Preamble

Research on Manuscript Screening Mechanisms for AI-Assisted Editing and Publishing
(General Publishing House of Shaanxi Normal University, Xi'an, Shaanxi 710062)

Abstract

[Objective] This study explores manuscript screening mechanisms for AI-assisted editing and publishing to improve screening efficiency and quality. [Methods] The paper outlines the current application status, analyzes core elements such as keyword matching and content quality assessment, details various screening processes, examines advantages and challenges, and proposes suggestions for improvement. [Results] The study clarifies the key points of each stage of the screening mechanism and identifies existing problems. [Conclusion] Optimizing data acquisition and integration, enhancing the interpretability of results, and integrating human review can effectively improve AI manuscript screening mechanisms.

Keywords

AI-assisted; Editing and publishing; Manuscript screening; Human review

In the context of the digital era, AI technology has been widely applied and plays an important role in academic publishing, and the editing and publishing of mathematics education journals is no exception. For editors of mathematics journals, the traditional manual method of screening manuscripts has become inefficient due to the significant increase in submissions. AI technology possesses powerful functions in data processing and intelligent analysis, providing effective technical support for manuscript screening and fundamentally transforming the modes of editing and publishing. In-depth exploration of using AI-assisted editing for manuscript screening can significantly improve the quality of mathematics education journal publishing and exert a significant influence on the industry.

1. Overview of the Current Status of AI Screening Applications

Currently, AI screening technology has been widely applied across the editing and publishing industry. From large publishing groups to emerging literary platforms and various academic journals, AI screening tools have permeated different stages of the editorial workflow. Publishing institutions fully recognize that AI technology can extract valuable manuscripts from a large volume of submissions, significantly improving the quality of publishing operations. AI offers distinct advantages in manuscript screening. Traditionally, manual screening required editors to review large volumes of manuscripts word by word, which was not only time-consuming but also struggled to achieve ideal efficiency. By leveraging the computational power and algorithms of AI technology, a large amount of manuscript information can be preliminarily screened in a short period. AI utilizes its high-speed computing advantages to greatly reduce the time required to screen massive submissions, significantly shortening the preliminary review period and accelerating the publishing process.

Regarding screening standards, AI can measure manuscripts across multiple dimensions. In terms of content, AI can accurately grasp core concepts and other information, using the publisher's preset topics and audience preferences as screening criteria to precisely judge the consistency between manuscript content and publishing requirements. Regarding linguistic expression, AI can scan for grammar and sentence fluency, identifying linguistic errors in a timely manner to provide a solid linguistic foundation for subsequent editing. In terms of structural integrity, AI can accurately judge the layout and paragraph settings of an article to ensure a reasonable architecture. It is evident that applying AI technology to screen manuscripts can significantly improve the efficiency of the publishing workflow, assisting editors in devoting more energy to polishing high-quality manuscripts. However, this does not mean AI is omnipotent. When reviewing manuscripts with complex semantic nuances or high levels of innovation, AI may fail to function correctly, making it difficult to replace the human judgment that editors provide based on their own knowledge and experience.

2.1 Keyword Matching Mechanism

Keyword matching is a vital component in screening mathematics education manuscripts. Given the current information-based environment, mathematics education journals receive a large volume of submissions covering diverse themes. The keyword matching mechanism assists editors in quickly and accurately selecting manuscripts that align with the journal's themes and requirements from a vast pool of submissions. Editorial teams can construct a keyword library based on long-term areas of focus, primarily including professional mathematical terminology and trending vocabulary in the education industry. Examples include concepts like "functions" and "equations" in junior high school mathematics, "derivatives" in senior high school mathematics, and trending terms such as "cultivation of core mathematical literacy."

Information-based manuscript processing systems can perform preliminary screening by comparing the title, abstract, and high-frequency words of a submission against the keyword library. For instance, if a manuscript focuses on the practice of teaching junior high school functions using information technology and frequently features keywords like "information technology means" and "teaching effectiveness improvement," it can be judged as closely related to the journal's preset themes. This mechanism significantly improves screening efficiency and prevents editors from wasting time and energy. As mathematics education continues to innovate, the keyword library must be updated promptly to incorporate cutting-edge educational concepts, methods, and topics, thereby adapting to new developments in educational research.

2.2 Content Quality Assessment Methods

Assessing the content quality of a manuscript ensures its value in terms of both academic rigor and writing standards. First, the scientific validity of the research must be evaluated. Educational research in mathematics must be grounded in corresponding theories. By combining specific cases and problem-solving methods, the research should ultimately align with the mathematical knowledge system and curriculum standards. For example, when introducing a new problem-solving technique, its rigor must be guaranteed to ensure it effectively assists students in solving problems.

Second, the depth and breadth of the content must be measured. This involves evaluating the depth of the mathematical problem analysis and whether it provides new methods and strategies for teaching activities to help students master knowledge efficiently. For instance, research on a specific subject concept should break away from traditional teaching methods to form innovative strategies that guide students to grasp conceptual connotations from different perspectives, thereby enhancing the depth of their thinking. The assessment also focuses on whether the manuscript covers multiple teaching stages and caters to students of different levels, demonstrating superior content breadth.

