Issue #2 (Volume 8 2024)

Studying public-private partnership practices in textbook publishing and distribution in Nepal offers insights into effective strategies for improving education quality through collaboration between government and private sectors. The objective of the study is to assess the effectiveness of public-private partnerships (PPPs) in enhancing education quality through improved textbook publishing and distribution mechanisms. The methodology employed in this study integrates descriptive and explanatory research approaches. It utilizes a structured questionnaire comprising 40 items rated on a 5-point Likert scale to evaluate different dimensions of PPPs concerning school textbook publication and distribution in Nepal. In this study, representatives from private organizations involved in textbook publishing and distribution in Nepal were interviewed. Sampling is conducted through random selection from a pool of 390 private organizations, aiming to ensure representation across various sectors. The model developed from this analysis had a strong explanatory power, with the identified independent variables explaining up to 48% of the variability in improving education quality through PPPs. The study concludes that emphasizing transparency, accountability, and effective communication within public-private partnerships significantly contributes to enhancing education quality through improved textbook publishing and distribution mechanisms, supported by strong correlations between these factors and overall education quality, as revealed by advanced statistical methods.

This study aims to explore past research trends and identify key future directions at the intersection of artificial intelligence and legal education. The study’s data were gathered from the Scopus database, comprising 68 selected documents spanning from 1999 to 2024. The research methodology involves the use of VOSviewer software for bibliometric analysis. The results reveal that research on artificial intelligence and legal education, while still limited, has been undertaken in various countries, focusing on five primary research directions, including: (1) Improving technical education systems in colleges and universities through educational technology and modern legal learning systems; (2) Application of artificial intelligence and algorithms in the legal field; (3) Applying computational theory and e-learning technology in legal education; (4) Legal education and legal knowledge; (5) Digital transformation in the field of legal training. Based on the research results, five future research directions on this topic are also proposed, including: (1) Application of artificial intelligence in analyzing and predicting legal trends; (2) Artificial intelligence in personalizing the legal learning experience; (3) Artificial intelligence and legal professional ethics; (4) Development of artificial intelligence tools supporting legal teaching and research; and (5) Integration of artificial intelligence into online learning systems for legal education.

The study aims to investigate how artificial intelligence (AI) influences women-led startups in Saudi Arabia, aiming to understand their unique experiences, challenges, and opportunities within the AI technology landscape. This study used a qualitative method, conducting 16 in-depth interviews with women entrepreneurs operating businesses in Saudi Arabia. The analysis was performed using thematic analysis with NVivo 12, uncovering key themes and insights. The findings reveal that cultural norms, societal expectations, limited awareness, and financial constraints are directly associated with women’s involvement in AI-driven businesses. Cultural biases emerged as impediments, underscoring the need for targeted interventions such as awareness campaigns and educational initiatives to dismantle ingrained biases and foster an environment that recognizes and celebrates the contributions of women in the tech and AI sectors. Educational programs, collaborations between academia and industry, and mentorship initiatives were identified as pivotal components to prepare women entrepreneurs to navigate the intricate landscape of AI adoption. Financial inclusion emerged as a critical pillar, advocating for equitable access to funding and resources tailored specifically for women-led AI startups. The study further emphasizes the importance of fostering supportive ecosystems that extend beyond financial aid. Creating networks for mentorship, guidance, and collaboration provides women entrepreneurs with platforms to share experiences and resources, enhancing resilience and the potential for success in the AI landscape.

Acknowledgment
The authors extend their appreciation to the Arab Open University for funding this work through research fund No. AOUKSA-524008.

This study aims to test and evaluate various pedagogical strategies that can improve students’ comprehension of Maritime English course materials and help them confidently participate in English-based assessments. A comparative research methodology was employed, focusing on integrating specific strategies and tools into the curricula of maritime programs. The study focused on the Norwegian case at NTNU in Ålesund, featuring a 3-year Shipping Management program (Bachelor’s degree) with 45-60 students. The findings from the Norwegian case demonstrate a significant improvement in students’ willingness and ability to engage in English-language assessments, highlighting the effectiveness of the pedagogical approaches implemented. The study results align with existing literature, highlighting the need for continuous innovation in pedagogical approaches to Maritime English education. By the end of the course, 72.2% of students felt comfortable taking the exam in English, compared to only 25.8% at the beginning of the semester. Weekly lectures in English with presentations were identified as the most helpful tool, followed by group work, homework, and vocabulary lists. The use of digital interaction and software tools received a high score – 8.0 out of 10.0. The significant improvement in students’ confidence in English, along with their strong overall ratings of teaching tools, demonstrates the effectiveness of these methods in overcoming initial language barriers. Further recommendations include combining traditional teaching methods with modern digital tools to enhance learning outcomes. By focusing on student-centered approaches and integrating both traditional techniques and technological tools, institutions can foster the development of a more proficient maritime workforce.

