AI and the pragmatics of curriculum change

heducationist1 Comment

Whilst some (many) academic staff and students voice valid and expansive concerns about the use of or focus on Artificial intelligence in education I find myself (finally, perhaps, and later in life) much more pragmatic. We hear the loud voices and I applaud many acts of resistance but we cannot ignore the ‘explosive increase’ in AI use by students. It’s here and that is one driver. More positive drivers to change might be visible trends and future predictions in the global employment landscape  and the affordances in terms of data analytics and medical diagnostics (for example) that more widely defined AI promises. As I keep saying this doesn’t mean we need to rush to embrace anything nor does it imply that educators must become computer scientists overnight. But it does mean something has to give and, rather than (something else I have been saying for a long time) knee-jerk ‘everyone back in the exam halls’ type responses, it’s clear we need to move a wee bit faster in the names of credibility (of the education and the bits of paper we dish out at the end of it) as well as the value to the students of what we are teaching and , perhaps more controversially I suppose, how we teach and assess them. 

Over the past months, I’ve been working with colleagues to think through what AI’s presence means for curriculum transformation. In discussions with colleagues at King’s most recently, three interconnected areas keep surfacing and this is my effort to set them out: 

Content and Disciplinary Shifts

We need to reflect on not just what might we add, but what can we subtract or reweight. The core question becomes: How is AI reshaping knowledge and practice in this discipline, and how should our curricula respond?

This isn’t about inserting generic “AI in Society” modules everywhere. It’s about recognising discipline-specific shifts and preparing students to work with, and critically appraise/ engage with new tech, approaches, systems and idea (and the impacts consequent of implementation). Update 11th June 2025: My colleague, Dr Charlotte Haberstroh, pointed out on reading this that there is an additional important dimension to this and I agree. She suggests we need to find a way to enable students to question and make connections explicitly: ‘how does my disciplinary knowledge help me (the student) make sense of what’s happening, how can it inform my analysis of causes/consequences of how AI is being embedded into our society (within the part that I aim to contribute to in the future / participate in as responsible citizen). Examples she suggested: In Law it could be around how it alters the meaning of intellectual property, in HR it’s going to be about AI replacing workers (or not) and/or the business model of the tech firms driving these changes. In history it’s perhaps how have we adopted technologies in the past and how does that help us understand what we are doing now.

Some additional examples of how AI as content crosses all disciplines: 

  • Law: AI-powered legal research and contract review tools (e.g. Harvey) are changing the role of administration in law firms and the roles of junior solicitors.
  • Medicine: Diagnostic imaging is increasingly supported by machine learning, shifting the emphasis away from manual pattern recognition towards interpretation, communication, and ethical judgement.
  • Geography: Environmental modelling uses real-time AI data analytics, reshaping how students understand climate systems.
  • History and Linguistics: Machine learning is enabling large-scale text and language analysis, accelerating discovery while raising questions about authorship, interpretation, and cultural nuance.

Assessment Integrity and Innovation

Much of the current debate focuses on the security of assessment in the age of AI. That matters a lot of course but if it drives all our thinking and feel it is still the dominant narrative in HE spaces, we will double down on distrust, always default to suspicion and restriction rather than our starting point being designing for creativity, authenticity and inclusivity. 

First shift needs to be moving from ‘how do we catch cheating?”’to ‘Where and how can we ‘catch’ learning?’ as well as  ‘how do we design assessments that AI can’t meaningfully complete without student learning?’ Does this mean redefining ‘assessment’ beyond the narrow ‘evaluative ‘ definition we tend to elevate? Probably, yes. 

Risk is real, inappropriate/ foolish/ unacceptable …even malicious use of AI is a real thing too. So, robustness by design is important too: iterative, multi-stage tasks; oral components; personalised data sets; critical reflection. All are possible without reverting to closed-book exams. These have a place but are no panacea. 

Examples of AI-shaping assessment and design:

AI Integration & Critical Literacies

Students need access to AI tools; They need choice (this will be an increasingly big hurdle to navigate), they need structured opportunities to critique and reflect on their use. This means building critical AI literacy into our programmes or, minimally, the extra-curricular space, not as a bolt-on, but as an embedded activity. Given what I set out above, this will need nuancing to a disciplinary context. It’s happening in pockets but I would argue needs more investment and an upping of the pace- given the ongoing crises in UK HE (if not globally) it’s easy to see why this may not be seen as a priority. 

I think we need to do the following for all students. What do you think? 

