Recognising Peer Review: Human Expertise, Reviewer Credit and the Role of AI

Recognising Peer Review: Human Expertise, Reviewer Credit and the Role of AI

Peer review is one of the defining features of modern academic and scientific life. Before an article appears in a journal or a monograph is accepted by a press, experts in the field read the work, evaluate its methods and arguments, and recommend revisions. This process is intended to protect quality, identify weaknesses and ensure that published research meets disciplinary standards. For many researchers, the time invested in reviewing is considerable; it is not unusual for a single thorough review to take several hours or even days.

Despite this effort, peer reviewing rarely receives the same recognition as authorship. Journal articles, books and conference papers can be listed on a curriculum vitae, counted in promotion exercises and used to demonstrate impact. Pre-publication reviews, by contrast, are often confidential and largely invisible. The absence of formal credit means that much of the intellectual labour that keeps the scholarly system functioning goes unacknowledged. In recent years, however, new tools and platforms have sought to change this situation by making peer-review work more visible and measurable.

1. Why Peer Review Matters

Peer review is more than a routine administrative step; it is central to the trust that scholars and the public place in published research. Reviewers assess whether a study’s design is sound, whether the data supports the conclusions, whether the literature has been represented fairly and whether the argument is coherent. They recommend revisions, point out omissions and help authors improve clarity. In many cases, reviews significantly transform manuscripts, turning competent drafts into high-quality publications.

Because the process is typically anonymous, reviewers do this work without public credit. In double-blind systems, neither author nor reviewer knows the other’s identity. In single-blind systems, authors are known to reviewers but not vice versa. These arrangements protect impartiality but also contribute to the invisibility of the reviewer’s contribution. As a result, when a scholar lists achievements, months or years of review work may not appear anywhere, even though this activity has supported journals, colleagues and the broader discipline.

2. Recording and Recognising Reviewing Activity

To address this gap, several services have emerged that allow reviewers to record and showcase their peer-review contributions. These platforms create verified profiles where reviewers can list the journals they have reviewed for, the number of reviews they have completed and, in some cases, brief descriptions of the types of manuscripts reviewed. The aim is to turn peer reviewing into a visible research output that can be cited alongside publications and other measures of academic engagement.

Typically, reviewers can add a completed review by uploading confirmation emails from journals, forwarding editorial acknowledgements or using automated integrations with participating publishers. The platform then verifies that the review took place. Reviewers can choose whether the content of their review remains private, anonymised or, where journal policies permit, openly accessible. In this way, the confidentiality of the review process is preserved, but the fact of having completed the review can still be recognised.

Publicly visible metrics—such as the number of reviews performed, the range of journals served and the time frame—can demonstrate sustained engagement in peer review. For early-career researchers, this evidence can be particularly valuable when applying for jobs or funding, as it indicates trust from editors and integration into scholarly networks.

3. Incentives and the Value of Reviewer Credit

Some platforms go beyond documentation by introducing incentive systems. Reviewers may earn “points” or “merits” for each verified review, for reviewing within specific time frames or for contributing open, post-publication commentary. In some cases, higher levels of activity unlock benefits such as discounts on professional tools, publishing fees or access to research-support services. These incentives recognise that peer review is skilled labour and encourage high-quality, timely contributions.

Editors can also benefit from these systems. Access to reviewer databases—where individuals’ disciplinary areas, review histories and responsiveness are visible—can help editors identify appropriate reviewers more quickly. Rather than relying solely on personal contacts or ad hoc searches, editors can consult profiles to find scholars whose expertise aligns with a manuscript’s topic. This can improve the quality of review panels and reduce turnaround times.

At a systemic level, recognising reviewing activity underscores the principle that peer review is part of a scholar’s professional portfolio, not an invisible obligation. When promotion and hiring committees see documentation of reviewing, they gain a more complete picture of a candidate’s contribution to their field.

4. Challenges and Limitations of Current Recognition Models

While these developments are promising, they also introduce questions. Some scholars worry that quantifying review work could encourage a focus on volume over quality. A list showing “fifty reviews completed” reveals nothing about the depth or usefulness of those reviews. Others are concerned about potential conflicts of interest if reviewers feel pressured to accept invitations primarily to accumulate metrics.

The confidentiality policies of journals must also be respected. Not all publishers permit the public disclosure of reviewer identities or review content. Recognition platforms therefore need flexible privacy settings and close coordination with editorial offices. Reviewers should never upload confidential manuscripts or internal communications; only the fact of having reviewed (and the journal name, if allowed) should be recorded.

