Automating Psychometric Assessment Using Generative AI: A Scalable Framework for Industrial Applications

Abstract

Abstract Large industrial organizations increasingly rely on psychometric assessment to support high-stakes workforce decisions across geo-graphically dispersed and safety-critical environments. Traditional assessment workflows, which depend heavily on manual scoring and expert psychological interpretation, face significant challenges related to scalability, consistency, and timeliness. This study presents a scalable, AI-driven framework that automates end-to-end psychometric assessment by integrating transformer-based generative artificial intelligence with classical psychometric theory and ethical governance principles. The proposed system employs a hybrid architecture that combines deterministic, rule-based psychometric scoring with machine learning-based anomaly detection, alongside a generative interpretation module fine-tuned on expert-authored psychological reports. The framework was empirically evaluated using large-scale assessment data from Middle Eastern industrial operations. AI-generated scores and interpretive reports were compared with those produced independently by licensed psychologists using blind evaluation procedures. Quantitative analyses assessed scoring agreement and reliability, while qualitative expert reviews examined interpretive quality, construct validity, and professional acceptability. Results demonstrated strong agreement between AI-generated and expert scores across the three instruments, with Pearson correlations ranging from .819 to .915 and intraclass correlation coefficients from .818 to .915. Exact categorical band agreement ranged from 56.7% to 68.9%, while agreement within one adjacent band exceeded 97% for every instrument. Blinded expert review detected no statistically significant differences between AI-generated and human-authored reports across the evaluated quality dimensions. Automation reduced mean workflow touch time from 109.2 to 5.2 minutes and mean elapsed turnaround from approximately 45.9 hours to 17.6 minutes, with 7.1% of automated cases escalated for human review. These findings indicate that a psychometrically grounded, governance-oriented generative AI framework can substantially reduce assessment processing burden while maintaining strong agreement with expert scoring and comparable expert-rated interpretive quality. This research contributes an integrated framework unifying generative AI, psychometric theory, and ethical oversight for full-cycle psychometric assessment automation in industrial contexts.