  • Critical AI literacy (what it is, how it works (and where it doesn’t),  all the mess in connotes)
  • Aligned with better Information/digital literacy (how to verify, attribute, trace and reflect on outputs- and triangulate)
  • Assessment and feedback literacy (how to judge what’s been learned, and how it’s being measured)

Some examples of where the discipline needs nuance and separate focus and why it is so complex: 

  • English Literature/ History/ Politics: Is the essay dead? Students can prompt ChatGPT to generate essays but how are they generating passable essays when so much of the critique is about the banality of homogenised outputs and lack of anything resembling critical depth? How can we (in a context were anonymous submission is the default) maintain value in something deemed so utterly central to humanities and social science study? 
  • Medical and Nursing education:I often feel observed clinical examinations hold a potential template for wider adoption in non medical disciplines. And AI simulation tools offer lifelike decision-making environments so we are seeing increasing exploration of the potentials here: the literacy lies in knowing what AI can support and what it cannot do, and how to bridge that gap.Who learns this? Where is the time to do it? How are decision made about which tools to trail or purchase? 

Where to Start: Prompting thoughtful change

These three areas are best explored collectively, in programme teams, curriculum working groups or assessment review/ module review teams. I’d suggest to begin with these teams need to discuss and then move on from there. 

  1. Where have you designed assessments that acknowledge AI in terms of the content taught? What might you need to modify looking ahead?
    (e.g. updated disciplinary knowledge, methodological changes, professional practice)
  2. Have you modified assessments where vulnerability is a concern? Have you drawn on positive reasons for change (eg scholarship in effective assessment design)? (e.g. risk of generative AI substitution, over-reliance on closed tasks, integrity challenges)
  3. Have you designed or planned assessments that incorporate, develop or even fully embed AI use?
    (e.g. requiring students to use, reflect on or critique AI outputs as part of their task)

I do not think this is AI evangelism though I do accept that some will see it as such because I do believe that engagement is necessary and actually an ethical responsibility to our students. That’s a tough sell when some of those students are decrying anything with ‘AI’ in it as inherently and solely evil. I’m not trying  to win hearts and minds to embrace anything other than that these tech need much broader definition and understanding and from that we may critique and evolve.

Future of Work?

heducationist1 Comment

I wanted to drop these two reports in one place. Neither of course are concerned with wider ethical issues of AI and I do not want to come over as tech bro evangelist but I do think my pragmatism and the necessary ‘responsible engagement’ approach many institutions are now taking is buttressed by the (like or no) trends we are seeing which are profound. Barriers (as seen through eyes of employers) to change struck me too as we can see the similar manifestations in educational spaces: skills gaps, cultural resistance and outdated regulation.

The Future of Jobs Report 2025 explores how global labour markets will evolve by 2030 in response to intersecting drivers of change: technological advances (especially AI), economic volatility, demographic shifts, climate imperatives and geopolitical tensions. Based on responses from over 1,000 global employers covering more than 14 million workers, the report predicts large-scale job transformation. While 14% of current jobs (170 million) are expected to be created, 8% (92 million) will be displaced, resulting in a net 6% growth. The transition will be skills-intensive, with 59% of workers needing retraining. Those numbers are enogh to make you gasp and drop your coffee.

PwC’s 2025 Global AI Jobs Barometer presents an incredibly optimistic analysis of how AI is reshaping the global workforce. From a billion job ads and thousands of company reports across the globe, the report suggests that AI is enhancing productivity, shifting skill demands and increasing the value of workers. Rather than displacing workers it argues that AI is acting as a multiplier, especially when deployed agentically. The findings provide a counter perspective to common (and I’d argue, perfectly reasonable and rational!) fears about AI-induced job losses.

Whilst I am still wearing the biggest of my cynical hats, I concur that the need for urgent investment in skills (and critical engagement) is imperative and, lest we lose any residual handle on shaping the narratives in this space, we need to invest much more of our efforts into considering where we need to adapt what we research, the design and content of our currcula and the critical and practical skills we need to develop. Given the timeframes suggested in these reports, we’d better get on with it.

Is AI like a cute puppy?

heducationist3 Comments
Audio version of this post

TL:DR? No, it is not, so why would you embrace it?

I have mentioned this before but it keeps cropping up so I am going to labour the point again. The idea of ‘embracing’ AI in education (or anywhere) can be seen to grow as a narrative throughout 2023 and was already on a steep upward trajectory prior to that.

A line chart showing the frequency of the phrase “Embrace AI” in published texts from 2000 to 2022. The horizontal axis runs from 2000 to 2022; the vertical axis shows tiny percentage values from 0 % up to 0.00000024 %. From 2000 through about 2014, the blue line hugs the baseline at essentially 0 %, with a very slight rise between 2006 and 2012 and a dip around 2014. Beginning around 2015, the line climbs steeply, reaching approximately 0.00000022 % by 2022. A tooltip at the year 2000 notes a value of 0.00000000 %.
Google Ngram viewer for ‘Embrace AI’

But a significant contribution to this notion came in this HEPI blog of 5 January 2024. Professor Yike Guo urges UK universities to move beyond mere caution and become active adopters of artificial intelligence. Drawing on 34 years of AI, data-mining and machine-learning research at Imperial College London and his  role as Provost at HKUST, he warned that AI is not a peripheral tool but a fundamental shift in the educational paradigm. His focus on structural, systemic and pre-existing issues in how we construct education such as  the persistence of rote memorisation in curricula mirrors my own case for using AI as an opportunity to leverage research-informed changes long needed. Professor Guo advocates for compulsory AI literacy modules that teach students to interrogate and collaborate with digital co-pilots and insists that the true value of education will lie in cultivating ethical reasoning, emotional intelligence and creativity which, importantly, are qualities that machines cannot replicate. He says (and I quote this a lot):

“…UK universities face a choice: either embrace AI as an integral component of academic pursuit or risk obsolescence in a world where digital co-pilots could become as ubiquitous as textbooks.”

