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<p>In academic publishing, productivity has always been easy to measure. Count the papers, track the citations, and the numbers appear to tell a story of progress. But the rise of generative AI (GenAI) is forcing the research community to ask a more uncomfortable question: What if more papers do not necessarily mean better science?</p><p><a href="https://news.cornell.edu/stories/2025/12/ai-gives-scientists-boost-cost-too-many-mediocre-papers" target="_blank" rel="noopener noreferrer"><u>Evidence</u></a> suggests that researchers using AI tools are publishing faster and in greater numbers than before. Yet alongside this surge in output, new concerns are emerging about narrowing research agendas, uneven benefits across researchers, and a growing strain on peer-review systems.&nbsp;</p><h2>Measured Productivity Gains but Reduced Thematic Diversity</h2><p>One of the clearest signals of AI’s impact on research productivity comes from a Cornell University <a href="https://news.cornell.edu/stories/2025/12/ai-gives-scientists-boost-cost-too-many-mediocre-papers" target="_blank" rel="noopener noreferrer"><u>study</u></a> which found that scientists using large language models (LLMs) such as ChatGPT published up to 50% more papers than before.</p><p>From one perspective, this is a positive development. AI tools can streamline time-intensive tasks like editing manuscripts, identifying relevant citations, or summarizing prior literature. However, increased output does not necessarily translate to deeper or more meaningful scientific progress.</p><p><a href="https://www.nature.com/articles/d41586-025-04092-3" target="_blank" rel="noopener noreferrer"><u>Large-scale analyses</u></a> of scientific literature reveal a striking pattern. Researchers who adopt AI tools tend to publish more papers and receive more citations, but their work increasingly concentrates within narrower thematic areas. Instead of expanding the intellectual frontier, AI-assisted research often deepens existing trajectories.&nbsp;</p><h2>Unequal Gains in the AI Era</h2><p>Another dimension of the productivity paradox concerns <strong>who</strong> benefits from AI-driven productivity.</p><p>A <a href="https://academic.oup.com/pnasnexus/article/4/2/pgae591/7996465" target="_blank" rel="noopener noreferrer"><u>study</u></a> published in PNAS Nexus found that the productivity boost associated with AI adoption is <strong>6</strong>.4% higher for male researchers compared with female researchers. This suggests that systemic disparities such as differences in access to resources, training, or institutional support may shape how effectively researchers can leverage AI tools.</p><p>Without careful attention, AI could amplify existing inequities in academia, benefiting groups already advantaged in research ecosystems. But unequal access to productivity gains is only part of the picture. As AI-assisted research output grows, concerns are also emerging about the quality and rigor of that expanding volume of work.</p><h2>The Rise of “Productivity Without Rigor”</h2><p>Editors and conference organizers are beginning to confront a more practical consequence of AI-driven productivity. <a href="https://www.nature.com/articles/d41586-025-03967-9" target="_blank" rel="noopener noreferrer"><u>Reports</u></a> from within the publishing industry describe a noticeable rise in low-quality or partially AI-generated submissions (sometimes referred to informally as “AI slop”). These manuscripts are often linguistically polished but lack methodological rigor or conceptual novelty, placing additional strain on peer-review systems already operating at capacity.</p><p>This trend also creates significant pressure on peer-review infrastructure. As the volume of submissions rises, reviewers must spend more time filtering out weak or poorly verified work, potentially slowing the evaluation of genuinely novel research.</p><figure class="image"><img src="https://easydash.enago.com/uploads/image_48b38c5426.png"><figcaption>Disclaimer: The above image is generated using Notebook LM for illustration and understanding purposes.</figcaption></figure><p>The result is a growing tension: AI <a href="https://www.enago.com/responsible-ai-movement/resources/ai-assisted-writing-scientific-integrity-research" target="_blank" rel="noopener noreferrer"><u>improves</u></a> writing fluency and productivity, but polished language can obscure weak scientific contributions, making it harder for editors and reviewers to distinguish meaningful research from superficial output.&nbsp;</p><h2>Rethinking What Productivity Means in Research</h2><p>The emerging evidence points to a paradox at the heart of AI-enabled research:</p><ul><li>Researchers can produce more papers faster</li><li>Yet the collective intellectual diversity of science may shrink</li><li>And the systems that evaluate research quality may become overwhelmed</li></ul><p>None of this suggests that AI is inherently harmful to research. On the contrary, AI tools can democratize access to scientific communication, reduce language barriers, and accelerate routine tasks that previously consumed researchers’ time.</p><p>However, it strongly suggests that publication counts alone are becoming an increasingly unreliable measure of scientific productivity.</p><p>If success continues to be measured primarily by publication counts, AI may simply fuel the <i>parti pris</i> in academia: producing more papers to meet institutional expectations. In such a system, AI becomes a powerful amplifier of the “publish-or-perish” culture already shaping research careers.</p><h2>What Should Researchers Do?</h2><p>Responsible AI use in science therefore requires a fundamental shift in how research contributions are valued. The question is not how many additional papers AI can help you produce, but whether it enables you to:</p><ul style="list-style-type:disc;"><li>Ask meaningful scientific questions</li></ul><ul><li>Add novel insights and expand knowledge</li><li>Explore diverse and emerging research areas</li><li>Strengthen methodological rigor and transparency</li><li>Enhance equity in global research participation</li></ul><p>The real risk is not that AI will change how research is produced, because it already has. The risk is that without clear standards, it will quietly redefine what counts as good science. But we understand that there is a gap in how these standards are perceived by researchers.&nbsp;</p><p>This gap is precisely what <a href="https://www.enago.com/responsible-ai-movement" target="_blank" rel="noopener noreferrer"><u>Enago’s Responsible Use of AI (RUAI)</u></a> initiative seeks to address. By collating clear guidelines, standardized disclosure practices, and guiding stronger human oversight, it brings much-needed structure to an ecosystem currently marked by inconsistency and uncertainty. It recognizes that responsible AI use cannot be left to individual judgment alone, rather requires coordinated action across researchers, publishers, and institutions.</p><p>As AI continues to accelerate research output, the question is no longer whether we can produce more, but whether we can do so without compromising integrity, diversity, and trust. Responsible use of AI offers a path forward, not just to manage AI adoption, but to shape it deliberately, so that scientific progress is not only faster, but fundamentally better. <a href="https://www.enago.com/responsible-ai-movement/workshops" target="_blank" rel="noopener noreferrer"><u>Let us know</u></a> if you need assistance in understanding publisher and university policies or in applying best practices for the responsible use of generative AI through the RUAI initiative.</p>

50% More Papers, Less Science? What Does This Mean For You as a Researcher?

