Artificial intelligence has brought profound changes to academic research and publishing; however, its potential negative impacts remain a subject of debate. For instance, concerns have been raised about practices like research paper mills factories, which may compromise the integrity of knowledge and the sustainability of academic journals. This raises important questions: what are the potential negative impacts of artificial intelligence on knowledge development and the sustainability of academic journals, particularly with the rise of research paper mills, and how can these challenges be effectively addressed?
Both require effort to produce, that's for sure. I'd say the standard fact-check or independent review should work. However AI tools can do the fact checking or may become advanced enough to ask important questions about the particular paper based on that body of work. Given a significantly increased context-window.
Artificial intelligence has significant potential to enhance knowledge development and improve academic workflows, but its misuse presents challenges, especially in the sustainability of academic journals and the integrity of scholarly work.
Muy interesante esta discución, en los ultimos años he venido realizando algunas investigaciones academicas con y sin apoyo de la IA y he notado la gran diferencia ya que la inteligencia artificial (IA) ha revolucionado la investigación y la publicación académica, ofreciendo herramientas que agilizan procesos y amplían el acceso al conocimiento. Sin embargo, su implementación también conlleva riesgos que pueden afectar la integridad del conocimiento y la sostenibilidad de las revistas académicas, por lo cual podria decir que las posibles efectos negativos de la IA en la investigación y publicación académica se basan en estos tres factores:
Por lo cual y con el fin de mitigay y desarrollar estrategias para abordar estos desafíos, sugiero lo siguientes puntos:
Por lo cual podemos cuncluir que no se trata de no usar o ver como un enemigo a la IA, aunque presenta riesgos, la clave reside en utilizar la IA como una herramienta complementaria que, bajo una supervisión ética y responsable, potencie la calidad y alcance de la investigación académica.
The integration of artificial intelligence (AI) in academic research and publishing presents both opportunities and challenges. There is a need to implement strategies that combine technological solutions with strong policy measures and a commitment to academic integrity. By staying vigilant against unethical AI use, we can preserve the credibility and sustainability of academic journals.
Good answers. I think the real threat is fabrication of empirical data. AI makes it too easy to do everything, but completely aiding new knowledge and purposefully 'hallucinating' are two different things. Particularly dangerous when generating stats and figures which make up the basis of empirical study. Additionally giving people with little knowledge in certain industries over-night thought leadership could propagate a whole new level of misinformation or the opposite. Something I'm currently studying. But the complete fabrication of empirical foundations is what scares me the most. Particularly in academic research and publishing.
Dear Jamal Kaid Mohammed Ali , as you are aware of, I have posted many answers in different discussion rooms about this issue.
Your new question deserves an answer. But, let me tell that artificial Intelligence may create problems in academic publishing, and also solve some the problems...
AI and ‘paper mills’: a big ethics battle
"‘Paper mills’, or the “production of fake scientific papers to order” is an on-going fight within the scientific community. This means data can be forged and faked to produce a significant paper that stands out from the million published yearly. The reason behind it? Recent technology developments made it easier and easier to fake data and to write deceitful papers.
Lately, with the introduction of OpenAI software, ChatGPT, the fight has become even more challenging. According to integrity research-analysts, AI can generate even more plausible fake data as well as fake microscopy images. And they are harder to detect than you may think...
So, experts want to fight fire with fire. A specific software has been created to identify ChatGPT generated responses. But another AI tool can modify data slightly, making it undetectable by the integrity software. Thus, the validation of the papers becomes a never-ending loop.
Nevertheless, the basis of AI is to keep learning and upgrading its responses. It may be hard to code and develop, but a fraud-identifying AI appears be the strongest opponent to face the problem..."
https://students.uu.nl/en/gsls/ai-and-paper-mills-a-big-ethics-battle
The paper mill problem: are AI tools the answer?
"An AI-based service designed to detect bogus scientific articles flagged 10–13% of submitted manuscripts in a pilot run, according to a blog post by Ivan Oransky for Retraction Watch. The fake papers were caught by publisher Wiley’s Papermill Detection service, which screens submissions ahead of editorial review. The multi-tool system is a promising development in ongoing efforts to ensure the integrity of published research...