I tend to agree with much of Professor Guo’s stance: AI will reshape (and already is)  higher education pretty profoundly but I find his call to “embrace” AI really troubling. This phrase seems to be everywhere in relation to AI. I hear it every day and I don’t  think it is helpful at all.  I embrace my wife and daughter (and, somewhat awkwardly, my son and my mum: it’s a generational thing I think!), a kitten, and even my Spurs-supporting mates last week when we finally won a trophy after 17 years of pain (see picture below). 

A photograph taken inside a dimly lit bar showing a joyous celebration among football supporters. In the foreground, an older man wearing glasses and a flat cap laughs with his mouth wide open as a younger man embraces him from behind, both arms wrapped around his shoulders. The younger man, in a light trench coat, leans in close, smiling broadly. Behind them to the right, two other fans—one in a yellow Tottenham Hotspur shirt bearing the name “Kane” and the number 7—are similarly embracing. The background is softly focused, revealing a few more patrons and industrial-style décor with exposed beams and abstract wall art.
Me being embraced by my Spurs buddy ‘JM’ (Photo: Tom Sweetland)

But I do not embrace people or things I neither know nor trust. I do not embrace strangers. Even when I employed someone to complete a loft conversion, and we came to know them well over the course of the (interminable) job, we still didn’t end up hugging each other. Some people love their phones too much and might kiss and hug them but I think they’re daft. These are tools, nothing more. ‘Embracing AI’ narratives only feed anthropomorphism. It also feeds binary narratives: are you ‘fully embrace’ or ‘outright reject’? Actually, reality demands something far more nuanced.

To these ends, I am constantly challenging the idea of embracing AI. So, instead, I argue for engagement. We can engage with affection, care, warmth and appreciation, but we can also engage with suspicion, trepidation, anxiety, distrust, even fear. Engagement accommodates critical scrutiny as readily as it does positive and productive collaboration.So, bottom line, let’s drop the idea of embracing AI but encourage critical engagement with AI (in all its diversity…what we conceptualise AI as is another thing that vexes me btw). Also: Come on you Spurs!

Blank canvases

heducationistLeave a comment

Inspired by something I saw in a meeting yesterday morning, I returned today to Gemini Canvas and Claude equivalent (still not sure what it is called). Both these tools are designed to enable you to “go from a blank slate to dynamic previews to share-worthy creations, in minutes.”

The resource I used was The Renaissance of the Essay? (LSE Impact Blog) and the accompanying Manifesto which Claire Gordon (LSE) and I led on with input from colleagues from LSE and here at King’s. I wondered how easily I could make the manifesto a little more dynamic and interactive. In the first instance I was thinking about activating engagement beyond the scroll and secondly thinking about text inputs and reflections.

The basic version in Gemini was a 4th-iteration output where after initial very basic prompt:

“turn this into an interactive web-based and shareable resource”

…I tweaked (using natural language) the underpinning code so that the boxes were formatted better for readability and to minimise scrolling and the reflection component went from purely additional text to a clickable pop-up. I need to test with a screen reader to see how that works of course.

I then experimented with adding reflection boxes and an export notes function. It took 3 or 4 tweaks (largely due to copy text function limits in browser) but this is the latest version. Obviously with work this could be made to look nicer but I’m impressed with initial output and ability to iterate and for functionality in very short time (about 15 mins total).

For the Claude one I thought I’d try having all those features including in-text input interaction from the start. Perhaps that was a mistake, because although the intial output looked great, the text input was buggy. 13 iterations later and I got the input fix. However, then the export function that I’d added around version 3 had stopped working so I needed to do a lot more back and forth. In the end I ran out of time (about 40 mins in and at version 19) and settled on this version with the inadequate copy/ paste function.

It’s all still relatively new and what’s weird about the whole thing is the continual release of beta tools, experiemtnal spaces and things that in any other context would not be released to the World. Nevertheless, there is already utility visible here and no doubt they will continue to improve. I sometimes think that my biggest barrier to finding utility is my own limited imagination. I defintiely vibe off seeing what others have done. This further underlines for me the difference and a significant problem we have going forward. ‘Here’s a thing.’ they say. “What’s it for?’ we ask. ‘I dunno,’ they shrug, ‘efficiency?’

My prompt for this was:
‘tech bros shrugging’

The Manus from U.N.C.L.E.

heducationistLeave a comment

‘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).

