experience and it is both ease of use and actualy utility that will drive use I think.

I’ll believe it when I see it

heducationistLeave a comment

I finally got round to trying out ‘Nano Banana’, the Google AI Studio image editor. It’s incredible that the ‘naming of AI things’ is as sensible as the naming of cables for Apple products and conducted by a team of 7 year olds. Anyway, long story short, this is pretty remarkable and pretty depressing all in one go.

Here are my first two edits. Each has two iterations. Prompts are followed by the new version in each case.

Unaltered image 1 (Woolwich Ferry, London, taken by me)

Insert a star trek like space ship realistically in the sky

it’s a bit too big, make it smaller, more distant

Boldly going 400 yards. Unless there’s a light breeze.

Unaltered image 2

I look bald in this. I need to be wearing a hat suitable for a spy

My daughter behind me needs to be my arch nemesis. make her stealthy and holding a water pistol

It looks nothing like my daughter and it’s amazing the AI found a hat that actually fits.

The little AI symbol in the corner is sure to fox anyone using such tools for nefariousness.

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

heducationistLeave a comment

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

heducationistLeave a comment

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.

Plus ça change; plus c’est a scroll of death

heducationistLeave a comment

Hang on it was summer a minute a go
I looked at my blog just now and saw my last post was in July. How did the summer go so fast? There’s a wind howling outside, I am wearing a jumper and both actual long dark wintry nights and the long dark metaphorical ones of our political climate seem to loom. To warm myself up a little I have been looking through some tools that offer AI integrations into learning management systems (LMS aka VLEs)* rather than doing ‘actual’ work. That exploration reminded me of the first ever article I had published back in 2004. The piece has long since disappeared from wherever I save the printed version and is no longer online (not everything digital lasts forever, thank goodness) but I dug the text out of an old online storage account and reading it through has made me realise how much things have changed broadly while, in other ways, it is still the same show rumbling along in the background, like Coronation Street (but no-one really remembers when it went from black and white to colour).

What I wrote back then
In that 2004 article I described the excitement of experimenting with synchronous and asynchronous digital discussion tools in WebCT (for those not ancient like me, Web Course Tools – WebCT- was an early VLE developed by the University of British Columbia which was eventually subsumed into Blackboard). I was teaching GCSE English and was programme leader for an ‘Access to Primary Teaching’ course and many of my students were part time so only on campus for 6 hours per week across two evenings. I’d earlier taught myself HTML so I could build a website for my history students- it had lots of text! It had hyperlinks! It had a scolling marquee! Images would have been nice but I knew my limits. When I saw WebCT, I was fired up by the possibilities of discussion forums and live chat. When I set it up and trialled it I saw peer support, increased engagement with tough topics, participation from ‘quiet’ students amongst other benefits. I was so persuaded by the added value potential I even ran workshops with colleagues to share that excitement.

See this great into to WebCT from someone in CS dept at British Columbia from 1998:

That is still me of course. My job has changed and so has the context, but the impulse to share enthusiasm for digital tools that foster dialogue and interaction remains why I do what I do. It was nice to read that and I felt a fleeting affection for that much younger teacher, blissfully unaware of the challenges ahead! Even so and forming a rattling cognitive dissonace that is still there, I was frustrated by the clunky design and awkward user interface that made persuading colleagues to use it really challenging. Log in issues took up a lot of time and balancing ‘learning’ use with what I then called ‘horseplay’ (what was I, 75?!) took a while to calibrate. Nevertheless, I thought these worth working through but, even with some evidence of uptake across the college I was at was apparent, there was a wider scepticism and reluctance. Why wouldn’t they? ‘it’s too complex’; ‘I am too busy’; ‘the way I do it now works just fine, thank you’. Pretty much every digital innovation has been accompanied by similar responses; even the good ones! I speculated about whether we needed a blank sheet of paper to rethink what an LMS could be, but concluded that institutions were more likely to tinker and add features than to start again.

2004? Feels like yesterday; feels like centuries ago
It was only 2003–4 (he says, painfully aware that I have colleagues who were born then), yet experimenting with an LMS felt novel and that comes over really clearly in my article. If you’d asked me this morning when I started using an LMS I might have said 1998 or 99. 2003 feels so recent in the contexct of my whole teaching career. What the heck was I doing before all that? Thinking back I realise that in my first full time job there was only one computer in our office and John S. got to use that as he was a trained typist (so he said). And older than me. In the article I was carefully explaining what chat and forums were and how they were different from one another, so the need for that dates the phenomeon too I suppose. Later, after moving to a Moodle institution, I became e-learning lead and engaged with JISC working groups- a JISC colleague who oversaw the VLE working group jokingly called me Mr Anti-Moodle because I was vocal in my critiques. It wasn’t quite acccurate- I was critical for sure but then, as now, I liked the concept but disliked the way it worked. Persuading people to adopt an LMS was hard as I said, and, while I have seen some brilliant use of Moodle and the like, my impression is that the majority (argue with me on this though) of LMS course are functional repositiories with interactive and creative applications the exception rather than the norm. The scroll of death was a thing in 2005 and it is as much of a thing now. It also made me think of current ‘Marmitey’ positions folk are taking re: AI. Basically, AI (big and ill defined as it usually is) has to come with nuance and understanding so binary, entrenched, one size fits all positons are unhelpful and, in my view, hard to rationalise and sustain.

