AI, or artificial intelligence, is a technology of creating algorithms and computer systems that mimic human cognitive abilities to perform tasks. Many industries are undergoing revolutions due to the advances and applications of AI technology. The current study explored a burgeoning field-Psychometric AI, which integrates AI methodologies and psychological measurement to not only improve measurement accuracy, efficiency, and effectiveness but also help reduce human bias and increase objectivity in measurement. Specifically, by leveraging unobtrusive eye-tracking sensing techniques and performing 1470 runs with seven different machine-learning classifiers, the current study systematically examined the efficacy of various (ML) models in measuring different facets and measures of the emotional intelligence (EI) construct. Our results revealed an average accuracy ranging from 50-90%, largely depending on the percentile to dichotomize the EI scores. More importantly, our study found that AI algorithms were powerful enough to achieve high accuracy with as little as 5 or 2 s of eye-tracking data. The research also explored the effects of EI facets/measures on ML measurement accuracy and identified many eye-tracking features most predictive of EI scores. Both theoretical and practical implications are discussed.
Keywords: emotional intelligence; eye tracking; machine learning; psychometric AI.