Explore why AI adoption boosts publication rates but narrows research diversity. Discover the productivity paradox, equity gaps, and responsible AI use in academia.

50% More Papers, Less Science? AI's Research Paradox

Anagha Nair

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<p>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.</p>

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<p>Transparency around artificial intelligence use in research writing is now a near-universal expectation across scholarly publishing. Major publishers, journals, and ethics bodies have introduced Generative&nbsp;AI disclosure requirements, often framed as a cornerstone of responsible research practice. Yet, despite this apparent consensus, the ecosystem lacks agreement on a more fundamental question: <strong>what exactly counts as “AI use” in research writing?</strong></p><p>While much of the latest <a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC12515416/" target="_blank" rel="noopener noreferrer"><u>debate</u></a> focuses narrowly on manuscript writing and editing, this framing overlooks a broader reality. AI tools now influence foundational research decisions, including literature discovery, study design, data analysis, visualization, interpretation, and decision-making—often well before a manuscript is drafted. Treating AI disclosure as a writing-stage concern alone risks obscuring its deeper epistemic impact on research itself.</p><h2>Existing Gap Between AI Use and Disclosure</h2><p>&nbsp;A <a href="https://www.nature.com/articles/d41586-025-00343-5" target="_blank" rel="noopener noreferrer"><u>2025 survey</u></a> of 5,000 researchers found more than 50% use Generative AI (GenAI) assistance for their research writing tasks. Additionally, detection <a href="https://arxiv.org/abs/2403.07183" target="_blank" rel="noopener noreferrer"><u>studies</u></a> at AI conferences identified AI modifications in 6.5-16.9% of peer reviews via phrase frequency spikes, indicating hidden integration.</p><p>However, a broad literature review of the available <a href="https://pubmed.ncbi.nlm.nih.gov/40675866/" target="_blank" rel="noopener noreferrer"><u>sources</u></a> indicate a significant gap between AI use and its disclosure. <a href="https://pubmed.ncbi.nlm.nih.gov/40675866/" target="_blank" rel="noopener noreferrer"><u>Bibliometric analysis</u></a> of 1,998 radiology manuscripts in Elsevier journals showed only 1.7% disclosed large language model use, mostly for minor edits and predominantly from non-English-speaking institutions.&nbsp;</p><p>Collectively, these findings highlight a stark gap between AI adoption and disclosure, a gap shaped by multiple forces, including uncertainty over what types of AI assistance meaningfully count as “use” at different stages of the research lifecycle.</p><h2>A Patchwork of Definitions Across the Research Lifecycle</h2><p>Across policy reviews and <a href="https://www.bmj.com/content/384/bmj-2023-077192" target="_blank" rel="noopener noreferrer"><u>comparative analyses</u></a>, a recurring theme emerges: AI disclosure requirements are fragmented and inconsistently defined. Most guidelines emphasize transparency in principle, but offer limited guidance on <i>where</i> in the research process AI involvement becomes ethically or scientifically material. Policies from major publishers show <strong>different interpretations of what qualifies as AI use requiring disclosure</strong>. For example:</p><ul><li><a href="https://www.sagepub.com/journals/publication-ethics-policies/artificial-intelligence-policy" target="_blank" rel="noopener noreferrer"><strong><u>SAGE</u></strong></a> distinguishes <i>assistive AI (no disclosure)</i> from <i>generative AI (disclosure)</i>, with specific but separate criteria across text, images, and analysis.</li><li><a href="https://www.nature.com/nature-portfolio/editorial-policies/ai" target="_blank" rel="noopener noreferrer"><strong><u>Nature Portfolio journals</u></strong></a>, by contrast, require authors to disclose the use of large language models or generative AI tools (apart from their use in copy-editing), but do <strong>not provide a formal classification of AI use by function</strong> (e.g., assistive vs. generative, surface-level vs. substantive).</li></ul><p>While some publishers have begun to draw explicit functional distinctions between different types of AI use, while others retain broad, principle-based language that leaves substantial room for interpretation. Terms such as “substantial contribution,” “meaningful assistance,” or “content generation” appear frequently in publisher policies without thresholds, examples, or decision criteria. As a result, identical AI uses may be disclosed in one journal and omitted in another. This inconsistency extends beyond writing into upstream research activities, where AI support is often invisible to editors and readers alike.</p><h2>The Narrow Focus on Writing Misses the Larger Risk</h2><p>Focusing disclosure policies primarily on manuscript preparation creates a misleading boundary and misrepresents where AI influence is most consequential. AI systems increasingly assist with:</p><ul><li>Identifying relevant literature and synthesizing prior work</li><li>Generating or refining research questions</li><li>Supporting statistical analysis and modeling</li><li>Producing data visualizations and figures</li><li>Identifying patterns that influence interpretation</li></ul><p>Each of these activities can shape how knowledge is produced, framed, and interpreted. Yet many fall outside the scope of current disclosure norms, not because they are ethically neutral, but because definitions of AI use remain underdeveloped.