研究论文

生成式AI辅助下用户认知特征对协作效果的影响研究

  • 王萍 ,
  • 黄容芳 ,
  • 龙秋易 ,
  • 杨玲
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  • 1 华中师范大学信息管理学院 武汉 430079;
    2 湖北省电子商务研究中心 武汉 430079
王萍,教授,博士,博士生导师,E-mail:ping.wang@ccnu.edu.cn;黄容芳,硕士研究生;龙秋易,硕士研究生;杨玲,硕士研究生。

收稿日期: 2025-05-27

  修回日期: 2025-08-13

  网络出版日期: 2026-07-30

基金资助

本文系国家社会科学基金一般项目“面向人智协同的用户与生成式AI信息交互行为研究”(项目编号:24BTQ060)研究成果之一。

The Impact of User Cognitive Characteristics on Human-GenAI Collaboration Performance

  • Wang Ping ,
  • Huang Rongfang ,
  • Long Qiuyi ,
  • Yang Ling
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  • 1 School of Information Management Central China Normal University, Wuhan 430079;
    2 Hubei E-commerce Research Center, Wuhan 430079
Wang Ping, Professor, PhD, Doctoral Supervisor, E-mail:ping.wang@ccnu.edu.cn; Huang Rongfang, Master's Candidate; Long Qiuyi, Master's Candidate; Yang Ling, Master's Candidate.

Received date: 2025-05-27

  Revised date: 2025-08-13

  Online published: 2026-07-30

Supported by

This work is supported by the National Social Science Fund of China project titled “Exploring Human-GenAI Information Interaction Behaviors Toward Collaborative Intelligence” (Grant No. 24BTQ060).

摘要

[目的/意义] 随着生成式人工智能(Generative AI,简称Gen AI)的普及,其在优化信息获取与内容创作方面展现出显著优势,但用户个体在使用生成式AI时的效能差异待深入研究。为探讨用户认知特征对生成式人工智能辅助下人机协作效果的影响,本研究重点分析用户对AI的认知(AI素养)与对自身的认知(学习风格)如何作用于任务完成质量与效率,为提升人智协作效率和个性化优化提供理论与实践参考。[方法/过程] 通过对照实验设计,将被试分为无AI辅助组和AI辅助组,完成流行性疾病科普材料撰写任务,采集任务表现(有效性、全面性、逻辑性、通俗性)、任务耗时及相关认知特征数据,并结合交互过程进行综合分析。[结果/结论] 研究结果表明生成式AI显著提升任务完成质量,尤其在全面性、逻辑性与通俗性方面,但人智协作组的质量仍低于AI独立生成结果。高AI素养用户任务效率较高,但AI素养对任务质量影响不显著,生成式AI的“低门槛高上限”特性降低了基础素养差异的影响。不同学习风格影响分化,同化型用户在内容全面性、逻辑性上表现更优。低AI素养同化型用户从AI辅助中获益最大,提升幅度为高素养同化型用户的3.7倍,生成式AI有效弥补了其能力短板。

本文引用格式

王萍 , 黄容芳 , 龙秋易 , 杨玲 . 生成式AI辅助下用户认知特征对协作效果的影响研究[J]. 图书情报工作, 2026 , 70(15) : 16 -32 . DOI: 10.13266/j.issn.0252-3116.2026.15.002

