RESEARCH PAPERS

Exploration of the User Dialogue Optimization Mechanism in the Context of Generative AI

  • Li Chunxing ,
  • Dai Qinquan ,
  • Zhou Jie ,
  • Wang Dongyi ,
  • Wang Xue
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  • 1 People's Hospital of Guangxi Zhuang Autonomous Region, Nanning 530021;
    2 School of Information Management, Wuhan University, Wuhan 430072;
    3 Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000;
    4 School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190;
    5 Centre for Science and Technology Studies, Leiden University, Leiden 2311EZ, Netherlands;
    6 Hubei University of Automotive Technology, School of Automotive Business, Shiyan 442002
Li Chunxing, Master’s Degree;Dai Qinquan, Doctoral Candidate;Zhou Jie, Master’s Candidate;Wang Dongyi, Doctoral Candidate, Corresponding Author, E-mail: wangdy37@mail2.sysu.edu.cn;Wang Xue, Associate Professor, PhD.

Received date: 2025-02-08

  Revised date: 2025-05-28

  Online published: 2026-04-08

Supported by

This work is supported by the Youth Program of the National Social Science Fund of China, titled “Research on Human-Intelligence Collaboration Mechanism and Generative Content Quality from the Perspective of Interactive Memory” (Grant No. 24CTQ028).

Abstract

[Purpose/Significance] In the process of using generative artificial intelligence(Generative AI), users often find that the generated content does not meet their expectations and must optimize their interaction with the AI to obtain satisfactory results. However, research on the mechanisms underlying dialogue optimization remains underdeveloped. [Method/Process] This study explored the mechanisms of user dialogue optimization in the context of generative AI. Using grounded theory, the study conducted a coding analysis of 22 interview transcripts and 6 online documents to investigate the influencing factors, strategy content, and behavioral outcomes of user dialogue optimization in the context of generative AI usage. Based on this, a theoretical model of the mechanisms of user dialogue optimization behaviors was proposed. [Result/Conclusion] The findings reveal that task, user, and technology factors influence the content generation, which in turn affects user dialogue optimization. Typical strategies for dialogue optimization include content provision and instruction clarification, output format and structure optimization, feedback mechanisms and iterative optimization, task decomposition and step-by-step execution, the use and optimization of prompt engineering, and the leveraging of external resources. These strategies lead to enhanced user experience, user learning and alignment.

Cite this article

Li Chunxing , Dai Qinquan , Zhou Jie , Wang Dongyi , Wang Xue . Exploration of the User Dialogue Optimization Mechanism in the Context of Generative AI[J]. Library and Information Service, 2026 , 70(8) : 69 -81 . DOI: 10.13266/j.issn.0252-3116.2026.08.006

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