Artificial Intelligence Applications in Journal Publishing

Li Ran, Zhu Dandan, Wang Yan
Editorial Department of Armed Police Logistics, Armed Police Logistics College, Hefei, Anhui 300309

Abstract

[Purpose] This study aims to thoroughly investigate the current state and potential advantages of artificial intelligence technology in journal publishing, providing theoretical foundations and practical guidance for the digital transformation and innovative development of the publishing industry. It seeks to promote simultaneous improvements in both the quality and efficiency of journal publishing while accelerating the intelligentization process of China's journal publishing sector. [Method] Through a comprehensive literature review and inductive analysis, this paper systematically examines the latest applications and trends of AI technology across various stages of journal publishing. [Results] Beginning with an overview of AI development, this article defines the scope of AI applications in journal publishing and explores in detail its implementation in three key areas: manuscript submission, review and proofreading, and typesetting. [Conclusion] The integration of AI presents unprecedented transformative opportunities for the publishing industry. Through precise application of AI technologies, publishing houses can significantly enhance operational efficiency and publication quality. Consequently, journal publishers should actively embrace AI to meet the demands of the new era and propel China's publishing industry toward higher levels of intelligence, precision, and efficiency.

Keywords: artificial intelligence; journal publishing; editing; application; new era
Classification Code: G230
Document Code: A
Article ID: 1671-0134(2025)03-116-04
DOI: 10.19483/j.cnki.11-4653/n.2025.03.025
Citation Format: Li Ran, Zhu Dandan, Wang Yan. Research on the Application of Artificial Intelligence in Journal Publishing [J]. China Media Technology, 2025, 32(3): 116-119.

As vital carriers for academic research and knowledge dissemination, journals play an irreplaceable role in advancing scientific progress, cultural exchange, and social development. As artificial intelligence technology demonstrates enormous potential and value across manufacturing, healthcare, finance, and other industries, it is inevitably being introduced into various workflows of journal publishing [1,2]. With its powerful capabilities in data processing, image recognition, and deep learning, AI has brought transformative changes to publishing operations. For instance, in manuscript proofreading, AI can rapidly identify grammatical, spelling, and formatting errors, significantly improving both efficiency and accuracy. In intelligent typesetting, AI not only reduces the workload of typesetters but also automatically designs layouts according to journal specifications and article content, creating more rational and aesthetically pleasing pages. Furthermore, natural language processing and data mining technologies in AI enable deep analysis and mining of journal articles to extract key information and innovative points, providing editors and readers with more precise content recommendations and services. Faced with these changes, integration with AI has become an inevitable trend [3]. As AI technology continues to mature, its applications in journal publishing will become more extensive and profound. Publishing institutions should actively embrace this transformation, strengthen integration with AI technology, continuously enhance their innovation capabilities and competitiveness, and provide higher-quality, more efficient services for academic research and knowledge dissemination. This paper employs inductive analysis to elucidate AI applications in journal publishing, offering reference for future research on deep integration between AI and the publishing industry.

1. Overview of Artificial Intelligence

The concept of Artificial Intelligence (AI) was formally proposed by John McCarthy at the Dartmouth Conference in 1956. Since then, AI has evolved through multiple stages—from infancy to preliminary exploration, and then to vigorous development—with each technological leap marking progress toward simulating and surpassing human intelligence. In the early research phase, due to technological limitations, scientists' aspirations for AI remained largely theoretical, and they failed to develop algorithms that could truly simulate human intelligence [4]. Research during this period remained at the level of theoretical discussion and preliminary experimentation, without forming a complete technical system or broad application scenarios.

