The rapid evolution of Industry 4.0 and 5.0 requires a dynamic and predictive approach to workforce development, particularly identifying emerging occupations and the required knowledge, skills, and abilities (KSA) within critical sectors. This study addresses the research question: What are the key emerging Industry 4.0 and 5.0 occupations in Mexico's INFOCOMM (Information and Communications Technology) sector, and what KSAs do they demand? The study proposes a strategic framework that leverages AI-powered tools, specifically natural language processing (NLP) and machine learning (ML), to develop a comprehensive and adaptive taxonomy of KSAs that meets the evolving needs of the INFOCOMM sector. The methodology involves collecting and analyzing extensive datasets from job advertisements, industry reports, and educational frameworks, allowing us to automate the extraction and classification of job requirements and competencies. Using NLP and ML, it was possible to systematically categorize and rank in-demand skills, providing a scalable model that can be updated dynamically as labor market trends emerge. The framework identifies key occupations driven by advances in automation, data analytics, AI integration, and digital platform management, with the most critical skills, including problem-solving, project management, and interdisciplinary teamwork. Similarly, significant knowledge areas such as cloud computing, cybersecurity, and AI systems are identified as foundational for the future workforce. This flexible framework offers a robust tool for academic institutions and industry practitioners. For academia, it enables the alignment of curricula with the competencies necessary for Industry 4.0 and 5.0. For industry, it serves as a strategic guide for workforce development, supporting targeted upskilling and reskilling initiatives. The model's flexibility allows for continuous updates, which ensures relevance despite ongoing technological advances. Future research will extend this framework to additional sectors and regions, refine its predictive capabilities, and explore its longterm impact on education and workforce productivity.

The article explores the transformative impact of gamification in education, highlighting the integration of theory and practice to create dynamic and engaging learning experiences using multi-sensory virtual reality environments, such as the Multi-Sensory Virtual Decision-Making Center (MVDC). Among the state of the art of gamification, it has been shown that gamification significantly improves students' attention, motivation, and knowledge acquisition, leading to better academic performance. In addition, it promotes the use of new technologies and encourages teamwork. The Multi-sensory Virtual Decision-Making Center facilitates the creation of immersive collaborative virtual environments where multiple decision-makers can participate remotely in real-time without losing the sense of presence, thus providing an enhanced user experience by conducting joint decision-making sessions remotely and in real-time. Therefore, this work proposes a novel approach to engineering education by combining the synergistic potential of gamification and MVDC, improving both the educational experience and the efficiency of remote and real-time collaborative decision-making. This proposal includes the realization of joint exercises in the classroom with the combination of these technological tools. The initial results are encouraging because they offer an improved user experience when performing collaborative gamification exercises in the classroom and promote efficient and engaging real-time remote decision-making. This work will provide valuable insights and recommendations for future research in this dynamic and promising field.

Skills taxonomies are a useful classification system for all skills that an employee must count at the workplace. Taxonomies provide a framework for seeking, classifying, evaluating, and indicating the skills that are essential for all kinds of professions. Taxonomies based on Future Skills usually have a visual framework to show their occupational structures and provide upskilling and reskilling support through various pieces of training. This article proposes a web framework using a dynamic taxonomic system based on knowledge, skills, and abilities (KSA) matrices for submitting course and occupation structuring for the Industry 4.0 workforce. For the methodological process, firstly, a matrix taxonomic structure based on KSA was created through a review of the literature; secondly, an analysis of the data related to KSA presented by FutureSkills frameworks was carried out, such as those of the World Economic Forum (WEF), NESTA Taxonomy, SkillsFuture of the Government of Singapore, the Standard Occupational Classification (SOC), among others. Once the base taxonomy was created with the data from the frameworks mentioned above, the dynamic aspects were developed through new documents and publications, applying natural language processing tools to enrich and complete the KSAs identified as trends. The INFOCOMM technology sector was chosen as a case study under the dynamic matrix taxonomy structure, applying NLP tools to extract and explain KSA trends and potentially new ones. Among the results obtained is a web framework where the taxonomic structuring created for the INFOCOMM Technology sector can be dynamically displayed. The preliminary results show that this framework can serve as an international reference guide to designing future educational plans for active learning and experiential in higher education institutions and for designing occupational profiles for the industry's future.

In their last international report, “Jobs of the Future 2023”, the World Economic Forum strongly emphasizes that the ability to forecast future skills is paramount in ensuring quality learning for the workforce in the context of Industry 4.0 and beyond because it enables individuals and organizations to adapt, remain competitive, and thrive in a rapidly evolving industrial landscape. This study focuses on addressing the dynamic nature of the modern workforce by forecasting the Knowledge, Skills, and Abilities (KSA) that will be in high demand in the future. The authors explore innovative strategies for delivering quality education tailored to the workforce's diverse needs, with a particular emphasis on inclusivity and accessibility. The project has a broad scope encompassing a comprehensive analysis of workforce trends, emerging skill requirements, and the development of effective reskilling and upskilling programs. It aims to bridge the gap between current skills and future job requirements by forecasting the skills in demand. The study anticipates several key results, including the identification of future skill demands, the development of customized learning programs, the implementation of technology-enhanced educational initiatives, and the establishment of comprehensive workforce development strategies. By forecasting future skills and developing inclusive learning programs, the project seeks to create a more equitable and accessible educational landscape, ensuring that individuals from all backgrounds and demographics are prepared for the opportunities and challenges of the future job market.

This paper proposes a dynamic Knowledge, Skills, and Abilities (KSA) matrix-based taxonomy for the Industry 4.0 workforce. The study methodology consisted firstly of identifying the KSAs through a literature review and secondly of a KSA relevance analysis using information from World Economic Forum (WEF) global reports and the Organization for Economic Cooperation and Development (OECD). Finally, we identified the correlation coefficients of the KSA matrix elements concerning the data on jobs and occupations using information from the European Skills, Competencies, and Occupations (ESCO), Occupational Information Network (O*NET), and the strategic intelligence platform of the World Economic Forum. One of the goals was to make the taxonomy compatible with existing and future machine learning methods (i.e., AI-ready) that will enable efficient and effective use of AI in mining and explaining existing and potentially proposing novel trends and strategies. Preliminary results show that the KSA Industry 4.0 Taxonomy can serve as an international reference guide for designing 2030 educational approaches to active and experiential learning in Higher Education Institutions.