People often ask me how I use AI in my research.

This is my answer.

There is a great deal of advice at the moment telling students and researchers either not to use AI at all or to embrace it enthusiastically. Much of the public discussion has focused on plagiarism, authorship, academic integrity and whether AI should be used in universities at all. These are important questions, and they deserve serious attention. Over the past year, however, I have become interested in another question that seems to receive much less attention:

How might AI change the conditions under which we think?

I do not mean that AI does the thinking for us. I mean that it may change how some of us organise ideas, remain engaged with difficult problems and recognise patterns that we have struggled to articulate.

I first became interested in AI because I am neurodivergent. I often know what I want to say but struggle to find the words, identify the structure or feel confident that I am making sense. Before AI, I sometimes avoided writing not because I lacked ideas, but because I could not see how to begin organising them. The longer I left the task, the larger and more difficult it became.

AI did not solve that problem, but it changed how I encountered it. Instead of staring at a blank page and trying to settle everything internally before beginning, I could start with a conversation.

This distinction matters because scholarship begins long before words appear on a page. It starts with curiosity and involves reading, noticing, connecting ideas, questioning assumptions and gradually developing judgement about what matters. Writing is one way that scholarship becomes visible, but it is not scholarship itself.

I enjoy research. I enjoy finding patterns, developing explanations and working out why something happens in the way it does. I also enjoy explaining ideas to other people. What I do not enjoy is the struggle that can arise between understanding something and finding a form in which I can communicate it. That struggle can absorb energy that I would rather devote to the ideas themselves.

AI helps reduce that friction. It gives me a space in which I can think through writing: testing an idea, reorganising it, objecting to an interpretation and trying again. It does not remove the intellectual difficulty of scholarship, nor would I want it to. What it changes is my capacity to remain connected to the part of the work that gives me pleasure.

One unexpected consequence is that I now enjoy writing more than I used to. Not because it has become effortless, but because the conversation keeps me curious. Instead of experiencing writing primarily as a struggle to make finished sentences, I can experience it as part of the research process itself. More often than not, I finish a session wanting to continue thinking rather than feeling relieved to have stopped.

Bring your own scholarship

One aspect of my practice often surprises people: I almost never begin by asking AI to find my literature.

I do not use AI as a substitute for literature searching, although I sometimes use it later to test whether I have overlooked an adjacent author or debate.  I start by searching for the literature myself. I decide what to read, what is relevant and why it matters. Those decisions shape the intellectual direction of the work, and I want them to remain mine.

I then bring that scholarship into the conversation. I explain why particular papers matter, where I think the gaps are, what puzzles me and what previous research may have overlooked. The quality of the conversation depends heavily on the quality and specificity of what I bring to it. AI is helping me think about scholarship that I have already begun, rather than deciding what my scholarship should become.

I sometimes ask whether other work might speak to the same idea. Occasionally AI suggests an author I already know but had not considered in that context, producing one of those gratifying of course—why didn’t I think of that? moments. But I still locate and read the work myself, and I check that every suggested source actually exists.

If you are an undergraduate or beginning researcher, this advice may sound frustrating because finding literature is precisely where you want assistance. I would nevertheless resist handing over that task. Learning how to search, judge relevance and understand why one source matters more than another are fundamental research skills. If AI selects the literature for you, it also begins to shape the questions you are likely to ask.

Learn to have a conversation

Thinking with AI is not simply about asking better questions. It is also about learning how to respond critically to the answers.

A polished paragraph can create the impression that the work is finished. For me, it usually signals the beginning of a more demanding stage.

I work through revised text paragraph by paragraph and sometimes line by line, asking:

• Do I actually agree with this?
• Is this what I would say?
• Do I fully understand the point?
• Is the claim supported by the evidence?
• Has AI overclaimed or removed an important qualification?
• Is this my judgement, or merely a plausible-sounding sentence?

Sometimes I delete entire paragraphs. Sometimes I rewrite almost everything. At other times, AI has expressed something more clearly than I could initially. The test is not whether a sentence sounds polished. It is whether I understand it, agree with it and am prepared to defend it.

I also spend a surprising amount of time disagreeing with AI. Sometimes it misunderstands what I am trying to say. When that happens, I correct it, but I also ask why the misunderstanding occurred. Was my explanation unclear? Had I skipped a logical step? Was I assuming knowledge that I had not provided? Its misunderstanding can expose weaknesses in my own articulation.

At other times, the disagreement concerns how AI has interpreted someone else’s work. It may describe an author’s argument in a way that sounds entirely plausible but does not match my reading of it. It may emphasise one part of a theory while overlooking the distinction that I think matters most, smooth over a tension that I regard as important, or make two bodies of work appear more compatible than I believe they are.