</p><p>This gap creates a paradox: visible AI use in writing is scrutinized, while less visible but potentially more influential AI use earlier in the research process often goes undisclosed.</p><h2>Editing, Automation, and Intellectual Influence: Where confusion begins</h2><p>The definitional challenge is compounded by long-standing norms around automation and support tools in research. Statistical software, reference managers, plagiarism detectors, and language editors have long been integrated into scholarly workflows without formal disclosure requirements.</p><p>AI blurs these categories. When automation shifts from executing predefined instructions to generating suggestions, interpretations, or summaries, the boundary between <i>support</i> and <i>intellectual influence</i> becomes blurred. <a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC12515416/" target="_blank" rel="noopener noreferrer"><u>Studies</u></a> consistently show that researchers struggle to determine when AI assistance crosses this line—particularly when the output is reviewed and approved by a human.</p><p>Without guidance that distinguishes between process efficiency and epistemic contribution, disclosure decisions are left to individual judgment, resulting in uneven reporting.</p><h2>Why Precision is Important to Responsible AI</h2><p>The absence of clear, lifecycle-wide definitions has implications that extend beyond compliance:</p><ul><li><strong>Trust</strong>: Readers cannot reliably interpret disclosure statements if similar AI uses are reported differently. A recent <a href="https://arxiv.org/abs/2512.06705" target="_blank" rel="noopener noreferrer"><u>analysis</u></a> of &gt;5.2 million papers showed a dramatic <strong>transparency gap</strong>. Only 0.1 % of 75,000 papers published since 2023 explicitly disclosed AI use, despite widespread policy adoption and actual use, suggesting trust issues when actual AI involvement is not known.</li><li><strong>Comparability</strong>: Inconsistent and incomplete AI disclosure affects the reliability of meta-research that seeks to assess AI’s prevalence, impact, and risks in scholarly communication. A <a href="https://link.springer.com/article/10.1186/s12910-025-01239-9" target="_blank" rel="noopener noreferrer"><u>comparative analysis</u></a> of AI policies in bioethics and humanities journals found 30% journals did not have a publicly available AI policy. This fragmentation makes it difficult to compare AI use across journals, weakening evidence-based policy development and obscuring the true role of AI in research practices.</li><li><strong>Governance</strong>: Editors and publishers lack shared enforceable standards. Confusion about policy application risks uneven editorial decisions. The <a href="https://link.springer.com/article/10.1186/s12910-025-01239-9" target="_blank" rel="noopener noreferrer"><u>BMC comparative study</u></a> discusses that lack of clear AI policies can confuse authors about whether to disclose AI use, and can result in different editors within the same journal applying different interpretations, weakening governance.</li><li><strong>Equity</strong>: <i>Definitional uncertainty and policy inconsistency create&nbsp;</i><a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC12515416/" target="_blank" rel="noopener noreferrer"><u>asymmetries</u></a><i> among researchers: those who disclose AI use may face perceived editorial risk, while others benefit from AI support without transparency. These dynamics disadvantage authors who seek to comply ethically, reinforcing inequities in scholarly recognition and trust.</i></li></ul><p>As a result, responsible AI governance in research depends not simply on disclosure mandates, but on definitions that align with how research is actually conducted. Rather than asking whether AI was used, the more meaningful question is: <strong>Did AI meaningfully influence research decisions, interpretations, or scholarly outputs?</strong></p><p>This shift enables proportional transparency, recognizing that not all AI use carries the same ethical or scientific weight.</p><h2>Toward a Holistic, Tiered Approach</h2><p>A lifecycle-aware disclosure framework distinguishes between:</p><figure class="table"><table><tbody><tr><td style="border:1.0pt solid windowtext;padding:0in 5.4pt;vertical-align:top;"><strong>Proposed Category of AI Use</strong></td><td style="border-bottom-style:solid;border-color:windowtext;border-left-style:none;border-right-style:solid;border-top-style:solid;border-width:1.0pt;padding:0in 5.4pt;vertical-align:top;"><strong>Definition</strong></td><td style="border-bottom-style:solid;border-color:windowtext;border-left-style:none;border-right-style:solid;border-top-style:solid;border-width:1.0pt;padding:0in 5.4pt;vertical-align:top;"><strong>Typical Examples to Consider</strong></td><td style="border-bottom-style:solid;border-color:windowtext;border-left-style:none;border-right-style:solid;border-top-style:solid;border-width:1.0pt;padding:0in 5.4pt;vertical-align:top;"><strong>Proposed Disclosure Expectation</strong></td><td style="border-bottom-style:solid;border-color:windowtext;border-left-style:none;border-right-style:solid;border-top-style:solid;border-width:1.0pt;padding:0in 5.4pt;vertical-align:top;"><strong>Expected Impact on Editorial Handling</strong></td></tr><tr><td style="border-bottom-style:solid;border-color:windowtext;border-left-style:solid;border-right-style:solid;border-top-style:none;border-width:1.0pt;padding:0in 5.4pt;vertical-align:top;"><strong>Routine Automation</strong></td><td style="border-bottom:1.0pt solid windowtext;border-left-style:none;border-right:1.0pt solid windowtext;border-top-style:none;padding:0in 5.4pt;vertical-align:top;">AI-enabled functions that support efficiency for mechanical tasks without shaping research content, reasoning, or credibility in any manner.