Abstract

[Purpose/Significance] With the widespread adoption of Generative Artificial Intelligence (Generative AI, Gen AI), its significant advantages in optimizing information retrieval and content creation have become evident. However, the differences in user performance when utilizing generative AI require further investigation. This study explores how users’ cognitive characteristics influence the outcomes of human-AI collaboration facilitated by generative AI. Specifically, it focuses on how users’ cognition of AI (AI literacy) and self-cognition (learning styles) impact task quality and efficiency. The findings provide theoretical and practical insights for enhancing human-AI collaboration and personalized optimization. [Method/Process] A controlled experiment was conducted, in which participants were divided into a non-AI-assisted group and an AI-assisted group to complete the task of drafting popular science materials on infectious diseases. Data on task performance (effectiveness, comprehensiveness, logicality, readability), task duration, and user cognitive characteristics were collected. The interaction process was comprehensively analyzed. [Result/Conclusion] The results indicate that Generative AI significantly improves task quality, particularly in comprehensiveness, logicality, and readability. However, the performance of the human-AI collaboration group is still lower than that of AI-only results. Users with high AI literacy exhibit higher efficiency, although no significant direct impact of AI literacy on task quality is found. This result suggests that the “low-threshold, high-ceiling” nature of Generative AI reduces differences caused by basic literacy levels. Learning styles show differentiated effects: assimilators perform better than accommodators in comprehensiveness and logicality. Furthermore, low AI literacy assimilators benefit the most from AI assistance, with an improvement 3.7 times greater than that of high AI literacy assimilators. This finding demonstrates that Generative AI can effectively compensate for the cognitive limitations of users with low AI literacy.

