Why I endorse AI in research, even when many academics won’t

AI has a bad reputation in some corners of academia.

The fears are real: plagiarism, misinformation, lazy thinking. The louder AI gets, the more some researchers double down on rejecting it altogether.

I use AI in some areas of my research.

Not because I’m lazy or I want a shortcut.

But because I believe efficient doesn’t have to mean unethical.

2 years in: here’s what I’ve learned

I’ve been working with research-focused AI tools for two years now. Not just ChatGPT, but Paperpal, SciSpace, Jenni AI, Julius AI, and others.

I didn’t start with blind optimism. Like many people in academic spaces, I was cautious and even sceptical. I started testing tools on small tasks: summarising an article I’d already read, rephrasing a paragraph I’d written myself, structuring a rough research idea into bullet points.

What I found wasn’t magic. Simply put:

The tools worked when I used them wisely.
They saved time when I knew what I wanted from them.
They raised new questions: ethical ones, practical ones, strategic ones.

Long story short, AI isn’t the threat. Misuse is.

We’re having the wrong debate about AI

What frustrates me is how the conversation around AI in research keeps missing the point.

It’s always framed as: “Should we use AI or not?”

But that’s not the right question. The better questions are:

• When should we use it?

• How should we use it?

• What are the trade-offs?

• What safeguards do we need?

Instead, it becomes this black-and-white debate: either you fully embrace AI and get labelled reckless, or you reject it outright and feel morally superior.

Neither is helpful.

Here’s what a more grounded approach looks like:

• Use AI to help structure your thoughts NOT generate your conclusions

• Use summarisation tools to speed up comprehension NOT replace it

• Use writing assistants to clarify your work NOT outsource your voice

• Always ask: “Would I be comfortable explaining how I used this tool in a viva or journal submission?”

That last one is a good ethical filter. I use it often.

Refusing to engage is its own risk

Let’s be blunt: AI isn’t going away.

Journals are drafting policies. Institutions are training staff. Funders are starting to ask questions. Whether you like it or not, the future researcher is one who knows how to navigate these tools critically, carefully, and confidently.

Avoiding AI might feel safe now. But in a year or two, it could look like a gap in your skills.

I’m not saying we should all jump on the hype train.
I’m saying we should stop pretending the train doesn’t exist.

So what’s the way forward?

Start small.
Pick one tool.
Use it on something low-stakes.
Compare its output to your own thinking.
Reflect on what it got wrong and what it made easier.

Then keep going. With a critical eye, but also a practical one.

That’s how I’ve approached it. Not with fear. Not with blind faith. But with curiosity and discipline.

We don’t need more noise in this space. We need researchers who know how to use these tools well, and who are willing to talk about it.

I'm happy to be one of them.

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