I do not assume that my interpretation is automatically correct. Instead, I return to the original text and ask what is producing the difference between our readings. Which passages support my interpretation? Have I overlooked something? Is AI relying on a familiar summary of the author rather than the particular argument made in the work I am using? Are we answering slightly different questions?

These disagreements are often extremely productive. They require me to explain not only what I think an author is saying, but why I have interpreted the work in that way and what follows from that reading. Sometimes I revise my interpretation. Sometimes I become more confident that the distinction I am making is necessary. Either way, the process helps prevent theory from becoming a set of familiar names and generic summaries. It makes me return to the substance of the argument.

These disagreements also led me towards a more difficult question: if AI can participate meaningfully in developing an argument, what remains distinctive about the researcher’s role?

One exchange from a paper I am currently writing has stayed with me. AI initially suggested that its role might be understood as contributing to the thinking while responsibility remained with me. That sounded plausible, but it also felt incomplete. Responsibility is sometimes reduced to being the person who signs their name and is held accountable if something goes wrong. It does not fully explain what the researcher contributes to the work.

Working through that discomfort led me to distinguish the roles more carefully. AI can suggest interpretations, identify patterns, test an argument and expose contradictions. The researcher brings the sustained relationship with the question: the reading, evidence, experience and judgement needed to assess those suggestions, as well as the care required to decide what matters and what can responsibly be claimed.

That care is not incidental. I care about the topic I am researching. I care whether the findings are accurate and whether the explanation does justice to the people and situations involved. I care about how the research is interpreted, where it travels and what others may do with it. The work matters to me beyond the moment in which the text is produced.

AI cannot sustain that relationship to the research. It can respond within a conversation, but it does not continue wondering about the problem afterwards. It does not remain answerable to research participants, affected communities or future readers. It has no enduring investment in whether the findings are used carefully, distorted or ignored.

The final responsibility therefore remains with the researcher, but that responsibility is not simply ownership of the text. It rests on the intellectual and ethical work of evaluating, rejecting, revising and standing behind what the paper ultimately says. Contesting the first answer helped me understand more clearly both what AI can contribute and what it cannot.

Once I have a reasonably developed argument, I deliberately change the role I ask AI to play. Instead of asking it to improve the prose, I ask it to challenge the work.

Prompts I return to include:

• What assumptions am I making here?
• Where is the argument weakest?
• What evidence would challenge this conclusion?
• Where have I overclaimed?
• What important counterargument have I not addressed?
• What question do you think I am really trying to answer?
• Where are the gaps, contradictions or changes in meaning across the paper?
• Is the paper consistent?

None of these prompts asks AI to generate my argument. They ask it to provide resistance: something against which I can test work that already exists.

I rarely ask AI to tell me what to think. I ask it to help me discover, clarify and challenge what I think.

Finding your voice

A common criticism of AI-generated writing is that it makes everyone sound the same. There is some truth in this. Asked to produce academic prose without sufficient context, AI tends towards smooth, generic language and familiar structures.

But AI cannot give you an academic voice. Your voice develops through reading, writing, teaching, making mistakes and gradually discovering the questions and distinctions that matter to you.

Long before generative AI existed, I started writing GeoFoodie because I wanted somewhere to explore ideas that did not yet belong in academic papers. Looking back, blogging has been one of the most valuable things I have done as an academic. It gave me room to experiment, explain complex ideas to wider audiences and gradually discover how I wanted to write.

It also helped me learn something important: voice is not simply a matter of tone or phrasing. It includes what you notice, what bothers you, which explanations you find inadequate and what kinds of questions you repeatedly return to.

Early-career researchers need some equivalent space. It might be a blog, a newsletter, a notebook or a regular practice of reflective writing. Voice develops through use. It cannot be outsourced.

What AI can do is help you recognise a voice that has already begun to emerge.

One afternoon, almost on a whim, I asked:

“What do you think my research is actually about?”

Looking across thirty years of publications, AI reflected back a pattern that I recognised immediately. Although I had written about entrepreneurship, migration, innovation, food insecurity and community resilience, the same underlying question appeared repeatedly:

What enables or constrains people from doing what they value, and how might those conditions be organised differently?

AI did not discover that research programme. I had spent three decades building it. I already understood that the different areas of my work were connected, but I tended to explain that continuity through the theories I had used. AI helped me express that continuity in language that was simpler, more memorable and easier to communicate to other people.

Later, I asked AI to examine years of my writing and describe how I appeared to think, rather than simply how I wrote. The resulting “voice prompt” was not mainly about turns of phrase. It identified recurring intellectual habits: questioning taken-for-granted explanations, looking for enabling conditions rather than individual deficits and asking what mechanisms connect people, resources and possibilities.

