AI-DRIVEN PSYCHOMETRIC PROFILING: INTEGRATING MACHINE LEARNING AND BEHAVIOURAL ANALYTICS FOR PREDICTIVE TALENT ASSESSMENT IN MODERN ORGANIZATIONS

Abstract

AI-driven psychometric profiling has emerged as a transformative approach for understanding talent potential, behavioural tendencies, and job performance in modern organizations. Traditional psychometric assessments rely on static questionnaires and subjective interpretation, often lacking predictive accuracy and adaptability to dynamic workplace environments. This study investigates an integrated framework that combines machine learning, behavioural analytics, and multimodal data streams to enhance the precision of talent assessment. The research examines how digital behavioural cues, linguistic markers, cognitive task performance, and interaction patterns can be processed through supervised and unsupervised learning models to generate robust psychological inferences. By evaluating datasets from diverse organizational contexts, the study identifies feature patterns that correlate strongly with job-relevant competencies such as adaptability, leadership potential, emotional stability, and problem-solving ability. Model validation is conducted through cross-validation, SHAP-based explainability, and fairness audits to minimize bias. Findings indicate that AI-enhanced psychometric systems outperform traditional assessments in predictive validity, early-risk identification, and talent mapping accuracy. However, the results also highlight ethical concerns including algorithmic bias, privacy intrusion, and transparency challenges. This research contributes an operational, scalable, and data-driven methodology for predictive talent assessment, offering organizations a scientifically grounded tool for workforce optimization and evidence-based HR decision-making.