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September 11, 2026
How to Assess Students Knowledge in the Age of AI
A researcher at HSE University has proposed a flowchart to help lecturers decide how to assess students who use artificial intelligence. It shows where the use of AI should be restricted and where it can be incorporated into the learning process. The article has been published in IT Professional.
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September 9, 2026
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­Social Disadvantage and Language Disorder: Is Dissociating Possible?

SSRN Electronic Journal. 2024. Article 4985214.
Rakhlin N., Li N., Momotenko D., Sukmanova A., Kosikova A., Grigorenko E.

The study addressed the difficulties inherent in identifying Developmental Language Disorder (DLD) in children of lower socioeconomic status (SES), known to underperform linguistically compared to children of middle/higher SES. We compared Russian-speaking children from urban (middle SES) and rural (low SES) environment on standardized language tasks. To tease out the “rurality” effect (a rural-urban gap) from the “DLD effect” (a gap between the high- and average-DLD risk groups), we compared children from two low SES rural populations: a population with a high prevalence of genetic DLD and a comparison population. Our results confirmed the association between rural poverty and lower language performance, as rural children underperformed on all assessments. The two rural samples, however, were only minimally different from each other on most subtests. A test of sentence repetition produced the largest “rurality” but the smallest DLD effect. Only Linguistic Operators, a receptive task involving processing syntactically complex sentences, yielded a small rurality and a large DLD effect, suggesting that the effect of rural poverty alone (without a high genetic DLD risk) does not extend to receptive sentence processing tasks. This potentially makes this task a good diagnostic tool for the detection of genetic DLD in lower SES children, and, in a combination with sentence repetition, a tool to distinguish such DLD from the effects of social disadvantage. We also compared the two rural samples on indices of narrative microstructure and found that most of them failed to accurately differentiate the high-risk genetic DLD from an average risk rural sample.

Language: English
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Keywords: language processingExpressive languageDLDdiagnostic tools
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