The test run involved over 270 Wiley journals, which rejected between 600–1,000 submitted manuscripts per month once they started using the tool. A spokesperson for Wiley told Retraction Watch that flagged papers would not automatically be rejected, but would be considered by an editor before being processed further. The publisher says it is partnering with Sage and IEEE for its next testing phase, and aims to roll out the service as early as next year...
Investigations into retractions should help ensure the integrity of published research, but there is growing interest in using new tools such as Papermill Alarm to help stop fake papers getting published in the first place. Wiley say their new service will complement the STM Integrity Hub, a resource developed by academic publishers that incorporates Papermill Alarm and other tools to help combat fake science.
While much discussion around developments in AI has focused on possible threats to research integrity, spotting bogus manuscripts could be an area where AI could help restore trust in published science."
https://thepublicationplan.com/2024/08/01/the-paper-mill-problem-are-ai-tools-the-answer/
https://retractionwatch.com/2024/03/14/up-to-one-in-seven-of-submissions-to-hundreds-of-wiley-journals-show-signs-of-paper-mill-activity/#more-128927
https://johnwiley2020news.q4web.com/press-releases/press-release-details/2024/Wiley-announces-pilot-of-new-AI-powered-Papermill-Detection-service/default.aspx
https://thepublicationplan.com/2023/01/17/raise-the-papermill-alarm-a-new-tool-for-identifying-potential-fake-articles/
https://stm-assoc.org/what-we-do/strategic-areas/research-integrity/integrity-hub/
https://thepublicationplan.com/2023/01/27/will-a-cross-publisher-integrity-hub-aid-the-battle-against-fake-research/
Neglect of hidden academic labour ‘bad for science’
Rise of AI and publication quotas have led to scientists “publishing rubbish”...
"Universities overlook activities such as peer review and journal editing when they hire or promote researchers, said Keyan Tomaselli, an emeritus communication studies professor.
Instead, he noted, institutions reward academics for publishing—a practice driven by the publication subsidy the country’s higher education department pays institutions for articles published in accredited journals. Some scientists even have yearly publishing quotas.
“The quota system must be thrown out the window,” Tomaselli said. “We must produce work that is symbolic, that is good, that is clear, and only submit for publication when it is ready for publication.”
Tomaselli is one of the scholars behind the Academy of Science of South Africa’s recently published statement on the need to recognise editors and peer review in academic settings..."
https://www.researchprofessionalnews.com/rr-news-africa-south-2024-11-neglect-of-hidden-academic-labour-bad-for-science/
https://www.researchprofessionalnews.com/rr-news-africa-south-2024-10-universities-urged-to-value-hidden-academic-labour/
How Emerging Tech Trends Are Shaping Scholarly Publishing
"In this post, I focus on four of the trends that are most relevant to publishing industry and discuss the challenges and opportunities they might bring...:
1. Agentic AI
2. AI governance platforms
3. Disinformation security
4. Spatial computing...
The future is undeniably exciting, with transformative possibilities on the horizon, but there is much that needs to be addressed to ensure these advancements are inclusive, ethical, lawful, reliable, and impactful."
https://scholarlykitchen.sspnet.org/2024/12/20/navigating-the-digital-frontier-how-emerging-tech-trends-are-shaping-scholarly-publishing/?informz=1&nbd=&nbd_source=informz
Springer Nature reveals AI-driven tool to 'automate some editorial quality checks'
"A new AI-driven tool to "automate a number of editorial quality checks" has been developed by Springer Nature to alert editors to "potentially unsuitable manuscripts" so that they can be held back from peer review. The AI tool has been developed in-house and will be integrated into Springer Nature’s next generation article submission and processing platform, Snapp. The latest tool follows the inclusion in 2024 of two AI tools to identify fake content...
Springer explained that the AI tool "supports editors and peer reviewers by quickly addressing manuscript quality issues", and the aim of the tool is to reduce the number of amendments needed, while "maintaining the integrity of a high-quality publishing process". The results gathered by the tool will be double-checked by a human expert before a final decision is made..."
