图书情报知识 ›› 2026, Vol. 43 ›› Issue (1): 25-38.doi: 10.13366/j.dik.2026.01.025

• 学术聚焦(1)·AI 与用户信息行为 • 上一篇    下一篇

生成式人工智能环境下用户信息搜寻与认知路径研究

牛庆萱, 蔡亚芳, 陈忆金   

  1. 华南师范大学经济与管理学院,广州,510006
  • 出版日期:2026-01-10 发布日期:2026-03-24
  • 通讯作者: 陈忆金(ORCID: 0000-0001-6289-9814),博士,教授,研究方向:网络信息组织、用户信息搜索行为,Email: chenyijin@m.scnu.edu.cn。
  • 作者简介:牛庆萱(ORCID: 0009-0001-4710-4259),硕士研究生,研究方向:用户信息搜索行为,Email: 2287099265@qq.com;蔡亚芳(ORCID: 0009-0009-1244-6273),硕士研究生,研究方向:用户信息搜索行为,Email: douyacaiki@163.com。
  • 基金资助:
    本文系国家社会科学基金项目“健康信息用户‘搜索即学习’过程中的知识结构演化规律研究”(20BTQ075)的研究成果之一。

User Information Search Behavior and Cognitive Paths in Generative Artificial Intelligence Environment

NIU Qingxuan, CAI Yafang, CHEN Yijin   

  1. School of Economics and Management, South China Normal University, Guangzhou, 510006
  • Online:2026-01-10 Published:2026-03-24
  • Contact: Correspondence should be addressed to CHEN Yijin, Email: chenyijin@m.scnu.edu.cn,ORCID: 0000-0001-6289-9814
  • Supported by:
    This is an outcome of the project "Research on Evolution of Knowledge Structure in Search as Learning Process of Health Information Users”(20BTQ075)supported by National Social Science Foundation of China.

摘要: [目的/意义] 探究生成式人工智能环境下用户在信息搜寻过程中的搜索行为、认知路径与认知转换三个方面的特征表现,并将其与传统搜索引擎用户进行比较,为生成式人工智能背景下搜索即学习特征研究、搜索工具改进提供参考。[研究设计/方法] 通过用户实验法,将实验被试分为传统搜索引擎组和生成式人工智能组,使用录屏与出声思维法收集数据,基于非参数检验分析方法进行数据分析。[结论/发现] 生成式人工智能组与传统搜索引擎组在搜索行为、认知路径和认知转换三个方面的表现存在显著差异。生成式人工智能加深了用户的搜索深度并增加了搜索宽度,但并未促使用户产生更复杂的学习。[创新/价值] 将生成式人工智能搜索工具纳入搜索即学习的研究范围,深入分析用户在生成式人工智能与传统搜索引擎下搜索行为和认知过程的差异,拓展了搜索即学习的研究情境和领域,有助于生成式人工智能的设计者帮助用户更好地使用自然语言进行信息搜索和学习。

关键词: 信息搜寻, 认知路径, 生成式人工智能, 搜索即学习

Abstract: [Purpose/Significance] This study explores the characteristics manifestations of users' search behavior, cognitive pathways, and cognitive transitions during information-seeking processes in a generative artificial intelligence environment, and compares them with those of traditional search engine users. The aim is to provide references for the research on search as learning features and the improvement of search tools in the context of generative artificial intelligence. [Design/Methodology] Through a user experiment, participants were divided into a traditional search engine group and a generative artificial intelligence group. Data were collected using screen recording and think-aloud method, followed by data analysis based on non-parametric tests analysis method. [Findings/Conclusion] Significant differences are observed between the generative artificial intelligence group and the traditional search engine group in terms of search behavior, cognitive pathways, and cognitive transformation. Generative artificial intelligence deepens users' search depth and increases search width, but does not promote more complex learning for users. [Originality/Value] Incorporating generative artificial intelligence search tools into the research scope of "search as learning", this study deeply analyzes the differences in users' search behavior and cognitive path when using generative artificial intelligence search tools versus traditional search tools. It expands the research context and field of "search as learning", helping designers of generative artificial intelligence to assist users in better using natural language for information query and learning.

Keywords: Information seek, Cognitive path, Generative artificial intelligence, Search as learning