The illusion of AI competence: A conceptual review of cross-dimensional miscalibration in generative AI use
Year: 2026
Type: article
Abstract: This article develops a conceptual account of the illusion of AI competence: a calibration failure in which gains in operating generative AI systems are misread as gains in the capacity to evaluate their outputs and in underlying domain knowledge. Drawing on cognitive offloading, the illusion of explanatory depth, the extended mind hypothesis, and research on automation bias and machine heuristics, the article specifies three analytically distinct dimensions of AI competence: operational (AI-O), evaluative (AI-E), and domain-task (DT), arguing that the illusion arises from cross-dimensional inference under asymmetric feedback. A structured but non-exhaustive conceptual review, organized into eight search arms including a deliberate disconfirming arm, maps the current evidence base and identifies where it is thin or contested. The resulting framework is presented as a directed causal model with three moderators, accompanied by two falsifiable implications concerning interface transparency and feedback disruption. The article concludes that the phenomenon is best understood as design-contingent rather than inevitable, with implications for AI literacy curricula and interface design.
Author: Rathapol Pradubwate
Institution: Srinakharinwirot University
Language: English
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,540
OpenAlex ID: W7224183406