AI-BASED AUTOMATED ASSESSMENT SYSTEMS AND THEIR ROLE IN EDUCATION QUALITY MONITORING: THE CASE OF UZBEKISTAN

Majidova , Yulduz, Janayev , Bunyod, Choriyev , Anvar, Mamatqulov , Mavlon

Муҳандислик ва Иқтисодиёт · 2026-yil

Annotatsiya

The rapid expansion of higher education in Uzbekistan — from 9% enrollment coverage in 2016 to 42% in2023, with more than 1.3 million students across 213 universities — has created a growing need for scalable, transparent,and consistent assessment practices. This paper examines the theoretical foundations, architectural frameworks,and practical implementation considerations of AI-based automated assessment systems (AAS) in the context of Uzbekhigher education. Drawing on peer-reviewed literature in natural language processing, machine learning-based grading,and education quality monitoring, the study proposes a three-layer AAS architecture comprising automated test grading,written-work evaluation, and practical assignment scoring modules. The analysis shows that AI-driven assessment toolscan reduce instructor workload by an estimated 40–60%, improve grading consistency, and provide real-time analyticaldashboards for institutional quality monitoring. Context-specific considerations for Uzbekistan — including multilingualassessment in Uzbek, Russian, and English, infrastructure readiness, and data governance — are discussed togetherwith implementation recommendations

Maqola ma’lumotlari
MualliflarMajidova , Yulduz, Janayev , Bunyod, Choriyev , Anvar, Mamatqulov , Mavlon
JurnalМуҳандислик ва Иқтисодиёт
Nashr sanasi2026-05-01
Jild4
Son5
TilIngliz

Kalit so‘zlar

automated assessment, artificial intelligence, education quality monitoring, higher education in Uzbekistan, natural language processing, automated essay scoring, e-learning

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