Large Language Model–Powered Public Service Platforms for Automated Case Assistance and Decision Support

Abstract

Large Language Models (LLMs) are emerging as a transformative technology in the modernization of public service delivery, enabling intelligent automation, natural language interaction, and context-aware decision support. Traditional public service platforms often rely on manual workflows, rule-based systems, and fragmented data sources, leading to inefficiencies and delays in case handling. This paper presents a comprehensive overview of LLMpowered public service platforms designed for automated case assistance and decision support. It explores how LLMs can be integrated with existing government systems to process unstructured data, interpret citizen requests, and generate actionable recommendations aligned with policy frameworks. The study outlines a layered architectural model incorporating user interaction interfaces, AI processing engines, data integration mechanisms, and governance controls. Key application areas such as social welfare administration, legal advisory systems, healthcare support, and citizen service portals are examined to illustrate practical adoption scenarios. Additionally, the paper discusses critical challenges including data privacy, model bias, explainability, and regulatory compliance, along with mitigation strategies such as human-in-the-loop validation and hybrid AI architectures. The findings suggest that LLM-driven platforms can significantly enhance efficiency, consistency, and accessibility in public services while supporting informed and transparent decision-making. The paper concludes by identifying future research directions focused on scalable deployment, ethical AI governance, and domain-specific model optimization for public sector applications.

Ähnliche Arbeiten