Searching for answers: meaning-making in AI-infused information seeking

Abstract

Introduction. This paper investigates the conceptual implications of the shift from searching for documents to searching for answers in AI-supported information-seeking systems. As large language model (LLM) technologies increasingly provide direct responses rather than directing users to sources, foundational questions arise concerning meaning-making, intentionality, and what it means to have a ‘conversation’ with an information system.

Method. We undertake a conceptual analysis grounded in philosophy of language and philosophy of technology, drawing particularly on Wittgenstein’s notions of language games and forms of life, Coeckelbergh’s theory of technology games, and Dennett’s intentional stance. Relevant literature on AI alignment, cognitive authority, and information evaluation is examined to frame the shift toward answer provision.

Analysis. Using these theoretical resources, we analyse the conditions under which humans treat AI systems as intentional or meaningful interlocutors, despite LLMs lacking intentions, understanding, or participation in language games. The analysis clarifies how meaning-semblant outputs may nevertheless be interpreted as meaningful within information practices.

Results. The investigation shows that while users engage LLMs as if they were intentional agents, LLM responses do not constitute meaning-making in the human sense. The shift to answer provision relocates interpretive and evaluative work to users, who must navigate meaning, trust, and cognitive authority without genuine source attribution.

Conclusion. To understand AI-provided answers in information seeking, it is necessary to distinguish between human meaning-making and machine-generated outputs and to reconsider how evaluation, intentionality, and trust operate in answer-oriented information environments.