scholarly publishing Archives

Beyond Automation: How AI Can Elevate Editorial Integrity in Scholarly Publishing

Posted on August 18, 2025August 18, 2025 by Ashutosh Ghildiyal

Scholarly publishing is undergoing a period of accelerated change, shaped by breakthroughs in artificial intelligence, automation, and the increasing complexity of manuscript submissions. Journal editors and publishing professionals now face a dual challenge: a surge in submission volumes and the rise of sophisticated content, sometimes generated by AI or originating from coordinated paper mills. Traditional editorial workflows are feeling the strain.

The real question is no longer whether to adopt AI tools, but how to implement them strategically, while safeguarding the trust, quality, and credibility that underpin scholarly communication.

Redesigning Workflows: A Strategic AI–Human Editorial Framework

Editorial teams today must manage more submissions, more complexity, and more scrutiny than ever before. The solution lies not in replacing human expertise but in integrating AI to complement it. A symbiotic AI–human workflow enables machines to handle repetitive, mechanical tasks while editors focus on nuanced decisions requiring judgment, context, and ethical oversight.

Here’s an eight-stage editorial framework designed to integrate AI thoughtfully across the manuscript lifecycle:

1. Intelligent Submission Processing

AI can take on routine intake tasks, such as metadata validation, file checks, and basic categorization, so that human attention starts at the right point: assessing complete, structured, and ready-for-review submissions.

2. Comprehensive Pre-Flight Screening

AI tools can conduct rapid, multilayered screening, such as:

Technical validation for formatting and completeness
Language and style analysis to flag readability or reference issues
Ethics and integrity checks to detect plagiarism, image manipulation, or data inconsistencies
Scope matching using natural language processing to route submissions to the right journals

3. Strategic Filtering and Prioritization

AI systems can flag submissions that fall short of minimum quality or ethical thresholds, freeing up editors to focus on promising or borderline cases where human judgment is most needed.

4. Enhanced Human Decision-Making

Editors, supported by AI-generated insights, make decisions based on originality, clarity, relevance, and novelty. Just as diagnostic tools support physicians without replacing their expertise, AI provides signals, but interpretation remains human.

5. Intelligent Reviewer Selection

AI can analyze reviewer expertise, past publications, availability, and diversity metrics to suggest potential reviewers. Human editors finalize the selection to ensure contextual fit and mitigate bias or conflicts of interest.

6. Augmented Peer Review

AI-powered tools can assist reviewers through:

Summarizing key manuscript points
Validating references and citations
Suggesting structural improvements to reviews
Supporting speech-to-text and generative drafting within secure, publisher-managed platforms

These tools reduce friction and enhance reviewer focus while ensuring that substantive review remains entirely human-led.

7. Decision Support, Not Decision Making

AI can help editors synthesize reviewer feedback and identify discrepancies, but the final verdict remains a human responsibility, grounded in ethical and academic considerations.

8. Quality Assurance and Production Support

Post-acceptance, AI can aid in formatting checks, metadata normalization, and disclosure validation. Editors retain final control to ensure accuracy, tone, and adherence to journal policies.

Elevating Peer Review, Not Replacing It

Peer review has long been criticized as slow or inconsistent, but it’s also an indispensable pillar of scholarly trust. Rather than overhaul the system, we must reinforce it by addressing systemic stress points, starting with reviewer support.

Key areas of improvement include:

Diversity: Ensuring a broad range of perspectives
Recognition: Acknowledging reviewer contributions
Training: Equipping reviewers with evolving standards and tools
Efficiency tools: Providing AI assistance to reduce friction, not intellectual rigor

New models, like transparent peer review and journal-level peer review quality ratings, can also strengthen accountability and trust.

Impact Across the Ecosystem

AI-powered workflows do more than help editors. They bring tangible benefits to every stakeholder:

Authors gain faster feedback and greater clarity
Reviewers experience less manual strain and better tools for structured evaluations
Publishers benefit from increased scalability, compliance, and process transparency
Institutions see enhanced credibility and streamlined editorial governance

When these tools are delivered within secure, publisher-controlled environments, they enhance, not erode, trust across the scholarly ecosystem.

Guiding Principles for AI Integration

To ensure responsible, effective AI adoption in editorial workflows, organizations should follow these principles:

AI should assist, not replace, human discernment
Efficiency should not come at the cost of oversight or ethics
Editorial decisions must always remain human-led
All AI-supported tasks must take place in secure, publisher-managed environments
Adopt solutions aligned with industry guidelines (e.g., COPE, STM Integrity Hub)

Equally important is selecting vendors and partners committed to ethical AI development, transparency, and accountability.

Building the Future of Editorial Integrity

The future of scholarly publishing lies not in choosing between human expertise and artificial intelligence, but in integrating both intelligently. AI can support scale, consistency, and early error detection, but the values of discernment, ethical reasoning, and deep engagement remain irreplaceably human.

