Large Language Models Across the Lifecycle of Scholarly Publishing

A special issue of Publications (ISSN 2304-6775).

Deadline for manuscript submissions: 31 October 2026 | Viewed by 1740

Special Issue Editor

Department of Library and Information Science, Keimyung University, 1095 Dalgubeoldaero, Dalseo-Gu, Daegu 42601, Republic of Korea
Interests: scientometrics; scholarly communications; social sciences
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Special Issue Information

Dear Colleagues,

This Special Issue examines how large language models are being integrated into the preparation, review and editorial handling of scholarly manuscripts. Rather than treating large language models solely as tools of authorship, the focus is on their broader influence on writing practices, peer review and editorial judgment within contemporary journal publishing.

As generative artificial intelligence becomes embedded across the manuscript lifecycle, the central challenge has shifted from simple questions of authorship attribution to more complex issues of disclosure, acceptable use and responsibility. Most journals now state that authors remain fully accountable for content produced with AI assistance, yet practical questions remain unresolved. These include how the extent of large language models use should be disclosed, which uses are considered acceptable or unacceptable and how editors and reviewers should respond when manuscripts or review reports exhibit strong indicators of artificial intelligence-generated language. This Special Issue invites contributions that engage directly with these unresolved editorial and ethical tensions.

Key Topics and Areas of Interest

Large Language Models in Manuscript Preparation

  • Artificial intelligence-assisted drafting, language editing and structural revision of manuscripts
  • Distinctions between acceptable support uses and disallowed forms of content generation
  • Effects of large language models use on writing quality, rhetorical structure and disciplinary voice

LLMs in Peer Review and Editorial Evaluation

  • Reviewer use of large language models in manuscript assessment and report writing
  • Editorial responses to manuscripts and reviews that exhibit artificial intelligence-dominant stylistic patterns
  • Limits of detection, interpretation of ‘artificial intelligence-like’ writing and implications for editorial trust

Integrity, Disclosure and Governance

  • Disclosure practices and reporting standards for large language model use beyond authorship attribution
  • Accountability frameworks when artificial intelligence-assisted text shapes scholarly arguments or evaluations
  • Journal policies addressing permitted and prohibited uses of large language models in manuscripts and reviews

Submission Guidelines

Both empirical and conceptual studies are welcome, including:

  • Qualitative analyses of author, reviewer and editor decision-making
  • Quantitative and text analytical studies of artificial intelligence-influenced manuscripts and peer review reports
  • Policy and governance-oriented contributions relevant to journal editors and publishers

Submissions should offer theoretically informed and empirically grounded insights into how large language models are redefining responsibility, disclosure and judgment in scholarly publishing.

Dr. Eungi Kim
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Publications is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (2 papers)

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24 pages, 3356 KB  
Article
The Attention Mismatch: Mapping the Structural Academic Governance Deficit in the Age of Generative AI
by Zhenning Guo, Haoran Mao and Fang Zhang
Publications 2026, 14(2), 27; https://doi.org/10.3390/publications14020027 - 17 Apr 2026
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Abstract
With the rapid advancement in Generative Artificial Intelligence (GenAI), AI-generated content (AIGC) lacking human cognitive oversight is increasingly permeating open web environments and academic communication systems. This study integrates longitudinal retraction data (Retraction Watch Database, 1990–2026), web-scale analyses of AI-content penetration (Common Crawl, [...] Read more.
With the rapid advancement in Generative Artificial Intelligence (GenAI), AI-generated content (AIGC) lacking human cognitive oversight is increasingly permeating open web environments and academic communication systems. This study integrates longitudinal retraction data (Retraction Watch Database, 1990–2026), web-scale analyses of AI-content penetration (Common Crawl, 2013–2026), and bibliometric mapping of governance scholarship (Web of Science Core Collection, Scopus, Google Scholar, 2020–2026) to diagnose the cross-level misalignment between synthetic-content diffusion, AI-related misconduct pressure, and governance attention. On this basis, it proposes a Normalized Coverage Index (NCI) to measure the relative relationship between scholarly attention to AI-related academic misconduct governance and the level of misconduct pressure observed through retraction data across disciplines. The results reveal pronounced asymmetries at the disciplinary level. Fields such as chemistry (0.04), physics, mathematics & statistics (0.11), and life sciences & biology (0.34) exhibit clear governance gaps, whereas Education shows a comparatively excessive level of attention (NCI = 29.26). Since 2022, AIGC has expanded rapidly across open web corpora, accompanied by a sharp rise in AI-related retractions, which also exhibit a longer detection lag than traditional forms of misconduct (2.77 years vs. 1.91 years). Although the volume of academic governance-related research has grown rapidly, its proportion within the broader body of AI-related research has declined, suggesting that scholarly attention to governance has not kept pace with technological diffusion. Consequently, a structural misalignment in governance—closely tied to the allocation of attention—has emerged within the academic system in the era of GenAI. This misalignment may pose potential risks to the robustness of the knowledge production system. Addressing it requires rebuilding epistemic infrastructure through provenance transparency, auditable workflows, and governance-aware seed corpora aligned with empirically concentrated risks. Full article
(This article belongs to the Special Issue Large Language Models Across the Lifecycle of Scholarly Publishing)
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When AI Writes the Letters: Recognizing Synthetic Authorship Patterns in Medical Publishing
by Elise Lupon and Grégoire Micicoi
Publications 2026, 14(2), 21; https://doi.org/10.3390/publications14020021 - 25 Mar 2026
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Abstract
The rapid integration of generative artificial intelligence into scientific publishing is reshaping how academic text can be produced, revised, and scaled. While transparent and limited use of AI for language support may be acceptable, a new structural vulnerability may be emerging in medical [...] Read more.
The rapid integration of generative artificial intelligence into scientific publishing is reshaping how academic text can be produced, revised, and scaled. While transparent and limited use of AI for language support may be acceptable, a new structural vulnerability may be emerging in medical publishing: the large-scale production of short, plausible, and weakly individualized correspondence across multiple specialties. In this viewpoint, we describe and conceptualize a pattern that may be termed synthetic authorship, defined not as undisclosed AI use alone, but as a reproducible mode of scholarly output structurally facilitated by automation. We focus particularly on letters to the editor, a format that combines brevity, rapid editorial handling, and formal indexation, and may therefore be especially exposed to this phenomenon. Based on recurring patterns observed in PubMed-indexed literature, including unusually high publication velocity, abrupt thematic dispersion, and stylistic uniformity across unrelated domains, we argue that such outputs may challenge the authenticity, epistemic value, and editorial function of scientific correspondence. We do not present empirical proof of misconduct, but rather outline a conceptual framework for understanding this emerging risk and propose proportionate editorial safeguards, including cross-domain pattern detection and contextual assessment of authorship coherence. As AI lowers the threshold for generating domain-plausible commentary at scale, scientific publishing must adapt its integrity frameworks accordingly. In this context, vigilance toward synthetic authorship may become an essential component of editorial responsibility and post-publication quality control. Full article
(This article belongs to the Special Issue Large Language Models Across the Lifecycle of Scholarly Publishing)
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