Rewilding higher education: weeds and wildflowers

heducationist1 Comment

Connie Gillies and Martin Compton

It was a privilege to offer reflections at Professor Cathy Elliott’s inaugural lecture, Rewilding the University recently. Her lecture was more than a celebration of an academic career: it was also a call to action. A provocation. A gentle but insistent reminder that education (and nature and the world!) does not need to look the way it does now. A packed lecture hall listened intently to Cathy’s arguments, ideas and jokes: it was a tough act to follow. Cathy said she hardly ever lectures but a skillful lecture is a thing of joy and is utterly compelling and we were lucky to witness one.  Here we share some reflections on Cathy’s ideas and how they have helped shape aspects of our own. 

Cathy made clear that rewilding is not a metaphor of neglect or abandonment, but of restoration, connection and flourishing. It recognises that overly managed systems, whether ecological or educational, can become depleted, homogenous and fragile. In both cases, monoculture and rigidity are warning signs: what Cathy referred to as ‘command and control’.  The invitation we heard was to value and support diversity, likewise in both nature  and education, to value what is often dismissed, and to allow for the possibility of unpredictable, unmeasurable growth.

This vision has shaped how we think about education and how we’ve each worked together with Cathy. Our own relationships, as a fellow academic (with similarly unconventional paths to current roles) and as a student (who had been disillusioned by educational experiences to the point of encountering Cathy’s course), and now as authors, as collaborators, is a component of the network that Connie has described as mycelial: Like subterranean fungal connections but nourishing ideas, allowing knowledge to travel, and making future growth possible. Like mycelium in forest ecosystems, these relationships and ideas remain largely invisible to the untrained eye, but they are foundational. They remind us that learning does not happen in isolation, but in intricate, collaborative webs.

When students sign up for Cathy’s Politics of Nature class, they often don’t fully grasp the lasting impact it will have on them. A friend once told Connie, “A Cathy Elliott module will change your life,” and while the statement may seem grand, it’s not far from the truth. For many, this course didn’t just teach content; it reshaped our approach to thinking, learning, and even our careers. Cathy’s teaching blends critical rigor with intellectual play, making the class a rare space where students can be both creatively curious and academically rigorous. Most importantly, she empowers students to discover their unique intellectual passions, encouraging them to contribute perspectives no one else could, simply because they aren’t anyone else.

Education, when rewilded, becomes an ecosystem. A space where mutual dependence is generative. A space where difference is not simply tolerated but required. It is through this lens that we’ve come to understand projects like ungrading, student co-authorship, and the politics of belonging, not as reforms, but as regenerative acts. These are not surface-level interventions, but shifts in the soil.

One of the most notable aspects of Cathy’s work is her broad intellectual curiosity. She’s not confined to any one field of study — from politics and nature to democracy, development, gender, race, disability and sexuality, Cathy’s academic interests are as diverse as they are profound. In an academic world that often pushes students toward ever-narrower specialization, Cathy’s approach encourages students to break free from this limitation.

Cathy’s teaching has long enacted this ethos. She nurtures students not through control but through trust. Her pedagogy invites learners to bring their whole selves, to make connections across disciplinary and personal boundaries, and to treat knowledge as something to be inhabited, not merely acquired. She encourages risk, slowness, reflection, and relationality which are qualities too often sidelined in institutional discourses of impact, efficiency and performance.

The dandelion is another metaphor Cathy draws on frequently and one we were also drawn to in our appreciation. Often dismissed as a weed, the dandelion (The French is ‘pissenlit’ which really does say everything about its reputation)  is in fact a profoundly restorative plant. It detoxifies soil, strengthens roots and nourishes ecosystems. It grows where it is not wanted and flourishes nonetheless. To children, it is a source of wonder, blown seeds, floating wishes,transformation, softness at one time, vibrant yellow before. But to adults, it is a nuisance to be removed. Cathy’s work, like the dandelion, asks us to reconsider who gets to decide what counts as valuable, as beautiful, as worthy. We need to ask ourselves to what extent have we constructed educational systems that we want to be like perfect lawns- predictable, clean, neat and each blade of grass much like the others. Cathy says: ‘don’t cut the grass and plant wildflowers instead!’ This is a literal and metaphorical phrase we can get behind!

This ethos extends into her work on gender, race and sexuality, which consistently challenges the structures that exclude some or  may diminish the presence or experience of others. In classrooms, in curricula, in institutional policy, she reminds us in her work that exclusion is never accidental, it is designed. But that also gives us pause for positive reflection: this means they can be redesigned. 

What we’ve come to understand through Cathy’s influence, and through our ongoing partnership, is that rewilding higher education is not a metaphorical indulgence, it is a pedagogical imperative. It calls us to rethink the terms of participation, the assumptions of merit, the rituals of assessment, and the conditions under which learning takes place. It also calls for attention to scale: recognising that large transformations begin with small shifts, relationships and new practices. 

