Generative Artificial Intelligence in Scientific Journal Publishing: Applications and Challenges

Editorial Office of Journal of Materials and Metallurgy, Shenyang, Liaoning 110819, China

Abstract

[Purpose] This study aims to thoroughly investigate the current applications of generative artificial intelligence in scientific journal publishing and the transformations it brings, providing theoretical foundations and practical guidance for the intelligent transformation of the publishing industry. [Methods] This research systematically analyzes the latest applications of generative AI technology across various stages of scientific journal publishing, as well as the opportunities and challenges it presents, using literature review, case study, and inductive synthesis methods. [Results] Beginning with the opportunities that generative AI brings to scientific journal publishing, this paper analyzes its applications in the publishing process and examines in detail the challenges faced by scientific journal publishing from two perspectives: publishing workflows and editorial career development. [Conclusion] The application of generative AI in scientific journal publishing presents unprecedented opportunities and challenges. It plays a crucial role in intelligent topic planning, improving review efficiency, and enhancing publishing quality and productivity. However, in the face of these challenges, publishing institutions must actively strengthen technology development and talent cultivation to achieve deep integration between AI technology and publishing operations, thereby more effectively promoting the dissemination of scientific research and meeting society's demand for high-quality academic outputs.

Keywords: generative artificial intelligence; scientific journals; publishing; applications; talent cultivation
Classification Code: G230
Document Code: A
Article ID: 1671-0134(2025)03-112-04
DOI: 10.19483/j.cnki.11-4653/n.2025.03.024
Citation Format: Lü Chong. Applications and Challenges of Generative Artificial Intelligence in Scientific Journal Publishing [J]. China Media Technology, 2025, 32(3): 112-115.

As an important platform for disseminating and exchanging research achievements, the significance of scientific journals cannot be overstated. They serve not only as a crucial window for researchers to understand the latest domestic and international research developments and master cutting-edge methodologies and technologies, but also as a key bridge for showcasing research results, sharing academic insights, and promoting academic exchange and collaboration. With the rapid advancement of technology, generative artificial intelligence, as a frontier technological approach, has demonstrated tremendous potential and value in the journal publishing field, bringing unprecedented transformations to traditional scientific journal publishing [1]. Numerous domestic and international scientific journals have already applied generative AI to various stages of the publishing process, achieving not only an intelligent reconstruction of publishing workflows but also greatly enhancing publishing efficiency and quality. In December 2023, the China Audio-video and Digital Publishing Association released the group standard Guidelines for the Application of Generative Artificial Intelligence Technology in the Publishing Industry, further promoting the standardized application of generative AI in publishing. Although generative AI has made certain progress in publishing applications, we must also clearly recognize that this technology remains in an exploratory stage. This paper begins with the opportunities that generative AI brings to scientific journal publishing, comprehensively analyzes its applications from the perspective of publishing workflows, and explores the challenges it poses to both publishing operations and editorial career development, aiming to provide assistance for journal editors and the publishing community in applying generative AI.

1. Opportunities Generative AI Brings to Scientific Journal Publishing

1.1 Optimizing Publishing Workflows and Enhancing Efficiency

Applying generative AI to scientific journal publishing can not only significantly optimize workflows across all stages but also substantially improve overall efficiency. During the creation and editing phase, generative AI, with its powerful deep learning and natural language processing capabilities, can quickly browse and analyze massive amounts of research materials, providing valuable writing inspiration and source materials for researchers, drafting paper manuscripts, and even automatically generating key sections such as abstracts, introductions, and conclusions, thereby greatly reducing authors' writing burden. Simultaneously, generative AI can automatically detect and correct common writing errors in grammar, spelling, and formatting, offering targeted revision suggestions such as optimizing sentence structure and enhancing clarity, making the editing process smoother and more efficient. In the review and evaluation stage, intelligent algorithms in generative AI can conduct preliminary screening of submissions, leveraging their powerful analytical capabilities to rapidly identify manuscripts that do not align with the journal's scope or fail to meet academic standards, thereby effectively reducing reviewers' daily workload and ensuring that only high-quality manuscripts enter the subsequent review process [2-3]. For manuscripts that meet journal requirements, generative AI can further provide content-based preliminary assessments, helping reviewers quickly understand the core value and innovative contributions of a paper, thus accelerating the review process. In the publishing and dissemination stage, generative AI, through big data analysis and intelligent decision support systems, can assist editors and management in making data-driven decisions, monitoring journal quality metrics such as review cycles, acceptance rates, and impact factors in real time, and promptly adjusting and optimizing publishing strategies to ensure the journal's long-term healthy development. Additionally, generative AI can deeply analyze readers' reading habits and interest preferences, precisely pushing the latest paper results highly relevant to their research fields, thereby enhancing user experience. To better expand into international markets, the language translation capabilities of generative AI can provide convenient and accessible content for global readers, promoting academic exchange and cooperation worldwide and enhancing the competitiveness and influence of journals in both academic and industrial spheres.

