图书情报工作

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2026 , Vol. 70 >Issue 16: 120 - 132

DOI: https://doi.org/10.13266/j.issn.0252-3116.2026.16.026

研究论文

生成式人工智能的学术写作“质能”与运用伦理

  • 曲奕同 ,
  • 李玉海 ,
  • 黄信毅
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  • 1 华中师范大学信息管理学院 武汉 430079;
    2 华中师范大学中国图书馆创新发展研究中心 武汉 430079;
    3 北京科技大学经济管理学院 北京 100083
曲奕同，博士研究生，E-mail：18760307636@163.com；李玉海，教授，博士，博士生导师；黄信毅，博士研究生。

收稿日期: 2025-06-25

  修回日期: 2025-12-09

  网络出版日期: 2026-08-20

基金资助

本文系中央高校基本科研业务费资助项目“多视角知识表示的知识组织理论及方法研究”（项目编号：CCNU24ZZ130）研究成果之一。

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The “Mass-Energy” Dynamics of Academic Writing and the Ethical Implications of Generative Artificial Intelligence

  • Qu Yitong ,
  • Li Yuhai ,
  • Huang Xinyi
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  • 1 School of Information Management, Central China Normal University, Wuhan 430079;
    2 Research Center for Innovative Development of Chinese Libraries, Central China Normal University, Wuhan 430079;
    3 School of Economics and Management, University of Science and Technology Beijing, Beijing 100083
Qu Yitong, Doctoral Candidate, E-mail: 18760307636@163.com; Li Yuhai, Professor, PhD, Doctoral Supervisor; Huang Xinyi, Doctoral Candidate.

Received date: 2025-06-25

  Revised date: 2025-12-09

  Online published: 2026-08-20

Supported by

This work is supported by the Fundamental Research Funds for the Central Universities titled “Research on the theory and method of knowledge organization of multi-perspective knowledge representation” (Grant No. CCNU24ZZ130).

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摘要

[目的/意义] 生成式人工智能的勃发与迭代，虽为科学研究注入了充沛的学术动能，但也催生出一定的伦理风险。拨开隐忧，建构伦理出路，以期AI和人文优势互补，削减学术写作对AI的过度依赖，增进学术领域的人机协同。[方法/过程] 基于小红书平台“AI写作”相关692条发文、4 947条评论进行高频词共现和LDA主题分类，并结合Chinese-BERT-wwm情感分析和高校学生半结构化访谈情况，试图发掘与剖析生成式人工智能在学术场景下的问题与困境。[结果/结论] 提取出智能辅助、写作质量、思维创新、学术规范4个高关注度主题。情感分析表明：AI智能辅助功能广受认可；AI写作质量的积极与消极情感几乎持平；AI思维创新的看法中担忧与反对情感占比最高；学术规范的消极情感略高于积极情感。只有明晰生成式人工智能的学术写作“质能”，才能使其成为知识呈现和科研成果转化的有力工具。

关键词： 生成式人工智能; 学术写作; 运用伦理

本文引用格式

曲奕同 , 李玉海 , 黄信毅 . 生成式人工智能的学术写作“质能”与运用伦理[J]. 图书情报工作, 2026 , 70(16) : 120 -132 . DOI: 10.13266/j.issn.0252-3116.2026.16.026

Abstract

[Purpose/Significance] The rapid emergence and iterative evolution of generative artificial intelligence have infused scientific research with substantial academic momentum, while simultaneously introducing complex ethical risks. By constructing feasible ethical pathways, this study aims to foster complementary strengths between AI and the humanities, mitigate excessive dependence on AI in scholarly writing, and enhance human–AI collaboration in academic contexts. [Method/Process] Drawing on 692 posts and 4 947 comments related to “AI writing” from the Xiaohongshu platform, this study employs high-frequency keyword co-occurrence analysis and LDA topic modeling. These results are further integrated with sentiment analysis using Chinese-BERT-wwm and semi-structured interviews with university students to uncover the challenges and dilemmas of applying generative AI in academic settings. [Result/Conclusion] Four high-attention topics are identified: intelligent assistance, writing quality, creative thinking, and academic norms. Sentiment analysis indicates that AI's intelligent assistance functions are broadly well-received; Attitudes toward AI writing quality are almost evenly split between positive and negative; concerns and opposition dominate perceptions of AI's impact on creative thinking; and negative sentiments slightly outweigh positive ones regarding academic norms. Clarifying the “mass-energy” of generative AI in academic writing is essential for transforming it into a powerful tool for knowledge presentation and the translation of scientific findings.

Key words： generative artificial intelligence; academic writing; application ethics

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