Are AI-Driven Editorial Decision Support Systems Effective in Scholarly Publishing?

Introduction

The rapid advancement of artificial intelligence (AI) has changed almost every aspect of scholarly communication, from how researchers search the literature to how manuscripts are written, submitted, and evaluated. One of the most significant developments inside editorial offices is the rise of AI-driven Editorial Decision Support Systems (EDSS). These tools are designed to help editors cope with ever-increasing submission volumes, growing expectations around research integrity, and pressure to deliver fast, fair, and transparent decisions.

AI-powered EDSS can now screen manuscripts for plagiarism and image manipulation, check references and basic statistics, suggest reviewers based on expertise and track record, and even generate preliminary recommendations such as “reject,” “revise,” or “send for peer review.” Advocates argue that these systems streamline workflows, improve consistency, and reduce bias. Critics, however, warn about over-reliance on opaque algorithms, the reinforcement of existing inequalities, and the danger of letting machines judge originality, nuance, or theoretical depth.

This article examines the effectiveness of AI-driven EDSS by exploring what they are, how they work, the benefits they bring, the risks they pose, and the best practices that can help journals use them responsibly. It concludes that AI can be extremely useful in editorial decision-making—but only when it is embedded in a carefully designed hybrid model where human judgement remains central and manuscripts are still prepared and checked with rigorous, human-performed proofreading and editing.

What Are AI-Driven Editorial Decision Support Systems?

Editorial Decision Support Systems (EDSS) are software tools that assist journal editors in evaluating manuscripts and managing the peer-review process. When these systems are enhanced with AI, they go beyond static rule-based checks and become adaptive, data-driven platforms capable of learning from large collections of published and submitted work.

AI-driven EDSS typically combine three main technologies:

• Machine learning: algorithms trained on historical data—such as past editorial decisions, citation patterns, and reviewer performance—identify patterns that can inform current decisions.
• Natural language processing (NLP): tools that “read” manuscripts, extract key concepts, analyse style and structure, and compare text to reference corpora for similarity or anomaly detection.
• Big data analytics: systems that integrate information about journals, authors, institutions, and citations to provide broader context for each submission.

In practice, EDSS do not replace the editor, but they prioritise, enrich, and structure information so editors can work more efficiently and make more informed decisions.

Core Functions of AI-Driven EDSS

Most AI-powered editorial systems provide some combination of the following functions:

• Manuscript screening: automatic checks for plagiarism, missing sections, incomplete references, formatting issues, and sometimes basic statistical or methodological red flags.
• Reviewer matching: recommending potential reviewers by analysing their publication history, keywords, previous reviews, and connections to the authors or topic.
• Integrity and ethics checks: image similarity analysis to detect potential manipulation, identification of suspicious citation patterns, and alerts for duplicate or salami-sliced submissions.
• Data and methods scrutiny: tools that verify internal consistency in tables and figures, check p-values against reported test statistics, or flag implausible sample sizes and effect sizes.
• Editorial recommendations: dashboards that summarise a manuscript’s fit with the journal’s scope, historical acceptance patterns, likely impact, and potential risk factors, often accompanied by a suggested decision.

By automating these tasks, EDSS can reduce the routine workload for human editors and enable them to devote more time to substantive questions about novelty, significance, and ethics.

Benefits of AI-Driven Editorial Decision Support Systems

1. Faster and More Efficient Manuscript Screening

Traditional editorial workflows often involve several days—or weeks—of preliminary checks before a manuscript even reaches peer reviewers. Editors or editorial assistants manually verify that submissions meet basic formal requirements, scan for obvious plagiarism, and decide whether a paper should be sent out for review or rejected at the desk.

AI-driven EDSS can complete many of these checks in minutes. They rapidly scan text for similarity against large databases, assess whether essential sections (such as abstract, methods, and ethics statements) are present, and verify that tables, figures, and references are formatted correctly. This offers several advantages:

• Significant reductions in editorial bottlenecks, particularly in high-volume journals.
• More predictable turnaround times for authors, who often face intense pressure to publish quickly.
• Early identification of submissions that clearly fall outside the journal’s scope or quality threshold, allowing editors to focus on more promising manuscripts.

2. Improved Accuracy and Consistency

Human editors can differ widely in how they interpret guidelines, spot issues, or apply desk-rejection criteria. Fatigue, time pressure, and unconscious bias all contribute to inconsistency. AI-based systems, by contrast, apply the same checks in the same way every time.

Properly configured EDSS can:

• Apply uniform screening criteria across all submissions, regardless of who is on duty that week.
• Detect plagiarism, text recycling, and citation manipulation with greater sensitivity than manual scanning.
• Highlight statistical inconsistencies or missing data that human readers might overlook, especially under time pressure.

While AI does not eliminate all forms of bias, consistent application of rules can help reduce some forms of idiosyncratic decision-making and support fairer treatment of authors.