1.2 Enhancing Publishing Resource Allocation and Editorial Value Creation

In the era of intelligence, the scope of editorial work and role positioning are quietly changing. Faced with the rapid development of AI technology, editors need to timely adjust their work strategies, learning to delegate tedious and highly rule-based tasks to intelligent systems, freeing up time and energy to focus on more challenging and creative intellectual labor. This transformation represents not only a reshaping of individual work capabilities but also a profound insight into and preparation for the future of the editorial profession. In scientific journal publishing, generative AI can conduct in-depth analysis of accumulated big data, revealing hidden patterns and trends to provide editors with valuable insights. By leveraging multiple information channels, editors can use participatory observation methods to capture potentially valuable new topics from massive amounts of information. These topics often represent frontier dynamics and hot trends in academic research, serving as important sources of inspiration for editorial planning [4]. While AI can process and analyze data, it still cannot replace the unique perspective and keen intuition of human experts in understanding the depth, breadth, and innovativeness of academic research. Therefore, on this foundation, editors must rely on their professional academic literacy and judgment to transform these insights into rigorous academic thinking for topic planning, ensuring accuracy and foresight in editorial decision-making. In actual publishing work, editors can also use big data analysis technology to deeply explore the meaning and connections behind data, creatively discover new research perspectives and methods, and further expand their cognitive boundaries to inject new vitality and momentum into academic journals. This creative approach to work not only enhances editors' individual capabilities but also provides strong support for the differentiated development of academic journals. Furthermore, the ability of AI technology to learn, model, and calculate based on massive data has significantly improved the accuracy of data analysis and prediction [5]. This technology can not only delve into all articles published since a journal's establishment to uncover their academic value and research trends but also continuously track papers currently under review and editing, updating data models in real time to improve prediction accuracy. More importantly, AI can effectively measure the academic value and dissemination risk of planned papers, providing scientific basis for editorial resource allocation. Meanwhile, the role of journal editors is no longer limited to traditional proofreading and layout design but extends more into the deep excavation and value assessment of papers. Using intelligent algorithms, editors can explore the potential value of papers with unprecedented depth and breadth, even predicting publication effects to some extent. Such foresight helps editors plan promotional strategies in advance, create momentum for paper publication, and further enhance the academic influence and brand recognition of journals.

2. Applications of Generative AI in Scientific Journal Publishing

2.1 Intelligent Topic Planning

In the editorial workflow of scientific journal publishing, generative AI can help editors break through personal preferences and knowledge limitations, providing a comprehensive and detailed perspective for topic analysis [6]. Compared with traditional manual topic planning, intelligent topic planning systems can not only generate topics that align with publication characteristics but also demonstrate unique advantages during the topic generation process. The collaboration between Elsevier and University College London (UCL) to establish the UCL Big Data Institute demonstrates that generative AI plays an important role in predicting and serving academic research hotspots. This collaboration not only validates the potential of generative AI technology in academic publishing but also provides editors with more precise topic recommendations to enhance their planning capabilities. Similarly, the Frontiers of Computer Science journal actively explored the use of advanced generative AI technologies such as GPT-4 and Alibaba Cloud Brain at the end of 2023 to optimize article review and topic selection processes, once again validating the significant effectiveness of generative AI technology in improving review quality and accelerating topic decision-making. These practical achievements not only confirm the enormous potential of generative AI technology in academic publishing but also point the way forward for the intelligent development of scientific journals, heralding the arrival of a new era of more efficient, precise, and innovative publishing and editing.

