Governing Artificial Intelligence in the Evolving Academic Publishing Ecosystem

Enis Karaarslan; Ömer Aydın

doi:10.57019/jmv.1899774

Governing Artificial Intelligence in the Evolving Academic Publishing Ecosystem

Abstract

As scholarly publishing navigates the rapid integration of artificial intelligence (AI), encompassing generative AI, natural language processing (NLP), large language models (LLMs), and autonomous agents, the editorial ecosystem must adapt to maintain its core integrity. This editorial evaluates the operational advantages of AI, such as enhanced efficiency and automated integrity checks, against systemic risks, including hallucination, bias, and confidentiality breaches. Drawing on recent empirical validations and policy frameworks from the Committee on Publication Ethics (COPE), the International Committee of Medical Journal Editors (ICMJE), and major publishers, we outline a comprehensive editorial policy for the Journal of Metaverse. This policy emphasizes human oversight, transparency, and domain-specific rigor. We also introduce practical implementation mechanisms, including an AI-use disclosure template, auditing protocols, and specific reporting requirements for metaverse research. Furthermore, this editorial explores the strategic use of generative AI for post-publication science communication, demonstrating how automated infographics and multimedia summaries can significantly enhance research visibility and broad accessibility. This document serves as both a functional policy framework and a contribution to the discourse on responsible AI integration in academic publishing.

Keywords

Ethical Statement

This study was conducted in accordance with the principles of scientific research and publication ethics. As a conceptual/literature-based study, it did not involve human participants, animal subjects, or primary data collection; therefore, ethics committee approval was not required. All authors confirm compliance with the ethical standards of the journal, including those stated in the Conflict of Interest declaration.

Thanks

The authors thank the reviewers for their constructive feedback, which helped improve the clarity and quality of the manuscript. Also, we want to thank to the Izmir Academy Association, publisher of the Journal of Metaverse.

References

Raitskaya, L., & Tikhonova, E. (2025). The 2025 Landscape of Generative AI in Scholarly Writing and Publishing: A Scoping Review of Uses and Ethical Approaches. Journal of Language and Education, 11(4), 5-51. https://doi.org/10.17323/jle.2025.29876
Elsevier (2025). Generative AI policies for journals. https://www.elsevier.com/about/policies-and-standards/generative-ai-policies-for-journals
Springer (2025). Authors, editors and peer reviewers supported with launch of new AI tool. Springer Nature. https://group.springernature.com/de/group/media/press-releases/ai-tool-to-help-streamline-integrity-and-ethics-checks/27730892
Anderson, P. (2025). Academic Publishing: Springer Nature Adds another Manuscript Tool. Publishing Perspectives. https://publishingperspectives.com/2025/01/academic-publishing-springer-nature-adds-another-manuscript-processing-tool/
Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J. (2023). GPT detectors are biased against non-native English writers. Patterns, 4(7). https://doi.org/10.1016/j.patter.2023.100779
Hosseini, M., & Resnik, D. B. (2025). Guidance needed for using artificial intelligence to screen journal submissions for misconduct. Research ethics, 21(1), 1-8. https://doi.org/10.1177/17470161241254052
Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International journal of information management, 48, 63-71. https://doi.org/10.1016/j.ijinfomgt.2019.01.021
Elali, F. R., & Rachid, L. N. (2023). AI-generated research paper fabrication and plagiarism in the scientific community. Patterns, 4(3). https://doi.org/10.1016/j.patter.2023.100706

