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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. (...) 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); If you’re currently anti-LLM, challenge yourself by reading Carrigan.
As a PhD student researching the impact of GenAI on university students (while also a new professor), it felt like this book was written for me. Carrigan immediately identified the scariest part of GenAI - its ability to dismantle the trusting relationships between faculty and students. By (at least partially) embracing AI in higher education, Carrigan shows we can simplify our workload to produce better quality work and enhance our means of thinking and engaging. This thought-provoking work increased my optimism about our future with GenAI.
Generative AI for Academics is not a guide for use, giving advice on better prompts or more engaging output, but a guide for refection, aiming to make the use of GenAI ‘routine without it becoming thoughtless’ (Carrigan 2025: 31).
A brisk, sensible map for using LLMs in scholarly life. It avoids both hype and doom, treating generative AI as a set of tools that demand judgment, not blind adoption. The tone is practical and reflective—ideal for faculty, PIs, and grad students who need shared language and guardrails.
By reframing generative AI as a dialogue partner and urging scholars to share their reflective practices, Carrigan offers academics across disciplines a way to navigate the uncertainty of higher education today.
The book serves as both a guide and a safeguard. It acknowledges the undeniable efficiency of AI tools while insisting that genuine learning depends on sustained reflection and human agency. Carrigan’s quiet insistence throughout the book is that technology does not diminish the value of intellectual effort. If anything, it raises the stakes. It demands that we become more conscious of how and why we think, not less. Right now, with these tools proliferating faster than our ability to think critically about them, that might be the most important thing any book about AI can say.
