Table of Contents
Background
Artificial Intelligence (AI) is no longer optional—it is becoming a foundational layer in modern digital workflows. In academic publishing, particularly within Open Journal Systems (OJS), editorial processes are often time-consuming, repetitive, and dependent on human consistency.
Editors, reviewers, and authors frequently deal with:
- High submission volumes
- Manual screening and formatting checks
- Inconsistent review quality
- Delays in communication and decision-making
- Difficulty maintaining efficiency with limited editorial resources
- Human error during repetitive administrative checks
- Time-consuming preliminary assessments before peer review
These challenges not only slow down the editorial process, but also affect the author experience and publication timeline. In many journals, editors spend too much time checking technical details and submission requirements instead of focusing on the scientific quality of the manuscript.
AI can help make these workflows faster and more efficient. Its purpose is not to replace editors or reviewers, but to assist them by handling repetitive screening tasks, detecting potential issues earlier, improving consistency, and helping editorial teams work more effectively. With AI support, journals can reduce manual workload while still keeping human evaluation and decision-making as the main part of the process.
To address these challenges, we developed AI-powered tools that are directly integrated into the OJS editorial workflow. These tools are designed to help editors during submission screening, technical checking, communication, and quality control processes. By combining AI assistance with human editorial oversight, journals can improve efficiency, consistency, and publication quality in a more practical and scalable way.
Challenges in Traditional OJS Workflow
Before integrating AI into the editorial process, many journals still rely heavily on manual workflows and human coordination for daily operations. Although OJS already provides a good system for managing submissions, reviews, and editorial communication, many important tasks still require significant manual effort from editors. As submission volumes continue to increase, these traditional workflows often become slower, more repetitive, and harder to manage consistently. This creates several common challenges that can affect editorial efficiency, review quality, and publication timelines.
Below are some of the most common challenges faced in traditional OJS editorial workflows.
1. Manual Initial Screening
The peer review process sits at the heart of academic publishing, serving as the primary quality gate for scientific and scholarly knowledge. Yet despite its critical importance, the workflow behind it remains riddled with inefficiencies that slow down publication timelines, frustrate authors and editors alike, and in some cases compromise the quality of what ultimately gets published. Understanding these pain points is the first step toward addressing them. One of the most immediate bottlenecks occurs right at the start of the process: initial screening.
Before a manuscript can move to the review stage, editors usually need to check it manually first. They need to see whether the topic fits the journal scope, whether the writing quality or format is acceptable, and whether the manuscript follows the basic submission guidelines, and others. For journals with large numbers of submissions, this process can take a lot of time. Editors may need to review hundreds of manuscripts one by one, and each submission requires careful attention before it can move forward. As submission volumes grow, the initial screening stage often becomes one of the main causes of delays in the editorial workflow.
2. Reviewer Selection and Review Quality Issues
The challenge deepens considerably when editors turn to the task of finding appropriate reviewers. Reviewer selection is far more nuanced than simply matching keywords to expertise. It requires editors to identify scholars who are not only knowledgeable in the specific subfield but also available, willing, and free from conflicts of interest. This process is heavily dependent on individual editor experience and personal networks, meaning the quality of reviewer selection can vary significantly from one editor to another, and from one journal to another.
In some cases, the selected reviewer may not have the exact expertise needed to fully evaluate the manuscript. This can result in feedback that is too general, less relevant, or not detailed enough to properly assess the quality of the research.
Even when the reviewer is suitable for the topic, another challenge still remains. Review quality can vary significantly between reviewers. Some reviewers provide detailed comments, constructive suggestions, and clear evaluations, while others may only give very short or limited feedback. This inconsistency can make the revision process more difficult for authors and can also affect the overall quality and fairness of the peer review process.
3. Workflow Delays and Human Dependency
While OJS provides a structured platform for managing editorial workflows, it does not by itself resolve the underlying human and process challenges that slow things down. Editors still navigate the system manually for many critical decisions, and the platform’s built-in communication tools — though more organized than raw email — still depend heavily on timely human responses.
