Semantic Mapping of AI-for-Government Research: Uncovering the Knowledge Architecture of Digital-Era Governance

This study presents a comprehensive bibliographic and semantic analysis of 3957 scientific publications on artificial intelligence (AI) in government and public administration. Using an integrated text- and network-based approach, we identify the main thematic areas and conceptual orientations shaping this rapidly expanding field. The analysis reveals a research landscape that spans AI-driven administrative transformation, digital innovation, ethics and accountability, citizen trust, sustainability, and domain-specific applications such as healthcare and education. Across these themes, policy-oriented and conceptual contributions remain prominent, while empirical and technical studies are increasingly interwoven, reflecting growing interdisciplinarity and methodological consolidation. By clarifying how AI research aligns with governance values and institutional design, this study offers actionable insights for policymakers and public managers seeking to navigate responsible public-sector AI adoption. Overall, the findings indicate that AI-for-Government research is moving from fragmented debates toward a more integrated, implementation-relevant knowledge base centered on trustworthy and value-aligned digital-era governance.