AI in academia

Meet the academics refusing to use generative AI

  1. Docter-Loeb, Nature (2026)

Researchers say they have their reasons for avoiding AI tools — and they’re sick of arguing about it.

Quotes

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.

She also thinks that using the tools would be counterproductive to her studies. “Coding is a skill I want to learn and develop, because it’s not the thing I’m the most confident in,” she says. She would rather try and do it herself, learning from her mistakes.

The indiscriminate adoption of AI threatens the foundations of academia

{2602.10165} The indiscriminate adoption of AI threatens the foundations of academia, R. Trotta (2026)

Artificial intelligence offers much promise, but its use in scientific research should be restrained so that the primary aim of academia – advancing knowledge for humans – is safeguarded.

Notes

Roberto Trotta is a physicist at SISSA that advocates for “scientific alignment” in the use of AI in research.

Quotes

The repercussions on the next generation of scientists might be even more severe: if the difficult, uncomfortable process of learning to reason like a scientist is replaced by a prompt to a chatbot, the critical thinking and depth of expertise that enables human scientists to supervise the output of LLM agents will be diminished. Research students might become little more than prompt engineers. Systemic changes to the role and funding of higher education and research institutions might ensue, as expensive PhD fellowships might swiftly be replaced by far cheaper API credits to run an army of AI agents on commercial platforms. Scientific literacy might wane within years, not decades.

Against the Uncritical Adoption of ‘AI’ Technologies in Academia

Against the Uncritical Adoption of ‘AI’ Technologies in Academia, Guest et al. (2025)

Under the banner of progress, products have been uncritically adopted or even imposed on users — in past centuries with tobacco and combustion engines, and in the 21st with social media. For these collective blunders, we now regret our involvement or apathy as scientists, and society struggles to put the genie back in the bottle. Currently, we are similarly entangled with artificial intelligence (AI) technology. For example, soft- ware updates are rolled out seamlessly and non-consensually, Microsoft Office is bun- dled with chatbots, and we, our students, and our employers have had no say, as it is not considered a valid position to reject AI technologies in our teaching and research. This is why in June 2025, we co-authored an Open Letter calling on our employers to reverse and rethink their stance on uncritically adopting AI technologies. In this position piece, we expound on why universities must take their role seriously to a) counter the technology industry’s marketing, hype, and harm; and to b) safeguard higher education, critical thinking, expertise, academic freedom, and scientific integrity. We include pointers to relevant work to further inform our colleagues.

Quotes

Students have always cheated. Bending and breaking the rules is human nature. And by the same token, educators are not police. We are not here to obsessively surveil our students — education is based on mutual trust. Therefore, our duty is to build mutually shared values with our students and colleagues. Especially when education is not valued, we as educators are obliged to show our students 11O. Guest et al.  that they are not just here to receive a degree: education is more than qualification (Biesta 2021). It is about preparing students to become a capable and active members of society.

Cited from Compulsive Technology: Computers as Culture, Tony Solomonides, Les Levidow (1985):

"The culture of AI is imperialist and seeks to expand the kingdom of the machine. The AI community is well organized and well funded, and its culture fits its dreams: it has high priests, its greedy businessmen, its canny politicians. The U.S. Department of Defense is behind it all the way. And like the communists of old, AI scientists believe in their revolution; the old myths of tragic hubris don’t trouble them at all."

The Agentification of Scientific Research: A Physicist’s Perspective

{2604.14718} The Agentification of Scientific Research: A Physicist’s Perspective, Xiao-Liang Qi [2026]

This article argues that the most important significance of the AI revolution, especially the rise of large language models, lies not simply in automation, but in a fundamental change in how complex information and human know-how are carried, replicated, and shared. From this perspective, AI for Science is especially important because it may transform not only the efficiency of research, but also the structure of scientific collaboration, discovery, publishing, and evaluation. The article outlines a gradual path from AI as a research tool to AI as a scientific collaborator, and discusses how AI is likely to fundamentally reshape scientific publication. It also argues that continuous learning and diversity of ideas are essential if AI is to play a meaningful role in original scientific discovery.

Advisory note

◼ Dark side

Quotes

To understand what AI brings to research, we must review common problems currently facing scientific enquiry. While challenges vary by field, several are universal:

1. Time Costs: Understanding industry progress and learning from others’ work requires immense time.

2. Loss of Tacit Knowledge: A vast amount of “intermediate” experience and data accumulated during research is not reflected in papers, forcing other scholars to explore from scratch.

3. Collaboration Limits: The scale of research collaboration is constrained by human communication costs, making large-scale and cross-disciplinary cooperation difficult.

4. Administrative Burden: Significant time is consumed by non-creative tasks, such as writing papers, peer review, writing grant proposals, and explaining work to others after the research is completed.

Notes

I couln’t disagree more. BTW when did “writing papers” become a non-creative task?