Tool-Augmented LLMs for Rapid Data Insights: Empowering Non-Expert Users in Open Government Data Contexts

Abstract

Open Government Data (OGD) initiatives aim to foster transparency and innovation, yet actual usage remains low due to limited user resources, low data literacy, and a lack of supportive tools. Adopting a Design Science Research (DSR) approach, this study explores how systems must be designed to enable non-expert users to effectively interact with OGD. We propose a design theory comprising design requirements, design principles, and design features, which we instantiate in a prototypical system based on the ChatGPT platform. The core design integrates large language models (LLMs) with tool augmentation techniques to enable fully automated data retrieval, analysis, visualization, and interpretation through natural language interaction. Initial formative evaluations indicate that tool-augmented LLMs can substantially lower interaction barriers for non-expert users, while limitations in accuracy and reliability remain. Our study contributes prescriptive design knowledge and practical guidance for developing advanced natural language interfaces for OGD platforms.