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.’

This paper explores the concept of place attachment, encompassing its meaning and representation among university students. It provides a comprehensive picture of place identity and place attachment among university students. The planned approach was to conduct a survey among students to examine their attachment to place from a green perspective. A questionnaire was used, and the data were analyzed using SPSS version 28 and Smart PLS 4. This paper summarizes the results of a survey of 245 students studying at the Budapest University of Economics and Business in 2022/23. The students were graduates in International Studies and Commerce-Marketing. The setting of the study was the university environment because it is an institution of higher education, a bastion of knowledge transfer, and the issue of attachment is very important in the relationship between educational institutions and students. The results confirmed that students perceived green energy (β: 0.283, t: 4.637 p: 0.000) and green solutions (β: 0.430, t: 9.155 p: 0.000) as having a significant effect on whether or not they perceived the institution as student-friendly. It is no coincidence, therefore, that students are satisfied with the green environment of the university (74%). They believe that the university has an adequate amount of green space (68%). However, the current situation could be improved, with a large proportion of students missing the widespread use of green (79%) and renewable energy (70%) in the institution.

Effective resource allocation is critical for academic libraries that offer reference consultations and information literacy instruction to support student success. The study aims to examine the time spent and the intensity and variation in information resource use across 671 reference consultations provided to undergraduate and graduate students at the Czech National Library of Technology, categorized by complexity levels. A case study methodology with quantitative analysis, including descriptive statistics and correlation tests, was applied. The results indicate that simple consultations require more extensive involvement of information resources with an emphasis on basic information literacy, while higher grades of consultations involve fewer resources but more frequent use of full-text databases. It is also shown that information resources are used consistently, with usage patterns reflecting the complexity of users’ assignments and questions. The analysis shows that there is a significant relationship between consultation complexity and both the time spent providing a consultation (correlation coefficient 0.276) and the time spent preparing for the consultation (correlation coefficient 0.262). The results suggest the need for strategic planning of human resources based on service complexity to increase the efficiency of consultations, as well as more conscious decision making regarding the use of information resources in consultation services.

Acknowledgment
The authors are grateful for the invaluable support provided by the Czech National Library of Technology during the research process. The research data offered were extremely helpful and have been essential to complete this research successfully.

The structure of the higher education market in 2012–2021 in 38 European countries was analyzed using concentration levels and Herfindahl-Hirschman indices based on the number of higher education institutions and their share in the QS World University Rankings, and the number of students. This market in 2021 has a low concentration: the 3 countries with the largest number of higher education institutions (Germany, Ukraine, France) covered about 36% of the market in total; the 3 countries with the largest number of universities in the QS (United Kingdom, Germany, Italy) – 5%; the 3 countries with the largest number of students (Germany, France, United Kingdom) – 37%; and the 3 countries with the largest number of foreign students (United Kingdom, Germany, France) – 5%. Using parametric and non-parametric comparison tests, it was found that although the number of higher education institutions and students does not generally depend on the population’s income level, the number of universities ranked in the QS and foreign students does. The correlation analysis revealed that GDP and GNI, population, and separately the employment and unemployment rates (for ranked universities and foreign students) are important factors that determine the uneven structure of the higher education market. The identified factors formed the basis for clustering countries using Ward’s hierarchical method, which revealed the clear existence of 3 clusters: the smallest of them accumulates the 4 largest European economies with the most ranked universities; the largest (24 countries) is quite diverse, which indicates relatively equal opportunities in the market and its unification.

Acknowledgment
Tetiana Vasylieva and Artem Artyukhov thank project 0122U000772, and Nadiia Artyukhova thanks project 0124U000545 for carrying out their part of this research.

The study explores the impact of artificial intelligence (AI) technologies on project management (PM) across different industries. It aims to assess how AI adoption in PM affects project efficiency. The study surveyed 159 project supervisors and specific project managers implementing projects from 7 industries in the Republic of Kazakhstan: software, green energy, engineering, construction, science, transport, and tourism. The research used variance and linear regression analyses to evaluate the relationship between AI adoption and project efficiency level measured by the Likert scale from 1 to 5 and test the associated hypotheses. The results show that AI adoption varies among industries, with software, construction, and scientific projects being the most active users. The study also found that the use of AI differed across eight project performance domains, with the stakeholder domain using voice technologies and process automation and the uncertainty domain using fewer tools. Projects with higher AI adoption rates showed higher efficiency scores (for example, in Software projects, the AI adoption rate is 3.2; the efficiency rate is 3.3), while those with lower efficiency levels (for example, in the Tourism industry, the AI adoption rate is 1.9; the efficiency rate is 2.2) showed the worst results. Decision-making systems, process automation, and voice technologies are the three most critical AI technologies PM professionals use to improve project efficiency.

Acknowledgments
This research has been funded by the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP19680313).