This document presents a systematic review of Multimodal Human–Computer Interaction. It shows how different types of interaction technologies (virtual reality (VR) and augmented reality, force and vibration feedback devices (haptics), and tracking) are used in different domains (concepts, medicine, physics, human factors/user experience design, transportation, cultural heritage, and industry). A systematic literature search was conducted identifying 406 articles initially. From these articles, we selected 112 research works that we consider most relevant for the content of this article. The articles were analyzed in-depth from the viewpoint of temporal patterns, frequency of usage in types of technology in different domains, and cluster analysis. The analysis allowed us to answer relevant questions in searching for the next steps in work related to multimodal HCI. We looked at the typical technology type, how the technology type and frequency have changed in time over each domain, and how papers are grouped across metrics given their similarities. This analysis determined that VR and haptics are the most widely used in all domains. While VR is the most used, haptic interaction is presented in an increasing number of applications, suggesting future work on applications that configure VR and haptic together.

Natural Language Processing (NLP) combines linguistics, computer science, and AI to enable computers to understand and interpret human language, making it crucial for analyzing large amounts of language data. This technology -paired with predictive models- has significant potential to forecast the relevance and evolution of skills needed in the industry, enhancing skills acquisition and alignment with job market demands and playing a key role in workforce development and educational planning. This paper comprehensively analyzes skills acquisition using NLP and predictive models. This analysis highlights significant advancements in NLP, showcasing its transformational impact on extracting and interpreting data from textual content. We conducted an extensive literature search under the systematic review guidelines, from which we selected the most relevant works for this analysis. This work examined how NLP techniques are used and adapted to extract meaningful insights from the textual content and identified which NLP models are employed to create taxonomies or classifications of skills. It explored how these models predict behaviors or outcomes in specific areas. The obtained findings show that NLP has constantly evolved in recent years, encompassing techniques that reinforce textual information extraction and underline the adaptability of NLP to various disciplines and contexts. Creating taxonomies and structured knowledge organization was a significant focus, highlighting its applicability in multiple fields. Finally, we discuss the ongoing evolution and adaptability of the NLP models to different disciplines and their integration with predictive models, which offer valuable insights that enrich the interactions between textual data and AI.

This study investigates the integration of future skills in engineering education at leading global universities, within the framework of Industry 4.0. We systematically analyzed academic publications from the top 10 universities listed in the QS World University Rankings since 2020, aiming to assess the extent of integration, key skill dimensions emphasized, and patterns of institutional collaboration. The results reveal significant trends in embedding future-oriented competencies into curricula, particularly through interdisciplinary and international initiatives. Our findings underscore the pivotal role of global collaboration in enhancing educational frameworks and equipping graduates with the skills demanded by the evolving industrial landscape. The study advocates for adaptable, dynamic educational models to align with rapid technological advancement and shifting industry needs. Future research should address the scalability of these integration efforts and their long-term impact on student performance and university-industry collaboration.

In the context of Industry 4.0, engineering education is evolving rapidly, requiring new approaches to continuing engineering education (CEE). Traditional methods struggle to meet the demands of professionals looking to update their skills. This study presents an experimental proposal using the Multisensory Virtual Decision Making Center (MVDC) to improve CEE training creation. The MVDC creates collaborative and immersive virtual environments, enabling real-time participation and decision-making with a greater sense of presence and complete data visualization. The proposed experiment compares the effectiveness of MVDC sessions with traditional video call sessions in developing CEE courses. By streamlining the training planning process for continuing education, the research aims to evaluate improvements in the quality of decisions, participation, and user satisfaction in this collaborative process. This study anticipates significant insights into integrating cutting-edge technologies such as MVDC for CEE, proposing it as a crucial tool for upskilling and reskilling. Expected outcomes include practical guidelines for implementing multisensory environments in CEE curriculum development, highlighting their potential to revolutionize the planning process. The research underscores the need for innovative approaches to meet the changing demands of the engineering workforce in the era of Industry 4.0. It lays the foundation for future studies that validate and expand these findings, paving the way for more effective and engaging CEE programs.

The purpose of this study was to examine how, in recent years, the top 100 universities in the QS ranking have integrated the development of Future Skills into their curricula to address the evolving demands of the labor market. The PRISMA methodology was used to conduct a comprehensive search and analysis focused on research articles and internal university documents, with significant contributions from American and European Higher Education Institutions. Various international reports by the World Economic Forum (WEF) and the Organisation for Economic Co-operation and Development (OECD) have identified and organized Future Skills based on their analysis and research. Regardless of how these skills are defined, all the stakeholders agreed that higher education institutions must focus on fostering them in undergraduates to meet labor market demands. Our research identified a significant trend in skill adoption, with social and metacognitive skills emerging as prevalent among 59% of top universities in general searches. On the other hand, while cognitive-metacognitive and technical skills were more frequently mentioned by 35% and 29% of these universities, collaboration was emphasized in 74% of the documents, mainly within social and medical sciences. The main conclusion was that while most universities are currently incorporating these skills into student profile development, collaborative efforts in advancing the understanding and implementation of other Future Skills will become essential for the seamless incorporation of graduates into the workforce. This fact reinforces the urgent need for further research to ensure comprehensive skill development, mainly focusing on emotional skills in diverse academic disciplines, such as natural sciences, engineering, agriculture, and humanities.