Cognitive AI in Psychological Testing: Improving Validity and Reliability in Personality Assessment Using Deep Learning Models

Abstract

Psychological testing has long relied on standardized instruments to measure personality traits, yet traditional assessment methods face persistent challenges related to measurement error, response bias, construct validity, and cross-cultural reliability. The emergence of cognitive artificial intelligence (AI) and deep learning models presents a transformative opportunity to enhance the psychometric foundations of personality assessment. This study develops a conceptual-analytical framework to examine how cognitive AI systems capable of learning, adaptation, and contextual inference can improve the validity and reliability of personality measurement. By synthesizing literature from psychometrics, personality psychology, machine learning, and cognitive computing, the paper demonstrates how deep neural networks, natural language processing, and multimodal data integration reduce construct contamination, mitigate social desirability bias, and enhance internal consistency and predictive validity. At the same time, the analysis highlights new methodological and ethical challenges, including algorithmic bias, model interpretability, and threats to psychological transparency. The findings suggest that cognitive AI does not replace psychological theory but augments it by enabling adaptive, data-driven, and context-sensitive personality assessment. The study contributes to psychological measurement theory by reconceptualizing validity and reliability as dynamic properties of human–AI assessment systems rather than static attributes of test instruments.