Employee AI Use and Readiness in Applied Higher Education: Awareness, Barriers, and HRM Implications

Artificial intelligence (AI) adoption in higher education increasingly represents a challenge in workforce development and organizational transformation, rather than a purely technological issue. This study re-analyzed survey data from 311 faculty members, training instructors, and administrative staff at the Public Authority for Applied Education and Training (PAAET), Kuwait, to examine the associations of AI awareness and perceived barriers with AI usage/readiness from a human resource management perspective. AI usage/readiness was treated as a self-reported indicator of AI-related human capital readiness rather than as a measure of demonstrated competence or comprehensive organizational readiness. Ordinal confirmatory factor analysis and structural equation modeling were conducted using robust diagonally weighted least squares/weighted least squares mean and variance adjusted (DWLS/WLSMV) estimation. The refined 12-item, three-factor model demonstrated satisfactory reliability and validity, although model fit was mixed across scaled and robust indices. A near-zero eigenvalue also indicated possible numerical instability, warranting cautious interpretation and independent confirmation. AI awareness was strongly and positively associated with usage/readiness (β = 0.782, p < 0.001), whereas perceived barriers showed a small positive adjusted association (β = 0.117, p = 0.019). Together, the predictors explained 66.0% of the variance in usage/readiness. The positive association with perceived barriers may indicate that employees who engage more extensively with AI are more likely to recognize institutional and technical constraints. Building on these findings, this study proposes two conceptual extensions for future validation: a five-level AI capability maturity model for higher education and a seven-domain AI-enhanced instructor competency framework. The findings emphasize the need to integrate employee capability, institutional support, responsible AI governance, and continuous learning to develop sustainable AI readiness. Given the cross-sectional, self-reported design, these findings should be interpreted as associations rather than causal effects.