The rapid evolution of e-learning platforms, propelled by advancements in artificial intelligence (AI) and machine learning (ML), presents a transformative potential in education. This dynamic landscape necessitates an exploration of AI/ML integration in adaptive learning systems to enhance educational outcomes. This study aims to map the current utilization of AI/ML in e-learning for adaptive learning, elucidating the benefits and challenges of such integration and assessing its impact on student engagement, retention, and performance. A comprehensive literature review was conducted, focusing on articles published from 2010 onwards, to document the integration of AI/ML in e-learning. The review analyzed 63 articles, employing a systematic approach to evaluate the deployment of adaptive learning algorithms and their educational implications. Findings reveal that AI/ML algorithms are instrumental in personalizing learning experiences. These technologies have been shown to optimize learning paths, enhance engagement, and improve academic performance, with some studies reporting increased test scores. The integration of AI/ML in e-learning platforms significantly contributes to the personalization and effectiveness of the educational process. Despite challenges like data privacy and the complexity of AI/ML systems, the results underscore the potential of adaptive learning to revolutionize education by catering to individual learner needs.

The opportunities and challenges related to sustainability impose not only a societal transformation but also a personal one. Higher Education Institutions (HEIs) have great potential to promote sustainability and shape future generations’ skills, knowledge, thinking, and actions. Addressing environmental, social, and economic pillars, education for sustainable development (ESD), or education for sustainability (EfS), is a catalyst for quality education towards a sustainable future. This paper aims to analyze the state of the art and prevailing trends in the scientific literature pertaining to ESD/EfS. In this regard, specific objectives were established to identify the following: (1) authors’ productivity over time; (2) countries’ contribution and collaboration; (3) the most relevant keywords; and (4) the most prominent themes—in terms of relevance and degree of development. To fulfill the research objectives, quantitative research employing bibliometric analysis was carried out in April 2023. The research data sample was collected from the Web of Science (WoS) database. By conducting the search using a combined topic (“education for sustainable development” OR “education for sustainability”), a total of 2827 documents in English were identified within a timeframe of 33 years (1989–2023). These documents served as the foundation for subsequent data processing, visualization, and analysis using Biblioshiny/Bibliometrix (version R-4.2.0), as well as Excel (version 2309) software. The visualization and analysis focused on examining the most prolific authors and countries, in addition to generating word clouds based on keywords. Moreover, the thematic map that was delineated into four quadrants—Basic, Motor, Niche, and Emerging or Declining themes—provides an overview of the researched subject. ESD—embracing environmental, social, and economic pillars—implies transformative learning, action-oriented pedagogies, and professional development. As a holistic framework, it requires interdisciplinarity, system thinking, critical and experiential thinking, problem-based learning, and the use of 4IR technologies to enhance the teaching-learning process.

Recent technological advancements in e-learning platforms have made it easy to store and manage students’ related data, such as personal details, initial grade, intermediate grades, final grades, and many other parameters. These data can be efficiently processed and analyzed by intelligent techniques and algorithms to generate useful insights into the students’ performance, such as to identify the factors impacting the progress of successful students or the performance of the students who are struggling in their courses and are at risk of failing. Such a framework is scarce in the current literature. This study proposes an interpretable framework to generate useful insights from the data produced by e-learning platforms using machine learning algorithms. The proposed framework incorporates predictive models, as well as regression and classification models to analyze multiple factors of student performance. Classification models are used to systematize normal and at-risk students based on their academic performance, with high precision and accuracy. Regression analysis is performed to determine the inherent linear and nonlinear relationships between the academic outcomes of the students acting as the target or independent variables and the performance indicative features acting as dependent variables. For further analysis, a predictive modeling problem is considered, where the performance of the students is anticipated based on their commitment to a specific course, their performance for the whole course, and their final grades. The efficiency of the proposed framework is also optimized by reliably tuning the algorithmic parameters. Furthermore, the performance is accelerated by empowering the system with a GPU-based infrastructure. Results reveal that the proposed interpretable framework is highly accurate and precise and can identify factors that play a vital role in the students’ success or failure.

We are witnessing the Fourth Industrial Revolution (4IR) in which technological discoveries and changes are coming at a dizzying speed generating tremendous transformations of organizations, the environment and societies. The purpose of the present paper is to analyze a set of challenges and opportunities of the workforce and workplace ecosystems in the context of 4IR. Innovation – as key ingredient of scaling-up processes – and the widespread implementation of specific 4IR technologies will lead to substantial, sometimes even disruptive changes of the labor market, but also to the workforce and workplace ecosystems’ transformation. Among others, artificial intelligence (AI), smart robotics, and intelligent automation are emerging technologies that dramatically change the ways of work, productivity, but also the types and number of available jobs. This paper has a few specific objectives: summarizing the main technologies and tools specific to 4IR and Industry 4.0; analyzing the major shifts related to the current structure of the workforce -highlighting the external and technological component (AI, robots, automation); analyzing the hybrid workplace (physical and digital); identifying some emerging technologies and their impact on the work ecosystem. The methodology of the study relies on qualitative methods - literature review, systematization of knowledge, observation/ participation (as experts in different national and international consultancy and training programs). The paper draws conclusions and some remarks regarding the intention to address other challenging subjects in the future, such as: rethinking performance management systems to reflect and tackle these challenges; skill gaps and the role of emerging technologies in supporting Society 5.0. - a more sustainable, inclusive and human-centric society.

In the context of today’s dynamic environment, agility and speed are two essential characteristics that apply to project management in the software development industry, as well as in many other industries. In order to meet the complex and continuous challenges of the digital age, the principles, techniques and methods of Agile Project Management and Scrum are expected to become more widespread, especially in software development, replacing or augmenting the traditional ones. This paper aims to identify trends in project management related to digital transformation and diffusion of Industry 4.0 technologies. A bibliometric analysis was carried out by searching the WOS database. The resulting documents were exported and processed in VOSviewer to fulfil the scope. The research revealed that, in the context of digital transformation, information technology supports the agile approach, agile transformation and agile project management. Furthermore, emerging technologies specific to Industry 4.0, especially artificial intelligence, and big data, can contribute significantly to all project phases. These emerging technologies can improve data processing and analysis, project forecasting, and risks prediction, can support decision making thus contributing to the success of the project.

Sustainable development is not exactly a new topic, but seen in the light of recent events, there is a growing need to identify new ways of putting sustainable development principles into practice. The present paper aims to analyze the influence that elements of organizational culture have on sustainable development to increase the efficiency and effectiveness of implementing sustainable development management in Romanian higher education institutions. Since organizational culture influences organizational strategies, the article proposes a conceptual model for determining the influence of organizational culture on the implementation of sustainable development management. An argument for carrying out this study is the need to increase sustainable development practices in the Romanian labour market, starting from the changes in the educational system. As a result, we mention a model that can facilitate changes in the university culture, with influences on the integration of modern and topical theories and practices, such as sustainable development. The obtained results indicate a low correlation between university culture and sustainability, which opens another perspective on the need for a change of mentality in the Romanian university environment. The change concerns both vertical and horizontal integration, with micro and macro effects. The results are important for the Romanian university environment, first of all, to identify good practices or at least to see the need to transform the existing organizational culture. For university decision-makers, it helps to base development trends on existing realities. Last but not least, it may raise the question of extending the usefulness and importance of the study to the level of triple helix models in which universities should be much more active.