</td><td style="border-bottom:1.0pt solid windowtext;border-left-style:none;border-right:1.0pt solid windowtext;border-top-style:none;padding:0in 5.4pt;vertical-align:top;">Spell-checking, citation formatting, transcription without summarization, plagiarism screening, file or figure formatting</td><td style="border-bottom:1.0pt solid windowtext;border-left-style:none;border-right:1.0pt solid windowtext;border-top-style:none;padding:0in 5.4pt;vertical-align:top;">Not required</td><td style="border-bottom:1.0pt solid windowtext;border-left-style:none;border-right:1.0pt solid windowtext;border-top-style:none;padding:0in 5.4pt;vertical-align:top;">Treated as standard digital infrastructure; no impact on peer review or authorship</td></tr><tr><td style="border-bottom-style:solid;border-color:windowtext;border-left-style:solid;border-right-style:solid;border-top-style:none;border-width:1.0pt;padding:0in 5.4pt;vertical-align:top;"><strong>Assistive Use</strong></td><td style="border-bottom:1.0pt solid windowtext;border-left-style:none;border-right:1.0pt solid windowtext;border-top-style:none;padding:0in 5.4pt;vertical-align:top;">AI support that improves workflow or presentation by reshaping the expression, but does not directly or greatly alter scholarly claims or conclusions</td><td style="border-bottom:1.0pt solid windowtext;border-left-style:none;border-right:1.0pt solid windowtext;border-top-style:none;padding:0in 5.4pt;vertical-align:top;">Language editing, paraphrasing for clarity, translation verified by authors, structural suggestions</td><td style="border-bottom:1.0pt solid windowtext;border-left-style:none;border-right:1.0pt solid windowtext;border-top-style:none;padding:0in 5.4pt;vertical-align:top;">Recommended (light-touch)</td><td style="border-bottom:1.0pt solid windowtext;border-left-style:none;border-right:1.0pt solid windowtext;border-top-style:none;padding:0in 5.4pt;vertical-align:top;">Disclosure recorded for transparency; no editorial escalation</td></tr><tr><td style="border-bottom-style:solid;border-color:windowtext;border-left-style:solid;border-right-style:solid;border-top-style:none;border-width:1.0pt;padding:0in 5.4pt;vertical-align:top;"><strong>Substantive Contributions</strong></td><td style="border-bottom:1.0pt solid windowtext;border-left-style:none;border-right:1.0pt solid windowtext;border-top-style:none;padding:0in 5.4pt;vertical-align:top;">AI use that contributes directly or indirectly to research outputs, while humans retain decision-making control</td><td style="border-bottom:1.0pt solid windowtext;border-left-style:none;border-right:1.0pt solid windowtext;border-top-style:none;padding:0in 5.4pt;vertical-align:top;">Drafting background sections, generating tables or figures, data classification, literature synthesis support</td><td style="border-bottom:1.0pt solid windowtext;border-left-style:none;border-right:1.0pt solid windowtext;border-top-style:none;padding:0in 5.4pt;vertical-align:top;">Required</td><td style="border-bottom:1.0pt solid windowtext;border-left-style:none;border-right:1.0pt solid windowtext;border-top-style:none;padding:0in 5.4pt;vertical-align:top;">Editors assess scope and appropriateness; reviewers may be informed</td></tr><tr><td style="border-bottom-style:solid;border-color:windowtext;border-left-style:solid;border-right-style:solid;border-top-style:none;border-width:1.0pt;padding:0in 5.4pt;vertical-align:top;"><strong>Interpretive Influence</strong></td><td style="border-bottom:1.0pt solid windowtext;border-left-style:none;border-right:1.0pt solid windowtext;border-top-style:none;padding:0in 5.4pt;vertical-align:top;">AI use that affects reasoning, interpretation, or conclusions</td><td style="border-bottom:1.0pt solid windowtext;border-left-style:none;border-right:1.0pt solid windowtext;border-top-style:none;padding:0in 5.4pt;vertical-align:top;">AI-supported interpretation of results, predictive modeling, inference generation, scenario analysis</td><td style="border-bottom:1.0pt solid windowtext;border-left-style:none;border-right:1.0pt solid windowtext;border-top-style:none;padding:0in 5.4pt;vertical-align:top;">Mandatory with detailed description</td><td style="border-bottom:1.0pt solid windowtext;border-left-style:none;border-right:1.0pt solid windowtext;border-top-style:none;padding:0in 5.4pt;vertical-align:top;">Heightened editorial scrutiny; possible methodological justification or limitations required</td></tr></tbody></table></figure><p>Such an approach aligns disclosure requirements with <i>function, influence, and accountability</i>, rather than with specific technologies or brand names. It also preserves a foundational principle consistently emphasized across ethics guidelines: human researchers remain fully responsible for the integrity of their work, regardless of AI involvement.</p><h2>Defining AI Use Is a Governance Imperative</h2><p>The scholarly community has moved quickly to demand transparency around AI. What it has not yet resolved is the definitional foundation that makes transparency meaningful.</p><p>As AI continues to shape research across the lifecycle, limiting disclosure discussions to writing alone risks misrepresenting its role in knowledge production. A holistic, impact-based understanding of AI use is essential—not only for consistent disclosure, but for sustaining trust in the scholarly record.</p><p>For <a href="https://www.enago.com/responsible-ai-movement/publisher-ai-guidelines" target="_blank" rel="noopener noreferrer"><u>Enago’s Responsible AI Movement</u></a>, this challenge conveys a central message: responsible AI adoption begins with clear definitions that reflect real research practice, not just policy intent.</p>

