The way people travel, organise their time, and acquire information has changed due to information technologies. Artificial intelligence (AI) and machine learning (ML) are mechanisms that evolved from data management and developing processes. Incorporating these mechanisms into business is a trend many different industries, including education, have identified as game-changers. As a result, education platforms and applications are more closely aligned with learners’ needs and knowledge, making the educational process more efficient. Therefore, AI and ML have great potential in e-learning and higher education institutions (HEI). Thus, the article aims to determine its potential and use areas in higher education based on secondary research and document analysis (literature review), content analysis, and primary research (survey). As referent points for this research, multiple academic, scientific, and commercial sources were used to obtain a broader picture of the research subject. Furthermore, the survey was implemented among students in the Republic of Serbia, with 103 respondents to generate data and information on how much knowledge of AI and ML is held by the student population, mainly to understand both opportunities and challenges involved in AI and ML in HEI. The study addresses critical issues, like common knowledge and stance of research bases regarding AI and ML in HEI; best practices regarding usage of AI and ML in HEI; students’ knowledge of AI and ML; and students’ attitudes regarding AI and ML opportunities and challenges in HEI. In statistical considerations, aiming to evaluate if the indicators were considered reflexive and, in this case, belong to the same theoretical dimension, the Correlation Matrix was presented, followed by the Composite Reliability. Finally, the results were evaluated by regression analysis. The results indicated that AI and ML are essential technologies that enhance learning, primarily through students’ skills, collaborative learning in HEI, and an accessible research environment.

The study makes, under a new configuration of the circular economy, a cross-country analysis based on the Competitiveness and Innovation Indicators in the E.U., i.e., two sub-criteria: private investments, jobs, and gross value added; and patents related to recycling and secondary raw materials as a proxy for innovation. The analysis proved that investments influence the number of patents, and participate in societal transformation. A further cluster analysis classified countries on the level of innovation. The cluster analysis in SPSS centres on significant potential, weaknesses, impact, and waste management control through blockchain technology. It is found that the factors that influence innovation, according to the Global Competitiveness Report, link the business dynamism and innovation capability with the capacity to sustain resilient ideas, such as competitive intelligence and social entrepreneurship. The discussions aim to prove that the efforts to rethink the circular economy principles contribute to its conceptual and societal transformation role through the implementation of innovative processes, inventive solutions, and blockchain technologies, and their social consequences to solve environmental problems. Once understood and accepted, CE will drive sustainable behaviour.

The paper aims to develop—based on a particular dual comparative analysis that follows the current European concerns—the concepts of competitiveness and innovation as pillars uprighting companies’ resilience, creating ecoinnovative jobs and social inclusion. In their struggle to meet the Circular Economy principles and Green Deal objectives, the countries chosen for analyses—Romania and Serbia—have started implementing added-value blockchain concepts in their societies to thrive in the resilient European market and build empowered societies. According to the World Economic Forum Global Sources of Competitiveness, skills considered in our study refer to businesses’ versatility and societies’ innovation capability. Based on specific data provided by Eurostat, the results showed a correlation between the ecoinnovation index and R&D personnel by sector and helped design a regression model. Hence, we demonstrate that R&D creativity, once stimulated through innovative teaching, blooms, having positive effects at society and market levels as reflected in the ecoinnovation index. Furthermore, cluster analysis within E.U. innovation helped identify strengths and weaknesses, provided new grounds in applying innovation, and led to further recommendations.