参考文献

[1] FEUERRIEGEL S, HARTMANN J, JANIESCH C, et al. Generative AI[J]. Business & information systems engineering, 2024, 66(1): 111-126.
[2] 中国 网. 政府 工作 报告( 全文 ) [EB/OL]. [2025-09- 05]. http://www.china.com.cn/lianghui/news/2025-03/12/ content_117762844.shtml. (China internet information center. Report on the work of the government[EB/OL]. [2025-09- 05]. http://www.china.com.cn/lianghui/news/2025-03/12/ content_117762844.shtml.)
[3] AI Q, ZHAN J, LIU Y. Foundations of GenIR[EB/OL]. [2025- 09-26]. http://arxiv.org/abs/2501.02842.
[4] CNNIC. 生成 式人 工智 能应 用发 展报 告( 2024) [R]. 中国 互联 网络 信息 中心, 2024. (CNNIC. Generative Artificial Intelligence Application Development Report (2024) [R]. Information Center of the China Internet Network, 2024.)
[5] NOY S, ZHANG W. Experimental evidence on the productivity effects of generative artificial intelligence[J]. Science, 2023, 381(6654): 187-192.
[6] SUN X, MA R, ZHAO X, et al. Trusting the search: unraveling human trust in health information from Google and ChatGPT[EB/ OL]. [2024-09-21]. http://arxiv.org/abs/2403.09987.
[7] KRUPP L, STEINERT S, KIEFER-EMMANOUILIDIS M, et al. Unreflected Acceptance—Investigating the Negative Consequences of ChatGPT-Assisted Problem Solving in Physics Education[EB/OL]. [2025-09-09]. http://arxiv.org/ abs/2309.03087.
[8] WANG P. The rationality of explanation or human capacity? understanding the impact of explainable artificial intelligence on human-AI trust and decision performance[J]. Information processing and management, 2024, 61(4): 103732.
[9] 邓俊, 易欣妍, 傅诗婷. 社交机器人如何提升用户社会临场感?表情包情感效价在人智对话交互中的作用[J]. 图书 情报 知识, 2023, 40(2): 29-39. (DENG J, YI X Y, FU S T. How to improve users social presence by social robots? the role of sticker’s emotional valence in Human-AI conversation interaction[J]. Documentation, information & knowledge, 2023, 40(2): 29-39.)
[10] INKPEN K, CHAPPIDI S, MALLARI K, et al. Advancing Human-AI complementarity: the impact of user expertise and algorithmic tuning on joint decision making[J]. ACM transactions on computer-human interaction, 2023, 30(5): 1-29.
[11] WEI XIONG, CHEN WANG, LIANG MA. Partner or subordinate? sequential risky decision-making behaviors under human-machine collaboration contexts[J]. Computers in human behavior, 2023, 139: 107556.
[12] 王俊, 谢青伶, 刘畅. 日常生活情境下用户与生成式人工智能交互行为分析[J]. 图书情报知识, 2025, 42(2): 60-69, 93. (WANG J, XIE Q L, LIU C. User’s interaction behavior with generative artificial intelligence in daily life contexts[J]. Documentation, information & knowledge, 2025, 42(2): 60-69, 93.)
[13] SEBASTIAN WANDELT, XIAOQIAN SUN, ANMING ZHANG. AI-driven assistants for education and research? a case study on ChatGPT for air transport management[J]. Journal of air transport management, 2023, 113(C): 102483.
[14] 王若佳, 范科鸣, 刘智锋, 等. 生成式人工智能环境下用户信息检索式行为研究[J]. 数据分析与知识发现, 2024, 8(Z1): 20-30. (WANG R J, FAN K M, LIU Z F, et al. A Study on user querying behavior in generative artificial intelligence environment[J]. Data analysis and knowledge discovery, 2024, 8(8/9): 20-30.)
[15] 宋小康, 赵宇翔, 宋士杰, 等. 社会技术系统范式下AI赋能的替代信息搜索:特征、理论框架与研究展望[J]. 图书情报知识, 2023, 40(4): 111-121. (SONG X K, ZHAO Y X, SONG S J, et al. The features, theoretical framework and research prospects of ai-enabled surrogate information searching: a sociotechnical system paradigm[J]. Documentation, information & knowledge, 2023, 40(4): 111-121.)
[16] 冷旭, 陈渝. 替代信息搜索情境下的用户转移意愿研究[J]. 情报杂志, 2024, 43(12): 186-197. (LENG X, CHEN Y. Users’ switching intention in surrogate information searching context[J]. Journal of intelligence, 2024, 43(12): 186-197.)
[17] 周涛, 吴晓颖, 邓胜利. 用户知识问答转移行为研究:从问答社区到生成式AI[J]. 情报杂志, 2024, 43(2): 200-207. (ZHOU T, WU X Y, DENG S L. Examining users’s witching behavior of knowledge Q&A: from Q&A communities to GenerativeAI[J]. Journal of intelligence, 2024, 43(2): 200-207.)
