Asymmetric transmission of AI and renewable energy equity shocks to U.S. oil sector performance: A regime-dependent analysis for sustainable investment

The increasing integration of Artificial Intelligence (AI) into education requires studying the motives for its use among students. This study aims to identify the key motivations for economics students to use AI and compare these motivations by grade level and gender. The study examines satisfaction with the use of AI and analyzes the number of AI tools used.

An anonymous empirical study was conducted among 264 students from the Faculty of Economics at Taras Shevchenko National University of Kyiv, Ukraine. Data analysis included descriptive statistical methods, non-parametric statistical methods, and exploratory factor analysis.

The study found that students’ main motivations for using AI are the automation of routine tasks (34.2%) and the need to save time (21.5%), while 18.7% use AI to compensate for lack of experience. Among Bachelor’s students, motivations such as automating routine tasks and saving time increased from 53% to 58% over the course of their studies, while lack of experience decreased from 22% to 15%. In contrast, Master’s students showed a decrease in routine automation (from 36% to 28%) but an increase in the need to compensate for lack of experience (from 15% to 28%) and to save time (from 18% to 25%). In terms of gender, men are more likely to use AI for learning and personal development, while women are slightly more likely to use AI for work. More than 38% of respondents say they need to use at least 2 AIs to achieve their goals.

Acknowledgment
This publication is based upon work from 24-PKVV-UM-002, ‘Strengthening the Resilience of Universities: Czech-Ukrainian Partnership for Digital Education, Research Cooperation, and Diversity Management,’ supported by the Czech Development Agency and the Ministry of Foreign Affairs under the initiative ‘Capacity Building of Public Universities in Ukraine 2024.’

The crumble of financial markets due to the recent crises has wobbled precariousness in the stock market and intensified the returns vulnerability of banking indices. Against this backdrop, this study intends to model the volatility of the Indian Bank Nifty returns using a battery of GARCH specifications. The finding of the present research contributes to the literature in three ways. First, volatility during the sample period, which corresponds to a time of stress (a bear market), is more persistent, with an estimated coefficient of 0.995695. Moreover, when volatility rises, it persists for a long time before returning to the mean in an average of 16 days. Second, for a positive γ, the results insinuate the possibility of an “anti-leverage effect” with a coefficient of 0.139638. Thus, the volatility of the Bank Nifty returns tends to rise in response to positive shocks relative to negative shocks of equal magnitude in India. Finally, the findings demonstrate that EGARCH with Student’s t-distribution offers lower forecast errors in modeling conditional volatility.

The modern approaches towards higher education systems management often tend to focus on separate universities’ performance, lacking the systemic view of the overall higher education systems’ competitiveness. Thus, the policymakers often fail in tailoring the higher education strategies to the mission of higher education in contemporary society.
The article focuses on providing a systemic insight into the global competitive positioning of the national higher education systems. Based on the suggested ranking methodology, the authors perform the evaluation and ranking of 94 higher education systems, highlighting the limitations of this method, and the cluster analysis, identifying 3 types of their competitive positioning: leaders, followers, and underperformers. Based on Pearson coefficients of skewness and kurtosis calculation, the article shows that globally the inequalities in terms of higher education enrolment rate are decreasing, while those of R&D institutions quality and university-industry collaboration in research remain unchanged. Therefore, upgrading higher education quality assurance systems becomes the main strategic priority for the developing countries in terms of ensuring their higher education systems’ competitiveness. Given the levelling of higher education attainment and its quality worldwide, the authors anticipate further specialization of the universities and broadening of their role within the national innovation system. The article shows that the more comprehensive the approach for evaluating the higher education systems performance – the better the policymakers may benefit in terms of higher education strategic management.