RESEARCH PAPERS

Dual-Agent Knowledge Assimilation in Human-AI Interaction: A Theoretical Model

  • Zhou Peng ,
  • Zhang Ji ,
  • Wang Shuoyu ,
  • Yao Wei
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  • 1 School of Business, Nankai University, Tianjin 300071;
    2 School of Business Administration, South China University of Technology, Guangzhou 510641;
    3 School of Economics and Management, Tianjin University of Science and Technology, Tianjin 300222
Zhou Peng, Doctoral Candidate; Zhang Ji, Doctoral Candidate, Corresponding Author, E-mail:zhangji102779005@163.com; Wang Shuoyu, Master's Candidate; Yao Wei, Professor, PhD.

Received date: 2025-04-22

  Revised date: 2025-07-03

  Online published: 2026-06-23

Supported by

This work is supported by major social science project of the Tianjin Municipal Education Commission, titled “Research on the Mission and Construction Path of a Chinese-Style Modernization Knowledge Management System” (Grant No. 2024JWZD33).

Abstract

[Purpose/Significance] The rapid evolution of next-generation artificial intelligence technologies has triggered a series of complex knowledge interaction challenges. It becomes a critical issue in advancing new quality productive forces through technological innovation. Investigating novel human–AI relationships and constructing theoretical models of their knowledge assimilation is of great theoretical and practical significance for promoting the development of knowledge management in interaction contexts. [Method/Process] This study adopted the lens of human-AI interaction to examine knowledge assimilation under emerging human-AI relationships. It identified key dimensions of the assimilation process, including knowledge agents, modes of knowledge representation, knowledge attributes, interaction patterns, and core features. On this basis, a theoretical model of dual-agent knowledge assimilation between human and artificial intelligence entities was proposed. [Result/Conclusion] Human-AI relationships are embedded in both exchange-based and co-ownership-oriented interpersonal dimensions, characterized by a duality of independence and interdependence. The evolving dynamics of human-AI interaction, together with heterogeneity in knowledge representation, give rise to new knowledge production mechanisms that integrate explicit knowledge, tacit knowledge, latent knowledge, and domains of ignorance. Four distinct modes of knowledge assimilation emerge—search-based, supply-based, embedded, and symbiotic—each associated with different knowledge demands and exhibiting dynamic mode-shifting effects. Ultimately, dual-agent knowledge assimilation is found to be a dynamic, non-linear evolutionary process. These findings offer valuable insights for fostering intelligent, collaborative, and creative interactions between individuals, organizations, and advanced AI systems, thereby advancing Human–AI innovation and knowledge co-creation.

Cite this article

Zhou Peng , Zhang Ji , Wang Shuoyu , Yao Wei . Dual-Agent Knowledge Assimilation in Human-AI Interaction: A Theoretical Model[J]. Library and Information Service, 2026 , 70(13) : 43 -55 . DOI: 10.13266/j.issn.0252-3116.2026.13.004

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