Category Archives: Dyslexia

The Blank Page Has Never Been Neutral: AI and What We Think Writing Proves

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There are serious ethical questions about generative AI, from its environmental costs and implications for employment to the appropriation of other people’s intellectual and creative labour. I share many of those concerns. But this essay is about something narrower, and more personal.

When I wrote previously about how I think with AI—using it as a conversational sparring partner to structure ideas, test arguments and interrogate my writing—the response was mostly thoughtful. But elsewhere, particularly on LinkedIn and occasionally in comments directed at me, I have encountered something stronger. Using generative AI in academic writing is not simply described as risky or unwise. It is described as morally wrong. On one occasion, the practice was described as ‘morally bankrupt’.

That is a striking phrase, and it reflects a particular intensity in the reaction to AI in academic writing. Writing sits close to the heart of what it means to be an academic. It is how we show our thinking, lay claim to our ideas and are judged by our peers. So the objection to using AI to write is not only that it might cause harm. It is that it seems dishonest—that the words, and perhaps the thinking, are not really one’s own. I understand that concern, and much of what follows takes it seriously. But the moral intensity of some of the reaction seems to me to outrun the actual shape of the problem.

In that heightened register, one side of the ethics receives rather less attention. Lowering barriers to producing academic knowledge matters too.

It matters for individuals, because academic writing is tied to publication, recognition, employment and promotion. But the stakes are larger than careers. There are people who find some of the mechanics of academic writing extraordinarily difficult who nevertheless have important things to say. Ideas, insights and potential breakthroughs sit in minds that do not easily convert complex thought into the polished, linear prose that academia rewards. Some of those ideas might change a field, alter policy or practice, or eventually improve people’s lives.

When barriers to writing keep those ideas out of the conversation, the loss extends beyond the person who struggles to publish them. It is a loss to collective knowledge.

That possibility belongs in the ethical calculation. If a technology can sometimes enable people to communicate knowledge that would otherwise remain inaccessible, we cannot evaluate it only by what it allows us to bypass. We also have to ask what it allows to become possible.

This does not settle the ethical question. But it makes blanket declarations that AI-assisted writing is inherently immoral much harder for me to accept, and it makes me wonder whether part of the unease comes from assumptions about what writing itself proves.

What do we think writing proves?

There is a familiar story about academic writing. We read. We think. We confront the blank page. We draft, delete and rewrite, and slowly, through that struggle, the argument emerges.

There is truth in this. Writing is not the transcription of completed thought. Sentences expose gaps in arguments. Trying to explain something reveals that we don’t understand it as well as we thought. Putting an argument into words forces questions of precision: is this really what I mean? Does this follow? Have I collapsed a distinction that matters?

But I am increasingly unconvinced that generating the initial words and doing that intellectual work with them are the same thing.

For me, they often compete. If a substantial part of my cognitive capacity is taken up with initiating a sentence, remembering how it began, organising it grammatically, spelling the words and holding in mind where the paragraph needs to go, there is less left over for asking what the sentence actually says. And academic writing never happens in a vacuum. Disciplinary conventions, word counts, reviewers’ expectations and institutional pressures such as REF all occupy some of the mental space in which thinking has to happen.

AI changes that distribution for me. The first words it offers do not have to be the right words. They give me something to think with, argue against and make more precise. The friction does not disappear; more of my attention goes to the friction that matters.

That is very different from a student asking an AI to read five papers they had not read, synthesise them and produce an essay which they then submit. There, almost everything the exercise was meant to accomplish has been bypassed. The student has not followed the authors’ arguments, puzzled over difficult passages, noticed tensions, weighed interpretations or worked out what they think. Nor have they done the work of selecting from that understanding and building an argument for someone else. What remains is the artefact that was supposed to be evidence of a process that never took place.

The problem there is not that the student avoided the blank page, or that a machine produced some of the words. It is that the learning did not happen. And just because some uses of AI bypass intellectual work, it does not follow that all do.

So perhaps the question is not whether AI removes struggle, but which intellectual activities a task is meant to require, and which of those AI is supporting rather than replacing.

Whose writing process have we universalised?

This matters to me particularly because I am neurodivergent. I am dyslexic and ADHD.

I cannot spell. I am quite open about this. More confusingly, I can spell the same word correctly and incorrectly in the same paragraph. I can know exactly what I want to argue while finding it extraordinarily difficult to impose the linearity needed to communicate an interconnected set of ideas. The blank page is not a neutral intellectual space for me.

None of this means I don’t need to write, or that learning to write well hasn’t mattered. I have spent a career doing precisely that. But it makes me suspicious when one process of producing prose becomes a moral benchmark for authentic intellectual work.