It felt fitting, then, that the very day after Cathy’s lecture, a special issue of the Journal of Learning Development in Higher Education was published. Co-edited by one of us and containing a piece co-authored by the other, the issue is seeded with many of these same ideas. It features students and a Vice Chancellor; early career ac academics and emeritus professors, reimaginings of assessment, and reflections on academic community that echo and extend Cathy’s provocations. The special issue is a timely continuation of many of the conversations we have had with Cathy, who, unsurprisingly, also has a paper in the special issue and was part of the King’s/ UCL editorial collective. 

We both have very different careers and are at very different ends of them! But we share the sense that the rigid, often foreboding and frequently distrustful academy could be rewilded. It doesn’t have to be this way; more importantly, it could be otherwise.

Meme-ingful reflections on AI, teaching and assessment

heducationist1 Comment

I did a session earlier today for the RAISE special interest group on AI. I thought I’d have a bit of fun with it 1. because I was originally invited by Dr. Tadhg Blommerde (and Dr. Amarpreet Kaur) who likes a heterodox approach (see his YouTube channel here) and 2. Because I was preparing on Friday evening and my daughter was looking over my shoulder and suggesting more and more memes. Anyway, I was just reading the chat back and note my former colleague Steve asked: “Is the rest of the sector really short of memes these days now that Martin has them all?” I felt guilty so decided to share them back.

My point: There’s a danger we assume students will invariably cheat if given the chance. This meme challenges educators to reconsider what they define as cheating and encourages transparent, explicit dialogue around academic integrity. What will we lose if we assume all students are all about pulling a fast one?

My daughter (aged 13) suggested this one. How teachers view ChatGPT output: homogenised, overly polished essays lacking individuality. My daughter used the ‘who will be the next contestant on ‘The Bachelor’ (some reality show I am told) image to illustrate how teachers confidently claim they can spot AI-generated assignments because “they all look the same.” My point: I think this highlights early scepticism about AI-produced writing but that we should as educators consider the extent to which these tools have evolved beyond initial assumptions and remind our students (and ourselves) that imperfections and quirks can define a style. Just ask anyone reading one of my metaphor-stretched, overly complex sentences. Perhaps, for too long we have over-valued grammatical accuracy and formulaic writing?

My point: It’s not just about AI detectors of course. It’s more that this is an arms race we can’t win. If we see big tech as our enemy then fighting back with more of their big tech makes no sense. If we see students as the enemy then we have a much bigger problem. Collective punishment and starting with an assumption of guilt are hugely problematic in schools/ unis much as they are in life and tyrannical societies in general. When it comes to revisiting academic integrity I am keen discuss what it is we are protecting. I am also very much drawn to Ellis and Murdoch’s ‘responsive regulation’ approach. I don’t think I’m quite on the same page regarding automated detection but I do agree regarding the application (and resourcing of) deserved sanction for the ‘criminal’ (willful cheats) along with efforts to widen self-regulation and move as many students as possible from carelessness (or chancer behaviours) to self-regulation is critical.

Pretty obvious I guess but my point is this: We also need to resist assumptions that all students prioritise grades over genuine learning and creativity. Yes, there are those who are wilfully trying to find the easiest path to the piece of paper that confirms a grade or a degree or whatever. Yes, there are those whose heads may be turned by the promise of a corner-cutting opportunity. But there are SO many more who want to learn, who are anxious because they know others who are being accused of using these tech inappropriately (because, for example, they use ‘big’ words… really, this has happened). ALSO, we need to challenge the structural features that define education in terms of employability and value. I know how to use chatgpt but I am writing this. Why am I bothering writing? Because I like it. Because – I hope- my writing, even when convoluted (much like this sentence) is more compelling. Because it’s more gratifying than the thing I’m supposed to be doing. Above all, for me, it’s because it actually helps me articulate my thoughts better. We must continue valuing intrinsic motivation and the joy students derive from learning and creating independently. But more than that: we need to face up to the systemic issues that drive more students towards corner cutting or willful cheating. By the way, I often use generated text in things I write. All the alt text in these images is AI generated (then approved / edited by me) for example.

This leads me to the next one. I mean I do use AI every day for translation, transcription, information management, easing access to information, reformatting, providing alternative media, writing alt text… Many don’t I know. Many refuse; I know this too. But we are way into majority territory here I think. Students are recognising this real (or imagined) hypocrisy. The only really valid response to this I have heard goes something like: ‘I can use it because I am educated to x level. first year undergrads do not have the critical awareness or developed voice to make an informed choice’. I mean, I think that may be the case to an extent or in some cases but it reminds me a bit of the ‘pen licences’ my daughter’s primary school issued: you get one when you prove you can use a pencil first (little Timmy, bless him, is still on crayons). Have you seen the data on student routine use of generative AI? It elevates the tool to some sort of next level implement but is it even? I think I could make a better case for normalisation and acceptance of a future where human / AI hybrid writing is just how it is done (as per Dr Sarah Eaton’s work- note the firve other elements in the tenets.)

