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In the rapidly evolving landscape of artificial intelligence, understanding the capabilities and limitations of large language models (LLMs) in specialized fields such as education is crucial. A study by Elena Kardanova, Alina Ivanova, Ksenia Tarasova, Taras Pashchenko, Aleksei Tikhoniuk, Elen Yusupova, Anatoly Kasprzhak, Yaroslav Kuzminov, Ekaterina Kruchinskaia, and Irina Brun, introduces a novel psychometrics-based methodology to assess LLM performance in the field of pedagogy. By focusing on the educational domain and developing a robust benchmark tailored for LLM evaluation, the authors offer new insights into the strengths and weaknesses of these models.

In an era where the demand for innovative educational strategies is paramount, the effectiveness of teaching methods in fostering student learning has come under scrutiny. A recent study by Evgeniy Terentev, Irina Shcheglova, Denis Federiakin, Yuliya Koreshnikova, and Jamie Costley delves into the contrasting realms of active and passive teaching approaches within the context of economics and management education at a leading Russian university. By examining how these teaching methodologies influence student performance across various cognitive levels, the authors aim to shed light on the vital role of instructional practices in preparing students for success in an increasingly complex and competitive knowledge economy.

As its central theme, the conference focused on finding a balance between upholding high academic standards at universities and promoting the well-being of both students and faculty. The first plenary session examined the phenomenon of student families, with conference participants discussing how these families fit into the broader demographic context and proposing measures to support them.