Congrats to Nadine Abdelmalek on winning the DAAD Prize 2025 for her #academicwork and her commitment to our #studentrepresentation, the #mentoring program for German schools abroad, and German Model UN. http://go.tum.de/833008 👏

📷A. Hüttenrauch

An experiment: how to use Claude Opus 4 to help myself say ‘no’ to stuff at work

Over the last three months I’ve radically reduced what I’m committed to at work, with a view to focusing on really matters to me. However this process has made me realise quite how bad I am at saying ‘no’, even when I genuinely intend to. Therefore I’m going to try and enrol Claude to help me with this process, by sharing every new invitation with it in order to inform my decision making. Here’s my prompt:

I’m a mid career academic who has varied interests and often struggles to retain my focus. I’ve identified the topics I want to fully commit to over the next phase of my career, but I still routinely find myself saying ‘yes’ to invitations which are vaguely interesting (e.g. connecting in an intriguing way to a core interest, or reflecting a wider interest outside my research agenda) or desirable in some way (e.g. that will involve going to places I want to visit, even if I don’t want to do the event). These are the projects I intend to focus on for at least the next few years:

🤖 Build a robust theory of LLMs 💼 Design & implement UoM training *💻 Contribute to DTCE’s success * *📚 Deepen expertise about Maggie’s work * 🙏 Build system to disseminate her work

I would like you to play the role of a critical friend, perhaps a senior mentor figure, willing to talk to me about every new invitation. I will commit to raising the invitation with you, in order to examine whether it directly *and *valuably contributes to one of my five commitments. If it doesn’t connect in some way then I will say ‘no’, even if my initial reaction is to say ‘yes’.

You should not try and talk me out of doing things. Your role is to ask me questions which help me examine my initial reactions, in order to assess them in relation to these core commitments. If I can’t substantially justify the relevance of the invitation I should never say ‘yes’ to it, even i there might be extrinsic reasons I am considering. While you should not simply push me to say no, I want you to critically interrogate my reasoning in order to ensure that I’m honest with myself and really can substantiate my claim. You should be academic in your style, collegial in your approach and forceful in your argumentation.

I would like you to build up an understand of my projects through our conversation. This is a secondary goal but it should inform your questioning, given the relevance which my understanding of the projects has to our primary undertaking. In this sense I am asking you to play the role of a reflexive technology, deepening my insight into *why *I am doing these things (why it has meaning and matters to me) through an accumulating understanding of *what *I am doing. I will take your insights seriously and you should attempt to draw connections and offer interpretations which go beyond my own understanding, though these should be framed as hypotheses rather than arguments.

I will report back later this year to reflect on whether this has worked!

#academicWork #claude #decisionMaking #reflexivity #work

Are you overworking as an academic?

I recently inventoried everything I was committed to doing and asked myself the following questions:

• Am I contractually obliged to do this?
• Has someone in a position of seniority directly asked me to do this?
• Do I care deeply about this? Does it excite me?
• Will I be letting a close colleague or friend down if I don’t do this?

It was surprising how many things I was doing for which the answer to this was ‘no’ 🤔

#academic #academicWork #acceleratedAcademy #careers

The allure of LLMs as professional support at a time of crisis within higher education

Machine writing has arrived at a time of intensifying pressure within many higher education systems. Financial constraints lead to changes in the organisation of academic work, particularly with regard to the role played by teaching. Political polarisation drives a greatest contestation of academic authority, sometimes even harassment of academics. The shifting plate tectonics of knowledge, stemming from social and technological transformation, create the risk that recognised expertise will be rendered redundant. Universities are increasingly torn asunder between leaders who see themselves as equipping their institution to survive in a hostile climate and academics who see the ensuing disruption as an expression of that very hostility (Rosenberg 2023).

Within this challenging landscape, large language models have emerged not just as technical tools, but as psychological presences in academic life. It can be immensely difficult to work in these conditions. This is exactly why we need to give serious thought to how LLMs might feel to academics under these circumstances. These friendly assistants are constantly available, willing to consider any request and always encouraging. They are never irritable, distracted, passive aggressive or tired. They never prioritise someone else over us. They don’t impose expectations on us. They can make mistakes, confuse us or act in ways contrary to our intentions. But as we become more skilled at talking with them, these occasions come to feel like the exception rather than the rule. In the seething cauldrons of ambient stress and interpersonal antagonism which many universities have become, at least some of the time, these are evocative characteristics. If we see our working life as assailed on all sides by hostile forces, if we see our jobs as under impending or future risk, the omnipresent ally able and willing to support us through the working day is going to be extremely attractive.