The conventions of academic writing are not necessarily ableist in themselves. But when facility with a particular writing process becomes a proxy for intellectual seriousness, we should ask whose capacities that process assumes. When someone says ‘just write it yourself’, they are often describing a process that has never cost them what it costs others—and it is difficult to see a cost that has never been yours.

For many neurodivergent scholars, the bottleneck is not having the idea, understanding the literature or making the connection. It is getting a complex web of thought into the sequential form academic prose demands. Everyone has finite cognitive capacity. The difference is that some parts of conventional writing consume a disproportionate share of mine, and those parts were never particularly good measures of the quality of my thinking.

The part you cannot see

There is another problem with judging intellectual labour by the production of text. Much of thinking is invisible.

Someone reading a transcript of my interactions with an AI would see me bringing ideas to it, asking questions, requesting alternative formulations, rejecting suggestions and revising text. They might reasonably conclude that the AI contributed to the writing.

It did.

But the transcript does not contain the papers I have read over decades, the connections I make away from or at the desk, the half-formed idea that sits in my head for three days before I realise what is bothering me, or everything I considered and rejected without ever typing it.

We have long since learned to be wary of the assumption that what is readily observable constitutes the whole of what is real. The same caution applies to writing. What we can see of someone’s process is not the whole of it.

Nor is the AI simply a sophisticated spellchecker. Something happens in the exchange. It can suggest a distinction I had not articulated, identify a contradiction, or offer a formulation that makes me see my own argument differently. Sometimes the thought develops through the dialogue. I bring knowledge, commitments and judgement; it brings possibilities generated from patterns in its training and the context I provide; I reject, alter or build on them.

But the relationship is not symmetrical. I care what the argument means. I have commitments to the research and to the people and ideas I work with. I have to decide whether a claim is defensible, and I have to take responsibility for it. That makes it surprisingly hard to say who ‘did the thinking’, while leaving responsibility rather less ambiguous.

It doesn’t save me time

People often assume that academics use AI because it is quicker. That isn’t my experience.

Writing a paper with AI takes me at least as long as writing without it, and sometimes longer. I argue with it. I ask it to challenge me. I reject what it produces. I go back to the literature. I re-explain things it has misread. I return to something we apparently settled two days earlier because it keeps bothering me.

But the most important time is not the time I spend at the screen.

The back and forth with an AI has a pace of its own. A response arrives in seconds, and it is easy to keep going: to ask again, adjust, accept, move on. That pace can override the urge to stop and ponder. I think this is one of the easiest ways to produce slop, even when every individual exchange feels thoughtful.

For people who write fluently, some of that pondering may happen within the writing process itself, in the pauses between sentences. For me, those pauses were never empty; they were filled with spelling, sequence and the effort of holding everything in mind. AI clears some of that away, but it does not create the pause. I have to build that in deliberately. I have to step away and let something sit.

And when the pondering works, it rarely arrives neatly. Ideas tend to come to me in big rushes, usually when I am nowhere near a computer, and because they arrive all at once, I can lose them before I get back. Making space for pondering is only half of it. The other half is catching what it produces, and then being able to act on it.

So the time this takes is not only mechanical. It is the time for the thinking that makes the rest worthwhile.

It has also changed my relationship to shame.

For most of my career, the shame I carried about my writing was attached to proofreading. I can spot errors in other people’s work with ease; I cannot see them in my own. Spelling mistakes and unpolished sentences occupied a disproportionate share of my attention and anxiety, despite saying very little about the quality of my thinking. Being able to worry less about whether a word is spelled correctly, and more about whether it is the right word, has been genuinely liberating.

But the shame has not gone. It has moved.

Now, when I ask an AI to set out a revised version of something we have been working through—to give the argument I have been building words and an order—I sometimes feel I am being lazy. Surely a proper scholar would integrate it herself?

I don’t think I am being lazy. Having the words and the order in front of me is exactly what lets me read, judge and push against them, rather than holding everything in my head while trying to build it. But the feeling lingers, and I know where it comes from. It is the ‘morally bankrupt’ comments, internalised. They carry the idea that the difficulty is the proof, even though for me the difficulty was never where the thinking lived.

Learning to listen to the niggle

The other thing AI has changed is how much attention I pay to the feeling that something is wrong before I can say what it is.

AI writes extraordinarily plausible prose. That is one of its attractions and one of its dangers. A paragraph can be elegant, coherent and entirely wrong for the argument I am making.