Finally, writing quality is a key focus, primarily ensuring fluent language and standardized formatting. Clear language accurately expresses the author's viewpoints, and a reasonable structure enhances the article's hierarchy. Typically, the structure of mathematics teaching articles follows a sequence of problem statement, methodological description, practical process, effect analysis, and conclusion, facilitating reader understanding. Standardized formatting requires adherence to requirements for font size, line spacing, and other elements. Evaluating these aspects allows for an accurate judgment of whether a manuscript meets the journal's publication standards. During the evaluation, editors should also focus on whether the manuscript reviews previous research and the author's familiarity with the field, thereby achieving innovation based on existing research.

2.3 Innovation Judgment Standards

Innovation serves as a critical criterion for measuring the publication value of a manuscript. First, innovation should be present in both content and methodology, such as proposing cutting-edge teaching ideas or designing innovative activities. For example, junior high school geometry teaching could incorporate VR technology to allow students to intuitively understand graphic transformations. Such pedagogical innovation provides students with a new experience, effectively stimulates interest, and achieves innovation in teaching content.

Second, authors need to develop innovative research perspectives. Traditional perspectives may not fully meet teaching needs; therefore, mathematical problems must be examined from new angles to achieve breakthroughs. For instance, analyzing students' psychological barriers from a psychological perspective and proposing feasible strategies can break the limitations of single-subject research and achieve perspective innovation. Finally, teaching achievements should be comprehensively organized to propose innovative viewpoints that promote the development of education and teaching. When judging innovation, editors can refer to relevant literature and research trends, using professional databases to ensure the manuscript's innovations are valuable. Additionally, editors may consider the operability of the innovation; if the results can be applied to actual teaching activities, the manuscript possesses significant publication value.

3.1 Detailed Preliminary Review Process

The first stage of screening mathematics manuscripts is the preliminary review. Editors can use AI tools for initial formal checks. Upon receiving a manuscript, its format is inspected; AI can quickly compare the manuscript against submission requirements. Regarding formatting, AI can check font, line spacing, abstracts, keywords, and citation styles to ensure that headings at all levels meet journal standards. It also verifies the completeness of information, including author names and contact details, which facilitates communication and provides background on the author. If there are significant formatting errors, editors can use AI markers to promptly communicate with the author for revisions. If errors are excessive and not corrected in time, the manuscript may be rejected immediately.

After the formal review, the content is examined. Editors use AI-assisted technology to review the theme and its relevance to the journal, assessing the connection between the content and mathematics teaching or teacher professional development. For example, if a mathematics teaching manuscript fails to specify whether it targets junior or senior high school or lacks specific teaching content, AI assistance can quickly generate a preliminary review result. Language fluency and typos also directly impact quality. If a manuscript clearly deviates from the journal's positioning, AI helps editors identify this quickly for immediate rejection. Once the quality is confirmed, editors form a preliminary opinion, noting strengths (such as novel cases) and weaknesses (such as methodological deficiencies or imprecise language), before moving to the peer review stage. This process is typically completed within two weeks, ensuring valuable manuscripts are identified quickly. During this stage, AI also assists in screening for academic misconduct, such as plagiarism, by comparing the text against vast databases.

3.2 Exploration of the Peer Review Process

The editorial department may invite industry experts or senior editors to conduct a peer review. Following the preliminary review, experts read the manuscript in detail, using AI tools to analyze content quality. AI can comprehensively examine everything from the scientific validity of teaching methods to the logic of the arguments. For a manuscript related to teaching "congruent triangles" in junior high school, AI tools evaluate the consistency between the teaching method and cognitive patterns, the method's effectiveness, and the article's structure. If paragraph design is illogical, AI can suggest rearrangements to improve the framework. AI also helps correct improper academic terminology to ensure precision.

Experts combine their experience with AI tools to make scientific judgments regarding the manuscript's innovative value in mathematics education. Furthermore, experts can use AI to analyze specific issues, such as the alignment of teaching methods with educational policies. With AI assistance, experts complete their review and form a report that clearly states their recommendation (e.g., acceptance or rejection) and explains the direction for necessary revisions. Editors then weigh these opinions to determine the final handling of the manuscript. If experts disagree, editors may organize discussions or invite additional experts for arbitration. AI assistance ensures that the peer review process maintains high quality, resulting in published manuscripts that hold significant value in their research field.

3.3 Analysis of the Final Review Process

The editor-in-chief or the editorial board completes the final review stage. This stage involves a comprehensive consideration of opinions from the previous two stages and a thorough weighing of the manuscript's content. The editor-in-chief or board can use AI tools to re-verify the educational value and the manuscript's fit with the journal's positioning. Beyond quality, factors such as the journal's current themes and publishing plans are considered, with AI providing data analysis to assist in these judgments.

