The 6E Learning by Design (LbD) model can enhance student teachers’ development of competence for integrating technologies in the classrooms including Artificial Intelligence (AI). However, teacher educators rarely use the 6E LbD model in supporting and encouraging student teachers to integrate AI applications in their classrooms effectively. To attract teacher educators to use the 6E LbD model, in the present study, we modeled learning activities for each phase. We also examined the impact of the 6E LbD model in supporting student teachers to integrate AI in the classroom. We adopted a Participatory Action Research approach implemented in two cycles. We collected data from 35 student teachers who were selected purposively. We collected data through observation, reflective journals and document analysis. We observed student teachers during the classroom activities and analysed their lesson plans and reflective journals, and interpreted data based on a thematic analysis. Findings show that there are various activities facilitating learning in different phases of the 6E LbD model. Also, findings show that the 6E LbD enables student teachers to develop skills and competence for integrating AI in their classrooms. The findings show that an evidence-based approach will motivate teacher educators to use the 6E LbD model.

This paper identifies skills required for workers in Industry 4.0 in France and provides insights to guide the development of a future research agenda. The research draws on a bibliometric analysis of 80 papers, mapping the technical and interpersonal skills needed for professionals to adapt and thrive in the evolving industrial landscape. The results highlight the importance of a multidisciplinary approach integrating competences in information technology, automation, robotics, artificial intelligence, data analysis, project management, interpersonal skills, adaptability, and collaboration. This holistic approach to skills development reveals six major domains: Technical, Flexibility, Inter-Agency, Soft Skills, Innovation, and Information Technology. The conclusions emphasize that the interconnection between these domains is essential to prepare a workforce capable of meeting the challenges and seizing the opportunities of Industry 4.0 in France. The research shows that there is a need for a multidisciplinary and integrated approach that combines technical and interpersonal skills in Industry 4.0 activities. It provides a solid foundation for formulating talent development strategies and educational curricula aligned with the demands of Industry 4.0. By proposing a future research agenda, this study not only highlights key areas for further exploration—economic impact, public innovation policies, and curriculum adaptation—but also contributes to the evolution of human resources in the digital transformation era.

Background: Industry 4.0 presents a volatile environment and is attentive to the need for changes in employees’ behavior. This paper aimed to investigate academic trends relating to the themes of Industry 4.0 (I4.0) and human resources (HR). Methods: a total of 354 articles on the thematic area published in the Scopus database between 2014 and 2023 were analyzed. Results: Industry 4.0 is related to various themes connected to the productive environment, with a focus on technology. Over time, topics related to human resources management have become integrated into research on Industry 4.0. In this scenario, research demonstrates the importance of studies on workers’ education and training. The findings presented in this paper are relevant for researchers, academics, and managers, serving as a guide for future research on I4.0. The main contribution is facilitating the analysis of the role of workers in the Industry 4.0 scenario for different professionals, such as researchers, teachers, and HR managers. Conclusions: the present study points out new possibilities for research on Industry 4.0 and HR. These possibilities should guide researchers and professionals in their implementation.

The aim of the present research is to identify and analyze the variables which help to effectively differentiate Quality of Life (QoL) and human labor in the G7 (Germany, France, Italy, Canada, Japan, United Kingdom, and United States of America—USA) and BRICS countries (Brazil, Russia, India, China, and South Africa) through a discriminant analysis. A discriminant analysis model is developed to classify countries as having a low, mid, or high QoL based on QoL and human labor variables. The variables used in the discriminant analysis were obtained between 2010 and 2022 from two platforms: NUMBEO variables capable of relating QoL to socioeconomic aspects and OECD’s (Organization for Economic Cooperation and Development) human-labor-related variables. Based on the results, the three variables that most discriminate the groups in order of importance are employed women in relation to the female population, the female labor force participation rate, and the female unemployment rate. Countries are classified as having a low, mid, or high QoL. The adopted technique will allow researchers and managers to classify and draw goals for action reorganization and investment in QoL and labor.