Are academics making an (em) dash for AI?

Stop calling it slop

Artificial intelligence writing is instantly recognisable, we are told—soulless, dispassionate, and devoid of the spark that marks genuine thought. Historian Jonathan Rees, in Academe this spring, calls it “bland, unspecific, pedestrian prose”. Journalist and UCL academic Sarfraz Manzoor, in a recent piece for The Independent, concluded that an AI article his students read was “competent but forgettable”. Scroll through r/professors on any given day and you will find dozens, if not hundreds, of colleagues enthusiastically nodding along and complaining bitterly about students submitting work that any fool can see was written by a machine.

Outside academia, the dismissal has become so culturally embedded that Merriam-Webster named “slop”—defined as digital content of low quality produced by artificial intelligence—its Word of the Year for 2025.

I have used AI routinely in my research, teaching and outreach for the past two years, and, in my experience, these confident, oft-repeated claims are wrong—wrong about the quality, wrong about the creativity, and wrong about the ease with which AI writing can supposedly be detected. Yes, there are of course real, compelling problems: the very genuine risk that students outsource thinking they should be doing themselves, the environmental costs of running these systems, and the danger that familiarity breeds credulity—that students accept AI output without the critical scrutiny they would apply to any other source.

But “it’s all slop” is simply not a critique. It is a lazy reflex that lets us avoid a more uncomfortable conversation. So let me add to that discomfort. More than half of this article was written by Anthropic’s Claude Sonnet 4.6. See if you can tell the output of the carbon-based neural net from that of its silicon-based counterpart. Feel free to make your case in the comments section below—which parts are mine and which were written by Claude? (A word of warning: counting em dashes will not help you. I have been liberally scattering them across my writing long before any algorithm or architecture was trained to do the same.)

The most threadbare—or, perhaps I should say, sloppiest—version of the anti-AI argument is that large language models are little more than sophisticated search engines (“uberbrowsers”), regurgitating their training data verbatim. The evidence strongly suggests otherwise—a 2024 study found that LLM reasoning is “unlike retrieval, and more like a generalisable strategy that synthesises procedural knowledge”. In other words, it doesn’t fetch answers. It applies a method that is often uncomfortably close to how a human researcher synthesises and extends knowledge.

I have seen this at first hand in both research and teaching. In a recent paper, we needed to translate a specialised mathematical expression from the literature into working code. I cut and pasted the relevant equation directly into Claude (as a low-resolution screenshot) and received working code within seconds. That code does not exist online. It was essentially reasoned—for a given definition of “reasoned”—into existence. The simulations of molecular diffusion in the same paper involved a more iterative, back-and-forth process with the LLM—but one that I believe many academics would recognise as collaborative problem-solving—and reduced work that would previously have taken days, if not weeks, to a matter of hours.

This is where the discomfort really deepens. Writing and debugging those codes would previously have been exactly the kind of task I would have set a PhD researcher or upper-level undergraduate project student—meaningful precisely because wrestling with the mathematics builds physical intuition. My mantra as a physics undergraduate was simple: “If I can’t code it, I don’t understand it.” That principle is not merely under pressure. It is dead in the water. The necessary pain of the learning process—and, crucially, its reward—has been short-circuited. And the reasoning capabilities that make this possible extend well beyond code generation.

When I fed ChatGPT one of our undergraduate quantum physics exam papers verbatim—no rewording, no restructuring, no assistance—it not only scored 95 per cent, but at one point reasoned its way toward an answer that was better than those found in some textbooks, steered along much as I might nudge a PhD student in a viva. That is not a search engine. That is something considerably more unsettling—and considerably more interesting. Whatever it is, slop it is not.

Philip Moriarty is professor of physics at the University of Nottingham.

AI has become part of my life’s noise, whether I like it or not

I don’t “use” artificial intelligence so much as interact compulsorily with it. The virtual AI assistant on the university website asks me how it can help me today (I ignore it). Microsoft Copilot wants to clean up my email messages (no thanks). I am urged to take training courses to make me “AI ready”. Even if I barely use it, AI uses me. Everything I have written in a 30-year career – books, journal articles, pieces for newspapers and magazines – has been stolen without my permission to train large language models. AI has become part of my life’s noise, whether I like it or not.