Sometimes reminder emails are missed, reviewer responses take longer than expected, and editorial discussions remain unresolved for days or even weeks. As a result, the progress of a manuscript often depends heavily on how quickly each person responds and follows up on their tasks. For journals with small editorial teams, managing a large number of active submissions can become overwhelming, even when the OJS system itself is already well configured.
4. Inconsistent Submission Quality
Then there is the issue of submission quality itself — a challenge that originates not with editors or reviewers, but with authors. Many submitted manuscripts are still not fully ready for peer review or even getting rejected in the initial stage. Some papers have unclear structures, weak abstracts, incomplete metadata, or poorly organized content. In some cases, the main findings or contribution of the study are not explained clearly enough, making the manuscript harder to evaluate. If you are an author, you should also be aware of the five most common reasons why articles are rejected and how to address them.
Because of this, editors often need to spend additional time requesting corrections, checking formatting, or guiding authors before the review process can continue. Even small issues can slow down the editorial workflow when they appear repeatedly across many submissions. When combined together, these challenges create a workflow that is time-consuming, heavily manual, and difficult to manage consistently. Although the traditional OJS workflow is functional, many journals still struggle with efficiency, reviewer management, communication delays, and submission quality control.
This is why many publishers are now looking for smarter and more scalable ways to support the editorial process without removing the importance of human editorial judgment.
How We Integrated AI to Improve Editorial Workflow in OJS
Running a high quality journal is harder than most people think. Behind every published article is a long chain of editorial work such as screening submissions, checking formatting, coordinating reviewers, following up unanswered reviews, handling revisions, and making publication decisions. Most of these tasks are still done manually, often requiring editors to spend hours inside the system handling repetitive processes one by one.
As submission volumes continue to grow, these challenges become even more difficult to manage. Editors are expected to move quickly while still maintaining consistency, publication quality, and fair editorial judgment. At the same time, reviewers may respond late, authors may submit incomplete manuscripts, and editorial teams often need to manage dozens or even hundreds of active submissions simultaneously. Even with OJS providing a structured workflow system, many important processes still depend heavily on manual effort and human responsiveness.
For a long time, many journals simply accepted this as part of the publishing process. But over time, we started asking a simple question. Could technology help reduce some of this workload without removing the important role of editors and reviewers?That question became the starting point for developing our AI tools for OJS. Our goal was never to replace editorial decision making, peer review, or human expertise. Instead, we wanted to build tools that could assist editorial teams by helping them work faster, more consistently, and more efficiently throughout the publication workflow.
The Disadvantages of Human Screening in Editorial Workflow
Before we talk about what we built, it is worth being honest about the problems we were trying to fix. Because if you have ever managed a journal, you will recognize all of them.
1. Screening submissions takes more time than it should
Every submission that comes in needs to be read, at least enough to know whether it belongs in your journal, whether the language is up to standard, and whether the author has followed your formatting guidelines. For a journal with a steady stream of incoming manuscripts, this alone can consume a significant chunk of an editor’s week. And much of that time is spent on submissions that never should have passed through in the first place.
2. Finding the right reviewer is never straightforward
It is not just about finding someone with the right expertise. You need someone available, someone without a conflict of interest, and ideally someone you have reason to believe will give a thorough and timely review. Most editors build this knowledge over years. New editors have to start from scratch. And even experienced editors will tell you that reviewer matching is as much art as it is science. The manual (traditional) peer review process can also lead to various shortcomings, such as delays and inefficiency, failure to detect methodological flaws, and others.
3. Submissions often arrive underprepared
This is not a criticism of authors — academic writing is genuinely difficult, and expectations vary widely across journals and disciplines. But the reality is that a large proportion of submissions arrive with abstracts that bury the point, structures that make it hard to follow the argument, and missing highlights that would have helped editors and reviewers understand the contribution at a glance. Every one of those issues adds work on the editorial side.
4. OJS is a great platform, but it does not solve everything
We want to be clear about this because it matters. OJS gives journals a solid foundation — structured workflows, role management, submission tracking. But it does not think alongside you. It does not flag a submission that is clearly out of scope. It does not tell an author that their abstract needs work before the manuscript enters the review queue. That gap between infrastructure and intelligence is exactly where we saw an opportunity.
