This paper scrutinizes how adaptive learning technologies and artificial intelligence (AI) are transforming today's education by making it personalized, accessible, and efficient as well as leading people to accepting, addressing, and mitigating sustainable development. Recently, education witnessed a remarkable technological surge driven by various advances in technology, which can be demonstrated by the increase of the number of scientific publications on this topic from just 1 in 1990 to 636 in 2023. Ongoing digitalization and technological revolution in education together with the novel approach to respect each student's unique learning style and abilities paved the way for adaptive learning technologies represented by the innovative tools that personalize educational experiences to cater to individual learners. All of that contributes to preparing more educated and informed citizens, drives innovation, and supports economic growth necessary for achieving a sustainable future. Our bibliographic study employs VOSviewer to conduct a bibliometric analysis of a total number of 3518 selected publications using the keywords “adaptive learning” and “AI” (represented by articles, proceeding papers, and book chapters) indexed in the Web of Science (WoS) database from 1990 to 2024. Our results demonstrate that recent technological changes played a key role in transforming adaptive learning, which was rather reinforced by the “digital surge” in education brought about by the COVID‐19 pandemic. Our findings can be useful for further development in the field of adaptive education where they can be employed by the relevant stakeholders and policymakers as well as by the scholars and researchers.

The electrical power sector plays an important role in the economic growth and development of every country around the world. Total global demand for electric energy is growing both in developed and developing economies. The commitment to the decarbonization of economies, which would mean replacing fossil fuels with renewable energy sources (RES) as well as the electrification of transport and heating as a means to tackle global warming and dangerous climate change, would lead to a surge in electricity consumption worldwide. Hence, it appears reasonable that the electric power sector should embed the principles of sustainable development into its functioning and operation. In addition, events such as the recent European gas crisis that have emerged as a result of the massive deployment of renewables need to be studied and prevented. This review aims at assessing the role of the renewable energy in the sustainable development of the electrical power sector, focusing on the energy providers and consumers represented both by businesses and households that are gradually becoming prosumers on the market of electric energy. Furthermore, it also focuses on the impact of renewables on the utility side and their benefits for the grid. In addition, it identifies the major factors of the sustainable development of the electrical power sector.

The use of machine learning and data-driven methods for predictive analysis of power systems offers the potential to accurately predict and manage the behavior of these systems by utilizing large volumes of data generated from various sources. These methods have gained significant attention in recent years due to their ability to handle large amounts of data and to make accurate predictions. The importance of these methods gained particular momentum with the recent transformation that the traditional power system underwent as they are morphing into the smart power grids of the future. The transition towards the smart grids that embed the high-renewables electricity systems is challenging, as the generation of electricity from renewable sources is intermittent and fluctuates with weather conditions. This transition is facilitated by the Internet of Energy (IoE) that refers to the integration of advanced digital technologies such as the Internet of Things (IoT), blockchain, and artificial intelligence (AI) into the electricity systems. It has been further enhanced by the digitalization caused by the COVID-19 pandemic that also affected the energy and power sector. Our review paper explores the prospects and challenges of using machine learning and data-driven methods in power systems and provides an overview of the ways in which the predictive analysis for constructing these systems can be applied in order to make them more efficient. The paper begins with the description of the power system and the role of the predictive analysis in power system operations. Next, the paper discusses the use of machine learning and data-driven methods for predictive analysis in power systems, including their benefits and limitations. In addition, the paper reviews the existing literature on this topic and highlights the various methods that have been used for predictive analysis of power systems. Furthermore, it identifies the challenges and opportunities associated with using these methods in power systems. The challenges of using these methods, such as data quality and availability, are also discussed. Finally, the review concludes with a discussion of recommendations for further research on the application of machine learning and data-driven methods for the predictive analysis in the future smart grid-driven power systems powered by the IoE.

Global economic trends have shown the progression of social inequalities and environmental deterioration in the grey economy. New economic practices and policies need to be developed in order to achieve the sustainable development goals (SDGs). A green economy (GE) has a correlative role with the implementation of sustainable development (SD), which could revive the grey economy, human well-being, and social equity, as well as substantially decrease environmental risks and ecological scarcities. This study aims to develop a hybrid methodological and mathematical approach to prioritize the most effective variables from classified GE and SDGs criteria (23 criteria) to implement SD. This study has deliberated over the Decision making trial and evaluation laboratory (DEMATEL) technique for considering interconnections among numerous criteria to collect the most effective variables (12 criteria) based on three pillars (3Ps) of SD. Likewise, the analytic network process (ANP) technique ranked these effective variables by considering their network relations based on three indicators. Lastly, integration was used to finalize and prioritize the most effective variables based on their weight from the ANP technique. This study will highlight the green economy with exclusive environmental issues and sustainable growth as the greatest effective variables among GE and SDGs criteria for SD implementation.

The deployment of renewable energy sources (RES) is considered to be an important objective for the energy sector in the European Union (EU). The EU Directive adapted in 2009 fixed mandatory national targets for the use of renewable energy in transport as well as for the share of RES in the gross final energy consumption. Contrary to previous studies, this paper does not examine the link between the RES and economic growth but rather focuses on real gross domestic product (GDP) and the implementation of national renewable energy targets. We employ panel data models for the case of the EU-28 countries covering the period between 2007 and 2017 that yield a low and positive relationship between the impact of GDP per capita and the share of RES in the final consumption. Our results show that there is a significant causality only from real GDP per capita to the share of renewable energy in final consumption, marking the potential of developed countries to consume more RES. We list some groups of countries according to these variables using a cluster analysis approach. Starting from the proposed panel data models, we constructed the scenarios for the 2020 for various shares of RES and different EU Member States. Overall, it appears that more attention should be attributed to policy proposals in which funding opportunities would be dependent on the achievements of national targets and economic benefits should be given to countries with very good performance in achieving high shares of renewable energy in their final energy consumption.