Exploring the Impact of Generative AI on Digital Inclusion: A Case Study of the E-Government Divide

This paper examines how Generative AI (GenAI) reshapes digital inclusion in e-government. We develop the E-Government Divide Measurement Indicator (EGDMI) across three dimensions: D1—Breadth of the Divide (foundational access, affordability, and basic skills), D2—Sectoral/Specific Divide (actual use, experience, and trust in e-government), and D3—GenAI Gap (access, task use, and competence). The index architecture specifies indicator lists, sources, units, transformations, uniform normalization, and a documented weighting strategy with sensitivity and basic uncertainty checks. Using official statistics and qualitative evidence for Serbia, we report D1 and D2 as composite indices and treat D3 as an exploratory, non-aggregated layer given current data maturity. Results show strong foundational readiness (D1 = 73.6) but very low e-government uptake (D2 = 19.9), indicating a shift of the divide from access to meaningful use, usability, and trust. GenAI capabilities are emergent and uneven (D3 sub-dimensions: access 47.8; task use 39.4; competence/verification 43.6). Cluster analysis identifies four user profiles—from “Digitally Excluded” to “GenAI-Augmented Citizens”— that support differentiated interventions. The initial hypothesis—that GenAI can widen disparities in the short run—receives partial confirmation: GenAI may lower interaction costs but raises verification and ethics thresholds for vulnerable groups. We outline a policy roadmap prioritizing human-centered service redesign, transparency, and GenAI literacy before automation, and provide reporting templates to support comparable monitoring and cross-country learning.