{"id":48716,"date":"2026-09-11T06:55:00","date_gmt":"2026-09-11T01:25:00","guid":{"rendered":"https:\/\/www.editage.com\/insights\/?p=48716"},"modified":"2026-09-10T22:00:53","modified_gmt":"2026-09-10T16:30:53","slug":"ai-in-peer-review-where-does-its-role-end","status":"publish","type":"post","link":"https:\/\/www.editage.com\/insights\/ai-in-peer-review-where-does-its-role-end","title":{"rendered":"AI in Peer Review – Where Does Its Role End?"},"content":{"rendered":"\n
\n

AI should help editors spend less time on routine tasks and more time exercising the judgment that makes peer review valuable in the first place.<\/p>\n<\/blockquote>\n\n\n\n

As part of the Peer Review Week 2026, we feature the interview of Maryam Sayab<\/strong>, Executive Director, Communications & Global Engagement, Asian Council of Science Editors | Dubai | UAE, Co-Chair | Peer Review Week Committee. The views she shares here are relevant to discussions today around where role of AI vs human in the peer review process. She is Interviewed by Dr. Radhika Vaishnav<\/strong>, Managing Editor, Editage Insights.<\/p>\n\n\n\n

Question 1: The Human Element<\/strong><\/strong><\/h2>\n\n\n\n

AI tools are increasingly being used to assist with manuscript screening, language assessment, and even reviewer recommendations. In your view, what aspects of peer review should always remain a human responsibility, regardless of how advanced AI becomes?<\/em><\/p>\n\n\n\n

When I visit editorial offices or speak with editors through Asian Council of Science Editor\u2019s capacity building programs, one concern comes up repeatedly. Interestingly, it isn’t “Should we use AI?” It’s “How much should we rely on it?” That tells me we’ve already moved past debating whether AI has a place in peer review. The real question now is where its role should end.<\/p>\n\n\n\n

I think AI is remarkably good at reducing the editorial burden. It can identify missing reporting elements, highlight inconsistencies, summarize manuscripts, or help editors prioritize submissions. Those are valuable contributions, especially when journals are dealing with increasing submission volumes and limited editorial resources.<\/p>\n\n\n\n

The danger is that we start measuring the success of peer review by how quickly we process manuscripts rather than by how well we evaluate them. Sometimes an editor reads a paper that is imperfectly written yet recognizes an idea that could genuinely influence the field. Sometimes reviewers disagree completely, and the editor has to decide which concerns matter most. Those are the moments where technology reaches its limits. They depend on scientific judgment, disciplinary experience, and ultimately someone willing to stand behind the decision.<\/p>\n\n\n\n

For me, AI should help editors spend less time on routine tasks and more time exercising the judgment that makes peer review valuable in the first place.<\/p>\n\n\n\n

Question 2: Beyond Technical Evaluation<\/strong><\/em><\/strong><\/h2>\n\n\n\n

A strong peer review often involves judging novelty, significance, and the potential impact of a study on the field. Do you think AI can ever meaningfully contribute to these assessments, or are they inherently human judgments? Why\/why not?<\/em><\/p>\n\n\n\n

I often hear people ask whether AI will eventually judge novelty. I’m not sure that’s the right question. Perhaps we should first ask what novelty actually looks like.<\/p>\n\n\n\n

If novelty simply meant finding something different from existing literature, AI might become very good at it. But truly significant research often doesn’t announce itself so clearly. It may challenge accepted thinking, combine ideas from different disciplines, or raise questions no one was asking before. Some of the most influential papers weren’t immediately recognized because they looked unconventional at first. Good editors know that not every important paper arrives looking important. Those qualities are difficult to recognize because they require perspective rather than comparison.<\/p>\n\n\n\n

I remember speaking with an editor who said one reviewer dismissed a manuscript as incremental, while another described it as potentially field-changing. They had read the same paper, yet interpreted its contribution very differently. That wasn’t because one reviewer had better information. It was because significance is shaped by experience, disciplinary context, and sometimes the willingness to recognize an unconventional idea.<\/p>\n\n\n\n

AI can certainly enrich those conversations by bringing relevant evidence to the table. But I still believe the conversation itself belongs to people.<\/p>\n\n\n\n

Question 3: Trust, Bias, and Accountability<\/strong><\/strong><\/h2>\n\n\n\n

Some argue that AI could make peer review more consistent and less biased, while others worry it could introduce new forms of bias and reduce accountability. How do you see the balance between human and AI involvement in maintaining trust in the peer review process?<\/em><\/p>\n\n\n\n

Whenever someone says AI will remove bias from peer review, I usually smile a little because editors know that reality is rarely that simple.<\/p>\n\n\n\n

Bias doesn’t disappear. It changes form. The publishing community has spent decades learning to recognize human bias. We now need to develop the same awareness of automation bias. Human reviewers bring personal experiences, disciplinary preferences, and unconscious assumptions. AI brings the patterns embedded in the data on which it was trained. Neither is automatically neutral.<\/p>\n\n\n\n