What Counts as “Generative AI Use” in Research Writing? The Definition Problem No One Has Solved!

Over 50% of researchers use GenAI, yet only 0.1% disclose it. Explore why AI disclosure policies fail without clear definitions of AI use across the research lifecycle, and a proposed tiered framework.

What Counts as Generative AI Use in Research Writing? The Definition Gap in Scholarly Publishing

<p>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.</p>

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<p>The recent <a href="https://www.medrxiv.org/content/10.1101/2025.10.24.25338574v1" target="_blank" rel="noopener noreferrer"><u>preprint</u></a> titled <i>“Authors self-disclosed use of artificial intelligence in research submissions to 49 biomedical journals”</i> reports a striking figure: only <strong>5.7% of the 25,114</strong> empirical biomedical manuscripts submitted between April and November 2024 openly disclosed any use of AI. This sharply contrasts with prior surveys, where between <strong>28%&nbsp;</strong>and<strong> 76%</strong> of researchers admitted to using AI tools in preparing their manuscripts. This gap between estimated AI use and actual disclosure highlights a growing tension: AI is rapidly reshaping biomedical research and writing, but transparency is dangerously lagging behind.&nbsp;</p><h2>The 5.7% Disclosure Rate — and Why It Matters</h2><p>The study analyzed 25,114 empirical manuscripts submitted across 49 biomedical journals published by BMJ between April 8 and November 6, 2024. Of these, only 1,431 (5.7%) included a self-report of AI use. The most commonly disclosed AI tools were generative chatbots (e.g., LLM-based writing assistants), accounting for 56.7% of disclosures; writing-assistants more broadly accounted for 12.7%. The main declared purpose: improving writing quality (87% of disclosing authors). Other uses like translation, data generation, literature search, data analysis, image processing, code writing, reference management, were reported far less frequently. This means most disclosures reveal only low-risk uses while higher-risk AI tasks remain unreported. Furthermore, a separate <a href="https://pubmed.ncbi.nlm.nih.gov/40675866/" target="_blank" rel="noopener noreferrer"><u>bibliometric analysis</u></a> of 1,998 radiology papers in Elsevier journals reported that only 1.7% disclosed the use of LLM’s (large language models).</p><h2>Why does this low rate of disclosure matter?</h2><ul style="list-style-type:disc;"><li><strong>Scientific Integrity and Reproducibility:</strong></li></ul><p style="margin-left:.5in;">&nbsp;The use of AI for writing, analysis, data generation, or translation can affect meaning, nuance, and even compromise data integrity. Without transparency, peer reviewers and readers cannot properly evaluate the provenance of text, analyses, or interpretations.</p><ul style="list-style-type:disc;"><li><strong>Erosion of Trust and Accountability:</strong></li></ul><p style="margin-left:.25in;">When AI use remains hidden, it fuels what some describe as a “hidden ecosystem” of AI-assisted writing and research. This puts accountability, authorship normativity, and the trustworthiness of the scientific record under radar.&nbsp;</p><h2>Is This Figure a Tip of the Iceberg?</h2><p>The answer remains “Yes!” Several independent studies suggest AI involvement in a much higher proportion of biomedical research, often without disclosure. A large-scale linguistic <a href="https://arxiv.org/abs/2406.07016?" target="_blank" rel="noopener noreferrer"><u>analysis</u></a> of over <strong>15 million biomedical abstracts (2010–2024)</strong> found a marked increase in so-called “style words,” which are adjectives and verbs that are typically found in LLM-generated text. Authors argue that these stylistic markers point to at least <strong>13.5%</strong> of 2024 abstracts having been processed by large language models.&nbsp;</p><p>These findings collectively suggest that the true prevalence of AI assistance in biomedical research is likely much higher than what disclosure statistics indicate. In other words: many papers may depend on AI for writing, editing, data handling, or even drafting, without any formal acknowledgment or structured disclosure.</p><h2>What are the Repercussions?<br>&nbsp;</h2><h3>1. Accountability</h3><p>When authors use AI but do not disclose it, responsibility for accuracy, integrity, and originality becomes dangerously murky. If AI-generated content contains errors or biased phrasing, who is responsible? Undisclosed use blurs that line.</p><h3>2. Accuracy and Validity</h3><p>Generative AI tools, irrespective of their latest updates and the extent of training, are not flawless. They can hallucinate facts, misinterpret data, fabricate citations, overstate significance, or introduce subtle biases. Without a clear record of what was AI-assisted (e.g., text generation, data summarization, image processing), peers and readers cannot reliably assess and cross-verify methodological rigor, reproducibility, or interpret results in context.</p><h3>3. Peer Review and Publishing Systems</h3><p>Systematic non-disclosure can create a creeping “shadow literature,” where parts of academic output are AI-augmented and unknown to reviewers, editors, or readers. This threatens long-term trust in published evidence, especially in highly-sensitive fields like medicine, where accuracy matters for patient safety and policy decisions.</p><h2>Where Do the Existing Guidelines Fall Short?</h2><p>Many publishers and journals have issued guidance on AI use. Major academic publishers like <a href="https://www.elsevier.com/about/policies-and-standards/generative-ai-policies-for-journals" target="_blank" rel="noopener noreferrer"><u>Elsevier</u></a>, <a href="https://authorservices.wiley.com/ethics-guidelines/index.html#22" target="_blank" rel="noopener noreferrer"><u>Wiley</u></a>, <a href="https://taylorandfrancis.com/our-policies/ai-policy/" target="_blank" rel="noopener noreferrer"><u>Taylor &amp; Francis</u></a>, and <a href="https://www.sagepub.com/journals/publication-ethics-policies/artificial-intelligence-policy" target="_blank" rel="noopener noreferrer"><u>SAGE </u></a>have issued policies prohibiting AI authorship while allowing limited, disclosed use for tasks like language editing, with human oversight required. However, existing practices are fragmented, inconsistent, and often insufficient to ensure real transparency. Moreover, these guidelines often vary not only between publishers, but also between journals under the same publisher, leaving authors confused, and disclosure optional, inconsistent, or omitted. In addition, a lack of unified language and structure, i.e., where to disclose (methods section, acknowledgments, cover letter), how detailed to be (just tool name or detailed description of use), what types of AI use require disclosure (writing, data processing, analysis, translation, etc.) — adds to the opacity. This confusion and heterogeneity can dissuade authors from disclosing, even when they rely heavily on AI.</p><h2>What a Comprehensive Disclosure Guideline Could and Should Offer</h2><p>Given the stakes, the scholarly community needs a unified, standardized framework for AI disclosure. Here’s what such a guideline should include:</p><h3>1. Clear Definition of What Constitutes the “Right AI Use”</h3><p>The guideline should broadly define AI use to include: generative writing assistants (e.g., large language models), translation tools, code-generation, data analysis/processing tools, image-processing AI (e.g., for radiology), reference management AI, and any other AI-driven assistance. This helps avoid narrow definitions that only consider “writing” as AI use.</p><h3>2. Mandatory Disclosure With Structured Format</h3><p>Instead of leaving disclosure optional or hidden in cover letters, journals should require a dedicated section (e.g., “AI-Assistance Declaration”) in manuscripts where authors mention the AI tools used (by name or version), describe what they were used for (writing, translation, data analysis, image processing, reference management, etc.), clarify whether AI was used to generate new content (text/data), or only to assist (editing, translation, polishing).</p><h3>3. Alignment With Broader Open Science and Reproducibility Practices</h3><p>AI disclosure should be integrated into larger frameworks of transparency: data sharing, code availability, methodology reporting. Especially for studies using AI for data analysis or image processing, journals should encourage (or require) sharing of AI-generated code, models, and data, where permissible.</p><h2>Towards a Transparent, Trustworthy AI-Augmented Future</h2><p>The fact that only <strong>5.7%</strong> of biomedical research manuscripts disclosed AI use, despite far higher estimates of actual AI involvement, is a wake-up call for the entire publishing ecosystem. AI tools are already reshaping how scientific research is conducted, written, and reviewed. But this transformation comes with risks: loss of transparency, dilution of accountability, compromised reproducibility, and potential erosion of trust in the scientific record.</p><p>A unified, community-wide guideline for AI disclosure that is mandatory, structured, transparent, is no longer an optional nicety. It is essential. Supported by frameworks like <a href="https://arxiv.org/abs/2303.03886" target="_blank" rel="noopener noreferrer"><u>AI Usage Cards</u></a>, and aligned with open-science and robust reporting practices, such guidelines can safeguard the integrity of biomedical research even as AI becomes deeply integrated.</p><p>If journals, publishers, researchers, and funders adopt clear, consistent disclosure standards now, we can reap the productivity and accessibility benefits of AI without compromising the core ethical values in publishing. If you believe the scientific community deserves clarity and not guesswork, then join <a href="https://www.enago.com/responsible-ai-movement" target="_blank" rel="noopener noreferrer"><u>Enago’s Responsible AI movement</u></a> to help set the standard and define what responsible AI looks like in global research!</p>

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Only 5.7% Disclosed AI Use! Why Researchers Need Unified Guidelines Urgently

A major biomedical study reveals that just 5.7 percent of manuscripts disclosed AI use despite widespread adoption. Explore the risks of non-disclosure and the urgent need for unified AI guidelines.