The psychological comfort offered by these systems creates a complex relationship that goes beyond their technical capabilities. When human relationships in academia become strained by institutional pressures, the consistency and apparent care of AI systems can feel like a welcome respite.

AI literacy is an important feature of how academics engage with the opportunities and challenges presented by LLMs; it’s essential that users of these models have a broad understanding of how they operate, how they’re trained and the limitations entailed by this (Carrigan 2024: ch 3). However it’s possible to have a cognitive understanding of these issues while still relating to the models in complex and potentially problematic ways. For example I’ve determinedly insisted on using ‘it’ if I have to refer to LLMs using a pronoun in conversation. Yet I recently slipped a ‘he’ into the conversation when referring to Anthropic’s Claude despite the fact I was half way through my second academic monograph on the subject. I immediately corrected myself but it stuck with me because it illustrates how these associations and assumptions can linger on in the psyche, complicating the reflective views we hold on a particular subject.

I know Claude isn’t a ‘he’ and I often remind my students of the same thing when I see them falling into this habit. Is there nonetheless part of me which feels that Claude is a ‘he’? Which imagines Claude as a ‘he’? Which wants Claude to be a ‘he’? The point I’m making is not one about my own psychology but rather illustrating how there’s more to our reaction to LLMs than can be adequately captured in the intellectual views and opinions we offer about them. You can’t ensure academics have an accurate and effective sense of what models are how to engage with them simply through providing routes to knowledge about LLMs, important though such knowledge undoubtedly is. I would suggest that we must go deeper and that writing is a fascinating frame through which to explore these issues.

#academicLabour #academicWork #claude #higherEducation #LLMs #support

We urgently need to talk about the temptations of LLMs for academics

If we want to understand how academics use large language models (LLMs) we need to begin with the reality of the conditions most of us are working within.

This is a temptation I’ve experienced in my own work. I felt it strongly for the first time when struggling to complete a co-authored piece for an impending deadline. There was an element in the article I believe it was important to include but my co-author felt much less strongly about.

I wasn’t happy with the contents of the article, as it was missing discussion of a topic which I felt was hugely important, yet I was tired and distracted in exactly the way that makes writing difficult. I knew what I wanted to include but not how to include it. The words were not flowing, the deadline was approaching and I didn’t want to let my co-author down. I knew there was material on my blog which I couldn’t directly reproduce but which could easily provide inspiration for Claude to write passages which matched my writing style. It was the first time I had seriously contemplated relying on machine writing to complete a formal publication. I could not see a satisfactory way of resolving my dilemma: I didn’t want to exclude this topic from the article, I didn’t want to let my co-author down but I was also too sleep-deprived to write the required text that afternoon.

It strikes me in retrospect that I wouldn’t have contemplated including machine writing if I hadn’t been confident that Claude could match my style. In previous months I had experimented with giving it samples of my writing, asking it to characterise the style in bullet point lists, then using these descriptions in order to refine a prompt to match how I write. I wasn’t certain but it felt like Claude could match my writing in a way which others would likely find utterly plausible. This was initially an exploration of how subterranean machine writing could become but in that moment of temptation I saw the consequences of this capacity for the first time. I wasn’t comfortable including machine writing that was declared to the reader, either explicitly in the text or tacitly by simply including passages written in a jarringly different style. Even if the publisher had been ok with this, which I hadn’t gone as far as to investigate, it would have felt like an abdication of my authoriality. I’m sure it would have made my co-author deeply uncomfortable as well. But if the machine writing wouldn’t be identifiable to anyone other than me? That was a different prospect which offered a way out of my dilemma. I could fill in the text with a short section, satisfying my intellectual requirement to cover the topic while also meeting the deadline for the article.

What made it even more tempting was this machine writing would have been expressing my own ideas. There was no sense of asking Claude to provide the ideas. I simply had the ideas in one form (notes on my blog) which I needed to translate into another form (a section of an article) but which I was not in the moment capable of acting on. In the end I couldn’t do it. I have rarely had such a vivid sense of the devil and angel on respective shoulders in a professional setting. I could see a practical case for acceding to the temptation, in that it would produce a better piece under the circumstances which I found myself in. But if I did then I felt I would inevitably do it in similar situations in the future. Even with the best planning, a standard which none of us can consistently meet, there will always be circumstances where we have writing responsibilities which outstrip our present capacities. If we develop a comfort with leaning on machine writing in those situations, I suspect the category will expand and we will gradually find ourselves relying on it in situations which would once have felt simply challenging rather than impossible. It’s a retreat from the trouble of writing, one which is particularly tempting when that trouble feels insurmountable, but which has the capacity to subtly unpick the moral psychology through which writing comes to be meaningful and satisfying to us.