How Our AI Solutions Help Journal Editor in Improving Workflow Process
We decided early on that we did not want to build something that lives outside OJS and asks editors to change how they work. The plugin had to sit inside the platform, fit naturally into existing workflows, and be usable by editors without any technical background. Those constraints shaped everything.
The plugin currently does three things:
It screens new submissions automatically
The moment a manuscript is submitted, the plugin runs an assessment in the background. It checks whether the topic fits the journal’s scope, looks at the quality of the language, and flags any obvious structural or formatting issues. By the time an editor opens the submission, there is already a plain-language summary waiting for them — not a score or a rating, just a clear picture of what they are looking at and what might need attention.
For editors managing a high volume of submissions, this changes the shape of the day. Instead of reading every manuscript from scratch just to decide whether it deserves further consideration, they can triage with confidence and focus their reading time on the submissions that genuinely warrant it.
It gives authors feedback before their manuscript enters review
One of the things we felt strongly about was getting quality feedback to authors earlier in the process rather than later. When a submission has structural problems or a weak abstract, the current workflow means an editor has to spot it, write up the issues, and send it back — sometimes weeks after submission. The author then revises and resubmits, and the clock starts again.
Our plugin runs a quality check at submission and surfaces actionable feedback directly to the author inside OJS. They can see what needs attention and address it before the manuscript moves forward. It does not guarantee a perfect submission, but it raises the baseline and reduces the back-and-forth that eats into editorial timelines.
It helps readers understand articles faster
This one came from a slightly different direction. Once an article is published, the typical OJS article page gives readers a title, an abstract, and a link to the PDF. That is often not enough for someone scanning a journal to decide whether a paper is relevant to their work.
The plugin generates a set of key highlights for each published article and displays them on the public article page. These are not the author’s own highlights — they are generated from the content of the manuscript and written in language that is accessible to a broader reader, not just specialists. It is a small addition to the page, but it makes a real difference in how quickly a reader can assess whether an article is worth their time.
List of Our AI Features in OJS Editorial Process
Our AI tools are designed to improve and make more seamless for the OJS editorial process through smarter, faster, and more consistent workflows. We integrate various AI-powered features to assist editors in handling manuscript evaluation, review analysis, revision recommendations, and editorial decision-making more efficiently.
Below are some of the AI features currently available in our system and continuously being improved to support a more intelligent, accurate, and editor-friendly publishing workflow. Our goal is to help publishers and editorial teams simplify complex editorial tasks while maintaining publication quality and consistency.
1. AI Screening on : Relevance, Language formatting, Novelty and Contribution, Methodology Quality.
Our AI screening feature helps editors evaluate incoming manuscripts through four main aspects such as relevance, language and formatting, novelty and contribution, and methodology quality. Each aspect is assessed deeply, allowing every manuscript to receive a detailed evaluation before going to the editor’s final decision. This approach gives editors a more structured and consistent way to assess submissions without relying entirely on manual reading. As a result, the early editorial process becomes faster, more efficient, and more objective, while editors can immediately understand the overall quality and suitability of a submission as soon as it enters the system.
Each assessment aspect has a specific role in the screening process. Relevance assesses the extent to which the manuscript aligns with the journal’s scope and focus, while language and format evaluate the clarity of writing, sentence structure, punctuation, and other related structural elements. Originality and contribution assess whether the research offers new or meaningful insights to existing knowledge, and methodological quality examines whether the research methods are clearly described and appropriate for addressing the research questions. The screening results are then presented in a structured summary that editors can use as an initial reference when handling submitted manuscripts.
Manuscripts with low scores in certain aspects can be identified early on, helping editors reduce the time spent on manuscripts that are unlikely to proceed. This also makes it easier for editors to evaluate and maintain consistency in evaluating each submitted article without being influenced by subjective factors.
2. AI Recommendation by providing Submission notes, and conceptual summary about submission
Once the screening process is completed, our AI generates a recommendation for the submission in two forms such as submission notes and a conceptual summary. The submission notes highlight key points that editors should pay attention to, including potential concerns, strengths, or sections that may require further review. Meanwhile, the conceptual summary provides a concise overview of the manuscript’s main idea and research focus. Together, these outputs help editors quickly understand the core of a submission without needing to read the entire manuscript at the initial stage.