Only 5.7 Percent Disclosed AI Use – Why Unified Guidelines Are Urgently Needed

<p>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.</p>

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<p>In a stark wake-up call for scholarly communication, a recent <a href="https://www.science.org/content/article/one-fifth-computer-science-papers-may-include-ai-content" target="_blank" rel="noopener noreferrer"><u>Science report</u></a> revealed: <i>about <strong>one in five</strong> computer science papers may contain content generated by artificial intelligence (AI).</i>&nbsp; What makes this even more alarming is the <a href="https://phys.org/news/2025-08-scientific-papers-written-chatgpt-science.html" target="_blank" rel="noopener noreferrer"><u>speed of escalation</u></a>. In early 2022, detectable AI-written text in such papers was negligible; by mid-2023 it began to surface sporadically; and by late 2024 it had surged to <strong>over 22% of abstracts</strong> in some subfields. This stunning, two-year rise has sent shockwaves through the research ecosystem because it spotlights a broader and accelerating trend of AI use in research writing, raising urgent concerns about authorship, accountability, and trust.</p><h2>What did the <i>Science</i> Study Find?</h2><p>According to the <i>Science</i> article, researchers observed a sharp surge in <strong>AI-generated language</strong> across scientific manuscripts, particularly since November 2022, when OpenAI released ChatGPT. By analyzing patterns in phrasing, especially in abstracts and introductions in more than 1.1 million scientific articles and preprints (2020–2024), they found that up to <strong>22.5% of computer science abstracts</strong> showed signs of large language model (LLM) involvement.&nbsp;</p><p>Other fields also saw increasing AI influence: electrical engineering (~18%), statistics (~12.9%), physics (~9.8%), and even mathematics (~7.8%). Notably, these increases accelerated sharply after the release of ChatGPT, indicating a structural shift rather than a temporary trend.</p><p>These numbers suggest that generative AI isn’t just a writing aid. What makes this trend especially concerning is the <strong>human detection gap</strong>. Reviewers, editors, and even experienced researchers are increasingly unable to differentiate between human-written and AI-written text. As a result, AI-modified writing is silently entering the literature, often <strong>without disclosure</strong>, and often <strong>without being noticed at all</strong>.</p><h2>The Other Side of the Coin: Predatory journals exposed by AI</h2><p>Meanwhile, a recent University of Colorado Boulder&nbsp;<a href="https://www.sciencedaily.com/releases/2025/08/250830001203.htm" target="_blank" rel="noopener noreferrer"><u>study</u></a> revealed that a different AI tool has&nbsp;been working to identify potential threats to scientific integrity. The system screened ~15,200 open-access&nbsp;journals and initially<strong>&nbsp;</strong>flagged more than 1,400 journals as suspicious. After expert human review, over 1000 were confirmed to exhibit hallmarks of predatory publishing. This means that <strong>roughly 1 in every 14 open-access journals may be operating without credible scholarly standards</strong>. This figure is explosive, yet still dramatically under-communicated in the broader academic community.</p><p>Some of the strongest red flags included missing or fake editorial boards, unusually high levels of self-citation, and authors with many institutional affiliations.</p><h2>Why These Two Threads Matter? And How They Connect</h2><p>Putting both trends together— AI quietly appearing in thousands of manuscripts and a different AI system exposing more than 1,000 suspect journals—reveals a landscape of both opportunity and peril for academic publishing.</p><h3>1. Scaling Risk &amp; Reward</h3><p>AI helps researchers write faster, polish text, and generate ideas. However, the same tools can produce authoritative-sounding yet misleading content (“hallucinations”), and may be used to inflate publication count with low-quality papers.</p><h3>2. Broken Transparency</h3><p>Many authors now generate substantial portions of their manuscripts using AI but do not disclose it. For example, the <a href="https://arxiv.org/pdf/2411.15218" target="_blank" rel="noopener noreferrer"><u>Academ-AI dataset</u></a> shows hundreds of suspected instances of undeclared AI usage in peer-reviewed literature. Without clear acknowledgement, AI-assisted writing can affect trust: readers, reviewers, and institutions may not realize what “authorship” actually means in these cases.</p><h3>3. Predatory Publishing + AI = Double Threat</h3><p>Predatory journals exploit academic pressure for “publish or perish.” With AI writing, it becomes easier and cheaper to generate manuscripts that appear credible but lack scientific rigor. This creates a dangerous “supply chain” between AI-generated text and journals willing to publish anything for a fee.</p><h2>Why This Matters? Risks and Implications&nbsp;</h2><h3>1. Transparency &amp; Disclosure</h3><p>One of the most significant problems is that AI use is rarely being disclosed. Without clear acknowledgment, readers and reviewers may misinterpret AI-assisted sections as entirely human-written. This opacity affects trust. As some journals still lack concrete guidelines, many authors may not even realize the ethical dimensions of non-disclosure.</p><h3>2. Quality vs. Quantity Trade-off</h3><p>While AI can accelerate the writing process, there’s a risk that parts of a manuscript, especially pivotal sections like introductions, become formulaic or hollow. If generative tools are overused, we might see a loss of the subtle creativity, rigor, or critical insight that characterizes strong scientific writing.</p><h3>3. Peer-Review Challenges</h3><p>If peer reviewers are unaware of AI involvement, they may fail to catch errors, hallucinations, or misleading phrasing. Given how LLMs can hallucinate plausible but false claims, this is not a hypothetical risk. The more LLM-generated content slips through unchecked, the more credibility the academic record could lose.</p><h3>4. Erosion of Genuine Authorship and Credit</h3><p>When AI writes portions of a paper (or entire drafts), how do we assess contribution? Traditional norms of authorship like who did the experiments, who wrote, who edited may blur. This could challenge how credit, responsibility, and accountability are allocated in research teams.</p><h2>What Can Be Done?</h2><p>Given the scale and speed of AI’s infiltration into scientific writing, the academic community must act quickly. Here are some practical steps forward:</p><ol><li>Standardized Disclosure Statements<br>Journals should require authors to clearly disclose where and how they used AI. The disclosure should include:</li></ol><ul><li>Which tool was used (e.g., ChatGPT, Claude)</li><li>The version or model (e.g., GPT-4)</li><li>When and how it was used (e.g., drafting introduction, polishing grammar)</li><li>The extent of use (percentage of text, sections)</li></ul><ol><li>AI-Usage Checklists for Submissions<br>Just as authors fill out conflict-of-interest or data-sharing checklists, they should also complete an AI-usage checklist. This could help editors and reviewers understand potential AI contributions and decide where to scrutinize more deeply.</li><li>Training for Reviewers and Editors<br>Peer reviewers and journal editors need to be educated on how to detect LLM-like writing. By learning common AI tropes — unusual phrasing, repetitive structures — they can better assess manuscript authenticity.</li><li>Open, Transparent Detection Tools<br>Develop and support open-source tools for detecting AI-generated text. Transparency in the detection process will build trust; black-box detectors may raise fairness issues (e.g., bias against non-native English writers).</li><li>Institutional &amp; Funder Policies<br>Universities, funding bodies, and research institutions should develop policies around acceptable AI usage. They should balance encouraging productivity with ensuring scientific integrity.</li></ol><h2>Why This Moment Matters</h2><p>The convergence of AI-assisted writing and AI-enabled journal policing represents a pivotal moment in the history of science. On one hand, generative tools can democratize writing, make knowledge creation more accessible, and speed up idea exchange. On the other hand, without guardrails, generative AI may erode the integrity of the research record, blur authorship, and bolster shady publishing practices. Yet there is hope. What we urgently need are community norms, shared policies, and transparency. With well-defined AI-disclosure guidelines, we can ensure that generative AI adds value to research, rather than becoming a reason for its degradation.</p><p>Organizations across the ecosystem are beginning to define what “responsible AI” truly means in research, and one of the most proactive voices in this space is Enago’s Responsible AI Movement. By offering frameworks, training resources, and guidance for researchers worldwide, <a href="https://www.enago.com/responsible-ai-movement" target="_blank" rel="noopener noreferrer">Enago’s movement</a> reinforces the principle that AI should augment and not replace human scholarship. Ultimately, the future of science depends not only on technological tools but on the values that guide their use.</p>