My point: The narratives around essential changes we need to implement ‘because of AI’ presents a false dichotomy between reverting to traditional exam halls or relying solely on AI detection tools. Neither option adequately addresses modern academic integrity challenges. Exams can be as problematic and inequitable as AI detection. It is not a binary choice. There are other things that can be done. I’ll leave this one hanging a bit as it overlaps with the next one.

My point: We need to critically re-evaluate how and why essays are used in assessment. We can maintain the essay but evolve its form to better reflect authentic, inclusive and meaningful assessments rather than relying on traditional, formulaic, high-stakes versions. Anyway I (with Dr Claire Gordon) have said it before, we already have a manifesto and Dr Alicia Syskja takes the argument to the next level here.

Really, though, you should have been there; we had a great time.

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

heducationist1 Comment

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

Old problem, new era

heducationist2 Comments

“Empirical studies suggest that a majority of students cheat. Longitudinal studies over the past six decades have found that about 65–87% of college students in America have admitted to at least one form of nine types of cheating at some point during their college studies”

(Yu et al., 2018)

Shocking? Yes. But also reassuring in its own way. When you are presented with something like that from 2018 (ie. pre chatgpt) you realise that this is not a newly massive issue; it’s the same issue with a different aspect, lens or vehicle. Cheating in higher education has always existed, but I do acknowledge that generative AI has illuminated it with an intensity that makes me reach for the eclipse goggles. There are those that argue that essay mills and inappropriate third party support were phenomena that we had inadequately addressed as a sector for a long time. LLMs have somehow opened a fissure in the integrity debate so large that suddenly everyone wants to do something about it. it has become so much more complex because of that but also that visibility could be seen positively (I may be reaching but I genuinely think there is mileage in this) not least because: 

1. We are actually talking about it seriously. 

2. It may give us leverage to effect long needed changes. 

The common narratives I hear are ‘where there’s a will, there’s a way’ and chatgpt makes the ‘way’ easier. The problem though, in my view, is that just because the ‘way’ is easier does not mean the ‘will’ will necessarily increase. Assuming all students will cheat does nothing to build bridges, establish trust or provide an environment where the sort of essential mutual respect necessary for transparent and honest working can flourish.  You might point to the stat at the top of this page and say we are WAY past the need to keep measuring will!  Exams, as I’ve argued before, are no panacea, given the long-standing issues of authenticity and inclusivity they bring (as well as being the place where students have shown themselves to be most creative in their subversion techniques!). 

In contrast to this, study after study is finding that students are increasingly anxious about being accused of cheating when that was never their intention. They report unclear and sometimes contradictory guidance, leaving them uncertain about what is and isn’t acceptable. A compounding  issue  is the lack of consistency in how cheating is defined. it varies significantly between institutions, disciplines and even individual lecturers. I often ask colleagues whether certain scenarios constitute cheating, deliberately using examples involving marginalised students to highlight the inconsistencies.  Is it ok to get structural, content or proof reading  suggestions from your family? How does your access to human support differ if you are a first generation, neurodivergent student studying in a new language and country? Policies usually say “no” but to fool ourselves that this sort of ‘cheating’ is not routine would be hard to achieve and even harder to evidence. The boundaries are blurred, and the lack of consensus only adds to the confusion.

To help my thinking on this I looked again at some articles on cheating over time (going back to 1941!) that I had put in a folder and badly labelled as per usual and selected a few to give me a sense of the what and how as well as the why and to provide a baseline to inform the context around the current assumptions about cheating. Yu et al. (2018) use a long established categorisation of types of cheating with a modification to acknowledge unauthorised digital assistance:

  1. Copying sentences without citation.
  2. Padding a bibliography with unused sources.
  3. Using published materials without attribution.
  4. Accessing exam questions or answers in advance.
  5. Collaborating on homework without permission.
  6. Submitting work done by others.
  7. Giving answers to others during an exam.
  8. Copying from another student in an exam.
  9. Using unauthorised materials in an exam.

The what and how question reveals plenty of expected ways of cheating, especially in exams but it is also noted where teachers / lecturers are surprised by the extent and creativity. Four broad types:

  1. Plagiarism in various forms from self, to peers to deliberate inappropriate practices in citation.
  2. Homework and assignment cheating such as copying work, unauthorised collaboration, or failing to contribute fairly.
  3. Other academic dishonesty such as falsifying bibliographies, influencing grading or contract cheating.
  4. In exams.

The amount of exam based cheating reported should really challenge assumptions about the security of exams at the very least and remind us that they are no panacea whether we see this issue through an ongoing or a chatgpt lens. Stevens and Stevens (1987) in particular share some great pre-internet digital ingenuity and Simpkin and McLeod (2006) show how the internet broadened the scope and potential. These are some of the types reported over time: 

  1. Using unauthorised materials.
  2. Obtaining exam information in advance.
  3. Copying from other students.
  4. Providing answers to other students.
  5. Using technology to cheat (using microcassettes, pre-storing data in calculators, mobile phones. Not mentioned but now apparently a phenomenon is use of bone conduction tech in glasses and/ or smart glasses).
  6. Using encoded materials (rolled up pieces of paper for example).
  7. Hiring a surrogate to take an exam.
  8. Changing answers after scoring (this one in Drake,1941)
  9. Collaborating during an exam without permission.