Sometimes the problem is obvious: a misunderstood concept, an invented causal relationship, a flattened theoretical distinction. The harder cases are when the prose looks perfect and something nevertheless niggles. Sometimes it has reproduced a conventional interpretation my argument depends on resisting. Sometimes it has resolved a tension I need to keep open, or made commensurable two things I think must stay distinct. Sometimes it has supplied a beautifully polished version of disciplinary groupthink.

Its completeness is not my completeness.

Textual completeness and intellectual completeness are different things, and fluency can conceal absence. Something I know from years of research may simply not be there. An assumption shared across much of the literature may have slipped in unnoticed. A concept may have been made cleaner than the reality it is meant to help us understand. Recognising those absences requires knowledge, and it requires judgement—which sometimes arrives before explanation.

Part of what makes the niggle possible is that the AI’s knowledge and mine are differently constituted.

My episteme is situated. It has been formed through a particular biography: decades of reading and research, conversations, empirical encounters, a great deal of thinking and pondering, much of it done while making coffee or walking somewhere else, and a reflexive attention to how writing itself works. The AI’s knowledge reflects patterns across a vast but selective body of material, shaped by what has been written down, made available and rendered legible to the model. Neither is complete. They are partial in different ways.

That difference is what makes the encounter useful. The AI is not an objective reader; it offers no view from nowhere. What I get is my own argument refracted through a differently constituted body of knowledge.

And the refraction works in both directions. Sometimes the AI misreads a distinction I am trying to make, and my first response is frustration. That isn’t what I meant. But the misreading can show me that a connection obvious to me has never actually made it onto the page—that it is obvious only from where I am standing.

That isn’t what I meant can reveal something about my communication.

That sounds right, but isn’t can reveal something about the limits of what has been generated.

So much for outsourcing the struggle.

Authorship, judgement and responsibility

This is why the distinction between AI writing and human writing seems increasingly inadequate to me.

Someone can ask an AI to generate a paper about something they barely understand, make a few superficial changes and attach their name to it. I struggle to regard that as meaningful authorship. But other uses of AI are not simply smaller steps towards it. They are a different kind of practice altogether.

Academic writing has always been more collaborative than the image of the lone author suggests. Colleagues shape our thinking. Reviewers and editors change our arguments. Co-authors rewrite our sentences. Some scholars dictate, and some rely on extensive language editing. What made us authors was never that every word was ours alone. It was that we understood the work and answered for it.

AI is different from all of these. Its scale, opacity and capacity to generate apparently knowledgeable prose matter. But those differences make it more important, not less, to ask where intellectual responsibility lies.

Who understands the argument?

Who knows the literature and evidence it rests on?

Who recognises when something important has been flattened or omitted?

Who decides what stays and what goes?

Who can explain why it matters?

Who is willing to stand behind it when somebody says it is wrong?

Those questions get closer to the ethics of authorship than asking whether every sentence began on a blank page.

Where does the niggle come from?

A fair objection to all this is that it works for me because I already have the experience. My niggle is built on decades of reading and research. What about a first-year student, or an early career researcher, who has not yet built that knowledge?

I don’t want to wave that away. AI may lower barriers most safely for those who already know a great deal, and carry most risk for those still learning.

But students face the same problem without AI. The judgement needed to read a difficult text well, and to build an argument from it, takes years to develop. It comes through practice, and it has to be taught. Much of that judgement has become tacit for experienced academics. Sometimes those best placed to make it visible are the people for whom it never came easily, because we have had to pull it apart to understand it.

AI can be one more place to practise—not faster or better, but differently. A student who has read a paper and formed a view can compare it with an AI’s summary and ask what has been smoothed over, or where it is confidently wrong. Used this way, AI makes the process of judgement visible rather than bypassing it. It does nothing for a student who uses it to avoid the reading.

This is also why I think the arts and humanities, and the interpretive traditions across the social sciences, matter more in an age of AI, not less. When fluent text is cheap, the scarce skill is no longer producing it but judging it: reading closely, weighing arguments, noticing what is missing and asking whose view of the world a text assumes. This essay itself would not exist without philosophy, literature and history. They gave me the questions to ask of the technology, and of myself.

Beyond the purity test

None of this makes the concerns about AI in academia disappear. We should worry about cognitive offloading, about students bypassing the work through which they learn, about vast quantities of text that nobody has thought through, and about fabricated references and a homogenised, polluted knowledge commons.

But worrying about how a technology is used is different from declaring its use immoral in itself. The first asks us to pay attention. The second can overlook why some people turn to it in the first place.

