Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{192038,
        author = {Deepanjali Gurung},
        title = {Ai In Academic Publishing: Promise, Peril, And the New Frontier of Integrity Protection},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {12},
        number = {no},
        pages = {1-4},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192038},
        abstract = {Artificial Intelligence (AI) is increasingly embedded across the academic publishing lifecycle, transforming editorial workflows, peer review processes, and the dissemination of scholarly knowledge. From automated manuscript screening and plagiarism detection to grammar enhancement and reviewer selection, AI-driven systems promise substantial gains in efficiency, scalability, and global accessibility. However, the rapid integration of these technologies has coincided with a marked rise in sophisticated publication misconduct, most notably the proliferation of organized paper mills that produce fabricated or manipulated research manuscripts for profit. This convergence presents a complex challenge for publishers, editors, and the wider research community.
This paper critically examines the dual role of AI in academic publishing—both as an enabling technology and as a necessary safeguard for research integrity. I have reviewed the key benefits of AI adoption, including workflow automation, improved discoverability, and reduced editorial burden, alongside the ethical, legal, and operational risks associated with over-reliance on automated tools. Particular attention is given to emerging AI-based systems designed to detect paper mills and other forms of suspicious activity, such as plagiarism and AI-text detectors, provenance analysis, and authorship and citation network models. Evidence from recent pilot implementations and academic studies suggests that these tools can flag a significant proportion of high-risk submissions, yet also exhibit limitations related to false positives, evolving evasion strategies, and algorithmic bias.
The paper argues that no single technological solution can fully address the integrity challenges facing scholarly publishing. Instead, sustainable progress will depend on hybrid editorial models that integrate AI-assisted screening with robust human oversight, transparent disclosure policies, and cross-publisher collaboration. By positioning AI as a complementary tool rather than a replacement for editorial judgment, the academic publishing ecosystem can harness its benefits while safeguarding trust, credibility, and the integrity of the scholarly
record.},
        keywords = {Artificial Intelligence, Academic Publishing, Research Integrity, Paper Mills, Peer Review, Scholarly Communication},
        month = {},
        }