Artificial intelligence is beginning to reshape almost every stage of academic publishing. From manuscript screening and plagiarism detection to language improvement, reviewer selection, statistical checking, and editorial decision support, AI tools are increasingly becoming part of scholarly communication. This raises an important question: Will AI fundamentally transform peer review and journal editing, or will it remain only a supporting tool?
The most likely future is not one in which editors and reviewers disappear. Instead, academic publishing is moving toward a hybrid model in which artificial intelligence performs repetitive, technical, and pattern-recognition tasks while human experts remain responsible for scientific judgment, ethical evaluation, originality, and final editorial decisions.
AI Is Already Entering the Publishing Workflow
Journal publishing involves a long sequence of activities. A manuscript is submitted, checked for completeness, screened for plagiarism, evaluated for suitability, sent to reviewers, revised by authors, reassessed, copyedited, formatted, and finally published.
Many of these stages involve tasks that can be partially automated.
AI-assisted systems can already help identify missing sections, inconsistent references, formatting problems, possible plagiarism, duplicated images, unusual citation patterns, and incomplete reporting. Publishers may also use automated systems to classify manuscripts according to subject area or to identify potential reviewers based on publication records.
For editors managing hundreds or thousands of submissions, such tools can reduce administrative workload and help identify problems earlier in the process.
AI-Assisted Manuscript Screening
One of the areas most likely to change rapidly is initial editorial screening.
Editors commonly receive manuscripts that are outside the journal's scope, poorly formatted, incomplete, methodologically weak, or inconsistent with submission guidelines. Checking these issues manually takes considerable time.
AI systems can assist by examining whether a manuscript contains required components such as an abstract, methodology, ethics statement, data availability statement, references, tables, and figures.
They may also flag language that appears inconsistent, identify possible citation errors, or detect similarities with previously published material.
This could allow editors to spend more time evaluating scientific relevance and less time checking routine technical requirements.
However, automated screening should not become a substitute for editorial judgment. An unconventional but valuable paper could be incorrectly classified by an automated system, while a technically polished paper may still contain weak science.
Can AI Improve Reviewer Selection?
Finding qualified reviewers is one of the most difficult tasks in scholarly publishing.
Editors must identify researchers with appropriate expertise while avoiding conflicts of interest. Many invited reviewers decline because of workload, making the process slow.
AI-supported reviewer recommendation systems could help by analysing publication databases, research topics, keywords, citation networks, and areas of expertise.
Such systems may generate a list of possible reviewers within seconds.
This could significantly improve efficiency, especially for multidisciplinary manuscripts.
Nevertheless, reviewer selection cannot rely solely on algorithms. A system may recommend researchers who recently collaborated with the authors, have institutional conflicts, or have relationships that are not visible from publication records.
Human editorial oversight remains essential.
Will AI Become a Peer Reviewer?
AI can already generate comments on research manuscripts. It can summarise articles, identify unclear statements, suggest methodological questions, detect missing explanations, and highlight inconsistent terminology.
These capabilities may make AI useful as a review assistant.
For example, a reviewer could use an approved AI system to help check whether reporting guidelines have been followed or whether a statistical description is internally consistent.
However, peer review involves much more than identifying textual problems.
A good reviewer evaluates whether the research question is important, whether the methodology is appropriate, whether the conclusions are justified, whether the work contributes something new, and whether the interpretation is reasonable within the broader field.
These judgments depend on disciplinary knowledge, experience, context, and scientific reasoning.
AI may support peer reviewers, but giving an automated system sole authority to recommend acceptance or rejection would raise serious concerns about transparency, accountability, and fairness.
Confidentiality Is a Major Concern
Unpublished manuscripts are confidential documents.
Reviewers and editors often receive research findings months or even years before public release. Some papers include commercially sensitive information, new inventions, patent-related material, medical information, or unpublished datasets.
Uploading such manuscripts to general-purpose AI systems without permission can therefore create confidentiality and data-governance risks.
Publishers and journals will need clear policies explaining whether reviewers may use AI tools and, if so, which systems are approved.
Secure publisher-controlled AI environments may become more common because they can provide useful automated assistance without transferring confidential manuscripts to external platforms.
AI and Research Integrity Detection
AI may have an especially important role in research-integrity screening.
Academic publishers face increasing problems involving paper mills, fabricated data, manipulated images, inappropriate citations, duplicated publications, and fraudulent authorship.
Automated systems can examine patterns that would be difficult for an editor to detect manually.
For example, AI may help identify repeated textual structures across suspicious papers, manipulated figures, improbable statistical patterns, unusual citation networks, or inconsistencies between tables and written results.