Researcher Alert! AI Writing Surges in Research: 1 in 5 Computer Science Papers and Over 1,000 Journals at Risk

A Science study reveals 1 in 5 computer science papers contain AI-generated content, while detection tools exposed 1,000+ predatory journals. Explore the transparency crisis threatening academic integrity and solutions for responsible AI use in research.

AI in Academic Publishing: 22% of Papers Use AI & 1000+ Predatory Journals Exposed

<p>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.</p>

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<p style="margin-left:0px;">With the rise of Generative AI (GenAI) tools, researchers now have access to assistants that can refine text, rephrase sentences, and even suggest new ideas. But what happens when AI’s role in the writing process becomes so significant that it blurs the line between assistance and authorship?</p><p style="margin-left:0px;">Without proper oversight, AI can unknowingly take on the role of a “co-author” by influencing or altering the content and key arguments of a <a href="https://www.enago.com/submission-preparation.htm"><u>research paper</u></a>, without any formal acknowledgement. The <a href="https://retractionwatch.com/2025/02/10/as-springer-nature-journal-clears-ai-papers-one-universitys-retractions-rise-drastically/"><u>recent surge</u></a> in AI-generated manuscripts and subsequent retractions in Neurosurgical Review highlights the evolving challenges in research integrity. Furthermore, at least <a href="https://www.aapsnewsmagazine.org/aapsnewsmagazine/articles/new-page3/feb25/meetings-feb25"><u>10% of scientific abstracts</u></a> are believed to likely undergo review by an LLM, based on linguistic analysis of overused words like ‘delve.’ This highlights the growing influence of AI in academic writing.</p><p style="margin-left:0px;">Table of Contents</p><ul><li><a href="https://www.enago.com/academy/ai-unintentional-co-author-ethical-risks/#How_AI_Can_Unknowingly_Become_a_Co-Author"><u>How AI Can Unknowingly Become a Co-Author</u></a></li><li><a href="https://www.enago.com/academy/ai-unintentional-co-author-ethical-risks/#Ethical_Implications"><u>Ethical Implications</u></a></li><li><a href="https://www.enago.com/academy/ai-unintentional-co-author-ethical-risks/#Preventing_AI_from_Unknowingly_Becoming_a_Co-Author"><u>Preventing AI from Unknowingly Becoming a Co-Author</u></a></li><li><a href="https://www.enago.com/academy/ai-unintentional-co-author-ethical-risks/#Additional_References"><u>Additional References:</u></a></li></ul><h2>How AI Can Unknowingly Become a Co-Author</h2><figure class="image"><img src="https://www.enago.com/academy/wp-content/uploads/2025/04/Medium.png" alt="How AI becomes a co-author unintentionally"></figure><h3>1. Generative AI Tools Rewriting or Altering Content</h3><p style="margin-left:0px;">Generative AI tools (like ChatGPT or others) and LLMs can do more than just <a href="https://www.trinka.ai/grammar-checker/?utm_source=academy&amp;utm_medium=referral&amp;utm_campaign=article"><u>fix grammar</u></a>. The tool might:</p><ul><li>Generate or rephrase key arguments or conclusions in ways that change their original meaning.</li><li>Paraphrase original findings or suggest edits that affects scientific accuracy.</li><li>Generate new insights or edits weren’t a part of the researcher’s initial thought process.</li></ul><p style="margin-left:0px;">If researchers rely too much on AI’s output without critically reviewing it, they may end up incorporating AI-generated content as their own. This gives AI an uncredited and significant intellectual ownership of portions of the paper.</p><h3>2. Lack of Transparency and Acknowledgment of AI’s Role</h3><p style="margin-left:0px;">Currently, AI and/or LLMs cannot be listed as an author, since it doesn’t take accountability or responsibility for its contributions. However, when researchers fail to disclose AI’s involvement, it might appear that AI played a bigger role than intended. This can happen when:</p><ul><li>AI-generated text is included without proper acknowledgment (such as in the “Methods” section or other areas).</li><li>The line between human-written and AI-generated content becomes unclear.</li><li>Researchers unknowingly rely on AI’s suggestions or edits, allowing it structure the paper, without acknowledging its role.</li></ul><p style="margin-left:0px;">This could lead to ethical concerns about academic integrity, as the AI is indirectly taking part in the research process without being properly credited.</p><h3>3. Unintended Creation of New Ideas</h3><p style="margin-left:0px;">Beyond editing, LLMs can suggest alternative hypotheses, interpretations, or conclusions. If a researcher adopts these suggestions without careful evaluation, the LLM is effectively creating new intellectual contributions, a role traditionally reserved for human authors.</p><p style="margin-left:0px;">For example, AI may suggest changes that alter the hypothesis or offer new ways to interpret data. If a researcher then incorporates these changes into their final manuscript without evaluating their validity or accuracy, the AI’s output could become a central intellectual contribution—effectively making it a “ghost contributor.”