Volume 5 • Issue 1 • PP: 37–42 • 2026
A Systematic Review of AI-Powered Uzbek Short-Answer Grading Using NLP and Teacher-Annotated Datasets
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© 2026 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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This paper presents a Systematic Literature Review (SLR) of AI-powered automated short-answer grading, with a particular focus on low-resource languages such as Uzbek. The review follows the PRISMA 2020 guidelines to ensure transparency and methodological rigor. Relevant peer-reviewed studies published between 2018 and 2025 were systematically identified, screened, and analyzed across multiple academic databases. In total, 33 studies were included in the final synthesis. The reviewed literature indicates that transformer-based models, including mBERT and XLM-R, generally achieve stronger performance than traditional machine learning approaches, while recent large language models show potential in few-shot and zero-shot grading scenarios. The findings also highlight that the limited availability of teacher-annotated datasets remains a major challenge for developing reliable automated grading systems in low-resource educational contexts.
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References
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