Azamfirei, R., Kudchadkar, S. R., & Fackler, J. (2023). Large language models and the perils of their hallucinations. Critical Care, 27(1), 120. https://doi.org/10.1186/s13054-023-04393-x
Dahl, M., Magesh, V., Suzgun, M., & Ho, D. E. (2024). Large legal fictions: Profiling legal hallucinations in large language models. Journal of Legal Analysis, 16(1), 64-93. https://doi.org/10.1093/jla/laae003
Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng,W., Feng, X., Qin, B., & Liu, T. (2025). A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. ACM Transactions on Information Systems, 43(2), 1-55. https://doi.org/10.1145/3703155
Vo, A., Nguyen, K. N., Taesiri, M. R., Dang, V. T., Nguyen, A. T., & Kim, D. (2025). Vision language models are biased. arXiv preprint arXiv:2505.23941. https://doi.org/10.48550/arXiv.2505.23941
Zhu, B., & Zhang, H. (2025). Debiasing vision-language models for vision tasks: a survey. Frontiers of Computer Science, 19(1), 191321. https://doi.org/10.1007/s11704-024-40051-3
Zielinski, C., Winker, M. A., Aggarwal, R., Ferris, L. E., Heinemann, M., Lapeña, J. F., Pai, S.A., Ing, E., Citrome, L., Alam, M., Voight, M., Habibzadeh, F., & WAME Board. (2024). Chatbots, generative AI, and scholarly manuscripts: WAME recommendations on chatbots and generative artificial intelligence in relation to scholarly publications. Current Medical Research and Opinion, 40(1), 11-13. https://doi.org/10.1080/03007995.2023.2286102
COPE (2023). Authorship and AI tools. COPE (Committee on Publication Ethics). https://publicationethics.org/guidance/cope-position/authorship-and-ai-tools
DiMaggio, P. J., & Powell, W. W. (2000). The iron cage revisited institutional isomorphism and collective rationality in organizational fields. In J. A.C. Baum,& F. Dobbin (Eds.), Economics Meets Sociology in Strategic Management. https://doi.org/10.1016/S0742-3322(00)17011-1
Diakopoulos, N. (2016). Accountability in algorithmic decision making. Communications of the ACM, 59(2), 56-62. https://dx.doi.org/10.1145/2844110
McKenna, N., Li, T., Cheng, L., Hosseini, M., Johnson, M., & Steedman, M. (2023, December). Sources of hallucination by large language models on inference tasks. In Findings of the association for computational linguistics: EMNLP 2023 (pp. 2758-2774). https://doi.org/10.18653/v1/2023.findings-emnlp.182
Xu, Z. (2025). Patterns and purposes: A cross-journal analysis of AI tool usage in academic writing. arXiv preprint arXiv:2502.00632. https://doi.org/10.48550/arXiv.2502.00632
Ayala, O., & Bechard, P. (2024, June). Reducing hallucination in structured outputs via Retrieval-Augmented Generation. In Y. Yang, A. Davani, A. Sil, & A. Kumar (Eds), Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track) (pp. 228–238). https://doi.org/10.18653/v1/2024.naacl-industry.19
Kirchenbauer, J., & Barns, C. (2024). Hallucination reduction in large language models with retrieval-augmented generation using wikipedia knowledge. ArXiv Preprint. https://doi. org/10.31219/osf. io/pv7r5.
Rakin, S., Shibly, M. A. R., Hossain, Z. M., Akbar, M. M., & Khan, Z. (2025, April). Leveraging the Domain Adaptation of Retrieval Augmented Generation Models for Question Answering and for Hallucination Reduction. In International Conference on Information Technology-New Generations (pp. 482-493). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-89063-5_42
Tonmoy, S. M., Zaman, S. M., Jain, V., Rani, A., Rawte, V., Chadha, A., & Das, A. (2024). A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models. arXiv preprint arXiv:2401.01313. https://doi.org/10.48550/arXiv.2401.01313
Alan, A.Y., Karaarslan, E., & Aydin, Ö. (2024). A RAG-based Question Answering System Proposal for Understanding Islam: MufassirQAS LLM. arXiv e-prints, arXiv-2401. https://doi.org/10.48550/arXiv.2401.15378
Alan AY, Karaarslan E, Aydın Ö. (2025). Improving LLM reliability with RAG in religious question-answering: MufassirQAS. Turkish Journal of Engineering, 9(3), 544-559. https://doi.org/10.31127/tuje.1624773.
Goddard, J. (2023). Hallucinations in ChatGPT: a cautionary tale for biomedical researchers. The American journal of medicine, 136(11), 1059-1060. https://doi.org/10.1016/j.amjmed.2023.06.012
Chelli, M., Descamps, J., Lavoué, V., Trojani, C., Azar, M., Deckert, M., ... & Ruetsch-Chelli, C. (2024). Hallucination rates and reference accuracy of ChatGPT and bard for systematic reviews: comparative analysis. Journal of medical Internet research, 26(1), e53164.