The useful part was not accepting that description as authoritative. It was testing it, refining it and arguing with it until I recognised myself in it.

If you already have a body of essays, blog posts, reports or dissertation chapters, you could ask:

• What questions do I keep returning to?
• What assumptions do I repeatedly challenge?
• What kinds of explanations do I resist?
• How do I usually structure an argument?
• What types of examples or contrasts do I use?
• Which ideas appear to matter most to me?

Do not expect the answers to be definitive. Treat them as interpretations. The value often lies in noticing where you agree, where you resist and what that resistance reveals.

AI does not necessarily make me faster

People often assume that AI makes writers more productive. I am not sure that it has made me faster in any straightforward sense.

Some of my best papers were written long before AI existed and drafted remarkably quickly. Others have taken months or years of revision. AI has not eliminated that variation, nor has it removed the intellectual difficulty of writing.

Its contribution is subtler. It helps me remain engaged with questions that might otherwise become overwhelming or frustrating. Rather than requiring every strand of an argument to be organised internally before I begin, it allows me to develop the structure through dialogue.

For me, this matters more than speed. I do not want to automate away the parts of scholarship I enjoy. I want to protect them.

I enjoy reading across a body of work and suddenly seeing a connection. I enjoy working out why a familiar explanation is inadequate. I enjoy developing an argument until it changes how I understand the problem. I enjoy explaining an idea in a way that enables someone else to see it differently.

What I find difficult is the labour of converting all of that into an orderly structure while trying to hold every part of the argument in my head at once. AI does not create the curiosity or intellectual pleasure. It helps prevent the struggle of communication from overwhelming them.

That is why I hesitate to describe AI primarily as a productivity tool. Productivity is usually understood as producing more in less time. The more important change for me is that it helps sustain attention, reduce avoidance and preserve the joy that made me want to become a researcher in the first place.

Removing barriers

I also use AI to remove barriers around technical tasks that are not central to my scholarship.

For example, I ask it to write Python code for figures and charts. I could not write that code unaided, and becoming a programmer is not one of my research ambitions. AI allows me to describe the analysis and the type of figure I need, generate the code and then refine the result.

This does not remove the need for scrutiny. I still check that the graph accurately represents the data, that labels and scales are appropriate and that the visual does not imply something the analysis cannot support. AI can generate the code, but the interpretation remains mine.

The benefit is not simply that a task takes less time. It is that a technical barrier no longer stands between me and the form in which I want to communicate my research. That leaves more time and attention for the parts of scholarship that require my knowledge and judgement—and that I find most rewarding.

Declaring AI use

Increasingly, journals and universities ask authors to declare how they have used AI. I have found it more helpful to describe the relationship and process than merely list isolated tasks.

Statements such as “AI was used for editing” may be accurate, but they reveal very little about how the technology entered the development of the work. Nor does a list of prompts and outputs adequately represent an extended conversation in which ideas were proposed, challenged, revised and sometimes rejected.

A declaration for work developed in this way might read:

Generative AI was used as a conversational resource during the development of this work to explore ideas, test alternative interpretations, identify connections across previous publications and improve the articulation of the author’s arguments. The author selected and read the literature, interpreted the evidence, developed the theoretical framing, critically evaluated all suggestions and made the final decisions concerning the claims and wording presented.

This explains what AI contributed without pretending that it either did everything or nothing. It also makes the author’s scholarly work visible.

A useful declaration should therefore answer more than What tool did you use? or What prompt did you enter? A useful declaration should explain the role AI played, what changed through the interaction, which suggestions were accepted or rejected, what knowledge was needed to evaluate them and which parts of the scholarly process remained the author’s responsibility.

To me, that is a more honest account of how I think with AI.

A final thought

If you are looking for a shortcut through university, AI may produce text for you, but it will not produce the curiosity, judgement and care through which scholarship develops.

Used uncritically, it can generate fluent nonsense, flatten distinctive voices and make weak arguments appear complete. It can also introduce fabricated evidence, inherited bias and misplaced confidence.

Used critically, however, it can become part of an environment that supports scholarship. It may help someone begin when the blank page feels impossible, stay with a difficult question for longer, see an overlooked connection or recognise a pattern in their own work.

The most important change AI has made for me is therefore not that I can produce more words. It is that I can remain more closely connected to the parts of academic work that I value: curiosity, explanation, intellectual play and the pleasure of gradually understanding something more clearly.

AI has not given me that joy. Research already gave me that.

What it has done is reduce some of the friction that can make the joy difficult to sustain through the long and sometimes frustrating process of turning thought into communicable scholarship.

If AI helps create better conditions for careful, critical and joyful intellectual work, then it has a legitimate place in my research practice.

If it replaces that work, then I have misunderstood both the technology and the nature of scholarship.