Innovation science and technologiy Volume 2 Issue 8 (2026) · pp. 73-82
DIGITAL GOVERNANCE OF THE NATIONAL HIGHER EDUCATION RANKING: AN AI-BASED MONITORING ARCHITECTURE
Mamajonova, Mukhlisakhon
Abstract
This article examines the national ranking of higher education institutions in Uzbekistan from the perspective ofdigital governance: not what the ranking measures, but how its indicators can be produced, verified, and monitored. The2026 regulation establishes 25 indicators and links ranking outcomes to strategic and funding decisions, yet it leaves thedata infrastructure behind the ranking undefined, and current practice relies on an annual campaign of manual reporting.Using document analysis of the indicator formulas and a design science approach, the study first maps every indicatorto its primary data elements, source system, and update cadence, and then classifies the indicators into three automation-readiness tiers. The results show that 76 of the 100 ranking points rest on administrative registers and externalmachine-readable databases rather than surveys, and that between 57 and 65 points, depending on institutional category,can be generated fully automatically from data already maintained in existing digital systems. On this basis, the articleproposes a four-layer monitoring architecture - source systems, an interoperability layer, an analytics and artificial intelligencelayer, and a governance interface - and assigns concrete AI functions to specific indicators: rule-based validation,cross-source verification, anomaly detection, and short-term forecasting. The discussion identifies four governance risksof such a system, including data-quality failures and metric gaming, and formulates safeguards for each.
digital governance, higher education, national ranking, education management information systems, data governance, artificial intelligence in the public sector, monitoring architecture.
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