This paper aims to contribute with a literature review on the use of AI for cleaner production throughout industries in the consideration of AI’s advantage within the environment, economy, and society. The survey report based on the analysis of research papers from the recent literature from leading database sources such as Scopus, the Web of Science, IEEE Xplore, Science Direct, Springer Link, and Google Scholar identifies the strategic strengths of AI in optimizing the resources, minimizing the carbon footprint and eradicating wastage with the help of machined learning, neural networks and predictive analytics. AI integration presents vast aspects of environmental gains, including such enhancements as a marked reduction concerning the energy and materials consumed along with enhanced ways of handling the resulting waste. On the economic aspect, AI enhances the processes that lead to better efficiency and lower costs in the market on the other hand, on the social aspect, the application of any AI influences how people are utilized as workers/clients in the community. The following are some of the limitations towards AI adoption as proposed by the review of related literature; The best things that come with AI are yet accompanied by some disadvantages; there are implementation costs, data privacy, as well as system integration that may be a major disadvantage. The review envisages that with the continuation of the AI development in the following years, the optic is going to be the accentuation on the enhancement of the process of feeding the data in real-time mode, IoT connections, and the implementation of the proper ethical approaches toward the AI launching for all segments of the society. The conclusions provide precise suggestions to the people working in the industry to adopt the AI advancements appropriately and at the same time, encourage the lawmakers to create favorable legal environments to enable the ethical uses of AI. This review therefore calls for more targeted partnerships between the academia, industry, and government to harness the full potential of AI for sustainable industrial practices worldwide.

The aim of our study is to provide information on how and to what extent professionals of art institutions in Hungary and Slovakia (contemporary galleries and museums) use artificial intelligence in their work processes. Our research focuses on the extent to which these institutions use artificial intelligence in the development of the institution’s operational strategy, or how they can embed the assumed usefulness of artificial intelligence in the operation of the institution, be it the creation of an exhibition, the textual processing of the professional life of an artist, or a about a tool that shapes the gallery’s marketing strategy. We conducted ten in-depth interviews in the two countries, the interviewees were selected using the snowball method. The interview took place among professionals and professionally credible artists who are actively active in contemporary fine art life. The results revealed that the use of artificial intelligence as a tool in the creative work processes is not a requirement in the field of culture, neither in Hungary nor in Slovakia. All the interviewees already had professional experience with AI, 90% of those interviewed would like to deepen their knowledge of the creative use methods of AI, e.g., by creating working groups in the workplace on an experimental basis. Based on our conclusions, we can say that artificial intelligence currently has no conscious strategic use in contemporary art institutions. It can be said that creative professionals are aware of the possibilities of using artificial intelligence in their own field of image, video, and text creation, but there is uncertainty on the part of creators and curators when it comes to copyright. The in-depth interviews provided source material for the compilation of a standardized set of questions for a larger survey of 300-500 people, proportional to the sample, so our presented results are partial results of a larger research.

Despite the surge of publication of chatbots in the recent years in the field of education, we have little to know how this area has been researched so far, and the metrics of this type of research is still not known. To address such gap, this article offers a descriptive bibliometric study of chatbot research in education, aiming at presenting bibliometric analysis on articles on chatbots in education that were published in journals indexed in the Web of Science (WOS) database specifically Social Science Citation Index (SSCI) and Science Citation Index Expanded (SCIE) between 2016 and 2023. Descriptive bibliometric analysis was used to examine the data gathered from the chosen publications. including the annual number of articles and citations, the most productive author, countries with the highest publication output, productive affiliations, funding organizations, and publication sources. The bulk of the articles on chatbots in education, according to our dataset, were published between 2016 and 2023. The United States of America tops the list of countries regarding research productivity. The United Kingdom and China were ranked as most second and third productive countries, in terms of publication outputs. “Luke Kutszik Fryer emerged as the most productive author in this research domain in terms of the number of publications.” The University of Hong Kong had the highest number of publications among affiliations, indicating their significant contribution to the field. Additionally, the journal “Computers in Human Behavior” stood out with the highest number of publications per year, highlighting its relevance in publishing research on chatbots in education. This research offers valuable insights and a roadmap for prospective researchers, pinpointing critical areas where success can be attained in the study of chatbots in education.

The digital era has ushered in significant advancements in Generative Artificial Intelligence (GAI), particularly through Generative Models and Large Language Models (LLMs) like ChatGPT, revolutionizing educational paradigms. This research, set against the backdrop of Society 5.0 and aimed at sustainable educational practices, utilizes qualitative analysis to explore the impact of Generative AI in various learning environments. It highlights the potential of LLMs to offer personalized learning experiences, democratize education, and enhance global educational outcomes. The study finds that Generative AI revitalizes learning methodologies and supports educational systems’ sustainability by catering to diverse learning needs and breaking down access barriers. In conclusion, the paper discusses the future educational strategies influenced by Generative AI, emphasizing the need for alignment with Society 5.0’s principles to foster adaptable and sustainable educational inclusion.

This study investigated the utilization of Artificial Intelligence (AI) in the Recruitment and Selection Process and its effect on the Efficiency of Human Resource Management (HRM) and on the Effectiveness of Organizational Development (OD) in Jordanian commercial banks. The research aimed to provide solutions to reduce the cost, time, and effort spent in the process of HRM and to increase OD Effectiveness. The research model was developed based on comprehensive review of existing literature on the subject. The population of this study comprised HR Managers and Employees across all commercial banks in Jordan, and a census method was employed to gather 177 responses. Data analysis was conducted using Amos and SPSS software packages. The findings show a statistically significant positive impact of AI adoption in the Recruitment and Selection Process on HR Efficiency, which in turn positively impacted OD Effectiveness. Additionally, the study indicated that the ease-of-use of AI technologies played a positive moderating role in the relationship between the Recruitment and Selection Process through AI and HR Efficiency. This study concludes that implementing AI tools in Recruitment is vital through improving HR Efficiency and Organization Effectiveness.

Cartography includes two major tasks: map making and map application, which is inextricably linked to artificial intelligence technology. The cartographic expert system experienced the intelligent expression of symbolism. After the spatial optimization decision of behaviorism intelligent expression, cartography faces the combination of deep learning under connectionism to improve the intelligent level of cartography. This paper discusses three problems about the proposition of “deep learning + cartography”. One is the consistency between the deep learning method and the map space problem solving strategy, based on gradient descent, local correlation, feature reduction and non-linear nature that answer the feasibility of the combination of “deep learning + cartography”; the second is to analyze the challenges faced by the combination of cartography from its unique disciplinary characteristics and technical environment, involving the non-standard organization of map data, professional requirements for sample establishment, the integration of geometric and geographical features, as well as the inherent spatial scale of the map; thirdly, the entry points and specific methods for integrating map making and map application into deep learning are discussed respectively.