2.2 Improving Review Efficiency

The rapid development of technology has not only greatly facilitated increasingly frequent academic exchanges but has also led to a sharp surge in the number of paper submissions received by academic journals each year. This trend poses an urgent challenge for academic journal publishers: how to efficiently and accurately screen out high-quality research papers from massive submissions. Traditional academic journal paper初审 (initial review) processes rely primarily on manual processing by editors, which is not only inefficient but also lacks interactivity, making it difficult to meet the needs of today's rapidly developing academic exchanges [7-8]. To effectively address this issue, an increasing number of journal publishers are exploring the use of generative AI's machine learning and natural language processing capabilities to help editors preliminarily screen high-quality manuscripts that meet journal requirements, thereby reducing editors' workload. In the review process, generative AI can quickly process large volumes of manuscripts, accurately screening out articles that meet journal requirements and effectively alleviating the heavy pressure of review work. Simultaneously, based on in-depth analysis of published papers, this technology can assist editors in evaluating the innovative value of manuscripts, providing solid data support for uncovering high-quality research. Its built-in similarity detection function can sensitively identify plagiarism, data tampering, and content duplication, strongly safeguarding academic integrity. Furthermore, generative AI can automatically generate intuitive charts and reports to help reviewers deeply understand the essence of papers and accelerate the review process [9]. More notably, it can conduct comprehensive assessments of the review process, identify potential biases or errors, and provide detailed review feedback, offering scientific basis for editorial decision-making. This series of applications not only strengthens the fairness, objectivity, and efficiency of academic evaluation but also ensures the accuracy of evaluation results, injecting strong momentum into the sustainable development of academic publishing while effectively reducing reviewers' burden [10].

With the widespread application of generative AI technology in peer review, publishers should currently focus on integrating this advanced technology with the professional wisdom of reviewers to fully leverage the unique advantages of both. This fusion strategy can not only significantly improve review process efficiency but also further enhance the capacity and accuracy of academic evaluation. For example, the SmartReview system introduced by Nature Communications magazine represents a successful model. The system uses advanced algorithms to conduct preliminary quality screening of submissions, quickly eliminating manuscripts that do not meet standards while precisely matching reviewers with relevant professional backgrounds and strictly examining any conflicts of interest in the review process to ensure impartiality. The SmartReview system continuously iterates and improves its performance, aiming to accelerate academic evaluation processes, enhance objectivity and efficiency, while maintaining the rigor and transparency of review procedures, laying a solid foundation for the high-level development of academic journals.

2.3 Improving Quality and Efficiency of Publishing

In China's journal publishing work, strict quality control in editing and proofreading is a key link in ensuring publications' academic influence and broad dissemination effect. Although the traditional "three reviews, three proofreads, and one final reading" system is cumbersome and time-consuming, it lays a solid foundation for publication quality, ensuring that every环节 (stage) has been carefully considered and meticulously polished. This system effectively reduces errors through layered checks and enhances the overall standard of publications, allowing readers to perceive their professionalism and credibility. To further refine the standards for editing and proofreading quality and promote the standardized development of the publishing industry, in June 2023 the National Press and Publication Administration released the Method for Determining and Calculating Errors in Book Editing Quality, which provides detailed elaboration on the determination of various errors in text, images, etc., offering clear and explicit operational guidelines for editors. It not only clarifies the classification and grading of errors but also details corresponding scoring methods, making editing work evidence-based, more scientific, and rigorous. Against this backdrop, generative AI demonstrates obvious advantages in collaborative proofreading for publishing. Its powerful data processing and analysis capabilities can significantly enhance the precision and efficiency of proofreading. Automated proofreading technology can quickly capture and correct common errors in text, grammar, and logic, substantially reducing the workload of editorial staff [11]. Meanwhile, the introduction of intelligent error correction systems makes editing work more efficient and precise, effectively avoiding omissions and errors caused by human factors. Additionally, generative AI possesses risk assessment and automated polishing functions, enabling it to predict and evaluate potential risks based on publication content and characteristics, allowing timely preventive measures. In terms of polishing, AI can adjust language style with precision, making publications more aligned with readers' reading habits and aesthetic preferences, which not only enhances readability but also strengthens market competitiveness. More notably, generative AI can conduct in-depth evaluation of literature quality and citation standards. It can quickly search and compare relevant literature to ensure citation accuracy and completeness [12,13]. Simultaneously, through deep analysis of literature content, AI can judge its academic value and innovativeness, providing valuable references and suggestions for editors. This functionality not only reduces the risk of false information but also enhances the overall quality of publications, injecting new vitality into the healthy development of the publishing industry.

