AI and Academic Research

Strange language or phrases found in research papers may be the result of AI-generated programs attempting to rewrite text to disguise plagiarism. (Nature, 2021)

In 2019, Christian Szegedy, a computer scientist formerly at Google and now at a start-up in the Bay Area, predicted that a computer system would match or exceed the problem-solving ability of the best human mathematicians within a decade. Last year he revised the target date to 2026.

The technology’s ability to read and summarize text is already making it a useful tool for scholarship. How will it change the stories we tell about the past?

Publishers should encourage researchers to use all tools at their disposal in order to make their work as accessible and impactful as possible, while continuing to educate researchers on how to find, review, and verify information. At the same time, publishers need to fast track peer review reforms to be ready to contend with even murkier lines between novelty and regurgitation — and fact and fiction.

Emerging ‘co-scientist’ systems use chatbots to mimic the deliberations of a research group. Nature asked researchers to test them out.

When researchers cede their scientific judgement to machines, we lose something important.

This commentary explores the role of ChatGPT in the peer review process, evaluating its benefits, limitations, and ethical implications.

As the availability and performance of artificial intelligence for language editing and translation continues to improve, we can imagine a future in which everyone can use their own language to write, assess, and read science. The question is, how can we achieve it?

Preprint servers are seeing a rise in submissions seemingly produced by paper mills or with help from AI tools.

A group of experienced faculty authors assign ChatGPT the task of generating a manuscript with mixed results and were able to avoid detection by modifying the text.

More instructors are teaching with it. Is it making their courses better or dragging the profession down?

AI is upending peer review, the time-honored tradition in which academics help judge which research should be elevated to publication — and which should go in the reject pile. Under the specter of ChatGPT, no one can be sure anymore that their intellectual labor is being read and judged by humans.

Soon, artificial intelligence (AI) will be able to supercharge peer review and detect problematic papers.

Large language model creates synopses of papers aimed at various reading levels to help scientists sift through the literature.

Scholars should log every step of the process of composition.

ChatGPT might not yet produce sparkling prose. But it can free scientists up to focus on more stimulating writing tasks.

This site allows researchers to discover, share, read and discuss arXiv papers with their lab, other researchers, developers, and enthusiasts in their field.

The authors tested ChatGPT to write about the pathogenesis of medical conditions. They documented the positive, negative, and rather troubling aspects of the newly introduced chatbot’s performance.

As AI continues to evolve at a rapid pace, the question no longer is whether AI will enter peer review, but how to efficiently and effectively harness and manage its deployment in a manner that is fair to authors and valuable in the editorial process

Instead of banning AI-generated text, many editors are requiring authors to disclose their use of AI tools to generate research.

Breakthroughs in pure mathematics can take decades. A new Defense Department initiative aims to speed things up using artificial intelligence.

The aim of this work is to present an overview of the current AI image generation tools for scientific illustration, including an analysis of their potential benefits and associated risks.

There are clearly some areas that ChatGPT can help research—coding survey responses, improving the writing for people who have English as an additional language, and formatting of bibliographies stand out

Milton Pividori spent a year and a half studying how best to use ChatGPT in research. Here, he highlights three key lessons.

This article speculates that AI will have a large impact on scholarship practices and that it is important to begin considering the implications immediately.

The increasing volume of research submissions to academic journals poses a significant challenge for traditional peer-review processes. To address this issue, this study explores the potential of employing ChatGPT, an advanced large language model (LLM), developed by OpenAI, as an artificial intelligence (AI) reviewer for academic journals.

This study used ChatGPT to analyze peer reviewer reports and found that women authors receive less favorable reviews than men.

Researchers tested ChatGPT by seeing if it could replicate a previously published article and found that it falsified facts and provided incorrect formulas in responses that were plausible and well-written.

At major computer-science publication venues, up to 17% of the peer reviews are now written by artificial intelligence. The author suggests guidelines are needed before things get out of hand.