The familiar LMS problem
Back to the LMS, from WebCT to Moodle and other common current systems, the underlying functionality has barely shifted (I mean from the perspective of your average teacher/lecturer or student). Many still say Moodle feels very 1990s (probably they mean early 2000s but I suspect they, like me, find it hard to reconcile the idea of any year starting with a 2000 could be a long time ago). Ultimately I think none of these systems offered a genuinely encouraging combination of interface and user experience and that is an issues that persists to this day. The legacy of those early design decisions lingers, and we are still working around them. People have been predicting the death of the VLE for years (including me) but it has not happened. When I first saw Microsoft Teams just before Covid, I thought here’s the nail in the coffin. I was wrong again. Maybe being wrong about the end of the LMS is another running theme.

Will AI change the LMS story?
So what about AI powered integrations? Will they revolutionise how the LMS works? Will they be part of the reason for a shift away from them? Unlikely in either sense is my best guess. Everything I see now is about embellishments and shortcuts that feed into the existing structure. My old dream of a blank-sheet LMS revolution has faded. Thirty years of teaching and more than twenty years using LMSs suggest that this is one component of digital education that will not fade away. The tools will keep evolving, but the slow, steady thrum of the LMS endures in the background. I realise that I have finally predicted non change so don’t bet on that as I have been wrong quite a bit in the past. What I do know is that digital discussions using tools to support dialogic pedagogies have persisted as have the issues related to them. Only 10-20% of my students use the forums! I hear that still. But what I realised in 2004 and maintain to this day is that 10-20% is a significat embellishment for some and alternative for others so I stick with what I said back then in that sense at least. Oh, and lurking is a legit and fine thing for yet others!

One of the most wonderful things about the AI in Education course (so close to 15,000 participants!) is the forums. They add layers of interest that cannot be planned or produced. I estimate only 10-15% of participants post but what a contribution they are making and its an enhancement that keeps me there and, I am convinced, adds real value to those not posting too.

*I’ll stick with LMS as this seems to be pretty ubiquitous these days though I am aware of the distinctions and when I wrote the piece about ‘WebCT’ the term VLE was very much go to.

From Enthusiasm to Resistance: Framing the Institutional AI Response

heducationist2 Comments

This set of slides accompanied a presentation I just finished as part of the University of Kent Digitally Enhanced Education Webinar series hosted and organised by Dr Phil Anthony. Phil has grown a huge following and it gave me a heck of a buzz of nerves when I saw how many people were in attendance.

My tuppence worth at the start was an exploration of the shifting dynamics of AI literacy efforts at King’s. Luckily I didn’t dwell on defining AI literacy (or other literacies) nor did I want to labour the actual interventions we have experimented with: colleagues from other institutions covered that ground better than I could have done. What I tried to do was to show one slice of the complexity in terms of staff attitudes and responses from enthusiasm to scepticism and to resistance and account a little for that. There are many key tensions, institutional dilemmas and the practical realities shaping engagement with generative AI. We’re still working on honing the approach at King’s College London, including support structures, faculty, department and programme level interventions and policy considerations and we definitely have yet to nail the way we resource the literacy work, programme and assessment modfication work and infrastructure work across the board. Talking about it though (and also this week compiling a report on AI related work) did make me realise how amazing so many folk are at work. The effort and willingness to share, debate, experiment, research, design etc etc from staff and students is remarkable.

Watch recording:

Not sure how useful the slides are without me wittering on but the links are there so Access the full slides here: tiny.cc/deew1 or click the image below

Transparent AI workflows

heducationist2 Comments

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/

Innovation, AI and (weirdly) the new PSF

heducationist1 Comment

Mark Twain almost certainly said: 

“substantially all ideas are second-hand, consciously and unconsciously drawn from a million outside sources, and daily used by the garnerer with a pride and satisfaction born of the superstition that he originated them” 

and he is also attributed with saying:

 “A person with a new idea is a crank until the idea succeeds”

Both takes, perhaps even while being a little contradictory, relate to the idea of innovation. In this post that I initially drafted in an interaction with GPT Pro Advance voice chat while walking to work, I have thrown down some things that have been bothering me a bit about this surprisingly controversial word. 

Firstly, what counts as innovation in education? You often hear folk argue that, for example, audio feedback is no innovation as teachers somewhere or other have been doing it for donkeys years. The more I think though, the more I’m certain actions/ interventions/ experiments/ adaptations are rarely innovative by themselves but what matters fundamentally is context. Something that’s been around for years in one field or department might be utterly new and transformative somewhere else.

