图书情报知识 ›› 2026, Vol. 43 ›› Issue (3): 156-168.doi: 10.13366/j.dik.2026.03.156

• 情报、信息与共享 • 上一篇    

代际人工智能鸿沟测度方法:量表开发与应用实践

胡开朗1, 闫慧2,3, 祝振媛2,4   

  1. 1.成都市高新区党工委党群工作部,成都,610095;
    2.中国人民大学信息资源管理学院,北京,100872;
    3.中国人民大学人工智能治理研究院,北京,100872;
    4.中国人民大学数字人文研究院,北京,100872
  • 出版日期:2026-05-10 发布日期:2026-07-26
  • 通讯作者: 祝振媛(ORCID: 0000-0003-3964-253X),博士,讲师,研究方向:情报分析、文本挖掘,Email: zhuzhenyuan@ruc.edu.cn。
  • 作者简介:胡开朗(ORCID: 0009-0003-3916-0415),硕士,研究方向:人工智能素养,Email: 2018202291@ruc.edu.cn; 闫慧(ORCID: 0000-0002-3649-1601),博士,教授,研究方向:社群信息学、数字不平等、人工智能与社会,Email: hyanpku@ruc.edu.cn。
  • 基金资助:
    本文系国家社会科学基金重点项目“人工智能对国民经济的颠覆性影响及信息治理研究”(23AZD093)的研究成果之一。

Measurement Methods for Intergenerational AI Divide: Scale Development and Application Practice

HU Kailang1, YAN Hui2,3 ZHU Zhenyuan2,4   

  1. 1.Party and Mass Work Department of Chengdu High-tech Zone, Chengdu, 610095;
    2. School of Information Resource Management, Renmin University of China, Beijing,100872;
    3.RUC Institute for AI Governance, Beijing,100872;
    4.Research Center for Digital Humanities of RUC, Beijing,100872
  • Online:2026-05-10 Published:2026-07-26
  • Contact: Correspondence should be addressed to ZHU Zhenyuan, Email: zhuzhenyuan@ruc.edu.cn, ORCID: 0000-0003-3964-253X
  • Supported by:
    This is an outcome of the Key Project "Research on the Disruptive Impact of Artificial Intelligence on the National Economy and Information Governance"(23AZD093)supported by National Social Science Foundation of China.

摘要: [目的/意义]随着以生成式人工智能为代表的通用人工智能技术(AGI)快速发展和广泛应用,“人工智能鸿沟”(AI鸿沟)应运而生。研究AI鸿沟的测度问题,可为弥合AI鸿沟的政策制定提供参考,提升数字社会的包容度。[研究设计/方法]采用半结构化访谈收集个案原始资料,通过编码形成初步AI鸿沟量表,而后对基于量表的预调研结果开展针对亲代与子代的问卷调查,最终修订形成正式量表。[结论/发现] 经过Cronbach's α系数检验、KMO 检验与Bartlett球形检验等方法验证后,构建适用于代际的AI鸿沟测量体系,包括接入鸿沟、心理鸿沟、素养鸿沟与效益鸿沟四个维度。量表应用结果表明,代际间存在显著的AI鸿沟,子代在接入、素养及整体维度表现优于父代。[创新/价值] 探究代际数字鸿沟在AI时代的测量理论与方法,丰富AI与社会的关系理论。

关键词: 通用人工智能, 人工智能鸿沟, 数字鸿沟, 量表开发

Abstract: [Purpose/Significance] With the rapid development and widespread application of artificial general intelligence(AGI) technology represented by generative artificial intelligence, the "AI divide" has emerged. A comprehensive portrayal of the AI divide can provide valuable reference for policy-making aimed at bridging the gap and enhancing the inclusiveness in the digital society. [Design/Methodology] This study used semi-structured interviews to collect raw data from individual cases. Based on the interview data, a preliminary AI divide scale was developed through systematic coding. Then, based on the pre-survey results of the scale, a questionnaire survey was conducted for both parents and offspring. Finally, a formal scale was revised and formed. [Findings/Conclusion] After verification through methods such as Cronbach's α coefficient test, KMO test, and Bartlett's sphericity test, an AI divide measurement system applicable to intergenerational contexts, which includes four dimensions: access divide, psychological divide, literacy divide, and benefit divide is constructed. The results of the scale application show that there is a significant intergenerational AI divide, with the offspring outperforming parents in the access divide, literacy divide and overall dimension. [Originality/Value] This study explores the measurement theories and methods of the generational digital divide in the AI era, enriching the theoretical framework on the relationship between AI and society.

Keywords: Artificial general intelligence(AGI), AI divide, Digital divide, Scale development