[18] 李阳, 孙建军. 大模型对情报学发展的影响思考[J]. 情报学 报, 2025, 44(2): 246-256. (LI Y, SUN J J. Reflections on the impact of large language models on the development of information science[J]. Journal of the China Society for Scientific and Technical Information, 2025, 44(2): 246-256.)
[19] LI X, JIN J, ZHOU Y, et al. From matching to generation: A survey on generative information retrieval[J]. ACM transactions on information systems, 2025, 43(3): 1-62.
[20] XIAOYUE MA A B, YUDI HUO. Drawing a satisfying picture: an exploratory study of human-AI interaction in AI painting through breakdown–repair communication strategies[J]. Information processing & management, 2024, 61(4): 103755.
[21] 孙榕, 李白杨. 生成式人工智能(GAI)背景下的新型数字鸿沟识别框架研究[J]. 图书情报知识, 2025, 42(3): 19-30. (SUN R, LI B Y. A framework for identifying the new digital divide within the context of generative artificial intelligence(GAI)[J]. Documentation, information & knowledge, 2025, 42(3): 19-30.)
[22] 张铮, 刘晨旭. 大模型幻觉:人机传播中的认知风险与共治可能[J]. 苏州大学学报(哲学社会科学版), 2024, 45(5): 171- 180. (ZHANG Z, LIU C X. The hallucinations of large language models: perceived risk and the possibility of Co-governance in Human-computer Communication[J]. Journal of Soochow University(philosophy & social science edition), 2024, 45(5): 171-180.)
[23] 吴丹, 孙国烨. 生成式智能搜索结果可信度研究[J]. 中国图书馆学报, 2023, 49(6): 51-67. (WU D, SUN G Y. The Credibility of the results of generative intelligent search[J]. Journal of library science in China, 2023, 49(6): 51-67.)
[24] 宋士杰, 赵宇翔, 朱庆华. 从ELIZA到ChatGPT:人智交互体验中的AI生成内容( AIGC)可信度评价[J]. 情报资料工作, 2023, 44(4): 35-42. (SONG S J, ZHAO Y X, ZHU Q H. From ELIZA to ChatGPT: AI-Generated content (AIGC) credibility evaluation in human-intelligent interactive experience[J]. Information and documentation services, 2023, 44(4): 35-42.)
[25] 王一博, 郭鑫, 刘智锋, 等. AI生成与学者撰写中文论文摘要的检测与差异性比较研究[J]. 情报杂志, 2023, 42(9): 127-134. (WANG Y B, GUO X, LIU Z F, et al. Detection and comparative study of differences between AI-Generated and scholar-written Chinese abstracts[J]. Journal of intelligence, 2023, 42(9): 127- 134.)
[26] JIMPEI HITSUWARI, YOSHIYUKI UEDA, WOOJIN YUN, et al. Does human–AI collaboration lead to more creative art? aesthetic evaluation of human-made and AI-generated haiku poetry[J]. Computers in human behavior, 2023, 139(C): 107502.
[27] UWE MESSER. Co-creating art with generative artificial intelligence: implications for artworks and artists[J]. Computers in human behavior: artificial humans, 2024, 2(1): 100056.
[28] ALAVI M, LEIDNER D, MOUSAVI R. Knowledge Management Perspective of Generative Artificial Intelligence (GenAI)[EB/ OL]. [2025-09-22]. https://papers.ssrn.com/abstract=4782875.
[29] 徐娟, 黄奇, 袁勤俭. 社会认知理论及其在信息系统研究中的应用与展望[J]. 现代情报, 2020, 40(6): 145-153. (XU J, HUANG Q, YUAN Q J. Social cognition theory and its application and prospect in the field of information system research[J]. Journal of modern information, 2020, 40(6): 145-153.)
[30] 李爱霞. 基于学习行为数据的认知特性刻画研究[D]. 上海: 华东师范大学, 2021. (LI A X. Research on characterization of cognitive features based on learning behavior data[D]. Shanghai: East China Normal University, 2021.)
[31] 林鑫, 周知. 用户认知对标签使用行为的影响分析—— 基于电影社会化标注数据的实证分析[J]. 情报理论与实践, 2015, 38(10): 85-88. (LIN X, ZHOU Z. Analysis of the influence of user cognition on tag use behavior[J]. Information studies: theory & application, 2015, 38(10): 85-88.)
[32] 周涛, 张春雷, 邓胜利. 基于C-A-C的生成式AI用户间歇性中辍行为研究[J]. 现代情报, 2025, 45(3): 40-50, 64. (ZHOU T, ZHANG C L, DENG S L. Research on Generative AI users’ intermittent discontinuance based on the C-A-C[J]. Journal of modern information, 2025, 45(3): 40-50, 64.)
[33] MEIER M, MAIER C, THATCHER J B, et al. Chatbot interactions: how consumption values and disruptive situations influence customers’ willingness to interact[J]. Information systems journal, 2024, 34(5): 1579-1625.
[34] 孙林辉, 杨露, 袁晓芳, 等. 基于场独立/ 场依存认知风格的消费者信息搜寻效率研究[J]. 情报理论与实践, 2023, 46(4): 122-130. (SUN L H, YANG L, YUAN X F, et al. Research on information search efficiency of consumers based on their cognitive style of field-independence / field-dependence[J]. Information studies:theory & application, 2023, 46(4): 122-130.)