https://www.thebookseller.com/news/springer-nature-reveals-ai-driven-tool-to-automate-some-editorial-quality-checks
The Ethics of Using Artificial Intelligence in Scientific Research: New Guidance Needed for a New Tool
"The use of artificial intelligence (AI) in research offers many important benefits for science and society but also creates some novel and complex ethical issues. While the issues raised by AI use will not necessitate a radical change in the established ethical norms of science, they will require the scientific community to develop new guidance for the appropriate use of AI. In this article, we provide a brief introduction to AI and how it can be used in research, examine some of the ethical issues raised by using AI in research, and offer recommendations for appropriate use of this technology. We recommend that: 1) researchers and software developers are responsible for identifying, describing, reducing, and controlling AI-related biases and random errors; 2) researchers should disclose and explain their use of AI in language that can be understood by non-experts; 3) if appropriate, researchers should engage with impacted communities, populations, and other stakeholders concerning the use of AI in research to obtain their advice and assistance and address their interests and concerns; 4) researchers may be liable for misconduct if they intentionally, knowingly, or recklessly use AI to fabricate or falsify data or commit plagiarism; 5) AI systems should not be named authors, inventors, or copyright holders but their contributions to research should be disclosed and described; 6) AI systems should not be used in situations that may involve unauthorized disclosure of confidential information related to human research subjects, unpublished research, potential intellectual property claims, or proprietary or classified research; and 7) education and mentoring in responsible conduct of research should include discussion of ethical use of AI."
Preprint The Ethics of Using Artificial Intelligence in Scientific Re...
Flood of AI-assisted research ‘weakening quality of science’
Wave of ‘low-quality’ papers threatening to overwhelm journals and peer reviewers, preventing them from assessing more meaningful research...
https://www.timeshighereducation.com/news/flood-ai-assisted-research-weakening-quality-science
Navigating Ethical Challenges in Generative AI-Enhanced Research: The ETHICAL Framework for Responsible Generative AI Use
"The rapid adoption of generative artificial intelligence (GenAI) in research presents both opportunities and ethical challenges that should be carefully navigated. Although GenAI tools can enhance research efficiency through automation of tasks such as literature review and data analysis, their use raises concerns about aspects such as data accuracy, privacy, bias, and research integrity. This paper develops the ETHICAL framework, which is a practical guide for responsible GenAI use in research. Employing a constructivist case study examining multiple GenAI tools in real research contexts, the framework consists of seven key principles: Examine policies and guidelines, Think about social impacts, Harness understanding of the technology, Indicate use, Critically engage with outputs, Access secure versions, and Look at user agreements. Applying these principles will enable researchers to uphold research integrity while leveraging GenAI benefits. The framework addresses a critical gap between awareness of ethical issues and practical action steps, providing researchers with concrete guidance for ethical GenAI integration. This work has implications for research practice, institutional policy development, and the broader academic community while adapting to an AI-enhanced research landscape. The ETHICAL framework can serve as a foundation for developing AI literacy in academic settings and promoting responsible innovation in research methodologies."
Preprint Navigating Ethical Challenges in Generative AI-Enhanced Rese...
Ethical AI governance: mapping a research ecosystem
"How do we assess the positive and negative impacts of research about- or research that employs artificial intelligence (AI), and how adequate are existing research governance frameworks for these ends? That concern has seen significant recent attention, with various calls for change, and a plethora of emerging guideline documents across sectors. However, it is not clear what kinds of issues are expressed in research ethics with or on AI at present, nor how resources are drawn on in this process to support the navigation of ethical issues. Research Ethics Committees (RECs) have a well-established history in ethics governance, but there have been concerns about their capacity to adequately govern AI research. However, no study to date has examined the ways that AI-related projects engage with the ethics ecosystem, or its adequacy for this context. This paper analysed a single institution’s ethics applications for research related to AI, applying a socio-material lens to their analysis. Our novel methodology provides an approach to understanding ethics ecosystems across institutions. Our results suggest that existing REC models can effectively support consideration of ethical issues in AI research, we thus propose that any new materials should be embedded in this existing well-established ecosystem."
Article Ethical AI governance: mapping a research ecosystem
Ethical guidelines for the use of generative artificial intelligence and artificial intelligence-assisted tools in scholarly publishing: a thematic analysis
"This analysis aims to propose guidelines for artificial intelligence (AI) research ethics in scientific publications, intending to inform publishers and academic institutional policies in order to guide them toward a coherent and consistent approach to AI research ethics...
The analysis revealed inconsistencies among publishers’ policies on AI use in research and publications. AI-assisted tools for grammar and formatting are generally accepted, but positions vary regarding generative AI tools used in pre-writing and research methods. Key themes identified include author accountability, human oversight, recognized and unrecognized uses of AI tools, and the necessity for transparency in disclosing AI usage. All publishers agree that AI tools cannot be listed as authors. Concerns involve biases, quality and reliability issues, compliance with intellectual property rights, and limitations of AI detection tools.
