Large language models (LLMs) are increasingly used in education, but evidence on their performance comes mainly from high-resource languages. We evaluated five commercial LLMs in Kazakh in a zero-shot setting: sentiment classification on 300 class-balanced KazSAnDRA reviews and question answering (QA) on the same 300 r...
M. Kemelbek, Z. Nurbekova, A. Sagimbayeva· Information· 0 citations
The widespread adoption of generative AI tools has created new methodological challenges for the validity of assessment in higher education. If a neural network can write code, how can a student's actual knowledge be assessed objectively? This study proposes a multifactor AI-assisted assessment model for the fair e...
Z. Nurbekova, M. Kemelbek, A. Sagimbayeva et al.· Frontiers in Education· 0 citations
In the context of rapidly evolving digital economy requirements, the intellectual support of students’ professional development based on the analysis of educational data and labor market demands has become an important challenge for higher education institutions. The aim of this study is to develop a methodology for th...
D. Naimanova, Z. Nurbekova, Z. Alimova et al.· Frontiers in Education· 0 citations
It is suggested that ML-guided prompt routing can improve perceived relevance of instructional materials while remaining transparent and easy to calibrate and integrate interpretable ML predictions with prompt-engineered LLMs can automate individualized content generation and improve learning outcomes.
Petr Tsekoyev, T. Sembayev, Z. Nurbekova· AI@DTESI· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