This feature is designed to support editorial decision-making rather than replace it. Editors still make the final decision, but the AI provides a clear starting point to make the process more efficient. The submission notes help editors identify whether a manuscript may need revision, further evaluation, or is ready to move forward in the review process. At the same time, the conceptual summary is especially useful when handling large numbers of submissions, allowing editors to prioritize their attention more effectively. By bringing these insights together in one place, the editorial workflow becomes smoother, faster, and more consistent.
3. Reviewer Key Highlight
After reviewers submit their feedback, our AI gathers the main points from all reviews into a centralized, clear and structured summary for the editor. This summary includes reviewer decisions, major concerns, suggestions, and important notes from each reviewer. Instead of reading every review in full, editors can quickly see the overall feedback and understand how reviewers responded to the manuscript. The summary keeps the important information while making it easier and faster to review.
This feature is especially useful when a manuscript is reviewed by several reviewers at the same time. Different opinions or repeated concerns are easier to identify when all feedback is combined in one place. Editors can use the summary to make better decisions on whether a manuscript should be accepted, rejected, or revised. It also helps reduce the effort needed to manage multiple detailed reviews and keeps the editorial process running more smoothly and efficiently. With this feature, even editors can also receive recommendations on whether to add additional reviewers or not. For example, if the current review results are unsatisfactory or for other reasons.
4. Reader AI Screening
Our AI screening results are not only used by editors and reviewers, but are also displayed directly on the article detail page for readers to see. Visitors can quickly view a structured overview of the article’s relevance, novelty, methodology, and any others seamlessly within these tools. This helps readers decide more easily whether an article is relevant to their interests or research needs.
This feature is especially useful for researchers and academics looking for references to cite. Instead of spending time skimming through an entire paper, readers can use the AI screening results as a quick guide to understand the article’s focus, contribution, and research approach. The summaries and screening indicators make it easier to find relevant literature and evaluate articles more efficiently.
5. AI Automated Archiving (Coming Soon)
One of the upcoming features currently in development is AI Automated Archiving. This feature will allow the system to automatically archive submissions that do not meet the journal’s minimum screening standards based on key metrics such as relevance, focus and scope, and other screening indicators. Instead of leaving unsuitable submissions in the active queue, the AI will move them to the archive automatically, helping editors focus on manuscripts with stronger potential for review and publication. This will make the editorial workflow cleaner, faster, and more organized.
In addition to score-based archiving, the AI will also be able to identify unnecessary submissions such as duplicates, incomplete manuscripts, or papers that clearly fall outside the journal’s scope. The archiving criteria and thresholds will be configurable, allowing journal managers to decide how strict or flexible the automation should be. Overall, this feature is designed to reduce administrative workload and improve submission management efficiency, and it will be available in a future update.
How to Get Started with Our AI Tools for Your OJS
Our AI-powered editorial tools are available exclusively through our OJS Hosting Service. We built these features to help journals simplify editorial work, reduce repetitive manual tasks, and improve a more seamless and effective publishing workflow directly inside OJS. Because of this, these tools are not released as standalone plugins or public extensions, making them a unique advantage for journals hosted on our platform.
Beyond AI-powered editorial assistance, our hosting environment also includes exclusive security technologies developed specifically for OJS by our expert security team that is internationally certified. These security tools include Guardian AI and Advanced Security Plugin, this is the first and the only one security tool built exclusively for OJS.These security tools designed to help journals stay fully protected from unauthorized access, suspicious activity, malicious attacks, and various other high-risk attacks.
By combining AI-assisted editorial workflows with specialized OJS security tools, our hosting service is designed to help journals work more efficiently while also providing a safer, more stable, and more reliable publishing environment for your OJS journal.
If you are interested in using these AI and security features for your journal, feel free to contact our team to learn more about our OJS Hosting services.
Rest assured, these AI-powered features are designed to support your editorial workflow by providing helpful indicators and related suggestions clearly. Meanwhile the final editorial decisions, publication approvals, and other important journal-related actions remain fully in your control.
