Keywords
Critical Librarianship
Artificial Intelligence
Epistemology
How to Cite
Abstract
This article critically examines the data, information, knowledge, and wisdom hierarchical model (DIKW) in library science contexts mediated by artificial intelligence. Rather than proclaiming its obsolescence, it proposes to challenge its limitations and explore non hierarchical, situated, and critical alternatives that allow us to understand knowledge production in algorithmic times. The central objective is to move the DIKW model toward a horizon of colliding knowledge capable of recognizing knowledge production as a dynamic, situated, and conflictive field. The methodology combines three approaches: critical autoethnography, epistemic interaction with artificial intelligence systems, and abductive semiotic derivation. This hybrid strategy allows for the articulation of lived experience, human-algorithmic dialogue, and contemporary theoretical frameworks. The results show that epistemic processes do not follow a linear progression, but rather are configured in nine interdependent phases, from radical ignorance to epistemic emptiness. It is also evident that algorithmic bias constitutes a form of structural epistemic injustice, rather than a technical accident. As conclusion, critical librarianship needs to abandon hierarchical models like DIKW and adopt frameworks that legitimize uncertainty, situated judgment, and epistemic plurality as valid knowledge practices in the age of artificial intelligence.
Received: 6 December 2025
Accepted: 14 April 2026
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