What is it about?
AI systems produce outputs that can look like knowledge, but a person has not necessarily learned simply because an output appears. This paper provides a formal framework for distinguishing genuine change in understanding from mere exposure to data. It defines information systems as infrastructure that creates conditions for understanding, rather than mechanisms that deliver knowledge directly.
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Why is it important?
It matters because we keep confusing output with informing. AI can produce more outputs than any person can absorb, but output is not yet informing. A system informs only when someone can take up what it provides in a way that changes their understanding. That distinction matters for how we design systems, how we judge AI, and how we decide whether a system is serving knowing or merely increasing volume.
Perspectives
I wrote this article because I kept running into the same problem: we often talk as if systems give us information, when what they really give us is output. A database can retrieve, a search engine can rank, and an AI system can generate, but the important moment happens when a person takes something up and is changed by it. That is what I mean by the informing act. I hope the article helps readers see that information is not just what a system produces. It is tied to apprehension, understanding, and the person who comes to know.
William Senn
Tarleton State University
Read the Original
This page is a summary of: The informing act: apprehension in AI-mediated systems, Journal of Documentation, June 2026, Emerald,
DOI: 10.1108/jd-03-2026-0125.
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