By building thoughtful, secure, and transparent editorial systems, where AI and human judgment work in tandem, publishers can meet the challenges of volume and complexity while reinforcing the trust that defines scholarly communication.

The responsibility is clear. The opportunity is real. The time to act is now.

At Integra, we celebrate the contributions of editorial professionals, recognizing their invaluable service to the scholarly community. Our advanced tools empower them, ensuring they continue to play a crucial role in advancing human knowledge through research. As a trusted partner, we offer human-led, technology-assisted solutions tailored for editorial, research integrity, and peer review management.

This post is adapted from an original blog article by Ashutosh Ghildiyal, first published on Editor’s Café. To read the full article, please visit: https://editorscafe.org/details.php?id=76

Navigating the Future: Top Predictions for Peer Review and AI Integration in Scholarly Publishing

Posted on December 13, 2023April 5, 2025 by Prakash Nagarajan

The scholarly publishing landscape is experiencing a transformative shift, primarily due to the evolving nature of the peer review process and the emergence of Artificial Intelligence (AI) in this domain. Historically, peer review has been the bedrock of academic publishing, fostering credibility and scholarly discourse. Concurrently, the Open Access (OA) Movement is democratizing knowledge access, while Open Peer Review (OPR) is redefining the transparency and accountability paradigms in academic scrutiny.

Current Trends in Peer Review

Traditionally, peer review has been a process shrouded in confidentiality and selectivity, ensuring that scholarly work meets the highest standards of research and publication. Its significance in maintaining the integrity of academic work cannot be overstated. However, this process is not without its challenges, such as time constraints and potential biases.

AI’s introduction into peer review marks a new era of efficiency and precision. AI algorithms are increasingly being used to streamline the review process, from initial manuscript sorting to detailed data analysis, hinting at a future where technology and human expertise coalesce to enhance scholarly communication.

Transparency and Openness have become pivotal in the modern peer review landscape. The shift towards open models is a response to the academic community’s demand for more transparent scholarly communication. Speed and Efficiency are also at the forefront of current trends, with various initiatives aimed at streamlining the review process. Moreover, AI’s role in enforcing compliance with OA standards and ethical guidelines is becoming increasingly prominent. From detecting plagiarism to ensuring data integrity, AI tools like AuthorPilot are becoming essential in maintaining the scholarly publishing ecosystem’s credibility.

AI’s Emerging Role in Peer Review

From 2012 to 2022, the publishing landscape underwent a significant transformation, with closed access models, which once dominated 70% of the market, giving way to open access models now embraced by 54% of publishers. For journal publishers navigating this shift, an AI-powered content creation and publishing platform offers numerous advantages, including the key benefit of intelligent automation to expedite workflows.

Automated Manuscript Screening

AI-driven screening streamlines manuscript evaluation in several key areas:

Ethical Standards: AI tools scrutinize manuscripts for ethical compliance, checking for necessary approval statements, consent processes, proper disclosures, and adherence to funder mandates like grant details and trial registrations.
Journal Compatibility: AI assists in preliminary checks to align manuscripts with journal criteria, evaluating article type, writing quality, data representation, and basic formatting.
Reporting Guidelines Adherence: Automated systems efficiently assess compliance with essential reporting standards set by the EQUATOR Network, ensuring research integrity

Data Analysis Tools

Advanced AI tools are assisting in the meticulous task of data verification within manuscripts. By analyzing data sets for consistency and accuracy, these tools are enhancing the reliability of research findings.

Top Predictions for the Future

Prediction 1: Enhanced Manuscript Matching

AI is expected to become increasingly sophisticated in aligning manuscripts with the most appropriate reviewers, based on expertise and research interests. This targeted approach promises to improve the quality and relevance of peer review.

Prediction 2: Bias Reduction

AI has the potential to significantly reduce human bias in the review process by providing objective assessments based on pre-set criteria, thus promoting fairness and impartiality in scholarly publishing. Publishers should look to include AI tools that would have checks such as DEI to aid the process.

Prediction 3: Real-time Collaborative Review and Post-Publication Peer Review

The future might see the advent of AI-enabled platforms facilitating real-time, collaborative review processes, allowing for more dynamic and immediate feedback between authors and reviewers. Alongside, PPPR is emerging as a dynamic and ongoing evaluation method, allowing for continuous scholarly discourse and assessment even after publication.

Challenges and Ethical Considerations

Addressing Bias in AI

While AI expedites peer review, it presents challenges. AI may struggle with assessing a paper’s relevance and fully understanding its context within existing literature. It might not accurately judge method suitability or data support for conclusions, potentially leading to reviews that lack original expert insight. Furthermore, AI risks inaccuracies due to ‘hallucination’ and biases from training data. Confidentiality concerns arise when feeding manuscripts into systems, posing potential copyright and plagiarism issues. Additionally, there’s a risk of overreliance on AI for content summarization.

