Editorial assistants (EAs) of journals are at the focal point of a significant shift in the publishing industry brought about by the emergence and development of artificial intelligence (AI).
Editorial Assistants have historically played a key role in ensuring editorial workflows, including reviewing submissions, communicating with authors and overseeing deadlines. However, their roles are evolving as a result of the rapid adoption of AI.
This article explains how AI is changing the function of EAs by allowing them to shift from mundane tasks to strategic decision-making and quality management of content along with highlighting the difficulties associated with the use of AI.
Streamlining routine tasks
A study showed that early screening significantly reduces the time to final decisions by more than 60%. Imagine that being done by AI.
Traditionally, EAs used to spend a lot of time on monotonous activities. However, AI tools now handle the majority of these activities, freeing up EAs’ time to focus on important aspects of the article.
- Plagiarism checks: Journals typically have a plagiarism threshold between 15% to 25%. Tools like iThenticate detect plagiarism and ensure ethical standards are met.
- Language and formatting: AI technologies like Grammarly and ProWriting Aid improve grammar and align articles with journal criteria.
- Submission compliance: Automated mechanisms ensure all the required files are included and correctly formatted.
This automation not only increases productivity but also allows EAs to devote more time to higher-value jobs that need human judgment and creativity. For example, rather than formatting references, EAs can now focus on ensuring submissions are ethical.
Enhancing the peer review process
AI dramatically increases the efficiency and accuracy of manuscript review.
- Initial screening: AI algorithms screen submissions to ensure they align with a journal’s scope, saving EAs time.
- Identification of the most appropriate reviewer for the manuscript: Intelligent tools link peer reviewers based on expertise, resulting in a faster and more accurate review process.
- Fraud detection: AI can discover patterns of ethical violations, such as duplicate submissions or data manipulation.
These features not only speed up the peer-review process but also improve its integrity, allowing EAs to focus on resolving complex issues of the review process.
Improving author support
AI-powered chatbots are transforming the way authors communicate with journals. These virtual assistants can:
- Answer frequently asked questions regarding submissions.
- Offer step-by-step instructions for manuscript submission.
- Provide real-time updates on submission status.
An article by Fiorillo et al. (2024) showcases how chatbots and AI can contribute to reducing article processing times. These tools enhance the author’s experience, reducing delays caused by incomplete or incorrect submissions. As a result, EAs can dedicate their efforts to resolving more nuanced author queries and building stronger relationships with contributors.
Data-driven details for workflow optimisation
AI’s data analytic skills enable EAs to optimise editorial workflows and journal strategy.
- Trend analysis: It informs journals about reader preferences and market trends and helps the journals shape their focus areas accordingly.
- Manuscript trackers: They help streamline submissions and manage deadlines.
- Analytics: They help discover high-impact content and guide EA’s decisions.
These details establish EAs as crucial publishing strategists, assisting journals in remaining competitive and relevant in an ever-changing research landscape.
Human element: Indispensable expertise
While AI is taking over many routine tasks, it is crucial to remember that human oversight is still essential.
AI is a double-edged sword
AI can help find fake science through various quality and plagiarism checkers; it can also create fake data, images and articles.
Human judgment remains crucial
It is essential to maintain ethical standards and ensure quality. Therefore, there is no question that human EAs will still be needed to guide decisions related to publications, but the assistance that AI provides is invaluable.
The new challenge
EAs need to ensure they get it right before they get left (behind). The integration of AI in editorial workflows means that EAs need to acquire new skills. These include AI literacy and the ability to work with AI-driven tools effectively. The role of an EA is evolving towards more strategic responsibilities that require these skills.
Roses come with thorns
While all may seem rosy with AI, there could be cases of logical malfunction (e.g., misinterpreting author names) leading to inappropriate decision-making. Therefore, EAs must critically judge and review all AI-generated suggestions, ensuring human opinion in the publication process.
Balancing opportunities and challenges
The integration of AI brings the roses along with its thorns:
- Job evolution: EAs must develop new skills to work effectively with AI tools.
- AI bias: Ensuring AI algorithms are fair without any bias is a continuous challenge.
- Accountability: Clear guidelines are essential to utilise AI in editorial decision-making. Also, the final accountable person will be EA and not AI.
While these challenges are real, they also represent opportunities for EAs to upskill and adapt, ensuring their roles remain relevant and impactful.
Conclusion: Embracing the future
AI is not replacing journal EAs; rather, it improves their capacities by automating boring activities, providing actionable insights and freeing up time for strategic and ethical decision-making.
This hand-in-hand approach between humans and technology supports the long-term growth and relevance of EAs in publishing. By automating monotonous duties and giving actionable information, AI enables EAs to concentrate on higher-level responsibilities like strategic decision-making and ethical monitoring.
A future in which technology complements human skills is already taking shape in academic and medical publishing. By using AI, EAs may increase efficiency while retaining the integrity and quality of scholarly communication, so ensuring their vital role is retained in the publishing industry.
References
- Johnston SC, Lowenstein DH, Ferriero DM, Messing RO, Oksenberg JR, Hauser SL. Early editorial manuscript screening versus obligate peer review: a randomized trial. Ann Neurol. 2007 Apr;61(4):A10-2. doi: 10.1002/ana.21150. PMID: 17444512.
- Frederickson RM, Herzog RW. Keeping Them Honest: Fighting Fraud in Academic Publishing. Mol Ther. 2021 Mar 3;29(3):889-890. doi: 10.1016/j.ymthe.2021.02.011. Epub 2021 Feb 13. PMID: 33581045; PMCID: PMC7935660.
- Fiorillo L, Mehta V. Accelerating editorial processes in scientific journals: Leveraging AI for rapid manuscript review. Oral Oncology Reports. 2024 Jun 1;10:100511.
- https://www.highwirepress.com/blog/practical-uses-of-ai-and-ml-in-scholarly-publishing/