What’s at stake here isn’t just a question of research ethics or academic integrity in the formal sense. There’s something more fundamental about our relationship to the creative process itself. The constraints we face as writers (whether time, energy or our own cognitive limitations) create the conditions in which genuine intellectual work happens. Without that productive friction, something essential to scholarly identity may be lost.

The use of machine writing in knowledge production is still in its infancy and, even with detailed empirical investigation, there is a limit to how far we could answer these questions in relation to an issue which is developing so rapidly. In raising them I’m trying to highlight the questions, rather than take a stance as to the answers. The assumption that human authoriality underpins what we write in monographs, edited books and journals is so axiomatic that it is difficult at this stage to think through what knowledge production looks like when it can no longer be assumed. Explorations of the potential implications often oscillate between feeling mundane, preoccupied by minutiae around the edges of practice which will otherwise feel unchanged, and feeling grandiose, making sweeping generalisations which tend to overstate the issues involved.

This is exactly why empirical investigation will be so crucial to stabilising our understanding of how academics are using machine writing, as well as what this use means for knowledge production. But what I’m trying to do is, rather than even offer a comprehensive review of the fragmented and pre-print heavy literature in its current stage, open up the conceptual issues involved with a view to supporting academics in reflecting on their writing practice in relation to the rapidly developing possibilities which machine writing offers in their mundane working life.

My suggestion is that difficulty is at the heart of how academics will tend to relate to the possibilities of machine writing. Conversational agents provide us with new ways of negotiating difficulties in the writing process. They can offer new perspectives on what we have written, help us elaborate upon what we are trying to say and provide detailed feedback of a form which would have previously required a human editor. The attempt to eliminate difficulty from the writing process will have downstream consequences for our own writing practice, as well as the broader systems through which (we hope) our writing makes an intellectual contribution.

The reason I’m focusing on the experience of joy in academic writing is not simply that this makes it less likely we will hear the siren song of machine writing in the first place. I will argue that if we rely on machine writing when confronted with difficulties, those experiences of joy are likely to become more elusive and perhaps even disappear altogether from our writing lives. It is only through staying with these difficulties, even when it’s uncomfortable and dispiriting, that we can make it through to the other side.

This isn’t to suggest we must reject these tools entirely. Rather, we might consider approaching them with the same deliberate intentionality that characterizes thoughtful writing itself. Perhaps the question isn’t whether to use AI writing assistance, but when, how, and with what awareness of what we might be surrendering in the process. The most dangerous temptation may not be using these tools, but using them unconsciously, without reflecting on how they reshape not just what we produce, but who we become as scholars through the process of producing it.

My concern is that the critical discourse, while accurate in many respects, fails to create the space for these conversations about practical reasoning by academics.

#academicLabour #academicWork #acceleratedAcademy #claude #higherEducation #writing

Generative AI and the creative confusion of academic writers

In his guide to productive academic writing the psychologist and writing coach Robert Boice distinguishes between non-starters and non-finishers. Even if there could be some academics who experience both problems in their writing, Boice suggests in his experience these are distinct problems with a different psychological basis underlying them. While “an inability to finish resembles its counterpart, an inability to start, in dynamics such as perfectionism” he observes that “the two forms of writing problems typically occur in different people”. He offers examples of writers he has worked with who are immobilised by the expectations they have imposed upon themselves, literally unable to get started without immediately comparing what they are able to write with what they feel they ought to write. They might get some words down but immediately stop to revise them, finding themselves caught in a loop of self-doubt which makes it impossible to proceed. He suggests that such writer are often “impatient to finish” if and when they manage to get started, relieved at overcoming their initial impediment and eager to bring the ordeal to a close. In contrast he writes that non-finishers “don’t struggle much over beginnings” because “they know that perfection comes later in multiple versions”. In fact some “non-finishers even write prolifically, without ever completely finishing their projects”. Their problem arises when a completed text has to be cast out into the world to be read by others, rather than the discomfort they feel when reading back a work in progress.

The point he is making is that perfectionism, ultimately a fear of failure, can be found lurking behind both of these challenges. The difference is a matter of where the perfectionism is located. For non-starters, the sense of what the writing should be makes it near impossible to get started. For non-finishers, the sense of what the writing should be makes it possible to bring their piece to a close. In both cases the writing is approached through a prism of expectation, imbued with expectations and obligations which squeeze out the possibility of enjoyment. If you constantly compare what you are writing to what you feel you should be writing then it is difficult to find enjoyment in the process. Rather than relaxing into putting thoughts into words, letting ideas pass through you onto the page, it will be a twitchy exercise in failing to live up to your own standards. The problem here is not having standards, as much how we relate to them. If they are a test you impose continually on yourself then you are, ironically, much more unlikely to meet your own standards. In denying the space to make mistakes, to fall short and to fail the possibility of growth and development is also lost. This is why once you get stuck in your writing, whether as a non-finisher or a non-starter, it can be hard to get going again. The more you see your work through a prism of your own imagined inadequacy as a writer, the harder it will be to actually write, let alone find enjoyment in the process.

