Artificial Intelligence Quotient:

Abstract

The term Artificial Intelligence Quotient (AIQ) has emerged across multiple disciplines including cognitive psychology, computer science, education, enterprise consulting, and marketing, each producing distinct frameworks for what AIQ is and how it should be measured. As there is no systematic comparative analysis to date, this paper surveys ten independently developed AIQ approaches and introduces a two-dimensional analytical model to differentiate them: whether AIQ is conceived as a stable trait or a perishable skill, and what each framework treats as its primary object of measurement. The analysis reveals that most frameworks cluster around a shared assumption that AIQ is best measured through performance, while calibration — the ability to accurately read where AI succeeds and fails — remains largely implicit. We argue that calibration is the central mechanism through which knowledge becomes performance, and that its absence from most frameworks represents a significant conceptual gap. The parallel emergence of AIQ across so many fields reflects a genuine shared intuition that deserves coordinated academic attention.