What concerns me more isn’t whether AI introduces bias. It’s whether people become less likely to question its recommendations simply because they came from a machine. Editors have always challenged reviewer reports, requested additional opinions, or overturned recommendations when necessary. We should apply exactly the same critical thinking to AI-generated outputs.<\/p>\n\n\n\n

Trust doesn’t come from choosing between humans and AI. It comes from ensuring that every important decision remains transparent, explainable, and ultimately owned by someone willing to stand behind it.<\/p>\n\n\n\n

Question 4: Lessons from Experience<\/strong><\/strong><\/h2>\n\n\n\n

Think about the most valuable peer review feedback you have ever received (or provided)? Could an AI system realistically replicate that kind of contribution?<\/em><\/em><\/p>\n\n\n\n

The reviews I remember most aren’t the ones with the longest lists of corrections. They’re the ones that changed the way the authors thought about their own work.<\/p>\n\n\n\n

One reviewer once wrote something remarkably simple: “I think you’re asking the wrong question.” It wasn’t criticism for the sake of criticism. It was an invitation to rethink the study from a different perspective. The revised manuscript became far stronger, not because more errors were found, but because the conversation shifted.<\/p>\n\n\n\n

That’s what makes great peer review different from technical editing. Good reviewers don’t simply identify weaknesses; they help authors discover possibilities they hadn’t considered.<\/p>\n\n\n\n

AI can already produce useful suggestions, summarize arguments, and even point out methodological concerns. But encouraging intellectual growth requires curiosity, empathy, and an understanding of why researchers ask the questions they do. That’s much harder to automate.<\/p>\n\n\n\n

\n

Technology will probably become invisible. Editorial judgment shouldn’t.<\/p>\n<\/blockquote>\n\n\n\n

Question 5: Looking Ahead<\/strong><\/strong><\/h2>\n\n\n\n

Imagine the peer review process ten years from now. What role would you ideally like AI to play, and what role should remain firmly in the hands of human reviewers and editors?<\/em><\/p>\n\n\n\n

If someone from 2015 walked into today’s editorial office, they’d probably be surprised by how many technologies we now consider routine. Plagiarism detection, reporting checklists, reviewer databases, image screening, and research integrity tools have quietly become part of everyday editorial work. I think AI will follow the same path.<\/p>\n\n\n\n

Ten years from now, I expect editors won’t spend much time talking about AI itself. They’ll simply expect it to assist with reviewer matching, summarize reviewer reports, flag integrity concerns, or identify inconsistencies before peer review even begins.<\/p>\n\n\n\n

Ironically, I think the more capable AI becomes, the more valuable experienced editors become. Not because they’ll do more administrative work, but because they’ll spend more time doing the things technology still struggles with: weighing conflicting opinions, recognizing promising but unconventional research, mentoring reviewers, and making difficult editorial decisions. Technology will probably become invisible. Editorial judgment shouldn’t.<\/p>\n\n\n\n

Question 6: Quick Take<\/strong><\/strong><\/h2>\n\n\n\n

If you had to choose just one quality that peer reviewers must preserve in an AI-assisted future, what would it be and why?<\/em><\/p>\n\n\n\n

 <\/em>If I could preserve just one quality, it would be curiosity.<\/em><\/p>\n\n\n\n

The reviewers who leave a lasting impression aren’t necessarily the toughest or the fastest. They’re the ones who genuinely want to understand the research before judging it. They read with the intention of understanding before judging. They challenge assumptions without dismissing new ideas.<\/p>\n\n\n\n

That kind of curiosity improves manuscripts, supports authors, and sometimes even changes the direction of a study. AI can process enormous amounts of information, but curiosity is what transforms information into insight, criticism into mentorship, and peer review into a meaningful scholarly conversation. I hope we never lose that.<\/p>\n","protected":false},"excerpt":{"rendered":"

AI should help editors spend less time on routine tasks and more time exercising the judgment that makes peer review valuable in the first place. As part of the Peer Review Week 2026, we feature the interview of Maryam Sayab, Executive Director, Communications & Global Engagement, Asian Council of Science Editors | Dubai | UAE, […]<\/p>\n","protected":false},"author":2830,"featured_media":48723,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[5884,6542],"tags":[2923,6544,44],"new_categories":[],"new_tags":[6471,6545,5830,5869],"series":[],"class_list":["post-48716","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-academic-publishing-trends","category-human-validation","tag-ai","tag-human-validation","tag-peer-review","new_tags-ai","new_tags-human-validation","new_tags-peer-review","new_tags-peer-review-week"],"acf":[],"yoast_head":"\nAI in Peer Review - Where Does Its Role End? | Editage Insights<\/title>\n<meta name=\"description\" content=\"The 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