However what it means to be ‘stuck’ transforms once you become familiar with the use of large languages models (LLMs) like OpenAI’s ChatGPT and Anthropic’s Claude. There are still difficulties inherent to the writing process which you routinely encounter: the paragraph you don’t know how to finish, the article you don’t know how to fit together or the deadline you are struggling to meet. The change arises because machine writing always offers a path forward. If you simply provide it with the rest of the paragraph, it will complete it for you. Claude suggested that I complete the paragraph you are reading by observing how this “newfound ease creates a profound shift in how we relate to writing – where being stuck once demanded we dig deeper into our thoughts or define our argument, we now face a constant choice between pushing through the difficulty or letting the machine smooth our path”. Not only did it complete the thought, it matched my style because I’d presented it with enough of my writing that it could plausibly write in my voice. In this case I used this to illustrate a point by retaining the distinct contribution of the conversational agent, framing it as an object which I could comment upon in order to develop my argument.

If I’m tired or stressed, working towards towards a deadline which I’m struggling to meet, this can be incredibly enticing. Over the two years I’ve been working with LLMs on a daily basis, I’ve noticed that I often keep working at a point where I would previously have downed tools and gone to rest. There are points where stopping would have been unavoidable, simply because you run out of energy and can no longer do what you were doing. Yet at this stage the LLM can now step in in order to keep you going by offering suggestions about a potential path forward. This can be incredibly dangerous in a sector already prone to celebrate, even demand, over work.

However if used in a more careful and restricted way it can be extraordinarily helpful. You know that feeling when an insight is on the tip of your tongue but you can’t quite put it into words? By sharing your sketchy ideas with the LLM it can nudge you into articulacy, helping you say what you couldn’t quite say yes. This capacity to crystallise half-formed thoughts goes hand-in-hand with the risk of working through fatigue. They are two sides of the same coin, in the sense that creative use of LLMs offers a way through what would have formerly been cognitive limits. This boundary-crossing ability raises important questions about intellectual labor and authorship. Where do we draw the line between helpful assistance and delegating our core intellectual work to machines?

There is a real and immediate danger that something important is lost if you are never stuck. Or if you see stuckness as a straight forwardly negative experience to be moved through as quickly and easily as possible. Creative confusion serves a vital function in scholarly work, forcing us to dwell with uncertainty until new connections emerge organically. This productive discomfort is what often leads to our most original insights, those moments when we transcend conventional thinking precisely because we couldn’t immediately find a path forward.

As Boice reminds us, “productive writing is much more than getting unstuck”. It’s not enough to simply move through writing problems, either on a single occasion or recurrently. To be a productive writer means building routines and dispositions which are sustainable and effective, rather than merely finding the way through the barriers which impede a particular piece of writing or writing in general. But if you remain caught by those barriers, unable to move forward, the possibility of being a productive writer remains foreclosed. The lure of machine writing lies in the diversity of ways in which it can help you become unstuck. Even if the full repertoire of means through which machine writing can help might not be available to an author, the simple fact of it being there as an ever present assistant designed to help has the potential to be an enormous moral, as well as practical, support.

My use frames it as an example for readers who might not be aware of how effectively frontier models can complete a paragraph. For those who are using these models on a regular basis, it’s no longer an intellectual curiosity to be observed but rather an immediate possibility inherent in the writing process. The relatively small numbers of sustained users of these models within the academic community creates an epistemic gap between those who are writing using the familiar apparatus of the 21st century academic writer (e.g. a word processor) and those who have incorporated conversational agents into their process. My experience has been that a significant shift occurs after months of routinely writing in dialogue with them, leading to an intense awareness of their capacities at points of intense difficulty.

The solution to the problem is not to engage in a humanist rearguard action to drive these emerging technologies out of academic life, but rather to affirm the joy which can and should be found in writing, as well as finding actionable ways to cultivate that connection in our writing practice. The problem is not GenAI itself but rather a soulless instrumentalism which relates to it through the frame of efficiency. Instead I suggest we can have a joyful relation to conversational agents, which sees this technology in terms of intellectual interlocution rather than machine writing.

#academicWork #academics #creativity #higherEducation #scholarlyWork #writing

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The Finnish Environment Institute is hiring no less than 7 new #postdocs, 3 years guaranteed funding, to a great, fully bilingual (Finnish-English) workplace!

A great opportunity for great #AcademicWork !

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