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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.
September 9, 2026
‘Balkan Hospitality Opens Doors: Studying Dialects on the Verge of Extinction
You cannot study spoken dialects from books. Instead, you need to go to a village, seek out its elders, and earn the trust of local residents before you can record hours of spontaneous stories. This is how Natalia Muravleva, Associate Professor at the Faculty of Humanities, conducts her research. Her internship in Serbia continued her long-standing study of dialects spoken by Macedonian settlers. In this interview, she discusses how diaspora cultural centres help researchers reach informants, why native speakers need to be interviewed only in their own language (otherwise, as she puts it, they may 'break'), and how a single field season helped her finalise her monograph. She also shares warm memories of autumn in Belgrade and of colleagues with whom grammar can be discussed in three languages at once.
September 9, 2026
Scientists Train Neural Network to Generate Process Plans from 3D Models
Researchers at the HSE FCS AI and Digital Science Institute have developed CAD2TechSpec, a framework that converts 3D models of mechanical parts into machining process plans—step-by-step instructions for machine tools. The solution aims to reduce the time required for the design and preparation of technical process documentation in mechanical engineering, aircraft manufacturing, and other high-tech industries. The study findings have been published in PeerJ Computer Science.

 

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Balancing autonomy and efficiency: how learner control shapes cognitive load and online learning outcomes

Interactive Learning Environments. 2026. Vol. 34. No. 4. P. 2609–2630.
Gorbunova A., Kseniia Adamovich, Savelyev A., Costley J.

This study examines the tradeoff between learner autonomy and efficiency in optimizingonline instructional design. While learner control is known to enhance personalizationand engagement, it may also increase cognitive demands and learning time,potentially compromising efficiency. To investigate this issue, we conducted anexperimental study comparing low and high levels of learner control across keydimensions: pace, place, content, and sequence. A total of 135 students participated,with one group following a fixed path (low learner control) and another navigating aflexible sequence (high learner control). Learning efficiency was assessed throughengaged time, self-reported cognitive load, and performance outcomes. The resultsshowed that the fixed path group achieved similar performance levels andexperienced similar cognitive load levels to the flexible sequence group, whilerequiring significantly less time. These findings suggest that while learner controloffers potential benefits, excessive autonomy may not lead to improved outcomes,moreover reduced learner control can lead to comparable performance and cognitiveload outcomes with lower time investment. This supports the design of moreefficient, less customizable online learning environments. This study contributes tothe ongoing discussion about the design of adaptive learning systems by highlightingtime as a critical component of instructional efficiency.

Research target: Education
Language: English
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Keywords: эффективность обученияinstructional designonline learningcognitive loadlearning efficiencyкогнитивная нагрузкапедагогический дизайнонлайн обучениеlearner controlсамостоятельный контроль
Publication based on the results of:
Using digital traces to study learners' behavioral strategies (2025)
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