3. Enhanced Peer Reviewer Selection

Identifying suitable reviewers is one of the most time-consuming parts of the editorial process. Editors must find experts with the right knowledge, sufficient availability, and no conflicts of interest. This is particularly challenging in niche or interdisciplinary fields.

AI-driven EDSS can search across large databases of published work and reviewer activity to identify candidates whose expertise closely matches the manuscript. These systems can:

• Suggest reviewers based on topic similarity, methods, and keywords, not just broad subject categories.
• Flag potential conflicts of interest by checking co-authorship networks, institutional affiliations, and recent collaborations.
• Optimise reviewer selection by considering past performance indicators such as responsiveness and review depth.

Used thoughtfully, this can diversify the reviewer pool and relieve over-burdened senior scholars while still maintaining quality control.

4. Strengthened Research Integrity and Ethical Compliance

Concern about research integrity has grown sharply in recent years, with high-profile cases of fraud, manipulated images, fabricated data, and paper mills. AI-based integrity checks are becoming a core component of editorial decision support.

Typical tools can:

• Use similarity detection (for example, through tools like iThenticate) to identify plagiarism and self-plagiarism.
• Apply image-forensics algorithms to reveal duplicated, spliced, or altered figures, especially in biomedical research.
• Assess statistical plausibility and consistency, flagging unusual patterns that may warrant closer human scrutiny.

These capabilities do not prove misconduct on their own, but they give editors vital signals that certain submissions require careful, human-led investigation.

5. Data-Driven Editorial Strategy and Journal Management

Beyond individual manuscripts, EDSS can aggregate data about submissions, decisions, and citations to provide editors-in-chief and publishers with strategic insights. Dashboards may show:

• Trends in submission volumes by topic, region, or institution.
• Patterns in acceptance and rejection rates over time.
• The relationship between editorial decisions and subsequent citation impact or downloads.

Editors can use this information to refine aims and scope statements, adjust peer-review procedures, or decide when to launch new article types or special issues. In this way, AI becomes a tool not just for individual decisions, but for long-term editorial planning.

Challenges and Limitations of AI-Driven EDSS

Despite these advantages, AI-powered editorial systems have important limitations that must be recognised and actively managed.

1. Lack of Deep Contextual Understanding

Even the most sophisticated AI models do not truly “understand” research in the way human experts do. They can detect patterns in text and data, but they struggle with the subtleties that often matter most in scholarly evaluation.

For example:

• AI may fail to recognise the theoretical originality of a paper that uses familiar language to introduce a genuinely new perspective.
• Complex, interdisciplinary manuscripts may be misclassified or undervalued because they don’t fit neatly into existing categories.
• Unconventional but rigorous methods might be flagged as “anomalous” simply because they deviate from past patterns in the training data.

These limitations mean that AI recommendations must always be weighed against expert human judgement, particularly for high-stakes or boundary-pushing work.

2. Ethical Concerns and Embedded Bias

AI systems learn from historical data—and historical data often reflect systemic inequalities. If an EDSS is trained on past editorial decisions that favour certain regions, institutions, or topics, it may reproduce and even reinforce those patterns.

Risks include:

• Preference for manuscripts from well-known institutions or frequently cited authors, at the expense of early-career researchers or authors from under-represented regions.
• Under-recommendation of research in emerging or non-Western disciplines that have less representation in the training corpus.
• Propagation of gender or language biases, for example if non-native English writing is penalised more harshly by automated language assessments.

To mitigate these issues, publishers must audit EDSS performance regularly, diversify training data where possible, and ensure that human editors actively correct for bias rather than passively accepting algorithmic output.

3. Over-Reliance on AI Recommendations

One of the greatest dangers is not what AI does, but how humans respond to it. When a system presents a neat score, colour-coded risk indicator, or suggested decision, editors may be tempted to treat it as authoritative—even when it conflicts with their own judgement.

Over-reliance can lead to:

• Editors rubber-stamping AI suggestions without performing a full assessment of borderline cases.
• Rejection of unconventional or critical scholarship that the system does not “recognise” as valuable.
• Reduced willingness to deviate from algorithmic norms, which can stifle intellectual diversity and innovation.

Clear policies are therefore needed to define AI’s role: EDSS should be treated as advisory tools, not as decision-makers.

4. Data Security and Privacy Risks

Editorial systems process highly sensitive information, including unpublished research, confidential reviews, and author identities. Integrating AI into these workflows raises questions about where data are stored, who has access, and how securely they are protected.

Journals must ensure that:

• Manuscript data are handled in compliance with privacy regulations such as GDPR.
• AI vendors implement strong encryption and access controls to prevent data breaches.
• Unpublished manuscripts are not used inappropriately to train generic language models or commercial tools without explicit consent.

Any breach of editorial data could undermine trust in peer review and expose authors’ work to premature disclosure or misuse.

5. Difficulty Evaluating Truly Novel Research

Because AI models draw heavily on existing literature, they are best at recognising patterns that resemble the past. Genuinely novel or paradigm-shifting work may appear unusual, low-impact, or poorly connected within the graph of prior publications.