The article highlights the significant knowledge gap and inconsistencies in guide-lines for AI use in scientific research. There is an urgent need for unified ethical standards, and guidelines are proposed for distinguishing between the accepted use of AI-assisted tools and the cautious use of generative AI tools. "
https://www.researchgate.net/publication/389105112_Ethical_guidelines_for_the_use_of_generative_artificial_intelligence_and_artificial_intelligence-assisted_tools_in_scholarly_publishing_a_thematic_analysis
"I was relieved to see the degree to which publishers have moved from “whether” to engage with artificial intelligence (AI) to “how.” Our industry has moved past paralysis and into a phase which recognizes that AI is not one thing, but rather presents opportunities for revenues and cost savings along with threats to scientific integrity, revenues, and copyright."
- Roy Kaufman
https://scholarlykitchen.sspnet.org/2025/06/09/ask-the-chefs-what-did-you-take-away-from-ssps-2025-annual-meeting/?informz=1&nbd=&nbd_source=informz
Editors’ Statement on the Responsible Use of Generative AI Technologies in Scholarly Journal Publishing
"Generative artificial intelligence (AI) has the potential to transform many aspects of scholarly publishing. Authors, peer reviewers, and editors might use AI in a variety of ways, and those uses might augment their existing work or might instead be intended to replace it. We are editors of bioethics and humanities journals who have been contemplating the implications of this ongoing transformation. We believe that generative AI may pose a threat to the goals that animate our work but could also be valuable for achieving those goals. In the interests of fostering a wider conversation about how generative AI may be used, we have developed a preliminary set of recommendations for its use in scholarly publishing. We hope that the recommendations and rationales set out here will help the scholarly community navigate toward a deeper understanding of the strengths, limits, and challenges of AI for responsible scholarly work ."
Article Editors’ Statement on the Responsible Use of Generative AI T...
Dear Jamal Kaid Mohammed Ali , there are so many resources here.
We Need AI Standards for Scholarly Publishing: A NISO Workshop Report
"In the past two years, there has been an explosion of interest in the possibilities of artificial intelligence tools, such as deep learning methods in scientific research, and — particularly in STM publishing — large language models for automated text generation. There is a growing list of issues that the publishing world is facing, from copyright infringement to the pressure of increased bandwidth on open systems, and from questions of authorship and research integrity to the second-order issues around increasing pace of content generation. The Scholarly Kitchen has covered many of these issues regularly as the community comes to identify this growing list of challenges. This blog alone has published 159 posts that touch on artificial intelligence (AI). At times, it seems the number of challenges is increasing faster than the number of solutions AI tools are solving. Several initiatives are underway to address these challenges. Notably last month, NISO ( National Information Standards Organization — full disclosure, my employer) hosted a series of workshops for scholarly publishing leadership to identify and prioritize efforts to address some of the challenges around AI and interoperability. These collective actions can help reduce the number of issues and we can all benefit from the opportunities AI provides...
The environment with AI systems is evolving rapidly and we need to advance solutions as quickly as possible. These workshops and the resultant report should help provide guidance on where the community should focus its efforts."
https://scholarlykitchen.sspnet.org/2025/06/12/we-need-ai-standards-for-scholarly-publishing-a-niso-workshop-report/?informz=1&nbd=&nbd_source=informz
https://scholarlykitchen.sspnet.org/archives/?_tsk-tax-term=artificial-intelligence
https://www.niso.org/niso-io/2025/05/envisioning-more-standardized-approach-ai-scholarly-publishing
Publishers’ and journals’ instructions to authors on use of generative artificial intelligence in academic and scientific publishing: bibliometric analysis
"Objectives: To determine the extent and content of academic publishers’ and scientific journals’ guidance for authors on the use of generative artificial intelligence (GAI)...
