Peer review is the bedrock of academic publishing — an essential filter through which science attains its rigor and respectability. Scholar-volunteers scrutinize methodologies, authenticate findings, critique analysis, and ensure ethical standards — usually without compensation. Their expertise lends credibility to research and safeguards the integrity of scholarly literature.
Initially, the arrival of generative AI models triggered alarm among publishers and academics. Common fears emerged:
- Authenticity & Originality: Is the work genuinely researcher-driven?
- Copyright & Plagiarism: Was text or data improperly reproduced?
- Validity: Are findings original and reproducible—or the product of “papermills”?
The reaction was swift: some journals outright banned AI-written manuscripts, while others demanded strict disclosures and set boundaries around AI usage. In parallel, a market of AI-detection tools emerged to flag AI-generated text. These efforts were all legitimate attempts by the research community to grapple with technological disruption—and protect the foundations of scholarly output.
Fast forward, and a new consensus has begun to form. More publishers are actively embracing AI-integrated tools—when used responsibly and transparently. From ethics guidelines to automated submission checks, technology is being woven strategically into publishing workflows.
AI’s role in peer review brings a deeper, more difficult challenge. The volume of article submissions continues to rise, driven by funding pressures, career benchmarks, and institutional expectations. Yet the system still relies on an overstretched pool of reviewers — most of whom receive no compensation for their time or expertise.
This imbalance has created growing pressure on both sides:publishers eager to keep up with output expectations, and researchers struggling to meet review demands. Against this backdrop, AI becomes a tempting shortcut — not just for Authors, but for Reviewers.
But here lies the core concern: the legitimacy of peer review depends on papers being evaluated by true subject-matter experts — not by automated systems. If reviewers begin outsourcing their judgment to AI, we lose more than efficiency; we lose accountability.
How do we know a paper has truly been peer-reviewed — by a human expert — if the"review" was delegated to a machine?
This is not an abstract risk. AI tools now promise to summarize manuscripts, flag weaknesses, even assess novelty. But a peer reviewer’s role is not just mechanical assessment—it is interpretative, contextual, and anchored in lived expertise. A review run through AI cannot replicate that. And as these tools grow more powerful and accessible, so does the danger that human responsibility gets diluted.
In this new landscape, the role of Editors and editorial teams becomes even more critical. They have always been the final gatekeepers of quality and judgment — filtering noise from insight, and maintaining the identity of a journal.
Commercial publishers, particularly in the case of proprietary journals (as distinct from society-owned or jointly managed titles), have in recent years shifted toward a more operational editorial model. While many of the appointed editors are academically accomplished, they may be selected not just for vision or independence but for their willingness to align with commercial priorities.
Editorial decision-making is often shaped — sometimes subtly — by performance indicators: how many submissions were rejected, how many eventually got published elsewhere, how fast the decision turnaround was. Editors are encouraged not to reject outright, but to "work with authors" toward eventual acceptance — effectively shifting from curator to collaborator.
But is that the Editor’s role? To validate quality — or to shepherd manuscripts toward publishability?
This quiet tension is rarely discussed publicly, but widely understood in publishing circles. The challenge is not about resisting change or rejecting technology — but about protecting the line between supporting science and optimizing throughput.
And here lies a surprising twist. In facing the AI dilemma, Publishers might find their strongest argument for their role. Editorial oversight — when rigorous and independent — adds genuine value. It’s this value that justifies Article Processing Charges (APCs), subscription fees, and trust in the brand of a journal.
In recent years, that value was under pressure — especially in Open Access publishing. Critics questioned whether Publishers, given that researchers provide both content and certification, were still earning their keep. But the "AI moment" offers a chance to push back: real Editorial scrutiny, with human intelligence at its core, is not replaceable.
The AI revolution has come for publishing — and we’ve adapted fast. But the crucible arrives with peer review. This is where the limits of automation become visible — and where human judgment becomes non-negotiable. As long as peer review remains expert-centric, supported by strong editorial leadership, trust in academic publishing can endure.
Perhaps, in fact, the pressure from AI will do something unexpected: it will remind us of the irreplaceable role of Editors — not just as workflow managers, but as stewards of quality and integrity. The editorial board may yet reclaim its place as the center of a journal’s identity.
This moment calls for a renewed focus on strong, independent editorial leadership — Editor-in-Chiefs and Editorial Boards that guide journals with integrity and conviction. Their role has always mattered. Now, it matters even more.