These are the main reasons for cheating across the decades I could identify (from across all sources cited at the end):

  1. Difficulty of the work. When students are on the wrong course (I’m sure we can think of many reasons why this might occur), teaching is inadequate or insufficiently differentiated.
  2. Pressure to succeed. ‘Success’ when seen as the principal goal can subdue the conscience.
  3. Laziness. This is probably top of many academics’ assumptions and it is there in the research but also worth considering what else competes for attention and time and how ‘I can’t be bothered’ may also mask other issues even in self-reporting. 
  4. Perception that cheating is widespread. If students feel others are doing it and getting away with it, it increases the cheating.
  5. Low risk of getting caught.
  6. Sense of injustice in systemic approach, structural inequalities both real and perceived can be seen as a valid justification. 
  7. External factors such as evident cheating in wider society. A fascinating example of this was suggested to me by an academic who was trained in Soviet dominated Eastern Europe who said cheating was (and remains) a marker of subversion so carries its own respectability)
  8. Lack of understanding of what is allowed and is not- students reporting they have not been taught this and degrees of cheating blurred by some of the other factors here- when does collaboration become collusion?
  9. Cultural influences. Different norms and expectations can create issues and this comes back to my point about individualised (or contextualised) definitions of what is and is not appropriate. 
  10. My own experiences, over 30 years, of dealing with plagiarism cases often reveals very powerful, often traumatic, experiences that lead students to act in ways that are perceived as cheating.

For each it’s worth asking yourself:

How much is the responsibility for this on the student and how much on the teacher/ lecturer and / or institution (or even society)?

I suspect that the truly willful, utterly cynical students are the ones least likely to self declare and are least likely to get caught. This furthers my own discomfort about the mechanisms we rely (too heavily?) on to judge integrity too.

This skim through really did make clear to me that cheating and plagiarism are not the simple concepts that many say they are. Also cheating in exams is a much bigger thing than we might imagine. The reasons for cheating are where we need to focus I think.  Less so the ‘how’ as that becomes a battleground and further entrenches ‘us and them’ conceptualisations.  When designing curricula and assessments the unavoidable truth is we need to do better by moving away from one size fits all approaches, by realising cultural, social and cognitive differences will impact many of the ‘whys’ and hold ourselves to account when we create or exacerbate structural factors that broaden likelihood of cheating. 

I am definitely NOT saying give wilful cheaters a free pass but all the work many universities are doing on assessment reform needs to be seen through a much longer lens than the generative AI one. To focus only on that is to lose sight of the wider and longer issue. We DO have the capacity to change things for the better but that also means that many of us will be compelled (in a tense, under threat landscape) to learn more about how to challenge conventions and even invest much more time in programme level, iterative, AI cognisant teaching and assessment practices. Inevitably the conversations will start with the narrow and hyped and immediate manifestations of inappropriate AI use but let’s celebrate this as leverage; as a catalyst.  We’d do well, at the very least, to reconsider how we define cheating, why we consider some incredibly common behaviours as cheating (is it collusion or is it collaboration for example or proof reading help from 3rd parties). Beyond that, we should be having serious discussions about augmentation and hybridity in writing: what counts as acceptable support? How does that differ according to context and discipline? It will raise questions about the extent to which writing is the dominant assessment medium, about authenticity in assessment and about the rationale and perceived value of anonymity. 

It’s interesting to read how over 80 years ago (Drake, 1941) many of the behaviours we witness today in both students and their teachers have 21st century parallels. Strict disciplinarian responses or ignoring it because ‘they’re only harming themselves’ being common. In other words, the underlying causes were not being addressed. To finish I think this sets out the challenge confronting us well:

“Teachers in general, and college professors in particular, will not be enthusiastic about proposed changes. They are opposed to changes of any sort that may interfere with long- established routines-and examinations are a part of the hoary tradition of the academic past”

(Drake, 1941, p.420)

Drake, C. A. (1941). Why students cheat. Journal of Higher Education, 12(5)

Hutton, P. A. (2006). Understanding student cheating and what educators can do about it. College Teaching, 54(1), 171–176. https://www.jstor.org/stable/27559254 

Miles, P., et al. (2022). Why Students Cheat. The Journal of Undergraduate Neuroscience Education (JUNE), 20(2):A150-A160 

Rettinger, D. A., & Kramer, Y. (2009). Situational and individual factors associated with academic dishonesty. Research in Higher Education, 50(3), 293-313. https://doi.org/10.1007/s11162-008-9116-5 

Simkin, M. G., & McLeod, A. (2010). Why do college students cheat?. Journal of Business Ethics, 94, 441-453. https://doi.org/10.1007/s10551-009-0275-x 

