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Two reviews of Generative AI for Academics

Notes from Mirjam Sophia Glessmer on reading the book:

The other day I read something (that I cannot find again) along the lines of “GenAI creates art for people who hate art, music for people who hate music, reading for people who hate reading”, and I have been thinking about that a lot. I have explored what GenAI can and cannot do (for example regarding discussing workshop planning, but also to help with analysing qualitative data [don’t use it — we explain why in this article Rachel Forsyth and I just published]). I have never used it to create “art” or to write for me, and that is because both graphical and written forms of expressing myself feel very personal and very important to me and I cannot imagine delegating either, not even to professional artists or writers. Unless you can read my mind and do EXACTLY what I envision, stay away from my writing and art! That said, I pre-ordered Mark Carrigan’s new book, “Generative AI for Academics”, already last summer. Similarly to his previous book, “Social Media for Academics“, it seems a bit risky to read an actual, printed book on such a quickly changing technology as GenAI, but I found that it takes a big enough perspective that at least the current landscape still seems well described.

https://mirjamglessmer.com/2025/02/05/currently-reading-generative-ai-for-academics-by-carrigan-2024/

And from Scientist Sees Squirrel in a great three-book reflection:

I’ll start with Mark Carrigan’s Generative AI for Academics (Sage, 2024). This is the most bullish of the three about potential uses for the technology. Carrigan outlines ways for academics to use LLMs in their work – including, but not limited to, their writing. I especially appreciate Carrigan’s argument that the way to go is to find ways to think with LLMs rather than using LLMs as a substitute for thought. This is very much in the spirit of what I’ve argued: that too many folks are conceiving of LLMs as substituting for writing skills. Instead, I think we should, and can, find ways they can be used to build writing skills.. Among the uses for LLMs Carrigan explores are “rubberducking” (explaining your ideas to an LLM to test and polish your ability to explain them, just as you might talk your ideas out to a friend, or your cat, or a rubber duck); asking an LLM to summarize your draft, using its errors to diagnose gaps in what you’ve written; and using an LLM to assist with translation of text between audiences (paper to blog post, for example). In each case, Carrigan suggests that the LLMs can help you think – and while they might also save you time and effort, it’s the thinking help that might matter most.

https://scientistseessquirrel.wordpress.com/2025/02/11/three-books-to-sum-up-where-were-at-with-ai-tools-for-writing/

Thank you both, I really enjoyed reading these! 😊

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