Maintaining Human Oversight

Despite AI’s growing role, the need for human judgment remains paramount. Balancing technological efficiency with human insight is crucial for maintaining the integrity and quality of the peer review process.

Conclusion

As we look forward, the integration of AI in peer review presents a landscape ripe with opportunities for enhanced efficiency, reduced bias, and greater transparency. However, this journey necessitates a cautious approach to preserve the sanctity and ethical standards of scholarly communication. We invite our readers to engage with this evolving paradigm and explore AI-based solutions with us.

Navigating New Frontiers with AI: Research Integrity in Scholarly Publishing

Posted on November 10, 2023April 5, 2025 by Prakash Nagarajan

The widespread adoption of artificial intelligence (AI) represents a pivotal moment in global history. Often described as a transformative ‘superpower,’ generative AI is accessible to anyone with a computer and internet connection. Its influence is already evident across various industries, signaling the onset of far-reaching and disruptive changes yet to be fully realized.

AI, a remarkable product of decades of scholarly research and development, stands as a paradox in the realm of scholarly communication. This field, pivotal in broadcasting progress across various research areas, now faces the dual impacts of AI. This article aims to examine AI’s role in scholarly communication, with a special focus on journal article publishing lifecycle, and to contemplate its future implications for all involved stakeholders.

Journal Publication Growth

A basic analysis using the Dimensions app reveals a significant increase in the number of journal articles published from 2012 to 2022. There was an 89% growth over the past decade, with the number of articles rising from 2.8 million in 2012 to 5.3 million in 2022. In this period, the industry also experienced notable growth in the number of open access journals, a model also adopted by commercial publishers too. These journals were a major contributor in providing platforms for disseminating research to a broader audience.

The surge in publication numbers, coupled with disruptions to traditional business models and increasing publishing costs, has compelled publishers to reevaluate their operational strategies. They have swiftly embraced various technological enablers throughout the publishing lifecycle. As digital content consumption has skyrocketed over the years, publishers have had to revise their workflows. The shift from print-only to online-ahead-of-print, and now to a predominantly online-only model for article publication, has become the norm.

AI in Journal Publishing Workflows

Journal publishers in the academic publishing sector were early adopters of technology, enhancing their publishing workflows significantly. This included using markup languages to partly automate content production and to aid in archival and retrieval. With AI-enabled systems and generative AI, today, we stand at a pivotal juncture with the potential to revolutionize the industry.

Disruptive innovation, a hallmark of any industry, opens up vast opportunities to delve into unexplored areas and confront challenges. This is particularly true for scholarly communication and journal publishing, which navigate their unique challenges in this unfamiliar landscape. At the same time, these fields are harnessing the potential that AI technologies offer.

AI-enablement has been a welcome change in the journal publishing domain which has been trying to accommodate to emerging business models and changing market demands. In an industry where speed of publication of journal has been continuously reducing, publishers have been quick to adopt technology solutions to manage both upstream and downstream publishing activities. There are many AI-enabled solutions for every stakeholder involved in the entire scientific publication life-cycle.

Safeguarding Research Communication Integrity in the Age of AI

The advent of social media and widespread smartphone use has led to a staggering increase in the volume of content published and consumed every minute. Journal publishing is also experiencing a surge in content, overwhelming the niche audiences it caters to. This content overload poses significant challenges for two crucial players in the scientific publication lifecycle: researchers and practitioners. The repercussions are profound, affecting individual careers and society at large, as funding is squandered on unrealized benefits. We have many examples on predatory journals, papermills and unethical practices in the academia that pose a significant damage.

Regardless of the publishing model, the rise of AI has highlighted the essential role of journal publishers in the ecosystem, particularly in curating valuable information through meticulous peer review. While AI tools may boost content production, they also risk increasing the volume of low-quality content creating a strain in the research publication pipeline. This surge in content necessitates a more rigorous editorial review process, which is both costly and labor-intensive. The entire ecosystem must collaborate and embark on a journey of discovery to address the challenges and potential short- or long-term threats that are emerging as we begin to uncover the full potential of AI.

Future with AI in Scholarly Communication

This exploration into AI within scholarly publishing not only highlights technological advancements but also raises critical questions about ethical considerations. It underlines the need for a balance between innovation and responsibility, urging the academic community to lead with foresight and ethical rigor. The article ‘AI & Machine Learning in Scholarly Publishing: Services, Data, and Ethics’ offers an interesting starting point on tackling these challenges and ensuring the integrity of scholarly communication. The path forward remains uncharted, reflecting the true essence of scholarship in this domain. It emphasizes the importance of finding solutions to issues affecting society at large, underscoring the role of academia in navigating unexplored territories and contributing to global knowledge.

Tag: scholarly publishing