The primary outcomes were the content of GAI guidelines listed on the websites of the top 100 academic publishers and scientific journals, and the consistency of guidance between the publishers and their affiliated journals. Among the top 100 largest publishers, 24% provided guidance on the use of GAI, of which 15 (63%) were among the top 25 publishers. Among the top 100 highly ranked journals, 87% provided guidance on GAI. Of the publishers and journals with guidelines, the inclusion of GAI as an author was prohibited in 96% and 98%, respectively. Only one journal (1%) explicitly prohibited the use of GAI in the generation of a manuscript, and two (8%) publishers and 19 (22%) journals indicated that their guidelines exclusively applied to the writing process. When disclosing the use of GAI, 75% of publishers and 43% of journals included specific disclosure criteria. Where to disclose the use of GAI varied, including in the methods or acknowledgments, in the cover letter, or in a new section. Variability was also found in how to access GAI guidelines shared between journals and publishers. GAI guidelines in 12 journals directly conflicted with those developed by the publishers. The guidelines developed by top medical journals were broadly similar to those of academic journals. Guidelines by some top publishers and journals on the use of GAI by authors are lacking. Among those that provided guidelines, the allowable uses of GAI and how it should be disclosed varied substantially, with this heterogeneity persisting in some instances among affiliated publishers and journals. Lack of standardization places a burden on authors and could limit the effectiveness of the regulations. As GAI continues to grow in popularity, standardized guidelines to protect the integrity of scientific output are needed."
Article Publishers’ and journals’ instructions to authors on use of ...
A Call to Publishers: Join the Responsible AI Movement
Protecting research integrity through collaboration and education...
The Responsible AI Movement supports publishers in navigating the evolving AI landscape by establishing clear standards and promoting responsible, transparent AI use in research publishing...
We aim for comprehensive AI guidelines in research publishing, including mandatory human verification disclosure and clear classification of AI use...
Empower researchers worldwide with clear guidelines and best practices for the ethical and transparent use of AI tools in manuscript preparation...
https://www.enago.com/responsible-ai-movement?
Perspectives from Publishing’s Top Table — Matthew Kissner on AI
"We’re working to shape artificial intelligence (AI), not just by licensing our content for model training, but by building strategic partnerships with developers and R&D-intensive companies in healthcare, pharma, and chemistry who use our content to speed up product development. Corporations fund 80% of total US R&D investment, and we’re working to help them innovate faster...
We’re at a pivotal moment where researchers are increasingly using Large Language Models (LLMs) and AI agents to intermediate their information discovery, meaning more machines are interacting with our content. We need to transform scholarly publishing from a “pages business” to a “pipelines business.”...
AI is creating new applications for content. Model builders value scientific and scholarly content because it’s accurate, well-organized, and digestible. We’re seeing corporations develop specialized vertical-specific models. Pharmaceutical companies, healthcare providers, and chemical companies are using our content to fine-tune general models for their specific domains. This aligns perfectly with our purpose as an industry, because building AI models on authoritative, evidence-based content improves access to validated, trustworthy information.
That’s not to say that there won’t be barriers to successful AI adoption. We know, for example, that authors are concerned about how to use AI correctly and ethically. We surveyed 5,000 researchers and found something telling: most of them believed AI would significantly change their work within two years, but they weren’t using AI tools extensively. Why? About 60% said they needed clear guidelines from publishers on safe, ethical use and proper disclosure..."
https://scholarlykitchen.sspnet.org/2025/07/02/chefs-de-cuisine-perspectives-from-publishings-top-table-matthew-kissner/?informz=1&nbd=&nbd_source=informz
How AI Threatens Scholarly Communities and What Publishers Can Do
"Imagine a researcher typing a complex scientific query into one of today’s AI-discovery and summarization tools. In seconds, they receive a concise, seemingly authoritative summary – no clicking through to journal websites, no navigating subscription paywalls, and no downloading branded PDFs.
To the researcher, this feels like pure convenience, perhaps even magic, but for publishers, it looks like disintermediation. What does this mean for scholarly journals, the communities they nurture, and the integrity they safeguard?
This is no hypothetical scenario: it’s already here. Tools such as Google’s AI Overviews, Perplexity, and ChatGPT now deliver instant answers that bypass publisher websites entirely. What looks like a routine dip in referral traffic is in fact a rewiring of the very infrastructure that has sustained scholarly communities for decades...
The future has already arrived. Publishers have a narrow window to respond thoughtfully and strategically. The choice is stark: adapt or risk becoming invisible."
https://scholarlykitchen.sspnet.org/2025/07/07/guest-post-when-the-front-door-moves-how-ai-threatens-scholarly-communities-and-what-publishers-can-do/?informz=1&nbd=&nbd_source=informz
Low-quality papers based on public health data are flooding the scientific literature
The appearance of thousands of formulaic biomedical studies has been linked to the rise of text-generating AI tools...