Bagaria, A. (2025). INSIGHT: An Interpretable Neural Vision-Language Framework for Reasoning of Generative Artifacts. arXiv preprint arXiv:2511.22351. https://doi.org/10.48550/arXiv.2511.22351
Kachwala, Z., Singh, D., Yang, D., & Menczer, F. (2025). Prefill-Guided Thinking for zero-shot detection of AI-generated images. arXiv preprint arXiv:2506.11031. https://doi.org/10.48550/arXiv.2506.11031
Mahara, A., & Rishe, N. (2026). Methods and trends in detecting AI-generated images: A comprehensive review. Computer Science Review, 60, 100908. https://doi.org/10.1016/j.cosrev.2026.100908
Frangou, S., Volpe, U., & Fiorillo, A. (2025). AI in scientific writing and publishing: A call for critical engagement. European Psychiatry, 68(1), e98. https://doi.org/10.1192/j.eurpsy.2025.10061
Rodriguez, M. A., & Bollen, J. (2008, October). An algorithm to determine peer-reviewers. In Proceedings of the 17th ACM conference on Information and knowledge management (pp. 319-328).
Charlin, L., & Zemel, R. S. (2013). The Toronto paper matching system: An automated paper-reviewer assignment system. In S. Dasgupta & D. McAllester (Eds.), Proceedings of the 30th International Conference on Machine Learning (Vol. 28, pp. 1–9). JMLR Workshop and Conference Proceedings.
Siddique, M. K. B., Rahim, S. A., Rahman, M. M., Lasker, S. P., & Rahim, M. M. U. R. (2025). Artificial intelligence in journal editing: Expanding horizons for scholarly publications - A mixed method study. Health Sciences Quarterly, 5(2), 109–119. https://doi.org/10.26900/hsq.2674
Mitchell, E., Lee, Y., Khazatsky, A., Manning, C. D., & Finn, C. (2023, July). Detectgpt: Zero-shot machine-generated text detection using probability curvature. In International conference on machine learning (pp. 24950-24962). PMLR. https://proceedings.mlr.press/v202/mitchell23a/mitchell23a.pdf
Ghosal, T., Verma, R., Ekbal, A., Saha, S., & Bhattacharyya, P. (2018). An AI aid to the editors. Exploring the possibility of an AI assisted article classification system. arXiv preprint arXiv:1802.01403. https://doi.org/10.48550/arXiv.1802.01403
Fiorillo, L., & Mehta, V. (2024). Accelerating editorial processes in scientific journals: Leveraging AI for rapid manuscript review. Oral Oncology Reports, 10, 100511. https://doi.org/10.1016/j.oor.2024.100511
Sage (2025). Artificial intelligence policy. Publication ethics policies. https://www.sagepub.com/journals/publication-ethics-policies/artificial-intelligence-policy
Naddaf, M. (2026). More than half of researchers now use AI for peer review—often against guidance. Nature, 649(8096), 273-274. https://doi.org/10.1038/d41586-025-04066-5
SAUC (2025). Editorial Policy on the Use of Artificial Intelligence (AI). Street Art & Urban Creativity Scientific Journal. https://visualcompublications.es/SAUC/politicaia
Jiang, S. (2025). Tenure under pressure: Simulating the disruptive effects of AI on academic publishing. arXiv preprint arXiv:2509.16925. https://doi.org/10.48550/arXiv.2509.16925
Jeimp (2024). Guidelines and Recommendations for Authors Using AI in Manuscript Preparation. Journal of European Internal Medicine Professionals. https://jeimp.com/index.php/pub/announcement/view/5
Gewaltig, M-O. (2025). AI Policies in Academic Publishing 2025: Guide & Checklist. Thesify.ai. https://www.thesify.ai/blog/ai-policies-academic-publishing-2025
Giugliano, A. (2025). No AI Assignments Built to Be AI-Resistant. Thesify.ai. https://www.thesify.ai/blog/no-ai-assignments-design
Dergipark (2025). Artificial Intelligence (AI) Policy. Sakarya University Journal of Education. https://dergipark.org.tr/en/pub/suje/page/18417
Hoover, A. (2023). Use of AI Is Seeping Into Academic Journals—and It’s Proving Difficult to Detect. Wired. https://www.wired.com/story/use-of-ai-is-seeping-into-academic-journals-and-its-proving-difficult-to-detect/

Details

Primary Language

English

Subjects

Information Systems (Other) , Artificial Life and Complex Adaptive Systems , Computer Software , Information Technology in Design

Journal Section

Editorial

Authors

Enis Karaarslan
0000-0002-3595-8783
Türkiye

Ömer Aydın ^*
0000-0002-7137-4881
Türkiye

Publication Date

April 5, 2026

Submission Date

February 28, 2026

Acceptance Date

April 5, 2026

Published in Issue

Year 1970 Volume: 0 Number: 6

DOI

https://doi.org/10.57019/jmv.1899774

IZ

https://izlik.org/JA39GS26NC

Cite

RIS / Bibtex

APA

Karaarslan, E., & Aydın, Ö. (2026). Governing Artificial Intelligence in the Evolving Academic Publishing Ecosystem. Journal of Metaverse, 6, 134-143. https://doi.org/10.57019/jmv.1899774