An Analytical Study on the Use of Artificial Intelligence in Measuring Human Intelligence

Abstract

Purpose: This study explores how Artificial Intelligence (AI) has emerged as a tool for measuring human intelligence and ascertains whether the AI-based methods in measurement can supplement, enhance or substitute psychometric evaluation in any way. The study will also explore how the fields of psychometrics and AI are converging, highlight the essential AI-based methods in intelligence assessment, point out their strengths and weaknesses, and discuss the issues of fairness, impartiality, validation, and responsibility in relation to them.

Method/Approach: The research employs a qualitative, theoretical, and interpretative methodology that draws on the narrative-thematic assessment of secondary literature sources. The pertinent articles from peer-reviewed articles and preprints along with institutional reports were subjected to thematic content analysis. The literature was categorised into theme groups as follows: classical psychometric principles, AI-based computerised adaptive testing, brain imaging-based prediction, behaviour and multi-modal signal analysis, AI processes used in the human intelligence tests, and issues of fairness and ethics.

Results/Findings: The results indicate that AI has been shown to be the most effective tool for enhancing the efficiency and accuracy of standard intelligence testing methods in terms of
computerized adaptive testing and automated scoring. Methods based on neuroimaging provide
predictions about intelligence that are statistically weak but scientifically valid; behavioral methods such as eye-tracking have proven to be promising but may require substantial resources to be applied effectively. Yet, there is no single AI method that can provide full construct validity, standardization and reliability, and fairness as compared to conventional intelligence tests administered by humans. Moreover, these results once again bring to light issues of algorithmic bias, consent, transparency, and instability of responses in generative AI methods.

Originality/Value: This research offers a coherent framework for comparing research studies on different aspects of AI in psychometrics, adaptive testing, neuroimaging, behavioral inference, and evaluation of AI technology. It presents a hybrid assessment model involving AI in adaptive delivery, scoring, and signal extraction while allowing qualified professionals to stay in charge of interpreting results and ensuring ethical integrity.

Type of Paper: Exploratory Research.