Consequences may include:

• Underestimation of transformative research that does not yet have a citation trail.
• Misclassification of manuscripts from fast-moving fields where the evidence base is still emerging.
• Increased pressure on authors to conform to established templates in order to pass automated checks.

This is another reason why experienced human editors remain essential for assessing originality and long-term potential.

Best Practices for Implementing AI in Editorial Decision-Making

To harness the strengths of AI-driven EDSS while minimising risks, journals and publishers can follow several best-practice principles.

1. Maintain a Human–AI Hybrid Model

AI should support, not replace, editorial expertise. Journals can:

• Use EDSS primarily for routine, high-volume tasks such as screening and reviewer matching.
• Require that all final decisions are made by named human editors who have read the manuscript and considered AI outputs critically.
• Encourage editors to override AI suggestions when justified, documenting their reasoning.

This preserves the benefits of automation while keeping accountability in human hands.

2. Ensure Transparency and Explainability

Authors and reviewers increasingly want to know how AI is being used in the editorial process. Journals should:

• Clearly describe, on their websites and in author guidelines, which AI tools are used and for what purposes.
• Prefer systems that provide explainable outputs rather than opaque scores—for example, listing specific issues detected instead of a single “quality index.”
• Maintain records of how AI-generated assessments contributed to decisions, so patterns can be reviewed and improved over time.

3. Audit for Bias and Fairness

Regular audits are crucial. Publishers can:

• Monitor acceptance and rejection rates across regions, genders, institutions, and disciplines after EDSS deployment.
• Compare AI-assisted decisions with expert independent evaluations on a sample of manuscripts.
• Adjust training data or model parameters where systematic unfairness is detected.

Ethical oversight committees or advisory boards can help guide this process and recommend corrective actions.

4. Protect Confidential Data

Strong data governance is non-negotiable. Journals should:

• Use vendors and systems that comply with recognised security standards and undergo regular security testing.
• Limit access to manuscript data strictly to authorised editorial staff and contracted service providers.
• Establish clear policies against using confidential submissions to train general-purpose AI models without explicit, informed consent.

5. Update and Monitor AI Systems Continuously

Scholarly publishing is a moving target. New article types appear, ethical standards evolve, and research methods change. AI tools must be maintained accordingly.

Good practice includes:

• Regularly re-training models on updated and more diverse data.
• Collecting feedback from editors and reviewers about false positives, missed issues, and usability problems.
• Collaborating with AI developers to ensure that changes in policies or guidelines are reflected in the system’s behaviour.

Implications for Authors and the Role of Human Proofreading

For authors, the rise of AI-driven EDSS changes the submission landscape in several ways. First, manuscripts are now assessed not only by human editors and reviewers but also by automated systems that are highly sensitive to structure, clarity, and technical correctness. Poorly formatted text, inconsistent terminology, or unclear reporting can trigger red flags long before a human expert reads the work.

This makes careful manuscript preparation more important than ever. Authors can improve their chances of a smooth journey through AI screening and human review by:

• Following journal instructions meticulously and ensuring that sections, references, tables, and figures are complete and consistent.
• Describing methods and data transparently, with clear links between research questions, analyses, and conclusions.
• Using professional academic proofreading and editing services to correct language errors, improve clarity, and align with academic style expectations.

Importantly, while AI writing tools may seem attractive for drafting or revising text, many universities and publishers now scrutinise AI-generated content and similarity scores. Human proofreading remains the safest way to refine a manuscript without increasing the risk of problematic overlaps or AI-style phrasing that triggers concern in similarity checks or integrity reviews.

Conclusion: How Effective Are AI-Driven EDSS?

AI-driven Editorial Decision Support Systems are already having a profound impact on scholarly publishing. They provide faster and more consistent screening, improve reviewer selection, support research integrity checks, and offer valuable data for editorial strategy. In these areas, they have proven themselves to be highly effective tools when carefully configured and overseen.

At the same time, AI has clear limits. It cannot fully replace the nuanced, context-rich judgement of experienced editors and reviewers. It can embed existing biases, misinterpret novelty, and create a false sense of objectivity if its outputs are accepted uncritically. Its use also raises serious questions about privacy, fairness, and accountability.

The most balanced conclusion is that AI-driven EDSS are most effective when they complement, rather than substitute, human expertise. Journals that implement them transparently, audit them regularly, and insist on human responsibility for final decisions can reap substantial benefits in efficiency and integrity. Authors, for their part, can adapt by preparing well-structured, honest, and carefully polished manuscripts—ideally supported by expert human proofreading services that respect academic and ethical standards.

AI will undoubtedly continue to shape the future of peer review and editorial decision-making. The key question is not whether AI should be involved at all—it already is—but how the scholarly community can ensure that its use strengthens, rather than undermines, the credibility and fairness of academic publishing.