I think this is all meant to give me Fomo: fear of missing out. But I’m not sure what I’m meant to be missing out on. I already know how to write, and AI’s data-processing power would not help me with the untidily human business of reading, thinking, sifting evidence and formulating arguments. “AI” feels to me like a generic marketing term for lots of different technologies. I know that in the applied sciences it has many excellent uses: predicting natural disasters, diagnosing diseases, recognising tumours, enabling real-time tracking of threatened species. But in the humanities – the study of the messy, granular, non-algorithmic human – it feels like an existential threat. AI is “here to stay”, they say. But which AI do they mean?

The American writer Wendell Berry wrote: “The modern mind longs for the future as the medieval mind longed for Heaven.” Something that feels like the future feels like the answer. In universities, we’ve come to think of AI like that: all we can do is embrace it or adapt to it. I would like to see a bit more critical thinking on AI in universities, instead of just cheerleading. Then we could look at the more excitable or apocalyptic statements about it with at least a tinge of historical scepticism. We could talk about the unchallenged power it gives to the super-rich and super-weird white men of Silicon Valley. And we could acknowledge its grotesque environmental costs. We used to have “please consider the environment before printing this email”. Why not “please consider the environment before using ChatGPT”?

I resent most of all that AI has made me suspicious of my students when I mark their work. I refuse to agree with my more pessimistic colleagues who say that coursework is dead and we will have to revert to AI-proof exams. This feels like buying into the hype that the exponential growth of AI is inevitable: the future has been decided, and all we can do is adjust to it. It cheers me that Gen Z has adopted a cynical vocabulary – slopper, groksucker, botbrained – to describe those addicted to AI. I have faith in young people that they will still want to use words for the reason they were invented: to communicate with others. You wouldn’t outsource your speech to AI, I tell them; why outsource your writing? Don’t you want to sound like yourself?

My perhaps naive hope is that the growth of computer-generated writing will put a premium on the human-generated variety. We only think of large language models as competitors because writing, and our expectations for it, have become so impoverished. We worry that the algorithms will take over because much human writing might as well have been done by algorithm. So perhaps we can use this moment to steer students away from the rubric-driven and professionally voiceless essays they have been taught to produce and get them to write in a voice that is distinctively their own. I want them to think of writing as a gift they give to others – the gift of taking pains to tell someone what they know or have seen. I want them to see writing as an imperfect, time-consuming and fundamentally human act.

Then we can leave AI to anyone who thinks writing is just a faceless, computational process. I am happy for those who trade in the stale, noun-drenched language of university management-speak to use as much digital assistance as they like. They might as well have been using AI all along.

Joe Moran is a professor of English at Liverpool John Moores University.

Shame and anxiety encourage avoidant responses to problems

I was stressed; I was sick; I was running out of time: the reasons students give me for using artificial intelligence-generated content in their academic work are the same reasons they have always given me for committing academic misconduct. One of my roles at my institution is as an academic integrity officer, which means I investigate potential breaches of the academic code of conduct.

Until a couple of years ago, the vast majority of cases referred to me were suspected plagiarism, which were usually easy to prove. AI cases are more slippery things; lecturers often refer assignments on the basis of writing “feeling” like AI, but of course I cannot fail a student’s assignment on gut instinct. I need evidence, and that can be difficult to find. If I use an AI detector (itself an AI engine), it can give me probabilities. Much of the time the work is based on building a case out of multiple probabilities rather than definites.

A core part of this process is giving the student an opportunity to speak to me about the assignment – there’s an irony here about the amount of human time and effort involved in detecting AI usage. But it is often the most enlightening part. Occasionally students deny any wrongdoing (and, important to note, I do sometimes clear students of wrongdoing). More often, though, after a little friendly questioning, students will admit that they used AI.

Sometimes students will say they have not used AI and then contradict themselves when I ask them questions in a slightly different way. Did you use Copilot to answer this question? Did you ask Gemini to find references for you? For a number of students, “using AI” suggests cutting and pasting a ChatGPT answer into their assignment, and they seem unclear of where the line for acceptable usage is. This can be made more difficult because of varying requirements between subject areas. In my own, history, we have a clear line that generative AI is not allowed at all; others permit it for planning purposes and for other routine tasks. All students are expected to declare any AI use, but in conversations with students it is often clear that they find it easy to move – or to slip – between licit and illicit uses of AI.

