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This paper evaluates the rise of "Generative Artificial Intelligence" (GAI) in its myriad forms, with the highest profile being the "Large Language Models" (LLMs). More importantly, it analyses the potentially disruptive impact of this ascendant technology on higher education and the global workplace. The findings of this paper indicate that students pursuing higher education tend to perceive GAI favourably, as it frees them from the toil of rote-learning. However, the view is rather mixed in the case of educators, who are still coming to grips with this seemingly disruptive technology. In the case of the global labour market, GAI has the potential to decimate legions of white-collar jobs once it eliminates inherent issues of biases, security and misinformation. Despite the media's constant labelling of GAI as a disruptive technology that has suddenly burst onto the technological scene, it is evidenced in this paper that the technology has taken nearly eight decades to reach today's level of technological advancement. Further, it is far from reaching its full potential, as it is still incorporating advances in pattern recognition, planning and problem solving, and quantum computing technologies. This study also warns of concentrating the power of this game-changing technology in the hands of a few major corporate titans. [ABSTRACT FROM AUTHOR]

The paper presents the results of the research on the influence of the COVID-19 pandemic on the dissemination of innovative e-learning tools in higher education. Research was carried out in Poland in December 2021 on a sample of 621 students. The main issue that was the subject of the author's analysis was the influence of the COVID-19 pandemic on the change in the use of innovative e-learning tools in university education. After conducting the research and discussing this and related research about e-learning during the pandemic, it was concluded that the percentage of students familiar with the analyzed e-learning tools has increased significantly during the pandemic. There has been a visible rise, especially in the usage of the following tools: MS Teams, Zoom, and Google Classroom. The most frequently used e-learning tools during the COVID-19 pandemic have been mainly videoconferencing tools such as MS Teams and Zoom. However, students also have used e-learning platforms and e-mails. The author's research identified three hidden factors (categories) of the used e-learning tools. They include the following categories: popular services and applications adapted to e-learning; popular applications for synchronous meetings adapted to e-learning; and other synchronous and asynchronous e-learning methods. The familiarity with information technology, as well as an interest in innovative e-learning tools, have positive influence on the ease of acquiring content in e-learning. Having the proper resources also positively influences the absorption of e-learning content. On the basis of the achieved results, the authors prepared a model of relations between students' interest in innovative e-learning technology and the resources they possess to participate in e-learning classes. This model enables us to assess which method—e-learning, traditional or hybrid—should be used in the given situation. The developed model can be useful for universities. They can assess the students' interest in innovative e-learning technologies and their level of technical resources using questionnaires and on this basis divide students into groups to prepare the optimal learning way—e-learning, traditional or hybrid. [ABSTRACT FROM AUTHOR]

Due to SARS-CoV-2 pandemic, higher education institutions are challenged to continue providing quality teaching, consulting, and research production through virtual education environments. In this context, a large volume of data is being generated, and technologies such as big data analytics are needed to create opportunities for open innovation by obtaining valuable knowledge. The purpose of this paper is to investigate the factors that influence the adoption of big data analytics and evaluate the relationship it has with performance and knowledge management, taking into consideration that this technology is in its initial stages and that previous research has provided varied results depending on the sector in focus. To address these challenges, a theoretical framework is developed to empirically test the relationship of these variables; 265 members of universities in Latin America were surveyed, and structural equation modeling is used. The findings identify compatibility, an adequate organizational data environment, and external support as factors required to adopt big data analytics, and their positive relationship is tested with knowledge management processes and organizational performance. This study provides practical guidance for decision-makers involved in or in charge of defining the implementation strategy of big data analytics in higher education institutions. [ABSTRACT FROM AUTHOR]