AI for Informal Workforce Development: Leveraging Generative Models to Teach Practical Trade Skills to Gig and Blue-Collar Workers

Abstract

The accelerating diffusion of artificial intelligence (AI) is reshaping global approaches to
workforce training, yet informal and gig-economy workers remain largely excluded from
structured upskilling systems. This study investigates how generative AI models—notably
Generative Adversarial Networks (GANs), transformer architectures, and large language
models—can be leveraged to teach practical trade skills to blue-collar and informal workers.
Building on frameworks from the International Labour Organization (ILO) and UNESCOUNEVOC,
the research adopts a conceptual–analytical design that integrates empirical evidence
and meta-analytic findings to develop a tri-layer AI upskilling model. The model comprises: (1)
a Generative Simulation Layer that replicates complex trade procedures through synthetic data
and VR demonstrations; (2) an Adaptive Feedback Layer utilizing transformer-based learning
analytics to personalize training; and (3) a Labor Analytics Layer linking performance data to
employment and income outcomes.
Comparative analysis reveals that AI-enabled simulations substantially enhance learning
efficiency (78% vs 42%), task accuracy (84% vs 55%), and safety compliance (83% vs 48%)
relative to conventional workshop training. Regional adoption trends between 2018 and 2022
show significant growth in Asia, Europe, and North America, demonstrating the scalability of
AI-driven vocational systems. Furthermore, cost-effectiveness assessments indicate higher
performance gains per training dollar when AI tools are integrated with VR environments.
The findings affirm that generative AI can democratize skill acquisition, strengthen
employability, and align informal workforce competencies with Industry 4.0 imperatives.
Nevertheless, equitable deployment demands inclusive digital infrastructure, affordable access,
and robust governance frameworks to ensure that AI-mediated upskilling contributes to
sustainable and just economic growth.