"Data from five large open-access health databases are being used to generate thousands of poor-quality, formulaic papers, an analysis has found. Its authors say that the surge in publications could indicate the exploitation of these databases by people using large language models (LLMs) to mass-produce scholarly articles, or even by paper mills — companies that churn out papers to order..."
https://www.nature.com/articles/d41586-025-02241-2
AI is all around us, whether in publishing workflows or in the promise and potential threats of LLMs. How do you see AI affecting your publishing life, research integrity, and the communities you serve?
"The AI horse has left the barn, so now it’s about how to harness all that’s positive about it while minimizing harmful risks.
We’re taking a pragmatic approach that balances innovation with responsibility. On the positive side, AI is helping us improve workflow efficiency, enhance author services, and potentially accelerate peer review. We’re exploring AI tools for better manuscript matching, language assistance for non-native English speakers, and improved data extraction and formatting.
However, we’re equally focused on the challenges AI presents for research integrity. We’ve strengthened our detection capabilities for AI-generated content and are continuously updating our policies to address evolving concerns. The key is working collaboratively with our community — authors, reviewers, and editors — to establish clear guidelines that protect the integrity of the scientific record while allowing for beneficial AI applications.
Research integrity remains our top priority, and we’re investing heavily in training, tools, and partnerships to ensure we stay ahead of potential misuse while harnessing AI’s potential to advance scientific communication and discovery. Like all current technological developments, AI brings both promise and challenges, but I see these disruptions as opportunities to better serve our authors and readers, streamline processes, and expand access to critical microbiology research globally..."
https://scholarlykitchen.sspnet.org/2025/07/18/chefs-de-cuisine-perspectives-from-publishings-top-table-melissa-junior/?informz=1&nbd=&nbd_source=informz
Emerging AI dilemmas in scholarly publishing
Artificial Intelligence (AI) continues to reshape the landscape of research publishing, offering powerful tools for authors, reviewers, and editors alike, while presenting unique ethical challenges. AI has transitioned swiftly from being an emerging curiosity to becoming an integral force in scholarly publishing...
"Early debates centred principally around AI-assisted language tools aimed at enhancing clarity of academic writing, detecting plagiarism, or simplifying administrative editorial processes. Today, in 2025, we are confronted with more sophisticated developments that touch almost every step of a research article’s life cycle. AI technology presently shapes not only the manuscripts themselves, such as drafting entire sections, enhancing analytical workflows, producing visual data and multimedia content, but also intervenes directly into the critical stages of peer review and editorial selection.
With this widening AI influence, new ethical concerns continually arise. How much disclosure is sufficient to transparently represent the AI technologies involved in research production and dissemination? Should editors place explicit limits upon authors’ use of generative AI? How will such standards be fairly enforced across diverse communities with differential access and expertise? How can researchers, reviewers, editors, and publishers maintain integrity and balance between efficiency, fairness, accuracy, and accountability?"
https://publicationethics.org/topic-discussions/emerging-ai-dilemmas-scholarly-publishing
Balancing AI and academic integrity: what are the positions of academic publishers and universities?
"This paper navigates the relationship between the growing influence of Artificial Intelligence (AI) and the foundational principles of academic integrity. It offers an in-depth analysis of how key academic stakeholders—publishers and universities—are crafting strategies and guidelines to integrate AI into the sphere of scholarly work. These efforts are not merely reactionary but are part of a broader initiative to harness AI’s potential while maintaining ethical standards. The exploration reveals a diverse array of stances, reflecting the varied applications of AI in academic settings. These range from conservative approaches emphasizing the need for stringent AI regulations to more progressive strategies that seek to leverage AI’s capabilities. The paper accentuates the importance of guidelines that are adaptable, transparent, and culturally sensitive, acknowledging the global context of academic research. It also highlights the pressing need for effective implementation and enforcement of these guidelines to ensure that the integration of AI into academia does not compromise the integrity of scholarly work. The conclusion emphasizes the dynamic balance required between embracing AI’s transformative potential and upholding the timeless values of academic rigor and ethical research. This comprehensive analysis aims to provide a detailed perspective on the evolving role of AI in academia, fostering a dialog on how best to navigate this new technological frontier while preserving the sanctity of academic scholarship."