During the final review, if AI identifies controversial issues, intelligent communication assistance systems can facilitate in-depth dialogue with authors or experts. Once a manuscript is accepted, authors are contacted to revise the format and content based on expert feedback. During this communication, AI tools can be used to mark revision requirements, ensuring authors have a clear path forward. Revised manuscripts can be re-screened by AI to ensure they meet the required standards. The final review is a critical stage that ensures all published manuscripts represent the journal's level and align with the development of mathematics education. For rejected manuscripts, AI can help generate detailed reports explaining specific reasons, such as methodological limitations or lack of innovation, helping authors improve their future research. In terms of management, AI can classify and archive accepted manuscripts into databases based on themes and research directions, facilitating systematic analysis of published content and providing references for future topic planning by predicting trending research topics.

4.1 Advantages

With the volume of submissions in the mathematics education publishing industry rising, AI screening systems effectively address efficiency issues. AI systems can complete the screening of tens of thousands of manuscripts in a short time. For a national mathematics journal receiving hundreds of submissions monthly, AI can reduce a process that previously took weeks to just a few days, leaving more time for editing and typesetting.

AI screening is also characterized by its objectivity. Manual screening is naturally influenced by human factors, such as an editor's personal knowledge, experience, and preferences, which can lead to inconsistent opinions or misjudgments. AI systems evaluate based on preset algorithms trained on large datasets, allowing for scientific judgments on keyword matching and citation standards without bias toward an author's reputation. This ensures all manuscripts are screened under a unified standard, guaranteeing fairness. For example, when screening manuscripts focused on rural education, an AI system will not be biased due to cognitive differences regarding rural mathematics education. Furthermore, AI systems have great potential for expansion; as technology improves, new algorithms can be integrated to optimize the system, such as using semantic analysis to identify innovative teaching concepts or knowledge graphs to evaluate interdisciplinary integration.

4.2 Challenges

Despite its advantages, AI screening faces certain problems, most notably bias in training data. AI systems rely on training data to make decisions. If data collection is biased—for example, if it primarily comes from developed regions or key schools—the screening results will reflect that bias. AI may struggle to make objective conclusions for manuscripts from remote areas or ordinary schools due to unfamiliarity with their data characteristics.

Additionally, the AI screening process is often difficult to explain. Editors or authors may not understand the basis for a "not qualified" judgment, as the system may not intuitively show whether the problem lies in keywords or teaching methods. This "black box" nature of decision-making can lead to user skepticism and lower acceptance of AI systems. In mathematics education publishing, screening results are directly linked to the recognition of an author's teaching achievements; thus, a lack of trust can hinder widespread application. Furthermore, AI systems show limitations in solving complex problems. Mathematics education research involves diverse factors like student differences, teaching environments, and policy changes. AI may struggle to perform the multi-perspective comprehensive judgment that an experienced expert can provide, necessitating manual review for complex manuscripts, which increases costs and limits the application scope of AI systems.

5.1 Optimizing Data Acquisition and Integration

Fully leveraging AI's auxiliary functions in all stages of manuscript screening can lead to precise and efficient information acquisition and integration. Data sources can be expanded beyond major academic platforms to include small publishing organizations, "grey literature," and local publications, ensuring the AI system makes objective judgments based on comprehensive information. Once acquired, data must be integrated. Since data comes from different sources, it varies in format and quality. Standardized data interfaces for metadata—such as subject classification and author background—should be established to facilitate data fusion. Furthermore, data linking techniques can connect manuscript data with market feedback or academic citation data (e.g., CNKI), allowing AI to evaluate publication value from multiple perspectives, such as the alignment of innovative content with market trends.

5.2 Enhancing Result Interpretability

Ensuring that AI screening results are interpretable is essential for gaining user acceptance. On one hand, visualization tools can be developed to intuitively present the AI screening process. For example, a visual interface could display scores for keywords and pedagogical evaluations, allowing users to recognize the basis for the screening. On the other hand, natural language explanation modules can be used. After generating a result, the AI can explain the conclusion in plain language—for instance, specifying that a manuscript failed because the keywords did not match teaching priorities or the description of the teaching method was imprecise. This allows editors and authors to understand the issues and provides a clear direction for revisions, effectively breaking the "black box" of AI conclusions.

5.3 Combining with Human Review

While AI screening excels in efficiency, human methods are still required for complex problems. In practice, the AI system can perform the preliminary review to filter out manuscripts that do not meet formatting or thematic requirements. The remaining high-quality manuscripts can then be handled by editors. Editors can manually review complex issues that AI cannot handle, such as the logical consistency of interdisciplinary integration. Additionally, editors can calibrate AI results and troubleshoot errors. Combining manual and AI screening leverages the efficiency of AI while maintaining the comprehensiveness of human review, ensuring scientific results and safeguarding the quality of the journal's manuscripts.

In summary, AI-assisted manuscript screening mechanisms have begun to emerge in the industry, demonstrating great potential for efficiency while facing various challenges. As technology develops, the screening process must be continuously optimized to leverage AI's advantages while overcoming its limitations. Emphasis should be placed on human-machine collaboration, allowing AI to deeply integrate with editorial expertise to drive the publishing industry forward in the digital wave and build a foundation for higher-quality content.

References

(Note: Citations [1] through [15] as provided in the source text)