AI-BASED AUTOMATED ASSESSMENT SYSTEMS AND THEIR ROLE IN EDUCATION QUALITY MONITORING: THE CASE OF UZBEKISTAN
DOI:
https://doi.org/10.5281/zenodo.20607527Keywords:
automated assessment, artificial intelligence, education quality monitoring, higher education in Uzbekistan, natural language processing, automated essay scoring, e-learningAbstract
The rapid expansion of higher education in Uzbekistan — from 9% enrollment coverage in 2016 to 42% in
2023, 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 Uzbek
higher 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 tools
can reduce instructor workload by an estimated 40–60%, improve grading consistency, and provide real-time analytical
dashboards for institutional quality monitoring. Context-specific considerations for Uzbekistan — including multilingual
assessment in Uzbek, Russian, and English, infrastructure readiness, and data governance — are discussed together
with implementation recommendations
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