This is hardly surprising, given how easily accessible generative AI tools are now, and also when students are being repeatedly told – by schoolteachers before they come to university,  by university administrations, by the media – that they will need AI in their future working lives. While I personally think AI poses serious issues for pedagogy, in most of these cases, use of AI masks wider problems within our student population that urgently need addressing.

Throughout my time as an educator, there have always been students who are stretched too thin: financially, emotionally and academically. But in the current socio-economic climate, it feels as if these problems are getting worse. The current generation of undergraduate students were impacted during their formative adolescent years by the Covid pandemic, which deepened pre-existing inequalities and had a significant impact on mental well-being. In 2022, the UK Office for National Statistics reported that 37 per cent of students reported moderate to severe depressive symptoms – but only 5.8 per cent officially disclosed mental health conditions to their universities.

It’s undeniable that this generation of youth is facing huge personal and more existential challenges: the cost of living crisis, fear about the global political situation, ecological disasters, all being fed to us 24/7 by social media. A survey by the American Psychological Association found that over half of 18- to 34-year-olds felt overwhelmed by stress and a staggering 74 per cent believe that it’s harder to connect with other people than it was in the past, suggesting a widespread sense of social isolation despite their digital connections.

I believe this is at the heart of what is causing many students to turn to AI. While my university has a robust personal tutor system, and well-signposted systems for seeking help, many students find it difficult to reach out and ask for what they need. This is exacerbated by students increasingly not attending class. Shame and anxiety, made worse by lack of input from both teachers and peers, encourage avoidant responses to problems. Instead assignments are left to the last minute and then completed – often with the assistance of GenAI – in a panic.

There will always be students who cheat. Some of them may do so from laziness. The majority of cheating cases, however, have more complex causes, and without addressing them, my colleagues and I will continue to receive more and more AI responses to assignments.

Rachel Moss is senior lecturer in history at the University of Northampton.

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We need to get off the dreamland bus that is driving assessment over a cliff

When I was asked to write about “how artificial intelligence is affecting universities”, I thought it was only fair to let a chatbot offer its answer to this question. It laid out a cogent, if rose-tinted, set of points. A decent B+ essay, I felt. 

However, AI said almost nothing about the single most destructive issue for universities on both sides of the Atlantic, in which I have spent my working life – that all typed work by students is now worthless as a means of assessing them and their knowledge. 

If I were a vice-chancellor, I might hang that up as a red notice in my office. Currently, some universities appear to me, I am afraid, to be in dreamland on this point. I am not qualified to say whether a psychoanalyst would call it repression or suppression.

This problem has to be recognised and acted upon right now. We need to get off the dreamland bus. When I was an undergraduate (at the University of Stirling), almost all my assessment was based on words I had written by hand on a page. The same principle held true when later I taught at one of the ancient universities in England, and a few years later at one of America’s older universities. 

It is now essential that we go back to handwritten assessment for virtually everything (my own department is gratifyingly taking action along these lines). 

Some will claim that typing might be made secure somehow in exam settings, but I do not buy that. A severe problem at many UK universities, and probably elsewhere, is that student numbers have swelled so significantly that making all assessment depend on handwriting will be seen by some as impossible. We can use technology to solve that. Machines can convert script for human examiners to assess.

My AI chatbot was also silent on how the technical revolution called artificial intelligence will deepen inequality between universities in the UK and elsewhere. Think of football. Television and slow-motion replays have led to a world where Real Madrid took in €1 billion in revenue last year. Football stars earn sums that might be considered insane. I see much the same happening in the world’s universities. A small band of elite professors and universities, who create the original ideas, will prosper. Other universities and other levels of academic staff will suffer reduced relative income and status; they will be out-clevered by AI and used instead as support teachers and hand-holders. Call it a bifurcation.

Generative AI does have some uses in academia. I have found it valuable when starting on a novel project or a set of lecture slides on a new topic. I begin by using the Web of Science or Scopus to find a list of the key articles in a subfield. Then AI provides a separate list. The overlap is highly imperfect, presumably because AI has to stick to publicly downloadable documents and to Google Scholar. But that lack of close overlap is helpful to me. It gives me two bites at the underlying issues.