3. Challenges Facing Scientific Journal Publishing in the Context of Generative AI

3.1 Challenges in Review and Assessment

In the review and assessment processes of academic journals, although generative AI demonstrates enormous potential in content generation with its efficient and rapid characteristics, the problems it brings cannot be ignored. Since generative AI does not yet possess the deep analytical and judgment capabilities of human experts when generating research results, effectively evaluating the authenticity and academic value of these outputs has become a significant challenge. The root of the problem lies in the fact that generative AI's output depends on training data and algorithmic models, which may introduce bias or misinformation, thereby affecting research fairness and accuracy. First, data imbalance is a major issue facing generative AI. Due to difficulties and limitations in data collection, training data often cannot comprehensively and objectively reflect the diversity and complexity of academic research. This imbalance leads to bias in AI-generated content, distorting the true picture of research. Such bias not only damages research fairness but also misleads readers and researchers, negatively impacting academic progress. Second, the application of generative AI in academic journal review involves data privacy issues. During content generation, AI may need to access and process large amounts of personal information and sensitive data. If this data is not properly protected, it may violate ethical standards and privacy regulations, causing serious legal consequences [14]. Moreover, even with data protection, the unpredictability of AI in generating content may create privacy leakage risks. Third, generative AI has limitations in understanding instructions and generating required content. Since AI's language understanding and generation capabilities are not yet fully mature, it may misinterpret instructions or generate content that does not meet academic standards. This not only affects review quality but may also damage the reputation of academic journals. Additionally, relying on generative AI for academic review may harm academic integrity. Since AI output may lack sufficient accuracy and credibility, leading to inaccurate evaluation results, it directly damages the authenticity and reliability of academic research. This reliance may also trigger new forms of academic misconduct, such as using AI to generate false research results, seriously impacting the reputation of academic journals and the credibility of academic research. Meanwhile, as generative AI technology continues to develop and improve, academic journals need to constantly adjust review standards to adapt to these changes. However, in practice, this process may lack sufficient resources and support, causing difficulties for journals in responding to new technological challenges. Editors and reviewers need to receive new technical training and education to better understand and apply generative AI technology, thereby ensuring the accuracy and fairness of academic review.

3.2 Challenges in Editorial Career Development

Faced with the rapid development of AI technology, some editorial staff adhere to old ways, adopt negative attitudes toward learning emerging technologies, or encounter numerous obstacles in the learning process. Therefore, under the continuous development of AI technology, continuing education for traditional editors has become an unavoidable issue. In 2017, the State Council's New Generation Artificial Intelligence Development Plan clearly stated the need to accelerate research on changes in employment structures and methods caused by AI and support higher education institutions, vocational colleges, and social training institutions in conducting AI skills training. Currently, the talent demand structure in the journal publishing industry is undergoing profound changes, with interdisciplinary talents becoming increasingly favored, especially those who are proficient in both traditional publishing expertise and skilled in using intelligent software. To respond to talent demands in the intelligent era, educational institutions such as Beijing Institute of Graphic Communication have added majors like Intelligent Science and Technology to cultivate interdisciplinary talents who can adapt to future industry development needs. In reality, whether they are recent graduates cultivated by universities or senior editors who have worked in editorial positions for many years, all must regard mastering and effectively using intelligent editing software as an indispensable professional skill. They should continuously stimulate innovative thinking and enrich practical experience based on their respective work experience to ensure sustained competitiveness in their professional fields [15]. Moreover, from a macro perspective of the entire industry, policy-making institutions should keep pace with intelligent technology development, carefully plan top-level strategies, establish sound regulatory frameworks, and accelerate the construction of standardization systems.

Generative AI is gradually penetrating and revolutionizing the traditional model of scientific journal publishing. While this process is accompanied by a series of challenges, the enormous opportunities it contains cannot be ignored. Faced with these profound changes, we should adopt a proactive attitude and fully utilize the unique advantages of generative AI in scientific journal publishing. Through reasonable application of this advanced technology, we can optimize publishing workflows, improve publishing efficiency, enhance the academic influence of journals, and thereby more effectively promote the dissemination of scientific research and meet society's demand for high-quality academic achievements.

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Author Bio: Lü Chong (1990—), female, Han ethnicity, from Shenyang, Liaoning, bachelor's degree, intermediate professional title, research direction: editing and publishing.

(Responsible Editor: Li Yansong)