LLM-Based Personalized Goal Recommendation Systems for Gig Workers: Evidence from Behavioral Economics and Human–AI Collaboration

Abstract

The rapid expansion of platform-mediated gig work has created an urgent need for scalable, adaptive mechanisms that can support worker productivity and well-being without resorting to coercive algorithmic management. This paper proposes and evaluates a system architecture that leverages large language models (LLMs) to deliver personalized goal recommendations to gig workers, integrating insights from behavioral economics and human–AI collaboration. Building on goal-setting theory, prospect theory, and nudge design, we argue that LLM-based systems can generate context-aware, individually tailored goals that balance intrinsic motivation with performance targets. We examine the structural trade-offs inherent in such systems, including the tension between autonomy enhancement and algorithmic paternalism, the risk of fairness violations across heterogeneous worker populations, and the challenges of maintaining transparency and explainability in LLM outputs. The paper synthesizes evidence from recent field experiments, including a large-scale study of gig workers that demonstrates the effectiveness of self-set versus assigned goals, and connects these findings to the operational design of recommendation systems. We also discuss governance frameworks necessary to ensure that LLM-based goal recommendation remains robust against distributional shifts, feedback loops, and exploitation. The analysis concludes that while LLM-driven personalization offers substantial promise for improving gig worker outcomes, its deployment must be anchored in participatory design, continuous auditing, and regulatory oversight to avoid reinforcing precarity. This work contributes a systems-level perspective to the emerging literature on AI-assisted labor management and provides actionable guidelines for platform designers, policymakers, and researchers.