However, with the substantial improvement in computer performance and the popularization of the internet in the 1980s, AI research entered a new spring. Machine learning and data mining became research hotspots, and emerging technologies such as decision trees, support vector machines, neural networks, and genetic algorithms laid a solid foundation for widespread AI applications. Entering the 21st century, AI has ushered in its third wave of development, with deep learning becoming a breakthrough milestone. Deep learning, a machine learning method based on artificial neural networks, achieves automatic feature extraction and classification of complex data by simulating the working principles of human brain neurons. This revolutionary technological breakthrough has enabled AI to achieve substantial progress in image recognition, speech recognition, and natural language processing, improving system accuracy and efficiency while driving extensive and profound applications across education, finance, healthcare, transportation, and other fields [5,6].

Today, AI has made remarkable advances in numerous domains. In smart agriculture, Huawei and its partners have launched smart farm solutions that enable precise monitoring and control of crop growth environments, increasing personalized planting ratios to 60% while improving pest and disease warning accuracy to 95% and effectively reducing pesticide usage. In intelligent education, New Oriental's AI-powered learning platform provides personalized learning paths based on student progress and ability, improving score efficiency by 40% and significantly enhancing learning interest and motivation. In smart home systems, Xiaomi's ecosystem enterprises have developed intelligent appliance systems supporting whole-house intelligent联动 (interconnection), allowing users to remotely control appliances via voice or mobile apps, increasing life convenience by 70% while reducing energy consumption by 20%. In intelligent transportation, Didi's AI traffic brain predicts urban traffic flow through big data analysis and optimizes route planning, reducing urban congestion indices by 20% during peak hours and decreasing passenger waiting times by 30%. In smart retail, Alibaba's unmanned supermarkets employ facial recognition and RFID technologies to enable autonomous shopping and rapid checkout, improving shopping experience by 65% and reducing operational costs by 40%. In environmental protection, Tencent's environmental big data platform uses AI algorithms to analyze environmental monitoring data, accurately predicting air quality trends with warning accuracy reaching 85%, helping governments take advance environmental protection measures. In intelligent security, Hikvision's intelligent video surveillance system combines deep learning algorithms to identify abnormal behaviors in real time and automatically trigger alarms, increasing security response speed fivefold and significantly improving public safety.

These machine learning algorithms have extensive practical applications, achieving remarkable results in fields ranging from image recognition and speech processing to natural language processing. They have not only enhanced computer intelligence but also brought great convenience to people's lives and work. As technology continues to develop, machine learning will undoubtedly play an even greater role in more fields, driving continuous advancement in AI technology.

This study argues that when exploring AI applications in journal publishing, true AI applications should be technological implementations based on machine learning algorithms. As a key branch of AI, machine learning builds predictive models through training on known data and applies them to unknown data for prediction and analysis—this process embodies AI's core capability of self-learning and adaptation. In contrast, traditional computer automation programs such as literature search, journal search, and reference checking software, while demonstrating certain automated features in data processing, cannot be considered true AI applications because they do not employ machine learning algorithms for training and optimization. These functions primarily rely on preset rules and algorithms for statistical data analysis, lacking self-learning and adaptive capabilities. However, if these traditional functions evolve further toward AI, they could become part of AI applications. For example, intelligent search engines can leverage machine learning algorithms to deeply analyze users' search records and preferences, providing more personalized search results. Big data-based personalized report generation systems can create customized reports by mining and analyzing user data. These functional upgrades not only improve data processing and analysis accuracy but also provide users with more intelligent and personalized service experiences.

3. Applications of Artificial Intelligence in Journal Publishing

Journal publishing is a complex and cumbersome process involving multiple critical stages such as manuscript reception, review, editing, and proofreading. These stages involve heavy workloads and often repetitive tasks that consume substantial time and energy from editorial staff. However, as AI technology continues to develop and mature, its applications in the publishing workflow are gradually demonstrating enormous potential and value.

