Generative AI and the semiosic reconfiguration of knowledge organization – a preliminary exploration

Purpose This paper adopts a transdisciplinary semiotic perspective to examine how the generative capacities of large language models (LLMs) reconfigure foundational assumptions in knowledge organization (KO) and meaning-making. It aims to understand how the dynamic, interpretive nature of AI-generated content challenges traditional classificatory frameworks and demands new theoretical approaches. Design/methodology/approach The study draws on semiotic theory to analyze the interpretive complexity introduced by LLMs. It synthesizes Juri Lotman’s notion of cultural explosion and Umberto Eco’s concept of encyclopedic competence with Peircean-informed communication models to situate AI-driven text production within the broader conditions that guide interpretation. Findings LLMs instantiate a dynamic textual environment that challenges conventional KO models built on stability. The paper finds that the justification for knowledge is shifting from a stable, human-validated “literary warrant” to a volatile, AI-driven “algorithmic warrant,” a change that traditional KOS are ill-equipped to handle. Research limitations/implications The paper is theoretical in nature and does not include empirical validation. However, it opens new avenues for methodological development in KO by emphasizing the need for models that can accommodate interpretive dynamism and semantic instability. Practical implications Understanding LLMs as agents of semiosis has implications for the design of future KO systems, suggesting the need for adaptive structures that reflect the fluid, contextualized nature of meaning production in AI-mediated environments. Originality/value The paper contributes to the emerging body of research at the intersection of semiotics and AI by integrating Lotman’s and Eco’s theories with Peircean-informed KO scholarship. It offers a novel theoretical framework for understanding the epistemological and organizational impact of generative AI technologies.