Article Balancing AI and academic integrity: what are the positions ...
Artificial intelligence is transforming academic publishing in both empowering and problematic ways. On the one hand, it democratizes access to scientific writing tools, supports multilingual researchers, and accelerates literature review, hypothesis generation, and even peer review. On the other, it risks undermining the integrity and sustainability of academic knowledge, especially with the rise of AI-generated paper mills — automated systems that mass-produce low-quality or fraudulent research.
These so-called paper mills can flood journals with superficially plausible but scientifically empty articles, overwhelming editors and reviewers, diluting the quality of scientific literature, and eroding trust in peer-reviewed outputs. If unchecked, this could destabilize academic publishing by forcing journals to process more submissions than they can rigorously evaluate, while also damaging the reputation of legitimate researchers whose work becomes indistinguishable from synthetic outputs.
To address these threats, multiple strategies are needed:
First, AI detection tools must be integrated into editorial workflows to flag potentially machine-generated or low-integrity submissions, though these tools themselves must be transparent, constantly updated, and peer-reviewed.
Second, we need a stronger culture of human accountability and transparency, where authors are required to disclose the role of AI in writing or data analysis, and where journals adopt clear guidelines on acceptable AI use.
Third, verification of scientific claims must become more robust — including reproducibility checks, data availability mandates, and more rigorous methodological peer review. Here too, AI can be part of the solution, supporting reviewers in identifying statistical inconsistencies or unoriginal content.
However, it's important to recognize that integrating AI detection into screening processes may unfairly penalize authors who use such tools ethically — for example, to translate their manuscripts into correct academic English. This is particularly relevant for non-native English-speaking researchers, who often rely on generative AI to enhance the clarity and grammatical quality of their work without altering the underlying scientific content. Several studies have highlighted how AI-detection systems can produce false positives, flagging such texts as AI-generated even when they are based on original research. This creates a new form of linguistic bias, disproportionately affecting non-Anglophone authors and potentially discouraging their participation in global scientific discourse.
It is therefore essential that the integration of AI in editorial workflows be accompanied by clear, context-aware policies that distinguish between ethical language assistance and unethical content fabrication. Without this nuance, we risk reinforcing existing inequalities in academic publishing rather than mitigating them.
Finally, academic incentives must change. As long as career advancement is tied to quantity over quality, the temptation to exploit generative AI for rapid publication will remain strong. A shift toward rewarding rigor, reproducibility, and collaboration is essential.
In essence, the same AI that threatens to flood our systems with noise can — if wisely governed — help defend the clarity, credibility, and sustainability of academic knowledge.
Beyond recommendations: expanding the ethical discourse on AI-assisted academic writing
"In response to Cheng et al.’s article on ethical recommendations for artificial intelligence (AI)-assisted academic writing, we propose an expanded ethical discourse to address the evolving role of AI in scholarly communication. While applauding the authors’ foundational framework, we argue for greater disciplinary specificity, clearer thresholds for AI contribution, and broader consideration of systemic risks including linguistic bias, environmental impact, and corporate concentration. We advocate for the development of a graded typology of AI involvement, institution-led regulatory mechanisms, and integration of ethical AI use into editorial and research training practices. These enhancements are essential for building equitable, transparent, and sustainable AI governance in academic publishing."
Article Beyond recommendations: expanding the ethical discourse on A...
The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search
"AI is increasingly playing a pivotal role in transforming how scientific discoveries are made. We introduce The AI Scientist-v2, an end-to-end agentic system capable of producing the first entirely AI generated peer-review-accepted workshop paper. This system iteratively formulates scientific hypotheses, designs and executes experiments, analyzes and visualizes data, and autonomously authors scientific manuscripts..."
Preprint The AI Scientist-v2: Workshop-Level Automated Scientific Dis...
AI in academic publishing
"AI is changing everything. It’s opening up so many fascinating new possibilities for the dissemination and discovery of knowledge. The progress in the last two years has been incredible, and I am curious about developments we are going to see in the coming years, especially in combination with the move towards open access, which means that academic knowledge will be more easily accessible for everyone.
