Danielle Crowley is getting tired of people telling her to use generative artificial intelligence (genAI). As a marine zoologist at Bangor University, UK, she says that she is pretty much the only PhD student in her cohort who does not use it. She has seen colleagues use genAI tools for coding and for getting the tone of e-mails right. On one occasion, she was even encouraged by a lecturer to use it to generate a conference poster.

She says her colleagues are often surprised to hear she hasn’t tried it and have suggested she uses it for applications such as coding. “I’ve had a lot of people go like ‘oh but you have to use it’,” she recalls. But Crowley has her reasons. She has concerns about the ethics of copyright, what she calls a lack of transparency from companies about how they’re using the data, the environmental effects of AI tools and the accuracy of what genAI models spit out.

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And verifying AI-generated information often defeats the purpose of using the tool for efficiency, say cynics. Tanisha Jowsey, a social scientist at Bond University in Robina, Australia, says that as a designated “AI champion” of the faculty, she is supposed to appraise models, work out what they’re good at and suggest how the faculty could be using them. But ironically, she stresses, checking them creates even more work.

She says that 95% of the time “it would be quicker for me to just do the thing myself than get the tool to do it and then have to check whether or not it’s done it right”. She also finds that it’s an ineffective tool for qualitative research: a view she expressed in a co-authored commentary article that was posted on the preprint platform SSRN.

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