[35] 孙晓宁, 姚青. 信息搜索用户学习行为投入影响研究:基于认知风格与自我效能[J]. 情报理论与实践, 2020, 43(10): 99-107. (SUN X N, YAO Q. An exploration of factors related to learning behavioral engagement on information search users: based on cognitive style and self-efficacy [J]. Information studies: theory & application, 2020, 43(10): 99-107.)
[36] 郭海玲, 宋佳琳. 社会认知视角下移动端视频用户学习型搜索行为的影响研究[J]. 情报资料工作, 2022, 43(3): 75- 83. (GUO H L, SONG J L. Research on the influence of mobile video users’ learning search behavior from the perspective of social cognition[J]. Information and documentation services, 2022, 43(3): 75-83.)
[37] PINSKI M, BENLIAN A. AI literacy for users – a comprehensive review and future research directions of learning methods, components, and effects[J]. Computers in human behavior: artificial humans, 2024, 2(1): 100062.
[38] WANG B, RAU P L P, YUAN T. Measuring user competence in using artificial intelligence: validity and reliability of artificial intelligence literacy scale[J]. Behaviour & information technology, 2023, 42(9): 1324-1337.
[39] CAROLUS A, KOCH M J, STRAKA S, et al. MAILS - Meta AI literacy scale: development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change- and meta-competencies[J]. Computers in human behavior: artificial humans, 2023, 1(2): 100014.
[40] 周琼, 徐亚苹, 蔡迎春. 高校学生人工智能素养能力现状及影响因素多维分析[J]. 图书情报知识, 2024, 41(3): 38- 48. (ZHOU Q, XU Y P, CAI Y C. An examination of ai literacy among university students: current status and multifaceted influencing factors[J]. Documentation, information & knowledge, 2024, 41(3): 38-48.)
[41] CELIK I. Exploring the determinants of artificial intelligence (AI) literacy: digital divide, computational thinking, cognitive absorption[J]. Telematics and informatics, 2023, 83(C): 102026.
[42] TSENG Y J, XIAO R, BOGART C, et al. Assessing the efficacy of goal-based scenarios in scaling AI literacy for non-technical learners[C]//Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 2. New York, USA: Association for Computing Machinery, 2024: 1842-1843.
[43] CHEN G, FAN J, AZAM M. Exploring artificial intelligence (AI) chatbots adoption among research scholars using unified theory of acceptance and use of technology (UTAUT) [EB/OL]. [2025-09-22]. https://journals.sagepub.com/ doi/10.1177/09610006241269189.
[44] 赵宇翔, 景雨田, 宋士杰, 等. AIGC赋能的提示素养:生成式ai时代的人智交互能力重构[J]. 情报资料工作, 2025, 46(3): 14-25. (ZHAO Y X, JING Y T, SONG S J, et al. Prompt literacy for AIGC empowerment: reconstructing Human-AI interaction capabilities in the generative AI era[J]. Information and documentation services, 2025, 46(3): 14-25.)
[45] 杜瑾. 美国高校图书馆人工智能素养教育服务实践研究及其启示[J]. 图书情报工作, 2025, 69(14): 135-148. (DU J. Research on the artificial intelligence literacy education services in American university libraries and its implications[J]. Library and information service, 2025, 69(14): 135-148.)
[46] KOLB D. Experiential learning: experience as the source of learning and development[M]// London: Pearson Education, 2014.
[47] KOLB A, KOLB D. The Kolb learning style inventory—version 3.12005 technical specifications[S/OL]. [2025-09-06]. https:// www.researchgate.net/publication/241157771_The_Kolb_ Learning_Style_Inventory-Version_31_2005_Technical_Specifi_ cations.
[48] MAYA J, LUESIA J F, PÉREZ-PADILLA J. The relationship between learning styles and academic performance: consistency among multiple assessment methods in psychology and education students[J]. Sustainability, 2021, 13(6): 3341.
[49] 孙晓宁, 季馥春, 刘思琦. 信息搜索用户的学习投入影响研究:基于Kolb学习风格与认知弹性理论[J]. 情报学报, 2023, 42(1): 90-102. (SUN X N, JI F C, LIU S Q. Research on the learning engagement of information search users: based on Kolb’s learning style and cognitive flexibility theory[J]. Journal of the China Society for Scientific and Technical Information, 2023, 42(1): 90-102.)
[50] HALTTUNEN K. Students’ conceptions of information retrieval: Implications for the design of learning environments[J]. Library & information science research, 2003, 25(3): 307-332.