But of course, progress in AI is also raising serious and fundamental questions: What will it mean for research integrity? For the copyrights of authors and publisher? For the role of human researchers? For how we define and validate knowledge?
I believe our role as a publisher is to ensure quality and scientific verifiability in tandem with the academic community, to help defend academic integrity, support our communities, and ensure that the tools and services we build genuinely help researchers and strengthen the research process..."
https://scholarlykitchen.sspnet.org/2025/07/25/robert-harington-talks-to-carsten-buhr-ceo-of-de-gruyter-brill-in-this-series-of-perspectives-from-some-of-publishings-leaders-across-the-non-profit-and-for-profit-sectors-of-our-industry/?informz=1&nbd=&nbd_source=informz
AI content is tainting preprints: how moderators are fighting back
Preprint servers are seeing a rise in submissions seemingly produced by paper mills or with help from AI tools...
https://www.nature.com/articles/s41562-025-02273-8
https://www.nature.com/articles/d41586-025-02469-y
This is the first study to examine the submission mechanisms, specifically via an ancillary tool, the ASP, through which journal editors attempt to monitor generative AI use and enforce their policies...
Sense and sensibility of article submission platforms are needed regarding verification of AI use: a stakeholders’ perspective
"The development of artificial intelligence (AI) tools that can potentially automate components of the research process is accelerating rapidly. For journal editors, the undeclared use of generative AI (GAI) or large language models (LLMs) like ChatGPT to generate academic writing is particularly concerning. In response to GAI, many journals have incorporated an AI declaration statement into their article submission platform (ASP). The utility of such declarations may be limited, given their lack of verifiability. While a paper’s acknowledgements and ethical declarations constitute the primary location where authors formally declare accountability for their work and commit to proper academic conduct, a journal’s ASP serves as a second tier of verification. The configurations of GAI/LLM declarations in this space have not yet been formally characterized or assessed... Drawing from the experience with top-ranked medical journals, we advise that they—as well as other medical journals—ensure that important ethical clauses that appear in their IFAs also appear in their ASPs, so that there is congruency among ethical statements related to AI use. Regarding GAI/LLM use, the biggest challenge remaining for the publishing industry is how to confirm the veracity of statements made on ASPs."
https://www.researchgate.net/publication/394940292_Sense_and_sensibility_of_article_submission_platforms_are_needed_regarding_verification_of_AI_use_a_stakeholders'_perspective
No Summer Break for Scholarly Communications: AI, Integrity, and Policy Updates
When it comes to AI and publishing, there is some hope. Many users are seeking very specific AI solutions grounded in hard data, which is why Cabells chose to focus on utilizing its Journalytics database of verified journals when developing CompassAI, which helps you determine which is the best journal by just submitting your abstract. There has been little escape this Northern Hemisphere summer from the incessant march of AI into every part of our lives, and the temptation is just to unplug everything and go and catch a few last rays of sunshine before it disappears for another year. As a short-term AI strategy, it may take some beating, but the colder days ahead at least offer some light relief as we develop smarter ways to use AI in our research and publishing lives...
https://blog.cabells.com/2025/09/03/no-summer-break-for-scholarly-communications/
https://cabells.com/compassai
Attitudes and perceptions of biomedical journal editors in chief towards the use of artificial intelligence chatbots in the scholarly publishing process: a cross-sectional survey
"Artificial intelligence chatbots (AICs) are designed to mimic human conversations through text or speech, offering both opportunities and challenges in scholarly publishing. While journal policies of AICs are becoming more defined, there is still a limited understanding of how Editors in chief (EiCs) of biomedical journals’ view these tools. This survey examined EiCs’ attitudes and perceptions, highlighting positive aspects, such as language and grammar support, and concerns regarding setup time, training requirements, and ethical considerations towards the use of AICs in the scholarly publishing process.
A cross-sectional survey was conducted, targeting EiCs of biomedical journals across multiple publishers. Of 3725 journals screened, 3381 eligible emails were identified through web scraping and manual verification. Survey invitations were sent to all identified EiCs. The survey remained open for five weeks, with three follow-up email reminders...
This study found that EiCs have mixed attitudes toward AICs, with some EICs acknowledging their potential to enhance editorial efficiency, particularly in tasks like language editing, while others expressed concerns about the ethical implications, the time and resources required for implementation, and the need for additional training."
https://link.springer.com/article/10.1186/s41073-025-00178-8
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