3.1 Streamlined Submission

The introduction of online manuscript management systems based on AI technology represents a comprehensive upgrade from traditional manual manuscript reception, addressing pain points of low efficiency and frequent errors while building a more efficient and convenient communication bridge between editorial offices and authors. During this transformation, Qinyun Manuscript Processing System and Magtech Manuscript Processing System have become preferred choices for numerous editorial departments due to their powerful functionality and user-friendly design [8]. Both systems integrate AI-powered automatic recognition technology, making manuscript information extraction unprecedentedly simple and efficient. Following the system's user-friendly interface guidance, authors can easily upload their manuscripts, triggering the intelligent analysis engine to automatically extract core information such as title, author names and affiliations, abstract, keywords, and funding information from complex document content. This intelligent processing flow significantly reduces author burden, avoids errors from manual data entry, and enables authors to focus on content creation rather than tedious formatting issues. For editorial offices, this means receiving more standardized and structured manuscript data, laying a solid foundation for subsequent processing.

Beyond efficient information extraction, AI technology has optimized the entire manuscript management workflow [9]. Traditionally, editorial staff spent considerable time manually registering manuscript information and maintaining complex Excel spreadsheets. Now, all manuscript-related information is automatically stored in the system, forming a well-organized, easily searchable database. Staff can quickly retrieve required information by simply setting search conditions, gaining clear visibility into article titles, author affiliations, research directions, and more, thereby greatly improving work efficiency. Additionally, the system supports diverse statistical analysis functions, including monthly/annual manuscript submission volumes, expert review quantity analysis, and manuscript acceptance rate monitoring, providing rich data support for editorial offices. This data not only forms the foundation for daily operations but also serves as an important basis for strategic planning and content optimization. By analyzing research directions and quality distributions of submitted manuscripts, editorial offices can better grasp academic trends, identify research hotspots, and timely adjust topic selection and column design to ensure the journal's content remains cutting-edge and attractive. Simultaneously, the data provided by the system proves invaluable for continuously optimizing review processes and rationally allocating expert database resources. Overall, AI-based online manuscript management systems have not only automated and intelligentized manuscript processing workflows but also provided comprehensive and precise decision-support systems for editorial offices, helping them stand out in fierce market competition and continuously enhance their editorial capabilities and academic influence.

3.2 Intelligent Review and Proofreading

The efficiency and quality of peer review constitute the cornerstone of academic journal survival and development, directly affecting both the academic value of published content and the length of publication cycles. When editors face diverse manuscripts, they cannot alone comprehensively and accurately grasp their academic frontiers and innovation, making expert reviewers' professional judgments particularly crucial [10]. To effectively improve review efficiency while ensuring quality, the application of AI technology becomes essential. This technology intelligently recommends the most suitable reviewers based on manuscript content, editors' specific requirements, reviewers' professional qualifications, past review experience, and domain expertise, significantly shortening review cycles while improving review precision.

In the AI era, journal editing primarily utilizes intelligent review assistance systems and sensitive word recognition systems. China implements a "three reviews and three proofreads" system requiring rigorous manuscript proofreading. Intelligent review assistance systems further enhance the intelligence level of the review process [11]. First, the system conducts in-depth analysis of paper content, including article structure, research methods, data sources, analysis processes, and conclusions. Simultaneously, it comprehensively examines authors' academic backgrounds, research fields, and previous publications. Based on this information and considering reviewers' actual needs—such as scheduling constraints, review priorities, and journal positioning—the system generates targeted review comments. These comments include evaluations of academic quality, innovation, and practicality, while accurately identifying potential weaknesses and providing specific, constructive revision suggestions for authors [12]. Notably, intelligent review assistance systems continuously learn and analyze vast amounts of review data. By comparing review standards and preferences across different fields and journals, the system gradually masters the characteristics and requirements of specific journals, generating more professional and appropriately positioned review comments. This not only improves review efficiency and quality but also provides strong guarantees for the journal's academic reputation.

Sensitive word recognition technology serves as a powerful tool for ensuring manuscript content compliance. Faced with sensitive word variants in manuscripts—such as phonetic substitutions, abbreviations, and split expressions—editors must remain vigilant. By designing more sophisticated sensitive word recognition algorithms, the system can efficiently and accurately identify and flag these potential risk points, effectively improving detection efficiency and ensuring healthy, positive journal content [13].