[51] SHAW R S. A study of the relationships among learning styles, participation types, and performance in programming language learning supported by online forums[J]. Computers & education, 2012, 58(1): 111-120.
[52] SPATHARIOTI S E, ROTHSCHILD D M, GOLDSTEIN D G, et al. Comparing traditional and LLM-based search for consumer choice: a randomized experiment[EB/OL]. [2025-09-07]. https:// arxiv.org/abs/2307.03744v2.
[53] ZHENG J, WONG K C, ZHOU J, et al. Large language model in ideation for product innovation: an exploratory comparative study[EB/OL]. [2025-09-29]. https://papers.ssrn.com/ abstract=4729982.
[54] XU R, FENG Y, CHEN H. ChatGPT vs. Google: A comparative study of search performance and user experience[EB/OL]. [2025- 09-28]. http://arxiv.org/abs/2307.01135.
[55] SHARMA N, LIAO Q V, XIAO Z. Generative echo chamber? effects of LLM-Powered search systems on diverse information seeking[EB/OL]. [2025-09-21]. http://arxiv.org/abs/2402.05880.
[56] 李丹丹, 朱晶晶, 甘利人. 国外网络用户认知实验研究[J]. 图书情报工作, 2009, 53(24): 69-73. (LI D D, ZHU J J, GAN L R. A review of cognitive experimental research on foreign network users[J]. Library and information service, 2009, 53(24): 69-73.)
[57] WANG B, RAU P L P, YUAN T. Measuring user competence in using artificial intelligence: validity and reliability of 2293-2303.
[63] YU R, XU X, PENG S. The impact of individual AI proficiency on human–agent collaboration: higher sensitivity to discern the comprehension ability of intelligent agents for users with higher AI proficiency levels[J]. International journal of industrial ergonomics, 2025, 107: 103745.
[64] 陈琦, 刘儒德. 当代教育心理学: 第3 版[M]. 北京: 北京师 范大 学出 版, 2019. (CHEN Q, LIU R D. Contemporary Psychology of Education [M]. Beijing: Beijing Normal University Press, 2019.)
[65] 周晓美, 谷桂芳, 尹雪燕. 新护士基于学习风格的规范化培训效果评价[J]. 护理学杂志, 2024, 39(7): 1-4. (ZHOU X M, GU G F, YIN X Y. Effect evaluation of standardized training for new nurses based on learning style [J]. Journal of nursing science, 2024, 39(7): 1-4) artificial intelligence literacy scale[J]. Behaviour & information technology, 2023, 42(9): 1324-1337.
[58] 程子佳, 陈翀. 面向流行性疾病科普的用户问题理解与答案内容组织[J]. 数据分析与知识发现, 2022, 6(2-3): 202-211. (CHENG Z J, CHEN C. Question comprehension and answer organization for scientific education of epidemics[J]. Data analysis and knowledge discovery, 2022, 6(2-3): 202-211.)
[59] TANG M, HOFREITER S, WERNER C H, et al. “who” is the best creative thinking partner? an experimental investigation of Human–Human, Human–Internet, and Human–AI CoCreation[J]. The Journal of creative behavior, 2025, 59: e1519.
[60] 宋筱璇, 刘畅. 搜索前后用户知识水平的评估及其变化情况分析[J]. 图书情报工作, 2018, 62(2): 108-116. (SONG X X, LIU C. Assessment of user knowledge level before and after searching[J]. Library and information service, 2018, 62(2): 108- 116.)
[61] FULBRIGHT R, MORRISON M. Does using ChatGPT result in human cognitive augmentation?[C]//International conference on human-computer interaction. Cham: Springer, 2024: 133-146.
[62] VACCARO M, ALMAATOUQ A, MALONE T. When combinations of humans and AI are useful: a systematic review and meta-analysis[J]. Nature human behaviour, 2024, 8(12):2293-2303.
[63] YU R, XU X, PENG S. The impact of individual AI proficiency on human–agent collaboration: higher sensitivity to discern the comprehension ability of intelligent agents for users with higher AI proficiency levels[J]. International journal of industrial ergonomics, 2025, 107: 103745.
[64] 陈琦, 刘儒德. 当代教育心理学: 第3 版[M]. 北京: 北京师 范大 学出 版, 2019. (CHEN Q, LIU R D. Contemporary Psychology of Education [M]. Beijing: Beijing Normal University Press, 2019.)
[65] 周晓美, 谷桂芳, 尹雪燕. 新护士基于学习风格的规范化培训效果评价[J]. 护理学杂志, 2024, 39(7): 1-4. (ZHOU X M, GU G F, YIN X Y. Effect evaluation of standardized training for new nurses based on learning style [J]. Journal of nursing science, 2024, 39(7): 1-4)
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