AI panic in academia: why professors fear AI & how to adapt

Why academics are worried (and they’re not entirely wrong)

Last week, I sat in an academic seminar as a colleague presented his research. But for the first time in years, the most ‘interesting’ discussion wasn’t about his methodology or findings. Instead, the room erupted when someone asked: “How do we know this analysis wasn’t done by ChatGPT?” What followed was thirty minutes of increasingly anxious debate about AI in academia, while our poor presenter’s actual research was forgotten entirely.

The next day brought an even more revealing moment. At a faculty meeting about updating our curriculum for “a rapidly changing world” (academic-speak for “AI is here, help!”), I watched my colleagues tie themselves in knots. The mere suggestion of teaching students how to understand and responsibly use AI tools was met with horror. “We’d be undermining the very foundations of education!” one professor declared, to vigorous nods around the room.

This got me thinking very hard and long about the future of research (and partly explain why I started this blog). If you’re new here, check out our first post where I discuss why ethical AI in research matters.” Why are academics freaking out about AI? Walk into any faculty lounge these days, and you’ll hear the whispers about how peer-reviewed publications have lost their value or how we can no longer evaluate the quality of student submissions in an objective way. 

As someone who’s spent the last decade in academia, I get it. The arrival of powerful AI tools feels like an earthquake beneath our ivory towers. Suddenly, the skills we’ve spent years mastering – critical analysis, research synthesis, academic writing – seem threatened by algorithms that can churn out papers in seconds.

But here’s the thing: while the academic community is busy panicking about AI, we’re missing a crucial opportunity to reshape how we do research and education. Yes, AI brings legitimate challenges to academic integrity and research quality. And yes, we need to think hard about these issues. But maybe – just maybe – we’re asking the wrong questions.

Instead of wondering whether AI will replace us, shouldn’t we be asking how it could make us better researchers and educators? Rather than seeing AI as a threat to academic integrity, could we view it as a tool to enhance our work?

In this post, I’ll break down why so many academics are worried about AI, but more importantly, why I believe these fears, while understandable, might be preventing us from preparing for a future that’s already here.

The 3 big fears: Authenticity, Bias & Inquiry

Let’s be honest: academics aren’t just being paranoid. I recently watched a colleague demonstrate how they could feed a research paper into ChatGPT and get a perfectly plausible peer review in minutes – a process that usually takes me hours, if not days, of careful reading and thinking. That’s both impressive and terrifying.

The concerns keeping academics up at night fall into three big buckets, and they’re all legitimate.

First, there’s the authenticity crisis. We’re seeing papers in prestigious journals getting retracted because authors secretly used AI to write sections or analyze data. In July 2024, Australian universities had to submit formal action plans to address AI risks to academic integrity, as documented by TEQSA (Tertiary Education Quality and Standards Agency). Recent studies estimate that between 10-60% of students are using AI in their studies in some form, with an unknown portion using it inappropriately. One senior professor told me she now spends more time trying to spot AI-generated content than actually evaluating her students’ ideas. It’s not just about catching cheaters – it’s about preserving the fundamental integrity of academic work. When we can’t trust whether a human actually did the thinking, we’ve got a problem.

Then there’s the bias bomb waiting to explode. AI models are trained on existing research – including all its historical biases, outdated conclusions, and methodological flaws. Just last month, I tested a popular AI tool on a dataset about what weights do people assign to different sources of knowledge (e.g., Western Scientific knowledge versus Indigenous or experiential knowledge). Unsurprisingly, it consistently undervalued Indigenous knowledge and Experiential knowledge as lacking rigour and “scientific” backing, apparently mirroring biases in its training data. We’re at risk of not just perpetuating these biases but amplifying them at an unprecedented scale.

But perhaps the deepest fear is about the very nature of academic inquiry (the death of academic inquiry?). A PhD student friend recently confided in me: “I feel like I’m competing with AI rather than using it to learn.” This cuts to the heart of what we do in academia. The messy process of wrestling with ideas, following dead ends, and gradually building understanding – can that survive in a world where AI offers instant, polished answers?

