###### Abstract
In this new industrial context [16], design principles such as decentralization, real-time capabilities, or virtualization, which go hand in hand with augmented and virtual reality, robotic automation and industrialization, big data analytics, cloud data, and computing or cybersecurity, have become the fundamental pillars of such a revolution. All of these factors contribute to the creation of an industry based on a set of interconnected systems that are capable of making decisions with little or no human intervention [17]. In this current industrial revolution, the three technological pillars are [18; 19; 20]: Internet of Things (IoT), Edge Computing (EC), and Artificial Intelligence (AI). Firstly, the Internet of Things provides us with the mechanisms needed to enable a dynamic, global network of heterogeneous objects based on standard communication protocols [14] that interact and cooperate to reach a common goal [21]. This multi-device integration and interconnection in a widely distributed network elevates the scope of traditional communication [22] from human-human to human-human, human-thing and thing-thing or Machine-to-Machine (M2M). Secondly, Edge Computing is a computational paradigm designed to split the computation process of the devices that are physically close to data generation points instead of sending the whole bulk of data to a remote entity. This makes it possible to address drawbacks such as latency, battery consumption, Wi-Fi availability, security, and privacy [23]. Finally, artificial intelligence facilitates automatic data analysis to extract knowledge models, a clear advantage in decision-making and optimization processes. The different AI methods analyze and extract actionable insights from all generated data [24], thus providing competitive advantages in manufacturing processes [25]. Industry 4.0, first named as such by the German government in 2011, is no longer a futuristic vision, but it has become a standard process for more and more industrial processes over time. The current industrial revolution is in full force, and there is an apparent demand for qualified professionals that are able and willing to respond to the needs and opportunities which arise in this new generation of industrial processes. However, do current university programs cover all the theoretical and practical aspects of Industry 4.0 to enable professionals to operate effectively in this paradigm? In the work [26], it was shown that technologies such as EC or IoT have a positive impact on the achievements of specific SDGs. Which areas (AI, IoT, EC) are more and which are less taught? What methods are used in teaching AI, IoT, and EC? How much do the real problems of the industry affect the educational teaching process? What are the academic needs in the field of AI, IoT, and EC and what could support the teaching process? Projects, practicals, and case studies should not only focus on current needs but also look to the future, considering current trends in research and taking advantage of market opportunities to solve real-life challenges (such as predictive maintenance [27]). This paper aims to present a study carried out in a university educational community on how knowledge of IoT, EC, and AI is transferred in the teaching process and how students acquire such knowledge from the academic teachers' point of view. To achieve this, a 7-stage methodology process is followed: (1) literature review, (2) university courses review, (3) defining the research questions, (4) questionnaire development, (5) survey, (6) survey results from quantitative and qualitative analysis, and (7) conclusions and development of recommendations. The review of the literature describes reaction of universities to the challenges of Industry 4.0. The university courses review contains information about actual training programs of selected universities in the field of AI, IoT, and EC. The questionnaires are prepared based on the extended Likert scale during the first phase.