That is one consolation in what is the most frightening moment for academics and higher education in modern history. When asked to educate “AI-literate” graduates for the workplace, lecturers must also somehow learn to blend their traditional abilities with a technology that potentially undermines the qualities that we seek to instil in students.

One last thought for the next generation of academics: good luck to you all. I mean it. Aiming for a university career is going to be a far, far riskier choice than I had ever imagined.

Andrew Oswald is professor of economics and behavioural science at the University of Warwick.

I have gone from avoiding AI search results to regularly using AI for deep research

The internet brought the information world to our fingertips, even if we seem to spend a lot of our time using this seemingly infinite resource to look at pictures of cats. With this massive information overload, however, came a new problem: search, curation, and knowing what you can trust and what you cannot. There is a reason that Google Search was so revolutionary and ultimately became so dominant when they streamlined the search process.

So I was initially very frustrated when Google started dominating search results with its own artificial intelligence summaries first, because it wasn’t that long ago when the AI results were extremely unreliable, and hallucinated all sorts of information with extreme confidence. My intuitive reaction was to ignore AI search results, scroll down, and try to find the information I needed myself. But what if it’s a new field that you don’t know much about and don’t know the “right” keywords to use for effective search? What if it’s a niche and obscure topic that requires scrolling through 20 pages of search results to see if someone is maybe mentioned once in some sort of meaningful way? What about the problem of languages: do you want to perform your search in multiple languages to be able to find the information you want (for example, if you are looking for specific records, legal advice, other things).

In their early inception, AI chatbots aspired to be the solution to all these questions, but they simply were not reliable or robust enough to be able to actually achieve that goal. Much has changed, however, in the three-and-a-half years since ChatGPT was first launched, and many competitors soon followed. With improvements in quality, I have gone from avoiding AI search results to regularly using AI for deep research, taking advantage of AI’s ability to rapidly find information across multiple sources in multiple languages, and, in particular its ability to dig up information from obscure forums that would take me forever to dig up, to troubleshoot simulations, clean up code, or find out about new fields.

However, there is an aspect to this that is really important: AI chatbots become much more sophisticated and reliable in the information they provide, but they have also become much better at hallucinating with confidence, including alarmingly fabricating fictitious but believable references that may be mostly correct but off by title, journal, year, or authors, or even completely fabricated papers by people who do legitimately work in that field. AI chatbots have always hallucinated but their confections used to be easy to spot – now they sound so reasonable that some of these fake references will sneak past experts in the field. And once these hallucinations work their way into the scientific literature, they are amplified and propagated seemingly endlessly in a citation cascade where others start citing AI hallucinations as fact.

More concerningly, as has been discussed extensively on social media by Mikael Elias among others, more recent AI image generators can generate completely fabricated scientific images that are difficult-to-impossible to detect, including fabricated source data, and fabricated statistical tests (ironically, a student of mine told me that when they fed actual data to an AI chatbot asking it to convert it into an image for them, the chatbot completely messed up their data). Thus, while AI is maximally streamlining our workflows, the potential for hallucinated and fabricated information polluting the literature, whether through malice or lack of proper oversight, is greater than ever before, and this creates a very serious problem for protecting the integrity of the scientific record.

Unlike many people, even though I do use AI in many ways in my daily life, professionally, beyond deep research, I do not personally use AI to help me write, especially grant proposals. It can be very tempting to use shortcuts with AI for speed, and, in particular, I can appreciate the value of AI tools for language improvement for the vast majority of scientists for whom English is not a native language. Nonetheless, the problem with AI chatbots is they regress to the mean, everything ultimately ends up looking very cookie cutter and identical, and thus you lose the personal spark of inspiration that makes your grant stand out from the crowd.

It’s hard to imagine that in less than half a decade, we have gone to a world where AI permeates every aspect of our daily lives, and the world before AI is no longer imaginable. There are still many pitfalls and hazards to using AI uncritically as part of our research workflows. However, as with anything else, provided it is used ethically and responsibly, with proper human oversight and validation, AI can be a valuable learning and research tool, which can make our workflows significantly more efficient, freeing time up for deeper thinking.

Lynn Kamerlin is a professor and Georgia Research Alliance Wasser Woolley Chair in Molecular Design at Georgia Institute of Technology.