This could strengthen research integrity.
At the same time, integrity systems must avoid treating automated flags as proof of misconduct.
A suspicious pattern is an indication that human investigation may be necessary, not necessarily evidence that an author has acted dishonestly.
Journal Editing Will Become More Automated
Editorial production is another area where AI is likely to have a major impact.
After acceptance, manuscripts traditionally undergo copyediting, proofreading, reference checking, formatting, metadata preparation, and typesetting.
AI can assist with many of these tasks.
It may identify grammar problems, standardise terminology, detect inconsistencies in abbreviations, check references, generate summaries, prepare keywords, and help convert manuscripts into publisher-specific formats.
This could accelerate publication and reduce production costs.
Editors may increasingly focus on higher-level tasks such as ensuring clarity, consistency, scientific accuracy, and adherence to ethical standards.
AI is therefore likely to change the role of journal editors rather than eliminate it.
The Risk of Automated Bias
AI systems learn from existing data. If historical publishing practices contain biases, automated systems may reproduce them.
For example, an AI system trained on previous editorial decisions could potentially favour certain writing styles, institutions, geographic regions, or established research topics.
This would be particularly concerning if automated recommendations influenced editorial rejection without sufficient human review.
Publishers will therefore need to evaluate AI tools for fairness, explainability, and consistency.
Authors should also have reasonable opportunities to challenge decisions when automated systems play a meaningful role.
Transparency about AI use will become an important element of editorial governance.
Will AI Reduce Peer-Review Delays?
Long peer-review timelines are a major frustration in academic publishing.
AI cannot solve every cause of delay, but it may improve efficiency.
Automated technical checks can shorten the period between submission and editorial assessment. Reviewer recommendation systems may help editors identify suitable experts more quickly. AI-assisted reminders and workflow management may improve communication.
Reviewers themselves may use approved tools to organise notes or check technical details.
If implemented responsibly, these improvements could reduce unnecessary delays without sacrificing review quality.
However, the biggest limitation will remain the availability of qualified human reviewers. AI cannot fully replace the expert judgment needed to evaluate original research.
New Responsibilities for Authors
As journals adopt more AI-based screening, authors will also need to become more careful.
Manuscripts may increasingly be checked automatically for citation consistency, data transparency, ethical declarations, reporting standards, image integrity, and possible AI-generated content.
Authors should therefore maintain accurate research records and ensure that all claims can be supported.
If generative AI has been used substantially during manuscript preparation, authors should follow the disclosure requirements of the journal or publisher.
Transparency will become increasingly important because publishing systems themselves will be more technologically sophisticated.
The Human Editor Will Remain Central
Despite rapid technological progress, journal editors perform roles that are difficult to automate fully.
Editors decide whether a manuscript is relevant to their readership, whether a controversial interpretation deserves publication, whether reviewer comments are reasonable, and whether methodological weaknesses are serious enough to affect conclusions.
They also handle ethical disputes, authorship conflicts, appeals, corrections, and retractions.
These responsibilities require judgment and accountability.
AI can provide information and recommendations, but someone must ultimately take responsibility for the editorial decision.
For this reason, human editors are likely to remain central to credible scholarly publishing.
A Hybrid Future for Peer Review
The future of peer review will probably be characterised by collaboration between people and machines.
AI may perform initial technical screening, recommend reviewers, assist with reporting checks, identify potential integrity concerns, and support language editing.
Human reviewers will continue to evaluate scientific significance, methodology, interpretation, originality, and disciplinary contribution.
Editors will remain responsible for balancing reviewer comments and making publication decisions.
The strongest publishing systems may therefore be those that combine automated efficiency with human intellectual oversight.
Conclusion
Artificial intelligence is likely to transform academic publishing significantly, but transformation does not necessarily mean replacement.
AI can help journals manage increasing submission volumes, improve technical screening, identify possible research-integrity issues, assist reviewer selection, support copyediting, and accelerate publication workflows.
At the same time, peer review and journal editing involve scientific judgment, ethical responsibility, confidentiality, and accountability that cannot simply be transferred to automated systems.
The future of academic publishing will therefore depend on how carefully AI is integrated.
Publishers should establish transparent policies, protect confidential manuscripts, evaluate automated systems for bias, and ensure meaningful human oversight. Reviewers should use AI only within permitted and secure frameworks. Authors should remain responsible for the accuracy and integrity of their work.
AI may become one of the most powerful tools ever introduced into scholarly publishing, but the credibility of science will continue to depend on human expertise, transparent processes, responsible editorial judgment, and trust in the scholarly record.