Despite these challenges, many academics consider the benefits of recognition to outweigh the risks. Documenting reviewing activity does not change the underlying purpose of peer review but brings long-overdue visibility to a core academic responsibility.

5. The Growing Role of AI in Peer Review

Alongside these developments, the peer-review process is increasingly influenced by artificial intelligence. Publishers already use automated tools to check for plagiarism, image manipulation and statistical anomalies. Some are experimenting with AI systems that screen submissions for basic methodological soundness, identify reporting inconsistencies or flag missing ethical approvals. Others deploy natural language processing tools to help editors sort submissions by topic relevance or detect potential peer-review fraud.

There is a real possibility that AI will eventually undertake more of the routine work currently performed by human reviewers. Algorithms might be used to generate structured summaries of long manuscripts, highlight potential weaknesses in study design or identify overlooked references. For overburdened editorial teams, such tools are attractive: they promise quicker triage and more consistent baseline checks. In high-volume fields, AI could filter out clearly unsuitable submissions before they reach human reviewers, allowing experts to focus on more promising work.

However, AI-based assistance also raises serious concerns. Automated systems are only as reliable as the data on which they are trained. If training data reflects disciplinary biases, those biases may be reproduced or amplified. AI models can misinterpret nuance, struggle with unconventional methods and fail to appreciate context—particularly in qualitative research or emerging fields. A manuscript that challenges dominant paradigms might be incorrectly flagged as “low quality” simply because it does not resemble the majority of previously published work.

There are also ethical and practical questions about confidentiality and security. If a manuscript is processed by third-party AI services, what happens to that text? Does the system store it? Could it inadvertently appear in other outputs or be used to train commercial models? Given current concerns about intellectual property, many journals and institutions are cautious about allowing proprietary AI tools to access unpublished research.

For these reasons, AI should be seen as a potential support for peer review, not a replacement for human judgement. Automated tools may help with consistency checks, reference formatting or basic language screening, but decisions about originality, significance, methodological soundness and ethical acceptability require expert human evaluation. The human reviewer understands context, weighs competing interpretations and recognises subtle contributions that do not fit standard patterns.

In an ideal future, peer review may become a hybrid process: AI systems handle routine mechanical tasks and preliminary screening, while trained reviewers focus on conceptual clarity, methodological rigour and disciplinary relevance. Institutions and publishers will need clear policies to ensure that AI is used transparently, ethically and in ways that support rather than undermine trust in the review process.

6. Practical Advice for Authors and Reviewers

For authors, understanding how peer review works—and how it is changing—has practical implications. Recognising that reviewers volunteer their time can foster a more constructive attitude toward revision requests. Detailed, thoughtful responses to reviewer comments not only improve the manuscript but also demonstrate professionalism and respect for the process. Authors should also consider documenting their own reviewing activities, whether through internal systems, personal records or external platforms, so that this contribution is not lost.

Reviewers, meanwhile, can use recognition platforms to track their work, ensure it is acknowledged and manage requests more efficiently. They should remain alert to how their expertise is represented publicly and make sure that any information they share complies with journal policies. At the same time, reviewers should approach AI tools with caution. While it may be tempting to use generative AI to draft reports quickly, reviewers remain personally responsible for the content of their assessments. Over-reliance on automated phrasing risks misrepresenting their views and may conflict with journal policies that restrict AI-generated text in confidential reviews.

Finally, editors and institutions have a role in fostering a culture that values peer review appropriately. Recognising review work in workload models, promotion criteria and award schemes signals that this labour is not invisible but central to the health of the scholarly ecosystem.

Final Thoughts

Peer review remains one of the most important mechanisms for maintaining quality and trust in academic and scientific publishing. While the process faces pressures—from rising submission volumes, limited reviewer availability and technological change—it also benefits from innovations that make review activity more transparent and recognised. Platforms that verify and showcase reviewing provide one way to acknowledge this essential work, while AI tools promise to assist with selected tasks if used cautiously and ethically.

Ultimately, the value of peer review lies in expert human judgement. No algorithm can fully replace the insight of a knowledgeable scholar who understands disciplinary debates, methodological nuance and the broader significance of a piece of work. As the publishing landscape continues to evolve, the challenge will be to combine new technologies and recognition systems with a renewed respect for the human expertise that lies at the heart of peer review.

For authors preparing manuscripts for peer review, and for reviewers who wish to ensure that their feedback is as clear and constructive as possible, our journal article editing service and manuscript editing service can help refine structure, clarity and academic tone, supporting a smoother and more effective review process.