3.3 Journal Typesetting

AI applications in journal typesetting are gradually reshaping the publishing industry, particularly through breakthroughs in automatic typesetting and automatic directory generation, which have brought revolutionary changes to editorial work [14]. This technology introduction not only greatly improves typesetting efficiency but also ensures consistency in layout design and high standards of publication quality, providing readers with more professional and convenient reading experiences.

In automatic typesetting, AI through deep learning algorithms can precisely identify and strictly adhere to journal-specific formatting specifications. These specifications typically encompass multiple dimensions including fonts, font sizes, line spacing, paragraph formats, and image/table embedding methods to ensure unified and aesthetically pleasing journal styles. Traditionally, these tasks required professional typesetters to complete manually, being time-consuming, labor-intensive, and prone to human error. AI, however, can automatically complete the entire process from article import to final layout presentation according to preset rules, including text alignment, paragraph adjustment, and text-wrapping around images, achieving highly efficient automation of typesetting work. This automation not only improves efficiency but more importantly ensures consistent layout design across every issue, enhancing the journal's professionalism and readers' reading experience.

In automatic directory generation, AI also demonstrates remarkable capabilities. Through in-depth analysis of article titles, author information, keywords, and other data, AI can intelligently construct the journal's directory structure. This process requires no human intervention yet can automatically generate well-organized, clearly structured directories, greatly improving directory accuracy and generation efficiency. For readers, this means they can more quickly locate content of interest, whether searching for articles on specific topics or looking up all works by particular authors. Additionally, automatic directory generation effectively reduces inconsistencies between directories and main content caused by human error, thereby improving overall journal quality and professionalism [15].

It is worth noting that AI applications in journal typesetting are not limited to automatic typesetting and directory generation. As technology continues to advance, AI can also intelligently recommend the most suitable layout schemes and visual effects based on journal brand characteristics and target readership, providing more creative space for personalized design [16]. Simultaneously, through integration with other AI technologies such as big data and natural language processing, AI can more deeply analyze reader behavior and preferences, providing strong data support and strategic recommendations for journal content optimization and personalized content delivery.

Although AI applications in journal publishing have achieved initial success, deep integration between AI and publishing remains in the exploratory stage, with a long and challenging road ahead. Editorial departments, as key forces driving this process, should actively embrace AI technology, explore more application scenarios in editorial workflows, and aim to reduce editorial staff workload while freeing up more time and energy to focus on creating high-quality, exquisite, and highly readable journal content.

The introduction of AI technology has brought unprecedented efficiency improvements to editorial offices. Through automated manuscript information processing, intelligent proofreading, intelligent typesetting, and automatic directory generation, editorial staff can be liberated from tedious formatting adjustments and data entry tasks, allowing them to devote more energy to content selection, optimization, and innovation. This will further enhance overall journal quality and strengthen journal appeal and competitiveness. Editorial offices need to continuously learn and adapt to new technologies, establish comprehensive AI-assisted editorial systems, cultivate interdisciplinary talent, and explore optimal pathways for deep integration with journal publishing.

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Author Biographies:

Li Ran (1987—), female, from Chaohu City, Hefei, Anhui Province. Works at the Editorial Department of Armed Police Logistics, Armed Police Logistics College. Bachelor's degree. Research interests: journal publishing and armed police logistics theory.

Zhu Dandan (1994—), female, from Wuhu City, Anhui Province. Works at the Editorial Department of Armed Police Logistics, Armed Police Logistics College. Intermediate professional title, Master's degree. Research interests: publishing and armed police logistics theory.

Wang Yan (1991—), female, from Qinhuangdao City, Hebei Province. Works at the Editorial Department of Armed Police Logistics, Armed Police Logistics College. Junior professional title, Bachelor's degree. Research interests: publishing and armed police logistics theory.

(Responsible Editor: Li Yansong)