Cognitive Ecosystems In Green Labour Markets: Integrating Ai-Driven Dual-Scaffolding Hr Technology For Sustainable Human Capital Development

Abstract

The structural transition toward a sustainable green economy imposes significant cognitive friction on the workforce due to rapid job displacement and the complexity of new green technologies. Modern Human Resource Technology (HR Tech) must expand its role from administrative automation to cognitive support systems that preserve sustainable human capital. This study introduces an AI-Driven Dual-Scaffolding Experiential Learning Model (DS-ELM) designed to optimize professional adult upskilling without inducing cognitive overload. Utilizing a quasi-experimental approach (Non-Equivalent Control Group Design), 37 senior industry professionals were divided into an experimental cohort (N=18, utilizing the AI-driven DS-ELM framework) and a control cohort (N=19, using conventional manual experiential methods). Quantitative analysis showed a profound competency leap in the experimental group, with mean performance scores elevating from 67.78 to 86.11 (Δ = +18.33, p=0.002), vastly outperforming the control group (Δ = +5.26). Crucially, during the complex artifact design phase, the unassisted manual group suffered from massive cognitive bottlenecks, scoring only 78.72 compared to a score of 92.16 achieved when generative AI was integrated as an epistemic partner. This study establishes a new theoretical model for sustainable labor economics, demonstrating that dual-scaffolding technologies shield workers from cognitive exhaustion, ensuring resilient employment and workplace sustainability in the green automation era.