In this study, we undertake a comprehensive bibliometric analysis of the cybernetics research field. We compile a dataset of 4856 papers from the ISI Web of Science database spanning 1975–2022, employing keywords related to cybernetics. Our findings reveal an annual growth rate of 7.56% in cybernetics research over this period, indicating sustained scholarly interest. By examining the annual progression of scientific production, we have identified three distinct periods characterized by significant disruptions in yearly publication trends. These disruptions have been thoroughly investigated within the paper, utilizing a longitudinal analysis of thematic evolution. We also identify emerging research trends through keyword analysis. Furthermore, we investigate collaborative networks among authors, their institutional affiliations, and global representation to elucidate the dissemination of cybernetics research. Employing n-gram analysis, we uncover diverse applications of cybernetics in fields such as computer science, information science, social sciences, sustainable development, supply chain, knowledge management, system dynamics, and medicine. The study contributes to enhancing the understanding of the evolving cybernetics landscape. Moreover, the conducted analysis underscores the versatile applicability across various academic and practical domains associated with the cybernetics field.

Over the past years, machine learning and big data analysis have emerged, starting as a scientific and fictional domain, very interesting but difficult to test, and becoming one of the most powerful tools that is part of Industry 5.0 and has a significant impact on sustainable, resilient manufacturing. This has garnered increasing attention within scholarly circles due to its applicability in various domains. The scope of the article is to perform an exhaustive bibliometric analysis of existing papers that belong to machine learning and big data, pointing out the capability from a scientific point of view, explaining the usability of applications, and identifying which is the actual in a continually changing domain. In this context, the present paper aims to discuss the research landscape associated with the use of machine learning and big data analysis in Industry 5.0 in terms of themes, authors, citations, preferred journals, research networks, and collaborations. The initial part of the analysis focuses on the latest trends and how researchers lend a helping hand to change preconceptions about machine learning. The annual growth rate is 123.69%, which is considerable for such a short period, and it requires a comprehensive analysis to check the boom of articles in this domain. Further, the exploration investigates affiliated academic institutions, influential publications, journals, key contributors, and most delineative authors. To accomplish this, a dataset has been created containing researchers’ papers extracted from the ISI Web of Science database using keywords associated with machine learning and big data, starting in 2016 and ending in 2023. The paper incorporates graphs, which describe the most relevant authors, academic institutions, annual publications, country collaborations, and the most used words. The paper ends with a review of the globally most cited documents, describing the importance of machine learning and big data in Industry 5.0.

This study aims to provide a comprehensive bibliometric analysis covering the common areas between artificial intelligence (AI) applications and research focused on local or regional contexts. The analysis covers the period between the year 2002 and the year 2023, utilizing data sourced from the Web of Science database. Employing the Bibliometrix package within RStudio and VOSviewer software, the study identifies a significant increase in AI-related publications, with an annual growth rate of 22.67%. Notably, key journals such as Remote Sensing, PLOS ONE, and Sustainability rank among the top contributing sources. From the perspective of prominent contributing affiliations, institutions like Duy Tan University, Ton Duc Thang University, and the Chinese Academy of Sciences emerge as leading contributors, with Vietnam, Portugal, and China being the countries with the highest citation counts. Furthermore, a word cloud analysis is able to highlight the recurring keywords, including “model”, “classification”, “prediction”, “logistic regression”, “innovation”, “performance”, “random forest”, “impact”, “machine learning”, “artificial intelligence”, and “deep learning”. The co-occurrence network analysis reveals five clusters, amongst them being “artificial neural network”, “regional development”, “climate change”, “regional economy”, “management”, “technology”, “risk”, and “fuzzy inference system”. Our findings support the fact that AI is increasingly employed to address complex regional challenges, such as resource management and urban planning. AI applications, including machine learning algorithms and neural networks, have become essential for optimizing processes and decision-making at the local level. The study concludes with the fact that while AI holds vast potential for transforming local and regional research, ongoing international collaboration and the development of adaptable AI models are essential for maximizing the benefits of these technologies. Such efforts will ensure the effective implementation of AI in diverse contexts, thereby supporting sustainable regional development.