Objective Structured Clinical Examinations are something I have been thinking about a lot because I believe they may inspire others to adapt this assessment approach outside health professions. . In medical education, they’re routine. In business or political economy observable stations to assess performance or professional judgement would probably be deemed innovative. Chatting with colleagues, they could instantly see how something like that might work in their own context, but with different content, different criteria and perhaps a different ethos. In other words, in terms of the thing we might try to show we are doing to evidence the probably impossible to achieve ‘continuous improvement’ agenda, innovation isn’t about something being objectively new; it’s about it being new here. It’s about context, relevance and reapplication.

Innovation isn’t (just) what’s shiny

Ages ago I wrote about the danger and tendency for educators (and their leaders) to be dazzled by shiny things. But we need to move away from equating innovation with digital novelty. The current obsession is AI, unsurprisingly,  but it’s easy to get swept along in the sheen of it, especially if, like me, you are a vendor target. This, though reminds me that there’s a tendency to see innovation as synonymous with technological disruption. But I’d argue the more interesting innovations right now are not just about what AI can do, but how people are responding to it.

Arguable I know, but I do believe AI offers clear affordances: supporting diverse staff and student bodies, support for feedback, marking assistance, rewriting for tone, generating examples or case studies. And there’s real experimentation happening, much of it promising, some of it quietly radical. At the same time I’m seeing teams innovate in the opposite, analogue direction. Not because they’re nostalgic, conservative or anti tech (though some may be!),  but because they’re worried about academic integrity or concerned about the over-automation of thinking. We’re seeing a return to in-person vivas, handwritten tasks, oral assessments. So these are not new but because they are being re-justified in light of present challenges. It could be seen as innovation via resistance.

Collaboration as a key component of innovation

In amongst the amazing work reflected on, I see a lot a claims for innovative practice in the many Advance HE fellowship submissions I read as internal and external reviewer.  In some ways, seemingly very similar activities could be seen as innovative in one place and not another. While not a mandatory criterion, innovation is:

  • Encouraged through the emphasis on evidence-informed practice (V3) and responding to context (V4).
  • Often part of enhancing practice (A5) via continuing professional development.
  • Aligned with Core Knowledge K3, which stresses the importance of critical evaluation as a basis for effective practice—and this often involves improving or innovating methods.In the guidance for King’s applicants, innovation is positioned as a natural outcome of reflective practice

So while the new  PSF (2023) doesn’t promote innovation explicitly, what it does do (and this is new) is promote collaboration. it explicitly recognises the importance of collaboration and working with others, across disciplines, roles and institutions as a vital part of educational practice. That’s important because whilst in the past perceptions of innovation have stretched the definition and celebrated individual excellence in this space many of the most meaningful innovations I’ve seen emerge from collaboration and conversation. This takes us back to Twain and  borrowing, adapting, questioning.

We talk of interdisciplinarity (often with considerable insight and expertise like my esteemed colleagues Dave Ashby and Emma Taylor) and sometimes big but often small-scale, contextual innovation comes from these sideways encounters. But they require time, permission and a willingness to not always be the expert in the room. Something innovators with a lingering sense of the inspiring, individual creative may have trouble reconciling. 

Failure and innovation

We have a problem with failure in HE. We prefer success stories and polished case studies. But real innovation involves risk: things not quite working, not going to plan. Even failed experiments are educative. But often we structure our institutions to minimise that kind of risk, to reward what’s provable, publishable, measurable, successful. I have argued that  we do something similar to students. We say we want creativity, risk-taking, deep engagement. But we assess for precision, accuracy, conformity to narrow criteria and expectations. We encourage resilience, then punish failure with our blunt, subjective grading systems. We ask for experimentation but then rank it. So it’s no surprise if staff, like students, when encouraged to be creative or experimental, can be reluctant to try new things.

AI and innovation

I think I am finally getting to my point. The innovation AI  catalyses goes far beyond AI use cases. It’s prompting people to re-examine their curricula, reassess assessment designs, rethink what we mean by original thinking or independent learning. It’s forcing conversations we’ve long avoided, about what we value, how we assess,  and how we support students in an age of automated possibility. Even WHETHER we should continue to grade. (Incidentally I heard, amongst many fine presentations yesterday at the King’s /Cadmus event on assessment an inspiring argument against grading by Professor Bugewa Apampa from UEL. It’s so good to hear clearly articulated arguments on the necessity of confronting the issues related to grading from someone so senior). 

Despite my role (Ai and Innovation Lead) some of the best innovations I’ve seen aren’t about tech at all. They’re about human decisions in response to tech. They’re about asking, “What do we not want to automate?” or “How can we protect space for dialogue, for process or for pause?” 

If we only recognise innovation when it looks like disruption, we’ll miss a lot. 

Twain, Mark. Letter to: Helen Keller. 1903 Mar 17 [cited June, 19, 2025] Available from: https://www.afb.org/about-afb/history/helen-keller/letters/mark-twain-samuel-l-clemens/letter-miss-keller-mark-twain-st