Human-Centered AI and Cognitive Adaptation: An Empirical Study of Human Responses to AIMediated Information Systems

Abstract

Artificial intelligence now shapes much of how information reaches people, who read it, and what they make of it. The environments it creates are probabilistic, often fluent without being grounded, and curated by systems whose workings stay hidden. Most research on this shift has gone to AI literacy, explainability, and the mechanics of human–AI interaction. Far less has gone to a prior question: how does human cognition itself change to cope? The paper addresses that question with the Human Cognitive Adaptation Framework (HCAF). One hundred thirty-one adults who use AI-generated and algorithmically curated content daily completed a 25-item, six-point inventory covering five facets—orientation, dimensional literacy, ambiguity tolerance, coherence recognition and critique, and relational navigation. The research examined reliability, the correlations among facets, and dimensionality. The full scale was highly reliable (α = .902) and the data factored cleanly (KMO = .841). One factor dominated: its eigenvalue of 7.93 carried 31.7% of the variance, and parallel analysis retained a single factor. The facets correlated strongly with one another (r = .46 to .69), which reads as one adaptive response rather than five separate skills. Within that single capacity, people recognized synthetic coherence more readily than they tolerated the uncertainty recognition exposes, so adaptation is not uniform across its parts. The research used these results to develop the HCAF, which treats adaptation as the work of staying oriented, judging coherence, and holding up under instability, and draws out what follows for human-centered AI, AI literacy, education, and the design of AI-supported decision environments.