25 Articles about AI & Academic Scholarship

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Do you use generative AI to help identify literature you missed? If so, how? – Dynamic Ecology

Two-thirds of universities report AI use among doctoral students – Times Higher Ed 

After the PDF: A new unit of knowledge for the AI era – research Information

AI in Scholarly Publishing — SSP Pulse Check Report – Scholarly Kitchen

AI Shift: Agentic AI is coming for quantitative research – Financial Times

Peer review needs a revolution. AI is already driving it – Scholarly Futures

In Memoriam: The Academic Journal (death by LLM) – Arxiv

Guidelines needed for the use of AI in the preparation or review of IRB, IBC, and IACUC applications – Tandfonline 

What to expect in scholarly communications in 2026 (?Or what AI believes could occur...)  – Scholarly Futures 

A Cross-Disciplinary Analysis of AI Policies in Academic Peer Review – Wiley  

Fine-Grained Detection of AI-Generated Writing in the Biomedical Literature – Bioxiv

Funders ‘should support shared AI tools for translational research’ – Research Professional News  

AI-generated commentaries and letters to the editor of peer-reviewed publications: editors and authors beware! - Tandfonline 

A bibliography of genAI-fueled research fraud from 2025 - Sharonkabel

Meet the author who has published more than 500 letters to the editor in a year – Retraction Watch  

Evaluating the Use of Large Language Models as Synthetic Social Agents in Social Science Research – Sciopen 

Publisher under fire after ‘fake’ citations found in AI ethics guide – The Times

The H-Index of Suspicion: How Culture, Incentives, and AI Challenge Scientific Integrity – NEJM

Researchers who use generative AI to write papers are publishing more – C&EN 

Deep Research, Shallow Agency: What Academic Deep Research Can and Can't Do – Aaron Tay

Will AI stop new curation-led publishing models thriving before they’ve even had a chance to grow? – Scholarly Futures

AI-assisted cheating could impact universities' global standings – Korean Times

AI Slop Is Spurring Record Requests for Imaginary Journals – Scientific American

Publisher under fire after ‘fake’ citations found in AI ethics guide – the Times   

Hack reveals reviewer identities for huge AI conference – Science.org

Three types of Perfectionists

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A study measured three types of perfectionism: self-oriented, or a desire to be perfect; socially prescribed, or a desire to live up to others’ expectations; and other-oriented, or holding others to unrealistic standards. A person living with an other-oriented perfectionist might feel criticized by the perfectionist spouse for not doing household chores exactly the “right” way. Socially prescribed perfectionism is “My self-esteem is contingent on what other people think.”

Perfectionists tend to devalue their accomplishments, so that every time a goal is achieved, the high lasts only a short time, like “a gas tank with a hole in it.” 

There are also different ways perfectionism manifests. Some perfectionists are the sleeping-bag-toting self-flagellants, always pushing themselves forward. But others actually fall behind on work, unable to complete assignments unless they’re, well, perfect. Or they might self-sabotage, handicapping their performance ahead of time. They’re the ones partying until 2 a.m. the night before the final, so that when the C rolls in, there’s a ready excuse. Anything to avoid facing your own imperfections.

Olga Khazan writing in The Atlantic

AI Definitions: Opaque AI

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Opaque AI – This is when an AI algorithm operates as a black box that we can’t understand. This can lead to AI systems inadvertently perpetuating and amplifying biases. AI transparency, on the other hand, allows for the examination and understanding of how these biases occur, leading to more ethical and fair AI systems. The level of AI opacity varies depending on the industry. For example, in highly regulated industries, transparency is paramount for legal and regulatory compliance.

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17 Articles about AI & Religion

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20 Articles about how AI is Affecting Jobs

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AI Can’t Touch These Skilled Trade Jobs. If Only Enough Humans Would Fill Them. – Wall Street Journal 

The AI question every job candidate on interview should be prepared to answer – CNBC

The job replacement AI machine - Axios 

Can AI do your job? See the results from hundreds of tests. – Washington Post

Behind the Curtain: The job replacement AI machine – Axios  

The Problem With Letting AI Do the Grunt Work Artificial intelligence is destroying the career ladder for aspiring artists– The Atlantic  

Workday and Alix Partners data shows AI's productivity paradox is real - Axios

Job Seekers Find a New Source of Income: Training AI to Do Their Old Roles – Wall Street Journal

Why the McKinsey layoffs are a warning signal for consulting in the AI age – Fast Company  

There's new evidence that instead of bringing on a job apocalypse, AI is creating more work and jobs - Axios 

Finding Meaningful Work in the Age of Vibe Coding  - KD Nuggets 

Entry-level tech workers describe the AI-fueled jobpocalypse – Rest of World

Replace your boss before they replace you – Replace your Boss

Say Goodbye to the Billable Hour, Thanks to AI – Wall Street Journal

AI may discriminate against you at work. Some states are making it illegal. - Washington Post

What AI Skills Should Hiring Employers Look For? How to Define and Seek Out Workers with “AI Literacy” – JD Supra

Spooked by AI and Layoffs, White-Collar Workers See Their Security Slip Away – Wall Street Journal

The Forrester AI Job Impact Forecast, US, 2025–2030 - Forrester

When A.I. Took My Job, I Bought a Chain Saw – New York Times 

The surprising truth about AI’s impact on jobs – CNN

Saying "no" to 1,000 things

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People think focus means saying yes to the thing you've got to focus on. But that's not what it means at all. It means saying no to the hundred other good ideas that there are. You have to pick carefully. I'm actually as proud of the things we haven't done as the things I have done. Innovation is saying "no" to 1,000 things. 

Steve Jobs

A way to create finished, bug-free programs without human intervention

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Users of Claude Code, Anthropic’s software-writing AI system, recently discovered a way to create finished, bug-free programs without human intervention. The trick: Write a small program that asks the AI, over and over again, to improve the code it has already written. Named the Ralph Wiggum technique, after the dimwitted but persistently optimistic “Simpsons” character, this simple trick is effective at forcing Claude Code to solve problems on its own. - Wall Street Journal

11 Articles about AI & the Bigger Questions

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Don’t Passionately Settle

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On golf courses, one may find some aging men and women whose chief remaining goal in life is to knock a few more strokes off their game. This dedicated effort to improve their skill serves to give them a sense of progress in life and there by assists them in ignoring the reality that they have actually stopped progressing, having given up the effort to improve themselves as human beings. If they loved themselves more they would not allow themselves to a passionately settle for such a shallow goal and narrow future. 

M Scott Peck, The Road Less Traveled

16 Articles about the Limitations of AI

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What Makes an AI More Persuasive

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A new study “examined what factors make one chatbot more persuasive than another and found that AI models needn’t be more powerful, more personalized, or more skilled in advanced rhetorical techniques to be more convincing. Instead, chatbots were most effective when they threw fact-like claims at the user; the most persuasive AI models were those that provided the most “evidence” in support of their argument, regardless of whether that evidence had any bearing on reality. In fact, the most persuasive chatbots were also the least accurate.” -The Atlantic

AI Definitions: AI engineers 

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AI engineers – AI engineers work on the front end of AI machines, building AI-powered applications. On the other side, data scientists help collect and clean data and work with AI to make sense of it. Unlike those working in traditional IT roles, AI engineers will fix the AI when it breaks by digging through the layers to determine why it went wrong and how to repair it. Like a plumber, they’ll snake the pipes to clear out the system and figure out how to avoid the problem next time. This will be particularly important for models that have been highly customized to an organization.

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