In 2024 STM assembled a Task and Finish Group (TFG) with the aim of creating a classification of the various possible uses of AI, including GenAI, in the preparation of manuscripts, and this article includes a chart of the uses.

The greater the rate of production of scientific papers, the more the noise, so the less attention truly novel papers will achieve, and thus will be less likely to break through into the canon.

At UC3, several of our latest initiatives involve integrating AI tools, with a particular focus on improving metadata and assisting researchers with creating best practice DMPs.

Although chatbots such as ChatGPT can facilitate cost-effective text generation and editing, factually incorrect responses (hallucinations) limit their utility. This study evaluates one particular type of hallucination: fabricated bibliographic citations that do not represent actual scholarly works.

The era of artificial scientific intelligence is here. As algorithms generate discoveries at scale, what role remains for human scientists?

Voice-to-text tools powered by artificial intelligence can make life easier for academics by replacing the keyboard with dictation and transcription.

Each singularity metioned in this article has the potential to so alter the nature of academic research that it could either restart the slowing engine of innovation or else create a crisis to derail it further.

This blog post introduces GAIDeT, a taxonomy for the structured disclosure of Generative AI (GAI) use in research and how it builds trust without adding any extra burden to stakeholders.

Instead of obsessing over replacement, we must explore how AI can amplify human thought — how it can free up our mental bandwidth so we can focus more on the meaningful, creative, and qualitatively rich aspects of our work.

To identify both benefits and risks of generative AI for our industry, the author tested ChatGPT and Google Bard for authoring, for submission and reviews, for publishing, and for discovery and dissemination.

An astrophysicist with an interest in historical research enlists the help of ChatGPT and finds the information that it provides to be unreliable.

For those at the interface of funding organizations and the scientific community, platforms such as ChatGPT can tackle menial tasks and free up time for relationship-building work such as coaching and mentoring.

An independent scholar shares his thoughts on Elicit, which can save time and effort by generating synonyms and related search terms leading to sources he might not have considered and then summarizing them to reduce reading time.

Scientists worry that ill-informed use of artificial intelligence is driving a deluge of unreliable or useless research.

A study of review reports identifies dozens of adjectives that could indicate text written with the help of chatbots.

The author argues that we need to reckon with what ChatGPT is doing to the classroom and to human expression.

Researchers are using ChatGPT to conduct research and write manuscripts which raises ethical questions about plagiarism, authorship, and fraud.

While LLMs (large language models) and RAG (retrieval-augmented generation) are promising, they fall short of human expert intelligence in many activities, and that gap varies widely depending on the task.

Tools such as ChatGPT can be used to generate almost-identical research papers that pass standard plagiarism checks. Hundreds are thought to have been published.

Language models are opening new avenues for inquiry in historical research and writing. But will they undermine the reading of primary sources?

To move beyond speculation, we need strong, systematic evaluations that can measure how well AI actually performs on the objectives of peer review, such as identifying errors and automating quality checks.

A chat app used for emotional support used a popular chatbot to write answers for humans to select. Controversy followed.

Many of the citations produced by ChatGPT are simply word salads formatted to look like legitimate citations.

Ai2 has been awarded $75 million from the U.S. National Science Foundation (NSF) and $77 million from NVIDIA as part of a jointly funded project with the NSF and NVIDIA to advance our research and develop truly open AI models and solutions that will accelerate scientific discovery.

A surge in AI-generated text has been detected in papers across many disciplines since the release of ChatGPT.

Driven by new technology called OpenAI o1, the chatbot can test various strategies and try to identify mistakes as it tackles complex tasks.

To help researchers conduct a systematic review or meta-analysis as efficiently and transparently as possible, the authors designed a tool to accelerate the step of screening titles and abstracts

While concerns about integrating AI into peer review are valid, it is essential to explore how experimentation with AI can address existing challenges and enhance the process.

As Peer Review 2025 turns its lens to the future, one question looms large: How will artificial intelligence (AI) reshape the way we conduct peer review? I think we have reached a point where we’ve gone beyond the if to the how.