We have been here before. In Phaedrus (275a-b), Socrates worries that writing will weaken memory and give people the appearance of wisdom without its substance. He was not entirely wrong. Writing did change how we remember, and it has spread terrible ideas as well as good ones. But we did not conclude that writing itself was immoral. We learned, slowly and imperfectly, to ask how it was being used, by whom and for what.

My concern is that in responding to a genuinely disruptive technology, we risk turning familiar practices of scholarship into a purity test, and universalising a relationship between thinking and writing that has always worked better for some minds than for others.

I began with the charge that using AI in academic writing is morally bankrupt. I have tried to take seriously the concerns that lie behind it. But I think the charge puts the moral question in the wrong place. What matters is not whether a machine touched the words. It is whether someone has done the thinking, can explain it, and is willing to stand behind it. Those standards are more demanding than a purity test, and much harder to police. They are also the ones that have always mattered.

And there is a moral question on the other side too.

My drive is full of half-written papers. Some are unfinished because I lost confidence, some because something newer and more interesting came along, and some because they deserved to be abandoned. But many are unfinished because I struggled to write them in the way academic norms expect. I doubt I am unusual in this.

If we are going to talk about the ethics of AI in academic writing, we should be willing to count those losses as well—the arguments never made, the insights never shared, the knowledge that never reached the people who might have used it.

The blank page has never been neutral. It has simply been easier not to notice what it kept out.

AI Is Not Your Author: How I think with AI

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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.

Using AI to understand the influence of research

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I have become a fan of Google’s AI tools. They are fantastic for summarising and revising text to make it clear, concise and well-formed. There are some limitations, of course. Gemini, for example, can make up sources, and it cannot access everything that one might think of as being ‘open access’. Notebook LM is great for cutting across articles and notes that the user feeds directly into the notebook. This helps bypass the making things up element. While Gemini seems to be better at producing concise text, Notebook LM can be a bit verbose; both have their uses.

I thought I would try an experiment with a paper that I am very familiar with because I wrote it. I wanted to see what sort of results I could get from Gemini on the influence or impact of the paper. The paper that I was exploring was my 2019 paper, “More than Just Food: Food Insecurity and Resilient Place Making through Community Self-Organising,” published in Sustainability (https://www.mdpi.com/2071-1050/11/10/2942).

Gemini’s process

Using the deep research function, I asked Gemini, “How has this paper been used by other researchers to shape their research?” and included a link to the paper. The benefits of the deep research function are that it tells you what the thinking process is and then produces a report based on what it finds. The PDF linked shows all the ‘thinking’ Gemini did to arrive at a final report. A few immediate observations about this. Firstly, Gemini does an excellent job cutting to the key contributions of the paper. Secondly, I knew pretty much where to look, but it encountered access problems, which are likely to limit the ability of Gemini to provide clear and meaningful answers to prompts that students or other researchers may have about a particular research topic. Thirdly, Gemini does try to find workarounds that seem plausible; however, again, it encountered access problems.

Gemini research steps for determining the impact of Blake (2019), More than Just Food – Google DocsDownload

The report it produces

This is the report that it produces from its somewhat limited ability to access citing literature, despite this literature being mostly open access. The first interaction did not include the (somewhat difficult to read) table at the end, but when I pointed this out, the table was added. You have the option to export the report as a Google Doc, which is really handy. What the report doesn’t do is what I wanted it to do, which was a review of all the papers that cited my paper. It does show where the contributions of the paper to the literature are, but not specifically how my work is being used. However, it is still nice to have a clear summary of not just what the contribution is, but also how it is a contribution. It is also really positive, which is a bit of an ego boost.

Shaping the Discourse_ The Enduring Influence of Blake (2019) on Food Insecurity and Community Resilience Research – Google DocsDownload

This summary of my research also tells me some other things about my own research. Given the number of contributions that it finds to a whole range of areas–something that is inherently a problem linked to my Dyslexic mind, as for me it is all interconnected. I clearly need to work on limiting the ways that I seek to make my research publications relevant by focusing on making one or maybe two contributions if I want others to use the work in their research. Too many contributions make it hard for others to see what is most important and then use that centrally in their own work.

AI for Research: A Realistic Look

My experiment with Gemini AI offered a fascinating look into how these tools gather and present information, revealing both their strengths and their current limitations when it comes to assessing a paper’s impact.

It clearly shows that AI tools, like Gemini, excel at providing quick summaries and pinpointing a paper’s main arguments, giving you a valuable head start in understanding its essence. AI can also help you understand a paper’s broader thematic contributions – how its central ideas resonate and are adopted in wider academic discussions. This encourages a more conceptual way of thinking about how research influences a field, moving beyond simply counting citations.

