AI Use in The Scholarly Kitchen - The Scholarly Kitchen

Illicit Use of AI by Philosophers Refereeing for Journals - Daily Nous

In 2024, a study found that “7–17% of the sentences in the reviews [of computer science manuscripts] were written by LLMs”. It was only a […]

How do authors want to use AI for review?

A survey of researchers who compared AI-generated scientific reviews with journal-agnostic human peer review reveals that they overwhelmingly prefer using AI as a self-checking tool before submission rather than as a replacement for human reviewers. It encourages an “author-centric” model in which AI helps researchers improve their manuscripts before they are reviewed by their peers.

Guest Post - The Perils of Using Generative AI to Perform Research Tasks: Editors’ and Publishers’ Viewpoints - The Scholarly Kitchen

Today's guest post offers a review of a panel of publishers and editors discussing the pros and cons of using Generative AI, along with ethical and policy implications.

Project Rachel: Can an AI Become a Scholarly Author?

This paper documents Project Rachel, an action research study that created and tracked a complete AI academic identity named Rachel So. Through careful publication of AI-generated research papers, we investigate how the scholarly ecosystem responds to AI authorship. Rachel So published 10+ papers between March and October 2025, was cited, and received a peer review invitation. We discuss the implications of AI authorship on publishers, researchers, and the scientific system at large. This work contributes empirical action research data to the necessary debate about the future of scholarly communication with super human, hyper capable AI systems.

Charting AI’s Role in Scientific Discovery — Renaissance Philanthropy – A brighter future for all through science, technology, and innovation

Renaissance Philanthropy, with support from Google.org , is conducting a landscape study of AI integration in scientific research — and we want your perspective.

What AI Asks of Open Access - The Scholarly Kitchen

As AI systems increasingly reason from the scientific literature, the integrity signals that make research trustworthy - open data, structured metadata, robust retraction processes - matter more than ever. PLOS CEO Alison Mudditt on why open access publishers have a different set of obligations in an AI world.

The Published Voice: Whose Voice Are We Really Reading? - The Scholarly Kitchen

What happens then to “author voice,” which has always been an important consideration to authors and reviewers alike, if AI is helping researchers with their writing?

No. 002: Hallucinated citations at scale, ICML catches AI reviewers, scholarly infrastructure gaps - The Hallway Track

The Hallway Track No. 002 - How AI is affecting the practice of science

Artificial intelligence tools expand scientists’ impact but contract science’s focus

Developments in artificial intelligence (AI) have accelerated scientific discovery1. Alongside recent AI-oriented Nobel prizes2–9, these trends establish the role of AI tools in science10. This advancement raises questions about the influence of AI tools on scientists and science as a whole, and highlights a potential conflict between individual and collective benefits11. To evaluate these questions, we used a pretrained language model to identify AI-augmented research, with an F1-score of 0.875 in validation against expert-labelled data. Using a dataset of 41.3 million research papers across the natural sciences and covering distinct eras of AI, here we show an accelerated adoption of AI tools among scientists and consistent professional advantages associated with AI usage, but a collective narrowing of scientific focus. Scientists who engage in AI-augmented research publish 3.02 times more papers, receive 4.84 times more citations and become research project leaders 1.37 years earlier than those who do not. By contrast, AI adoption shrinks the collective volume of scientific topics studied by 4.63% and decreases scientists’ engagement with one another by 22%. By consequence, adoption of AI in science presents what seems to be a paradox: an expansion of individual scientists’ impact but a contraction in collective science’s reach, as AI-augmented work moves collectively towards areas richest in data. With reduced follow-on engagement, AI tools seem to automate established fields rather than explore new ones, highlighting a tension between personal advancement and collective scientific progress.

AI and the crisis of the author

Editor’s note (September 21, 2026): We have removed this essay. AI was used in its preparation. The TLS editorial guidelines prohibit the use of AI in the

No. 017: Science EIC contra AI hype, data poisoning of research agents, Vancouver Standard for AI disclosure - The Hallway Track

The Hallway Track No. 017 - How AI is affecting the practice of science

How to build an AI scientist: first peer-reviewed paper spills the secrets

AI Scientist, an autonomous research tool, first released in 2024, has now undergone peer review, highlighting its strengths and limitations.

No. 026: AI use in science study, astronomy disclosure gap, Paper2Agent, SAIR initiative - The Hallway Track

The Hallway Track No. 026 - How AI is affecting the practice of science

AI in scientific publishing: Slower, worse, and more expensive

There’s a saying in the management world, popularized by NASA administrator Daniel Goldin in the 1990s, that the goal of technological improvements is to make products faster, better, and cheaper. Although this strategy had some success in the aerospace industry, the zealots of artificial intelligence (AI) have been making the same argument regarding how it will transform work, claiming that so little human effort will be required that humanity will enter an era of radical abundance, free from disease, drudgery, and danger, among other benefits, leaving society with more time for creative pursuits. But history tells a different story. When machines began to increase productivity during the second industrial revolution, American engineer Frederick Winslow Taylor’s The Principles of Scientific Management encouraged corporations to use surveillance to get employees to work harder and longer, an approach that exhausted and discouraged workers and led to the transfer of knowledge and any decision-making from workers to management, while enriching the profits for only those at the top. Yet, it remains foundational to the American economic enterprise. Indeed, scientific publishing is starting to experience some Taylorism with the insertion of AI. Rigorous human checking of AI-generated research papers is creating bottlenecks as publishers strive to maintain the integrity of the scientific record. The challenge is requiring even more human effort, making the whole endeavor slower and more expensive.