High-impact research articles were generated by analyzing the quality of reports generated by ChatGPT and assessing the application’s impact on the research framework, data analysis, and the literature review. The study also explored concerns around ownership and the integrity of research when using AI-generated text.

This study investigates how models with advanced natural language processing capabilities can be used to reduce the time-consuming process of
writing and revising scholarly manuscripts.

Technology is making the old ways of teaching ineffective. A new way is starting to emerge.

This article reviews author guidelines of 100 academic publishers for use of AI in creating articles.

AI tools can assist researchers in designing studies, analyzing data, and drafting manuscripts, enhancing both efficiency and analytical rigor.

Stanford Medicine researchers created a team of virtual scientists backed by artificial intelligence to help solve problems in their real-world lab.

Academics may be leaning on a novel strategy to influence peer review of their research papers — adding hidden prompts designed to coax AI tools to deliver positive feedback.

Unlike human historians who explicitly document their methodologies, AI systems are creating the historical archives of the future without crucial transparency around how sources are selected.

An instructor at the University of Louisville has been compiling Academ-AI, a database of articles with likely, but undisclosed, chatbot-generated language. In a preprint last month, Glynn analyzed 500 of those papers and found that about 20 percent of them appeared in venues whose publishers explicitly require AI use to be disclosed.

Artificial intelligence (AI) tools now exist to aid almost every aspect of the research process, from hypothesis generation and data analysis to manuscript drafting and publication. What does the future hold for researchers and publishers as AI use continues to increase?

In the coming months, 4000 researchers from a variety of disciplines and countries will weigh in on guidelines that could be adopted widely across academic publishing which has been struggling with authors using AI tools to research and write papers.

As part of an innovative experiment, Springer Nature has become the first publisher to create a whole new academic book by empowering authors to use GPT as part of the integrated workflow.

A researcher tests ScopusAI and finds its performance to be less than ideal.

Experts speculate about whether AI technology may transform and disrupt the way we live.

The authors believe that generative AI has become inescapable. As a tool that is capable of generating content, its implications for how scholarly research is conducted and for scholarly publishing and communication are potentially transformative.

Tired of spending countless hours on peer reviews? An AI-assisted workflow could help.

As researchers dive into the brave new world of advanced AI chatbots, publishers need to acknowledge their legitimate uses and lay down clear guidelines to avoid abuse.

This study aims to systematically analyze the acknowledgment of generative AI tools, particularly ChatGPT, in academic publications. It delves into patterns across multiple dimensions, including geographical distribution, disciplinary affiliations, journals, and institutional representation.

While current data indicate that researchers are increasingly adopting AI assistance for manuscript preparation, disclosure rates remain surprisingly low, despite evolving publisher requirements.

Artificial intelligence (AI) natural language processing (NLP) systems, such as OpenAI’s generative pre-trained transformer (GPT) model (https://openai.com) or Meta’s Galactica (https://galactica.org/) may soon be widely used in many forms of writing, including scientific and scholarly publications (Heaven 2022).1 While computer programs (such as Microsoft WORD and Grammarly) have incorporated automated text-editing features (such as checking for spelling and grammar) for many years, these programs are not designed to create content. However, new and emerging NLP systems are, which raises important issues for research ethics and research integrity.

A test of ChatGPT for conducting literature reviews suggests that it fabricates papers and DOIs.

The author argues that choosing the right AI tool for the job and prompting well is critical to generating good search results.

An article published in Scientific American written by a researcher who had GPT-3 write a paper and then submitted it for publication.

The author argues that AI programs like ChatGPT are limited because they can gather information but are unable to tell if it is factually correct, so they generate mistakes.

Scientists are using ChatGPT and other AI tools to help them with their scientific research and grant writing. The results are mixed and the tools are still subject to biases and errors.

Chemistry faculty argue for and against the use of AI to generate review articles.

Publishers are increasingly using AI tools to screen papers and check them for plagiarism, language use, and other factors.