How I use AI in my academic work
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There’s a lot of noise about AI at the moment, and not much nuance.
This isn’t a defence of AI or a warning about it. I just want to share how I actually use it in my work—as a researcher and as a coach—because the conversation is often polarised, and the middle ground is more useful.
Here are three ways AI has genuinely helped me:
1. It helps me begin
Sometimes I use AI to get over the inertia of starting something—especially things I’ve been putting off. A blank screen can feel like a real barrier when I’m tired or unsure. Having something rough to react to makes it easier to get into motion.
Even when I don’t end up using the output, it helps me feel more confident taking on projects that I might otherwise delay—whether that’s a piece of analysis or a new coding script. It’s the sense that I have backup if I get stuck. That’s often enough. I hear this can be particularly useful for individuals with ADHD, where the hardest part is often taking the next functional step. AI is like your buddy cheering you along.
2. It’s not where my ideas come from
I don’t use AI to generate my research ideas, my arguments, conclusions, or content. That part still comes from me. I did recently put my paper through AI and told it to tell me everything wrong with it, and I can see that it may have come up with new ideas from that. So that’s something I’d have to consider in future.
What it’s good for is sorting through thoughts, helping me summarise something I already understand, or creating a structure I can then adapt. But when I’ve asked it to rewrite things “more clearly,” the results usually sound less like me—and sometimes cheesy or worse. Too polished, I suppose. I can end up putting back the complexity or the edge that actually matters to me.
So I’ve learned not to use it at that stage. If it’s diluting my voice, it’s not useful. And always let your conscience be your guide. If it feels like cheating, it probably is.
3. It’s helpful for admin
I don’t see any value in doing repetitive tasks manually if they can be done well with AI. Tidying up notes, generating tables, writing summaries of meeting recordings—if a tool can take that off my plate, great. I even use it to convert my grumpy marking feedback into a helpful criticism sandwich for students.
This is about getting some time back to focus on the parts that require actual thinking whilst creating an even better output.
If you’re interested, I'll be showing exactly some of the tricks I use in my May webinar in The Sisterhood, AI as your invisible academic assistant.
There was a recent Claude report showing that students are using AI not just for productivity, but for creative work—writing, composing, developing ideas. Not to avoid the work, but to engage more deeply with it.
That’s how I think about it too. It’s not replacing anything. But it is changing how I approach certain tasks. And I’d rather be transparent about that than pretend it’s not happening.
If you’re experimenting with AI in your own work, I’d love to hear how. And if you’re not, that’s also fine. The aim isn’t to do everything more efficiently. It’s to do what matters—better.
If you’re interested in leveraging AI in your research and teaching, you can check out my webinars Workshopping Grants with AI or Enhanced Teaching with AI, or join the Sisterhood where you get all my webinars included.