</p><h3>4. Influencing the Final Version Without Accountability</h3><p style="margin-left:0px;">As the use of AI grows, there may be instances where researchers rely so heavily on AI tools that the final version of the paper is more reflective of the AI’s suggestions than the researcher’s original thoughts. This can happen if:</p><ul><li>AI makes substantial structural changes (e.g., reorganizing paragraphs, rewording sections, or altering argument flow).</li><li>Large portions of text are rewritten by AI with minimal human intervention.</li><li>The researcher does not critically assess the changes or its impact</li></ul><p style="margin-left:0px;">At this point, AI isn’t just assisting, it’s shaping the paper in ways that could be seen as authorship-level contributions.</p><h2>Ethical Implications</h2><p style="margin-left:0px;">Academic authorship comes with responsibility. Authors must stand by their work, ensure its accuracy, and be accountable for ethical concerns like plagiarism or misrepresentation. AI, however:</p><figure class="image"><img src="https://www.enago.com/academy/wp-content/uploads/2025/06/Medium-1.png" alt="What AI cannot do"></figure><p style="margin-left:0px;">In long run, the use of AI-generated text without proper disclosure can lead to more retractions. For <a href="https://www.researchprofessionalnews.com/rr-news-europe-innovation-2024-10-ai-scientific-publishing-is-not-prepared-for-this/"><u>instance</u></a>, of more than 10,000 papers retracted in 2023, almost 100 papers were seen to be written using GenAI. <a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC10828852/"><u>Several publishers</u></a> have emphasized that AI should not be considered a co-author because it lacks accountability for the research, and human authors must take full responsibility for the content. They should also ensure full disclosure of AI use in academic papers, specifically for AI-assisted <a href="https://www.enago.com/copy-editing"><u>copy editing</u></a>.</p><h2>Preventing AI from Unknowingly Becoming a Co-Author</h2><p style="margin-left:0px;">To maintain research integrity, researchers should take the following precautions:</p><ol><li><strong>Transparent Declaration</strong>: Researchers must disclose the use of AI tools in the Methods or relevant sections of their paper, as outlined by the ethical guidelines.</li><li><strong>Critical Evaluation</strong>: Researchers should critically assess AI-generated content before accepting it, ensuring that it does not alter the original research’s meaning.</li><li><strong>Maintain Human Control</strong>: AI should only be used structural editing and text clarification upon proper evaluation of the generated output.</li><li><strong>Authorship Guidelines</strong>: Clear guidelines should be followed, ensuring that only human contributors who are accountable for the research are listed as authors. AI tools should be acknowledged for their technical assistance, but not as co-authors.</li><li><strong>Ownership of Ideas</strong>: Researchers should maintain ownership of the ideas, analysis, and intellectual contributions in their paper, using AI only to support their work without substituting or overshadowing it.</li></ol><p style="margin-left:0px;">AI is undoubtedly a powerful asset in academic writing, but its role must be carefully managed. As we foresee the future, journals and institutions are expected to update their policies to define AI’s acceptable level of involvement and mandate explicit declarations in research papers. While disclosure norms are still evolving, researchers are encouraged to establish transparency, critical evaluation, and clear authorship practices to ensure that AI remains a tool—not an unintended co-author.</p><h2>Additional References:</h2><p style="margin-left:0px;"><a href="https://publicationethics.org/guidance/cope-position/authorship-and-ai-tools"><u>https://publicationethics.org/guidance/cope-position/authorship-and-ai-tools</u></a></p><p style="margin-left:0px;"><a href="https://link.springer.com/journal/10462/submission-guidelines?srsltid=AfmBOophhMQb-im36jZ7qRIW6VyvJgBN7m8wEqoxnQYqYOXhWv1hS6R4"><u>https://link.springer.com/journal/10462/submission-guidelines?srsltid=AfmBOophhMQb-im36jZ7qRIW6VyvJgBN7m8wEqoxnQYqYOXhWv1hS6R4</u></a></p><p style="margin-left:0px;"><a href="https://www.elsevier.com/about/policies-and-standards/the-use-of-generative-ai-and-ai-assisted-technologies-in-writing-for-elsevier"><u>https://www.elsevier.com/about/policies-and-standards/the-use-of-generative-ai-and-ai-assisted-technologies-in-writing-for-elsevier</u></a></p><p style="margin-left:0px;"><a href="https://pmc.ncbi.nlm.nih.gov/articles/PMC10828852/"><u>https://pmc.ncbi.nlm.nih.gov/articles/PMC10828852/</u></a></p>

Is AI Becoming an Unintentional Co-Author? Understanding the Risks and Ethical Concerns

What happens when AI’s role in the writing process becomes so significant that it blurs the line between assistance and authorship? Read now!

AI as Unintentional Co-Author: Risks Ethics | Responsible AI

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