The aim of this paper is to introduce a research project dedicated to identifying gaps in green skills by using the labor market intelligence. Labor Market Intelligence (LMI). The method is primarily descriptive and conceptual, as the authors of this paper intend to develop a theoretical background and justify the planned research using Natural Language Processing (NLP) techniques. This research highlights the role of LMI as a tool for analysis of the green skills gaps and related imbalances. Due to the growing demand for eco-friendly solutions, there arises a need for the identification of green skills. As societies shift towards eco-friendly economic models, changes lead to emerging skill gaps. This study provides an alternative approach for identification of these gaps based on analysis of online job vacancies and online profiles of job seekers. These gaps are contextualized within roles that businesses find difficult to fill due to a lack of requisite green skills. The idea of skill intelligence is to blend various sources of information in order to overcome the information gap related to the identification of supply side factors, demand side factors and their interactions. The outcomes emphasize the urgency of policy interventions, especially in anticipating roles emerging from the green transition, necessitating educational reforms. As the green movement redefines the economy, proactive strategies to bridge green skill gaps are essential. This research offers a blueprint for policymakers and educators to bolster the workforce in readiness for a sustainable future. This article proposes a solution to the quantitative and qualitative mismatches in the green labor market.

Since its inception in 2013, “The Belt and Road Initiative” has become an important engine driving global economic growth. The initiative has not only promoted infrastructure construction in countries along the Belt and Road but also strengthened financial integration, unimpeded trade, people-to-people exchanges, and policy communication. In this context, higher education, as an important avenue for talent training and scientific and technological innovation, is of great significance to promoting the economic and social development of countries along the Belt and Road. By strengthening academic cooperation with Chinese universities, Kyrgyzstan can enhance its curriculum, adopt advanced teaching methods, and integrate cutting-edge research to foster more skilled labor. In addition, innovation and technology transfer through higher education partnerships can drive sustainable economic growth and diversification. This paper explores the strategic path of integrating higher education into the Belt and Road. Initiative, focusing on academic collaboration, enhancing R&D capabilities, and fostering an entrepreneurial ecosystem.

Adolescent childbearing is a crucial problem challenging policymakers in sub-Saharan African countries. The objective of this study is to show how teenage pregnancy and motherhood is related to social determinants like place of residence, education level and wealth quintiles, and consequently to suggest pragmatic actions susceptible to control the burden of teenage pregnancy. Disaggregated data were analyzed using data covering the decade 2012–2022 and provided by Demographic Health Surveys. In each country considered, the index of dissimilarity (ID) was computed to illustrate the variation of teenage pregnancy and motherhood according to the level of education, the rural-urban residence and the income quintiles. Recent statistics were also used for a comparison between countries. This study showed that childbearing affected 22.7% of African adolescents (15–19 years). However, the rate of adolescent childbearing varied from 40.4% in Nigeria to 5.2% in Ruanda. Moreover, huge differences were found in each country. Teenage girls living in rural areas, illiterate or with low level of education and suffering from poverty are more likely to be early married and to be exposed to pregnancy. The rate of adolescent childbearing is higher in Sub-Saharan African countries compared with countries from Latin America and World Health Organization Eastern Mediterranean. Most of the 31 countries considered in this study suffer from high rate of adolescent childbearing and large iniquities by place of residence and/or education level and/or wealth quintiles. Consequently, policymakers should adopt urgent and efficient strategies to reduce (and ideally to end) early marriage and teenage pregnancy by developing a policy that targets disadvantaged girls living in remote areas, having low or no decent income and suffering from illiteracy or low level of education.

This paper focuses on the analysis of educational institutions’ communication on social media, with an emphasis on the individual type of content used by these institutions to increase engagement and interaction with current and potential students. The authors examine how educational institutions tailor their communication content on Facebook and Instagram to meet the expectations and needs of their target audience. The analysis includes content evaluation, frequency of posts, user interaction, and integration of multimedia elements. In our research we focused on private school segment from kindergartens, through primary to secondary schools. The paper also presents an analysis of the differences of communication on different platforms (Facebook and Instagram) and their impact on the digital communication strategy of private schools. The results suggest that despite the increasing popularity of Instagram and higher interaction, educational institutions are communicating more on Facebook.

Introduction: Chatbots are increasingly utilized in education, offering real-time, personalized communication. While research has explored technical aspects of chatbots, user experience remains under-investigated. This study examines a model for evaluating user experience and satisfaction with chatbots in higher education. Methodology: A four-factor model (information quality, system quality, chatbot experience, user satisfaction) was proposed based on prior research. An alternative two-factor model emerged through exploratory factor analysis, focusing on “Chatbot Response Quality” and “User Experience and Satisfaction with the Chatbot.” Surveys were distributed to students and faculty at a university in Ecuador to collect data. Confirmatory factor analysis validated both models. Results: The two-factor model explained a significantly greater proportion of the data’s variance (55.2%) compared to the four-factor model (46.4%). Conclusion: This study suggests that a simpler model focusing on chatbot response quality and user experience is more effective for evaluating chatbots in education. Future research can explore methods to optimize these factors and improve the learning experience for students.

Using generative artificial intelligence systems in the classroom for law case analysis teaching can enhance the efficiency and accuracy of knowledge delivery. They can create interactive learning environments that are appropriate, immersive, integrated, and evocative, guiding students to conduct case analysis from interdisciplinary and cross-cultural perspectives. This teaching method not only increases students’ interest and participation in learning but also helps cultivate their interdisciplinary thinking and global vision. However, the application of generative artificial intelligence systems in legal education also faces some challenges and issues. If students excessively rely on these systems, their ability to think independently, make judgments, and innovate may be weakened, leading to over-trust in machines and reinforcement of value biases. To address these challenges and issues, legal education should focus more on cultivating students’ questioning skills, self-analysis abilities, critical thinking, basic legal literacy, digital skills, and humanistic spirit. This will enable students to respond to the challenges brought by generative artificial intelligence and ensure their comprehensive development in the new era.