Universities play an essential role in preparing human resources for the industry of the future. By providing the proper knowledge, they can ensure that graduates will be able to adapt to the ever-changing industrial sector. However, to achieve this, the courses provided by academia must cover the current and future industrial needs by considering the trends in scientific research and emerging technologies such as Artificial Intelligence (AI), Internet of Things (IoT), and Edge Computing (EC). This work presents the survey results conducted among academics to assess the current state of university courses, regarding the level of knowledge and skills provided to students about the Internet of Things, Artificial Intelligence, and Edge Computing. The novelty of the work is that (a) the research was carried out in several European countries, (b) the current curricula of universities from different countries were analyzed, and (c) the results present the teachers’ perspective. To conduct the research, the analysis of the relevant literature took place initially to explore the issues of the presented subject, which will increasingly concern the industry in the near future. Based on the literature review results and analysis of the universities’ curricula involved in this study, a questionnaire was prepared and shared with academics. The outcomes of the analysis reveal the areas that require more attention from scholars and possibly modernization of curricula.

Knowledge and skills in the field of Artificial Intelligence (AI), Internet of Things (IoT), and Edge Computing (EC) are more and more important for industry. Therefore, it is crucial to know what current students and future employees can offer to the industry. University students develop their knowledge and skills to support the industry in implementing modern technologies in the future. It can be expected that the first source of information for students will be lectures and other activities at the university. However, they may obtain knowledge from other sources. This article presents the results of research conducted among students assessing their own knowledge and skills in the field of IoT, AI, and EC. The research was preceded by an analysis of curricula at selected universities in terms of topics related to AI, IoT, and EC. Based on the results of the analysis, survey questions were prepared. The developed questionnaire was made available to students. The research sample for the survey participants was 563 students. The results obtained were analyzed. The results of the analysis show which issues are better known to students and which are worse. The information presented in this paper can be a source of information for the industry that can assess the competences that are or will be available on the labor market in the near future. Additionally, universities can obtain information on the areas in which there are competency gaps and which methods of teaching AI, IoT, and EC are better perceived by students.

In face-to-face learning environments, instructors (sub)consciously measure student engagement to obtain immediate feedback regarding the training they are leading. This constant monitoring process enables instructors to dynamically adapt the training activities according to the perceived student reactions, which aims to keep them engaged in the learning process. However, when shifting from face-to-face to synchronous virtual learning environments (VLEs), assessing to what extent students are engaged to the training process during the lecture has become a challenging and arduous task. Typical indicators such as students’ faces, gestural poses, or even hearing their voice can be easily masked by the intrinsic nature of the virtual domain (e.g., cameras and microphones can be turned off). The purpose of this paper is to propose a methodology and its associated model to measure student engagement in VLEs that can be obtained from the systematic analysis of more than 30 types of digital interactions and events during a synchronous lesson. To validate the feasibility of this approach, a software prototype has been implemented to measure student engagement in two different learning activities in a synchronous learning session: a masterclass and a hands-on session. The obtained results aim to help those instructors who feel that the connection with their students has weakened due to the virtuality of the learning environment.

Recently, the integration of coaching methodologies within educational environments has garnered significant attention due to their potential to enhance student learning outcomes and overall academic performance. While the literature extensively examines the effectiveness of coaching in a plethora of scenarios (e.g., sports, business, and entrepreneurship), there remains a notable research gap regarding its application within undergraduate educational environments, where coachees have unique characteristics in terms of background, age/maturity, resources availability, and population size. This literature review aims to address this gap by synthesizing existing research on the implementation of coaching strategies in educational settings and how they can cope with a large number of students. Through a comprehensive analysis, this study elucidates the current state of knowledge, identifies prevailing methodologies, and evaluates the outcomes associated with employing coaching techniques in group contexts. Moreover, this work explores the potential benefits and challenges of utilizing coaching strategies in group sessions, offering insights into their scalability and applicability within diverse educational settings. The findings of this review underscore the importance of further research in this domain to advance our understanding of how coaching methodologies can be effectively adapted to facilitate collaborative learning experiences and promote student success on a broader scale. Ultimately, this study advocates for the integration of group coaching approaches as a promising avenue for enhancing the efficiency and accessibility of traditional tutoring and mentoring practices in education.

Emergency Remote Teaching (ERT) aims to swiftly adapt conventional face-to-face educational methods to alternative (typically virtual) formats during crises. The recent COVID-19 pandemic accentuated the vulnerability of traditional educational systems, revealing limitations in their ability to effectively withstand such unprecedented events, thereby exposing shortcomings in the adopted ERT strategies. The goal of this study is to discuss the establishment of resilient, sustainable, and healthy educational systems in non-crisis times, which will enable teachers and students to make a smoother and less stressful transition to Emergency Remote Teaching (ERT) when necessary. A comprehensive hybrid approach, combining quantitative (interviews) and qualitative (online survey) methods has obtained data from 276 professors in 29 countries. These data have been used to identify a range of challenges related to ERT and their perceived level of difficulty. The methodological and social challenges (overshadowed by technical issues at the beginning of the crisis) identified in this research—such as the lack of personal contact or poor feedback from students—have been found to be the most demanding. From the collected insights regarding the perceived level of difficulty associated with the identified challenges, the present study aims to contribute to making higher education systems more robust in non-crisis times.

Practical Learning of Artificial Intelligence on the Edge of Industry 4.0 (PLANET4) is a cross-disciplinary initiative funded by the European Commission under the Erasmus+ program that embodies the triple helix model of collaboration between academia, industry, and administration. It aims to bridge the gap between academic teaching and practical applications in the context of Industry 4.0. PLANET4 focuses on developing hard skills in artificial intelligence, industrial Internet of things, and cloud and edge computing, along with the soft competencies required to manage changes in the industrial ecosystem. It involves three phases: (1) training needs analysis, taxonomy development, and workshop design; (2) collection of best practices and training material design; and (3) implementation of the PLANET4 learning course. This paper presents the materialization of these phases into a blended learning course, highlighting the integration of the triple helix model and the European Commission’s support for academia-industry collaboration with the aim of improving education quality and promoting Industry 4.0 innovation in Europe.