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Ten propositions about generative AI and the future of academic writing

This is Claude's summary of the arguments from the first 20k words of my new book on AI and writing, as well as the how to write blog post series I did earlier this summer.
  1. The advent of generative AI doesn’t just pose practical challenges for academic writing; it forces us to confront fundamental questions about why we write and what we hope to achieve through our writing. These are questions which have always been there, lurking beneath the surface of our practice, but which we’ve too often neglected to articulate clearly.
  2. There’s a real risk that the instrumental use of generative AI will exacerbate existing pathologies within academic writing culture, leading to a flood of mediocre outputs which are ‘good enough’ to meet institutional metrics but lack the spark of genuine intellectual engagement. This isn’t just about the quality of individual pieces; it’s about the cumulative effect on our knowledge systems.
  3. The distinction between functional and expressive documents in academic writing becomes crucial when considering the role of generative AI. While there might be a place for AI assistance in producing functional texts like abstracts or summaries, we should be extremely wary of outsourcing the creative, expressive aspects of our writing to these systems.
  4. Generative AI offers us an opportunity to recalibrate our relationship with writing, potentially helping us to cultivate what I call an ‘ecology of ideas’. Used thoughtfully, these tools can support us in capturing fleeting thoughts, making connections, and elaborating on our ideas in ways that enhance rather than replace our own creative processes.
  5. The joyfulness we find (or don’t find) in our writing practice isn’t just a personal matter; it’s likely to be a key factor in determining whether generative AI becomes a tool for creative augmentation or a crutch for increased productivity at the expense of quality. Cultivating this joy isn’t a luxury – it’s an essential part of maintaining the integrity of academic knowledge production in the face of technological change.
  6. The integration of AI into academic writing practices calls for a new kind of reflexivity. We need to be more conscious than ever of why we write, how we write, and what we hope to achieve through our writing. This heightened self-awareness can actually deepen our engagement with our work.
  7. The ability to use generative AI effectively as a writing tool may become a new form of academic literacy. However, the most crucial skill won’t be prompt engineering, but rather the ability to engage with AI outputs critically and creatively, using them to enhance rather than replace our own thinking.
  8. The pressure to increase quantitative outputs in academia, potentially exacerbated by AI tools, risks further eroding the space for deep, reflective scholarship. Paradoxically, maintaining the joy in our writing process might be our best defence against this pressure.
  9. The concept of ‘writing with’ rather than ‘writing through’ AI offers a promising model for integrating these tools into academic practice. It positions AI as a collaborator rather than a replacement, preserving the central role of human creativity and critical thinking in scholarship.
  10. The potential for AI to blur the boundaries between reading, writing, and thinking in academic work is both exciting and concerning. While it might enhance our cognitive processes, it also risks short-circuiting the valuable incubation period that complex ideas often require.
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