The Evolution Of Sustainability In Human Resource Technology: Reconstructing Ai-Augmented Experiential Learning For Sustainable Labour Economics

Abstract

The intersection of sustainable labour economics and human resource technology (HR Tech) demands human capital development strategies that can mitigate rapid technological displacement. While experiential learning has long been a cornerstone of professional training, its classical application often triggers cognitive overload when adults face highly complex, digitally multidimensional tasks. This study analyzes the effectiveness of an AI-Augmented Experiential Learning Model (AI-Augmented ELM) in enhancing complex operational planning and professional competencies within vocational leadership frameworks. Employing a quasi-experimental design (Non-Equivalent Control Group Design), 37 vocational professionals were assigned to an experimental group (N=18, receiving AI-Augmented ELM with prompt engineering guidance) and a control group (N=19, receiving conventional experiential training). Quantitative descriptive and inferential analysis revealed that the experimental group achieved a substantial competency leap, with mean scores jumping from 67.78 to 86.11 (Δ = +18.33, p=0.002), significantly outperforming the control group (Δ = +5.26). Paradoxically, during the manual phase of active experimentation, the control group—which autonomously integrated generative AI tools without rigid structural constraints—scored higher in baseline artifact creation (92.16) than the pure manual group (78.72), demonstrating that manual execution creates a severe cognitive bottleneck. This paper conceptualizes the shift from classical learning to AI-Augmented ELM as a core pillar of sustainable human resource technology, shielding the workforce from cognitive depletion and fostering resilient, future-proof employment.