And yet… something about this panic feels familiar. I recently dug up academic articles and media discussions from the 1990s warning that the internet would destroy serious research. ‘Students will just copy from websites!’ they warned. Sound familiar? I even had a friend joke to me that they imagined some academics (or intellectuals, as academics were then called) kicking against the invention of ‘writing’ as it would derail memory.  

So here’s where I think we need to shift our perspective: These challenges aren’t unique to AI. Every major technological shift in academia – from calculators to internet search – has forced us to rethink how we teach, research, and evaluate learning. The key isn’t to resist change but to shape it thoughtfully.

The question also isn’t whether AI will change academia – it already has. The real question is whether we’ll shape that change or be shaped by it. In the next section, I’ll explain why I believe we need to move beyond fear and toward practical adaptation. But first, I’d love to hear from you: what AI-related changes are you seeing in your academic field? Drop a comment below.

The elephant in the room 

Let’s tackle the elephant in the room: original thought. As a researcher who’s spent years developing my own academic voice, I understand the visceral reaction many have to AI-generated content. The concern isn’t just about cheating – it’s about the very essence of academic work.

“My identity as an academic researcher is bound up in my ability to think, read and write critically,” as one professor recently put it to me. “Do I want to ship this out to AI? No – where would that leave me as an academic?” This sentiment resonates with many scholars who see their analytical and writing skills as core to their professional identity.

However, this frames the discussion as an either/or choice between human thought and AI assistance. The reality is more nuanced. Think about how we use other tools for thinking – citation managers, statistical software, or even the humble word processor. Each of these augments rather than replaces our cognitive abilities.

The key distinction lies in how we use these tools. When I use AI while working on a paper, it’s not to outsource my thinking but to enhance it. AI can help surface connections across large bodies of literature, challenge my assumptions by pointing out counter-arguments I might have missed, or help me articulate my ideas more clearly. The original insights, critical analysis, and intellectual synthesis still come from me – the human in the loop.

As one colleague put it, “I’ve realized I’m not just using a tool – I’m participating in a dialogue.” This collaborative approach allows us to focus more of our energy on what humans do best: asking novel questions, making creative connections, and bringing deep disciplinary understanding to complex problems.

The question isn’t whether AI will replace academic thinking – it won’t. The question is how we can use it to push our thinking further. In my experience, AI is most valuable not when it gives us answers, but when it helps us ask better questions. As David Warlick puts it, “when information becomes abundant, the value is in the question.” This insight feels particularly relevant to our AI moment in academia. When AI can generate plausible answers in seconds, our value as academics shifts from being answer-providers to being skilled questioners, helping shape the inquiries that drive meaningful research and learning forward.

“when information becomes abundant, the value is in the question.”

David Warlick

Taking a step forward

Fellow academics, let’s be honest with ourselves. We can wring our hands about AI in faculty lounges, or we can do what we do best: think critically and adapt thoughtfully. The real challenge isn’t deciding whether to use AI – as my department chair bluntly put it last week, “that ship has not only sailed, it’s halfway across the ocean.”

Through my own stumbles and experiments over the past year, I’ve discovered some approaches that actually work. Not theoretical frameworks or administrative ‘wishlist items’, but practical strategies that have transformed how I research and teach. Here are the four most impactful ones: (1) Redefine rather than restrict – instead of banning AI tools outright, redefine what meaningful assessment looks like, (2) Focus on what humans do best – critical thinking, creative synthesis, and nuanced judgment, (3) Make transparency the default – normalize open discussion about AI use with disclosures, and (4) Build collective AI literacy – develop institutional frameworks and training programs.

In my next blog in this series, I will be developing these strategies and sharing my own experiences applying them. Until then, the most successful approaches I’ve seen treat AI as a collaborative tool rather than a replacement or threat. Think of it like the introduction of calculators in mathematics – they didn’t eliminate the need for mathematical thinking; they changed what we focus on teaching.

Of course, implementing these changes isn’t easy. But pretending we can hold back this technological tide isn’t realistic. The question is: how will we shape AI’s role in academia – before it shapes us?

“If you’re interested in how AI is reshaping academic research, check out our introduction to AI Ethics Lab where I outline why this conversation is so important.”