However, these tools are not perfect. It’s crucial to always cross-reference information, be aware of potential ‘hallucinations’ (where the AI invents facts or sources), and recognise that AI may not have access to all relevant literature, even if it’s publicly available. While AI is a powerful tool, it doesn’t replace the need for researchers to master traditional, comprehensive literature search strategies. Interestingly, observing Gemini’s ‘thought process’ can even offer students a blueprint for developing their own effective search strategies using academic databases like Google Scholar, Web of Science, or Scopus. This combination ensures both thoroughness and accuracy in your research.  

Finally, using AI like Gemini can help you refine a paper’s core contribution. By summarising its perceived impact, it can highlight if a paper’s scope is too broad or if it attempts to make too many distinct contributions. For greater impact and easier adoption by other researchers, focusing on one or two central, clearly articulated contributions per publication can make your work more digestible. You can even use AI prompts to help revise your writing for better focus. It might also reveal thematic connections you hadn’t considered, sparking new ideas for future research.

You are someone’s world: Neurodiversity

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‘To the world you may be one person; but to one person you may be the world.’

Dr. Seuss

The University of Sheffield Geography Society runs a campaign in November seeking to highlight issues students may face around mental health. This year they asked me to participate, so I am sharing my experiences of Dyslexia.

Dr Megan Blake, Senior Lecturer in Human Geography, Interdisciplinary Researcher and Food Security Expert

Estimates suggest that one in five people are neurodiverse.  This statistic does not mean that one in five people you will meet at university will be neurodiverse.  There are a lot of barriers that limit the ability of neurodiverse people to access a university degree.  Some of these are structural—how universities measure success and design knowledge acquisition—some are about perceptions of neurodiversity.

I am dyslexic.  I have always been dyslexic, as it is something you have when you are born.  Dyslexia is a specific learning disability linked to how we process and remember language, how it manifests will be different for different people.  I struggle with spelling, punctuation, proofreading, accurate copying, keeping focused in my writing, retrieving words under pressure, right and left, short term memory, calendars, and how I experience time.  I don’t have the usual problems with reading comprehension that many dyslexic people do, probably because I had a lot of reading support as a child.  I am also a lateral and interdisciplinary thinker, creative, can identify patterns, and think in complex systems.  The latter I see in my head but cannot always convert to words, so I draw diagrams.

When I was a child, I felt stupid because I had to go to the remedial reading group, and I could not spell.  I was not tested as a child for dyslexia because, at that time, people thought girls did not have dyslexia.  So, I was just not intelligent.  Except, I was super bright at some things.  Later, at university, I was not tested because the tutor thought there would be stigma, and as I was doing well, it was most likely that I had ‘good strategies’.  I do, but I also spend a lot longer and become discouraged and exhausted doing things that my colleagues can do quickly and with little effort.  Not being tested meant that I did not receive the legally required necessary adjustments for achieving success and a work-life balance. 

I have also struggled with feelings of self-worth and imposter syndrome due to the widely held biases that exist. Assumptions that suggest people with dyslexia have no place in an academic setting. Finally, in my early 50’s I was tested, and my long-held suspicions were confirmed. Interestingly, the way dyslexia is diagnosed is through a series of tests. What specifically indicates dyslexia is being very, very good at some tasks and not very good at others. For example my problem solving skills are well above average (in the top 5%), but my rapid naming skills are well below overage (in the bottom 5%). This confirmation has enabled me to get the help I need. I also learned to recognise that because of how my brain functions, I am one of a minority of people who can think in ways that linear thinkers cannot.  This difference helps me to solve problems and to be an expert in my field.  Dyslexic brains existed before humans developed reading and writing.  To exclude people based on this social construction is to ignore what we have to contribute. 

My advice? There are some practical things you can do, and I think this works for any neurodiverse person. Start by keeping a diary of what you struggle with or what tasks make you anxious, as well as those things that come easily for you and which you enjoy.  This notetaking will help you identify and prioritise those activities that give you a positive feeling.  If you find that you have to do those less comfortable activities, try to find out what support there might be.  It could be learning a work-around or identifying a piece of software or technology.  It might be something as simple as how you arrange your workspace.  I encourage you to get tested if you think you may be neurodiverse.  Just knowing can be pretty empowering.  Find others with the same issues with whom to talk.  They can help you identify strategies and help you feel less alone.  Finally, remember that your weakness is also your strength. Take pride and celebrate what you bring to the table, and don’t dwell on what causes you to struggle. 

Some hints and tips that I have learned are available here: https://geofoodie.org/2019/04/09/dyslexic_academic/