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Some researchers who refuse to use AI have been accused of being anti-progress \u2014 similar to the nineteenth-century Luddites who resisted the new machinery they feared would replace their jobs \u2014 but they say their views are more nuanced than that.Credit: Chronicle\/Alamy<\/p>\n
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.<\/p>\n
She says her colleagues are often surprised to hear she hasn\u2019t tried it and have suggested she uses it for applications such as coding. \u201cI\u2019ve had a lot of people go like \u2018oh but you have to use it\u2019,\u201d 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\u2019re using the data, the environmental effects of AI tools and the accuracy of what genAI models spit out.<\/p>\n
She also thinks that using the tools would be counterproductive to her studies. \u201cCoding is a skill I want to learn and develop, because it\u2019s not the thing I\u2019m the most confident in,\u201d she says. She would rather try and do it herself, learning from her mistakes.<\/p>\n
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Marine biologist Danielle Crowley has concerns around the ethics and environmental impacts of generative AI tools.Credit: Laura Oatley<\/p>\n
GenAI has become a hot topic over the past few years, as technology companies compete to release the most impressive model for public use. Researchers are using these tools for tasks such as writing papers<\/a>, peer <\/a>review<\/a> and coding<\/a>. It can save<\/a> them time, mental energy and sometimes money. But Crowley and others who are purposefully abstaining often find themselves judged by their peers.<\/p>\n \u201cA lot of people say \u2018it\u2019s the future, everyone is using it\u2019,\u201d she says. Not using it, she continues, \u201ckind of feels like showing up to a function and saying you don\u2019t drink\u201d.<\/p>\n Efficient, but at what cost?<\/p>\n According to a Nature survey of about 5,000 researchers<\/a> published in May last year, scientists are split on the ethics of AI use in academia. More than 90% of respondents felt it was acceptable to use AI for editing or translating their own text, but fewer were open to the idea of using it to generate text directly. And only a minority said they had actually used AI tools in their work. About one-quarter of respondents used them to edit their papers, whereas only 8% had used them to translate, summarize or write a first draft.<\/p>\n More recently, a survey<\/a> of 3,234 researchers published last November by the academic publisher Elsevier found that 58% of researchers used AI in their work, up from 37% the previous year. In terms of how researchers use or would like to use AI tools, 61% said to locate new research, 51% said for collecting and summarizing literature and 41% said for preparing grant applications. Those surveyed were generally positive about the potential of the technology to boost efficiency.<\/p>\n Hugh Possingham, a mathematician and conservation scientist at the University of Queensland in Brisbane, Australia, is among the researchers who are not using AI. He has made a conscious effort to avoid any sort of genAI \u2014 instead pledging on LinkedIn to rely on \u201cnatural <\/a>stupidity\u201d<\/a>.<\/p>\n As a mathematician, Hugh Possingham has seen examples of \u2018hallucinations\u2019 in AI-produced writing.Credit: Queensland Government (CC BY SA 4.0)<\/p>\n \u201cI\u2019ve never used any of them at all,\u201d he says. Even though AI has become integrated into many everyday functions, he\u2019s never clicked the button that generates or summarizes text when writing an e-mail, for example.<\/p>\n He complains especially about the errors he has spotted in AI-produced writing. AI sometimes hallucinates<\/a>: providing false or misleading information with conviction. \u201cI read a master\u2019s thesis where the person cited had died ten years before the paper was published, which is a masterful act,\u201d he says.<\/p>\n Why universities need to radically rethink exams in the age of AI<\/p>\n <\/a><\/p>\n Audrey Moores, a chemist at McGill University in Montreal, Canada, has seen AI create mistakes in her field, too. She\u2019s witnessed various AI-generated representations of chemicals that are incorrect. She first noticed this in a chemistry journal featuring \u201cnonsensical\u201d molecules, but it has become enough of a trend in presentations and other journals that she and a colleague wrote a comment article1<\/a> calling on the chemistry community to ban the use of genAI for certain tasks.<\/p>\n \u201cIt\u2019s like you\u2019re asking a three-year-old to draw a chemical,\u201d says Moores. And an AI model has \u201cnever gone through the chemistry courses a human would\u201d, she adds. Even when presented with the task of drawing a simple molecule such as caffeine, it might fail. (Nature, along with other publishers, has guidelines that prohibit the use of AI-generated images.)<\/p>\n 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 \u201cAI champion\u201d of the faculty, she is supposed to appraise models, work out what they\u2019re good at and suggest how the faculty could be using them. But ironically, she stresses, checking them creates even more work.<\/p>\n Tanisha Jowsey, a social scientist, says the extra checks needed for AI-produced work can slow workflows.Credit: photo supplied by Tanisha Jowsey<\/p>\n She says that 95% of the time \u201cit 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\u2019s done it right\u201d. She also finds that it\u2019s an ineffective tool for qualitative research: a view she expressed in a co-authored commentary article2<\/a> that was posted on the preprint platform SSRN.<\/p>\n Other downsides<\/p>\n Another major concern that researchers cite is ecological impact. The data centres that fuel genAI systems use a lot of energy<\/a> and water. A study published in Patterns estimates that in 2025, the carbon footprint of AI systems globally could have been between 32.6 million and 79.7 million tonnes of carbon dioxide, and the water footprint could have been 312.5 billion to 764.6 billion litres3<\/a>. To put it into perspective, that is comparable with the carbon footprint of New York City as a whole, the study says.<\/p>\n The potentially detrimental environmental consequences are one of many reasons Crowley abstains from using genAI tools. \u201cEspecially when my project is working on climate change, it didn\u2019t feel right to use this tool that basically did the same as other tools, but used more energy to do so,\u201d she says.<\/p>\n Other scientists also cited ethical concerns. Sustainability scientist Juan Rocha thinks that using AI tools helps large, private companies train their algorithms to be even better \u2014 and, in the long run, potentially displace human workers. \u201cYou are being used by AI, you are not using AI,\u201d says Rocha, who works at the Stockholm Resilience Centre in Sweden. \u201cWe\u2019re giving liberties and we\u2019re making labour in the future obsolete, diminishing the importance of work at the university.\u201d<\/p>\n Michaela Socolof, a psycholinguist at the Massachusetts Institute of Technology in Cambridge, also has concerns about how AI scrapes information. \u201cThe primary reason that I\u2019m against generative AI is because it is trained on the work of authors who have not given their consent,\u201d she says, seeing it as plagiarism. \u201cThis is just stealing the work of writers and artists.\u201d<\/p>\n Sustainability scientist Juan Rocha considers the future implications of well-trained AI models.Credit: Jesper Ahlin Marceta, Swedish Young Academy<\/p>\n Writing complaints<\/p>\n![\"Portrait](\"https:\/\/www.europesays.com\/ie\/wp-content\/uploads\/2026\/05\/d41586-026-00508-w_52359098.jpg\"\/)
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