Anagha Nair

A fabricated reference is not an ordinary error: it is an edge in the citation graph that points at nothing, and there is no paper to retract. This article traces how phantom citations propagate through reference-copying, why they are harder to remove than retracted “zombie” papers, and what a two-layer verification habit looks like in practice.

A Nikkei investigation found 17 arXiv preprints carrying instructions hidden in white text and 0.001pt fonts, aimed at any reviewer who pastes the manuscript into an LLM. This article explains how prompt injection works, what the studies since have shown about how easily it swings ratings, and what authors and reviewers should change before their next submission.

Publisher AI guidance now runs to dozens of documents that disagree on wording, detail and placement, which leaves researchers guessing at what counts as reportable AI use. This article sets out what a strong disclosure template actually contains, with weak versus strong phrasing, and a seven-point checklist to run before submission.

Publishers now treat generative AI as a tool, never a co-author, which means authors remain fully accountable for every citation and claim they submit. This article explains why editors read “ChatGPT wrote it” as evidence of weak oversight, what the documented failures reveal, and why disclosure alone is not enough without demonstrated verification.

Autonomous AI systems can now generate hypotheses, design experiments, write manuscripts, and even pass elements of peer review. This article explores the rise of AI scientists, their current capabilities and limitations, and the critical role researchers must play in verifying, governing, and responsibly integrating AI into the research process.

Researchers are increasingly turning to AI for writing and analysis — but many feel guilty about it, even when their use is fully ethical. New data reveals a growing AI guilt complex in academia, driven not by misconduct but by unclear norms, shifting authorship expectations, and the absence of standardized guidelines.

With over 1.3 million scientists generating millions of weekly queries on ChatGPT, AI is rapidly becoming embedded in research workflows. While it accelerates discovery and simplifies complex tasks, it also introduces hidden risks like citation errors, bias, and reduced critical evaluation. This article explores how AI is evolving into research infrastructure and outlines six practical strategies to help researchers maintain control, accuracy, and intellectual independence.

Generative AI tools are helping researchers publish 50% more papers—but is quantity driving down quality? This article explores the productivity paradox in academia, examining how AI adoption creates both opportunities and risks for research integrity, equity, and peer-review systems.

Wiley surveyed 2,400+ researchers worldwide and found GenAI is now embedded in research workflows. But beyond rising adoption, three trends stand out: expectations are becoming more realistic, institutional policies are lagging behind usage, and heavy reliance on general-purpose tools is creating new risks for research integrity, privacy, and disclosure.

Most journals now require AI disclosure but what actually counts as "AI use"? With over 50% of researchers using GenAI tools yet fewer than 2% disclosing it, the problem isn't transparency policies it's the lack of clear, lifecycle-wide definitions of AI involvement. This article examines why current disclosure norms fall short and proposes a tiered, impact-based framework for responsible AI governance in research.

Nearly 1 in 5 AI-generated references are completely fake. Learn how hallucinated citations threaten research integrity.

New evidence shows that fewer than 6 percent of biomedical manuscripts disclose AI use, exposing critical gaps in transparency, accountability, and trust across scholarly publishing.

Discover how AI is reshaping academic publishing: from 22% of computer science papers containing AI-generated content to over 1,000 predatory journals exposed by detection tools. Learn about the transparency crisis and what the research community must do now.