From Search to Questioning: Rethinking Knowledge Access in the Age of Artificial Intelligence

Abstract This paper offers a conceptual reflection on the transformation of knowledge access in the age of artificial intelligence. For decades, knowledge systems have been built upon assumptions of classification, categorization, and search, relying on predefined disciplinary structures and keyword-based retrieval. While effective in contexts of well-defined information needs, these systems increasingly reveal structural limitations as contemporary knowledge becomes more interdisciplinary, contextual, and dynamic. Rather than framing recent developments as merely technological advances, this paper interprets the shift from “search” to “questioning” as a structural transition in how knowledge is organized, accessed, and used. It examines the constraints of traditional classification systems, the limits of digitization practices that prioritize scanning over structural interpretation, and the challenges of designing systems that must coexist with legacy infrastructures such as libraries, databases, and citation frameworks. The paper does not propose a finalized technical model. Instead, it raises critical questions about the future design of knowledge systems, with particular attention to issues of epistemology, power, and access. By emphasizing transparency, contextualization, and traceable references, it argues that future knowledge systems should support meaningful questioning rather than merely faster retrieval. This work is intended as a conceptual contribution to discussions on knowledge organization, information retrieval, and AI-assisted knowledge systems. Keywords: Knowledge systems, Question-driven access, Information retrieval, Artificial intelligence, Interdisciplinary knowledge