In light of the accelerated growth of artificial intelligence (AI), large language models (LLMs) have become a central topic of interest in scientific research and practical applications across various fields. The present paper aims to perform a comprehensive systematic review of the scientific literature on LLMs in education published between 2023 and 2024, based on a dataset from the Web of Science, which includes 507 documents from 322 sources. The accelerated dynamics of research in this field are confirmed by the high annual growth rate of 369.66%. The study identifies the themes presented in the scientific literature by using thematic maps and analyzing the evolution of said thematic maps. In addition, Latent Dirichlet Allocation (LDA) and BERTopic are used to outline the research field more clearly. Due to LDA’s ability to discover high-level research topics using probabilistic discovery and BERTopic’s ability to capture deeper semantic patterns and the emergence of various topics by searching, this paper first identifies the main research topics in the extracted dataset, which are then discussed in the paper through a review of applications. As a result, a range of applications are discovered in areas related to teaching and learning, academic assessment, integrity, academic feedback, medical education, ethics, bias, regulation, and social challenges. The conclusions provide a roadmap for researchers, practitioners and stakeholders in highlighting the current situation of LLMs in educational practice, while opening the door for future explorations in this domain.

Massive Open Online Courses (MOOCs) are a relatively new educational model that provides free access to educational content regardless of location or time. Despite these benefits, MOOCs encounter significant challenges, such as low completion rates, high dropout rates, and inconsistent participant comprehension, often due to the absence of simulations and practical activities. Incorporating sustainable education principles into MOOCs could bring benefits to the long-term effectiveness of the learning process, ensuring conscious learning practices. To address the issue of MOOC dropout rates and assess the scientific interest in this area, a bibliometric analysis was conducted on a dataset of 193 papers sourced from the ISI Web of Science database, spanning from 2013 to 2023. Papers were selected based on relevant keywords for the study. The analysis highlights key academic institutions, leading authors, and publication trends within this field. It reveals a strong and growing interest in MOOC dropout rates, with an annual growth rate of 40.04%. Research trends were identified by analyzing n-grams from keywords, titles, abstracts, and keywords plus, supplemented by a detailed review of the most cited papers globally. A collaborative network analysis was performed to explore author collaborations, their global distribution, institutional affiliations, research objectives, and study directions. The findings from the most cited papers show an increasing focus on understanding the factors contributing to MOOC dropout rates and developing strategies to address these issues.

The primary objective of this research paper is to conduct a bibliometric analysis of the prevailing research landscape pertaining to agent‐based modeling (ABM). This analysis encompasses an examination of key contributors, affiliated academic institutions, influential publications, and prominent journals within the domain. To achieve this, a dataset consisting of 11,477 scholarly papers retrieved from the ISI Web of Science database has been curated, using keywords specifically related to ABM, spanning the period from 1996 to 2024. Employing n ‐gram analysis techniques on titles, keywords, abstracts, and keyword‐plus fields has unearthed a multitude of domains wherein ABM has been applied with notable success. Our findings, as delineated in this paper, underscore a sustained and robust growth in scholarly interest in the realm of ABM during the specified temporal span, characterized by an impressive annual growth rate of 25.29%. Furthermore, our study contributes to the identification and analysis of salient keywords and emerging trends, thereby elucidating key research trajectories within this domain. The identification of collaborative networks among authors, their respective academic affiliations, and the geographical distribution across various countries and territories offers valuable insights into the global proliferation of ABM as a research methodology. The findings offer valuable insights into the widespread applications of ABM across various domains, including climate change, social networks, supply chain dynamics, public health studies, financial market analysis, and population dynamics. The results of the study can help in guiding future research and practical applications of ABM in these and other multifaceted areas.