Stevens, G. E., & Stevens, F. W. (1987). Ethical inclinations of tomorrow’s managers revisited: How and why students cheat. Journal of Education for Business, 63(1), 24-29. https://doi.org/10.1080/08832323.1987.10117269 

Yu, H., Glanzer, P. L., Johnson, B. R., Sriram, R., & Moore, B. (2018). Why college students cheat: A conceptual model of five factors. The Review of Higher Education, 41(4), 549-576. https://doi.org/10.1353/rhe.2018.0025 

Gallant, T. B., & Drinan, P. (2006). Organizational theory and student cheating: Explanation, responses, and strategies. The Journal of Higher Education, 77(5), 839-860. https://www.jstor.org/stable/3838789 

CPD for critical AI literacy: do NOT click here.

heducationistLeave a comment

In 2018, Timos Almpanis and I co-wrote an article exploring issues with Continuous Professional Development (CPD) in relation to Technology Enhanced Learning (TEL). The article, which we published while working together at Greenwich (in Compass: Journal of Learning and Teaching), highlighted a persistent challenge: despite substantial investment in TEL, enthusiasm for it and use among educators remained inconsistent at best. While students increasingly expect technology to enhance their learning, and there is/ was evidence to supports its potential to improve engagement and outcomes, the traditional transmissive CPD models supporting how teaching academics were introduced to TEL and supported in it could undermine its own purpose. Focusing on technology and systems as well as using poor (and non modelling) pedagogy often gave/ give a sense of compliance over pedagogic improvement.

Because we are both a bit contrary and subversive we commissioned an undergraduate student (Christina Chitoroaga) to illustrate our arguments with some cartoons which I am duplicating here (I think I am allowed to do that?):

We argued that TEL focussed CPD should prioritise personalised and pedagogy-focused approaches over one-size-fits-all training sessions. Effective CPD that acknowledges need, relfects evidence-informed pedagogic apparoaches and empowers educators by offering choice, flexibility and relevance, will also enable them to explore and apply tools that suit their specific teaching contexts and pedagogical needs. By shifting the focus away from the technology itself and towards its purpose in enhancing learning, we can foster greater engagement and creativity among academic staff. This was exactly the approach I tried to apply when rolling out Mentimenter (a student response system to support increasing engagement in and out of class).

I was reminded of this article recently (because fo the ‘click here; clck there’ cartoon) when a colleague expressed frustration about a common issue they observed: lecturers teaching ‘regular’ students (I always struggle with this framing as most of my ‘students’ are my colleagues- we need a name for that! I will do a poll – got totally distracted by that but it’s done now) how to use software using a “follow me as I click here and there” method. Given that the “follow me as I click” is still a thing, perhaps it is time to adopt a more assertive and directive approach. Instead of simply providing opportunities to explore better practices, we may need to be clearer in saying: “Do not do this.” I mean I do not want to be the pedagogy police but while there is no absolute right way there are some wrong ways, right? Also we might want to think about what this means in terms of the AI elephant in every bloomin’ classroom.

The deluge of AI tools and emerging uses of these tech (willingly and unwillingly & appropriately and inappropriately) means the need for effective upskilling is even more urgent. However we support skill development and thinking time we need of course to realise it requires moving beyond the “click here, click there” model. In my view (and I am aware this is contested) educators and students need to experiment with AI tools in real-world contexts, gaining experience in how AI is impacting curricula, academic use and, potentially, pedagogic practices. The many valid and pressing reasons why teachers might resist or reject engaging with AI tools: workload, ethical implications, data privacy, copyright, eye-watering environmental impacts or even concern about being replaced by technology are a significant barriers to adoption. But adoption is not my goal; critical engagement is. The conflation of the two in the minds of my colleagues is I think a powerful impediment before I even get a chance to bore them to death with a ‘click here; click there’. In fact, there’s no getting away from the necessity of empathy and a supportive approach, one that acknowledges these fears while providing space for dialogue and both critical AND creative applications of responsibly used AI tools. In fact, Alison Gilmour and I wrote about this too! It’s like all my work actually coheres!

Whatever the approach, CPD cannot be a one-size-fits-all solution, nor can it rely on prescriptive ‘click here, click there’ methods. It must be compassionate and dialogic, enabling experimentation across a spectrum of enthusiasm—from evangelical to steadfast resistance. While I have prioritised ‘come and play’, ‘let’s discuss’, or ‘did you know you can…’ events, I recognise the need for more structured opportunities to clarify these underpinning values before events begin. If I can find a way to manage such a shift it will help align the CPD with meaningful, exploratory engagement that puts pedagogy and dialogue at the heart of our ongoing efforts to grow critical AI literacy in a productive, positive way that offers something to everyone wherever they sit of the parallel spectrums of AI skills and beliefs.

Post script: some time ago I wrote on the WONKHE blog about growing AI literacy and this coincided wiht the launch of the GEN AI in HE MOOC. We’re working on an expanded version- broadening the scope of AI beyond the utterly divisive ‘generative’ as well as widening the scope to other sectors of education. Release due in May. It’ll be free to access.