The article deals with distance education, which as a teaching method had to be suddenly introduced in schools and higher education institutions as a result of the global pandemic situation. The paper captures the second wave of Poland’s pandemic situation in relation to global circumstances and the methods of conducting distance learning used across the globe. The purpose of this study was to investigate first-year students’ expectations about the education shift to distance learning. GETAMEL, which is the adapted General Extended Technology Acceptance Model for E-Learning, was used in the study. The study analyzed the influence of Experience, Subjective Norms, Enjoyment, Computer Anxiety, and Self-Efficacy on students’ expectations in the context of distance learning during the COVID-19 pandemic. To test the research model presented during the research, The Partial Least Squares method of Structural Equation Modeling was used. An online survey was created to conduct the research, which collected data from 670 Polish first-year undergraduate students. The acquired data were analyzed using the SmartPLS 3 software. The results of the research indicated that the most important factors that influence the feelings of students and can convince them to change from teaching in the classroom to teaching in the distance learning model are the feeling of pleasure in this form of education and a sense of self-efficacy. The results of this study may be of particular interest to education practitioners, including teachers, and a starting point for further research on e-learning models, including, in particular, the understanding of students’ expectations regarding distance learning.

Since OpenAI released ChatGPT, the discussion on its usage in education has been conducted by students and teachers of every education level. Also, many studies have been performed on the tool’s possibilities and the threats related to its usage, such as incomplete or inaccurate information obtained or even plagiarism. Many universities worldwide have introduced specific regulations on ChatGPT usage in academic work. Furthermore, research on using ChatGPT by students and their attitudes towards it has appeared. However, a research gap exists in higher education teachers’ acceptance of AI solutions. The goal of this research was to explore the level of acceptance of the usage of ChatGPT by academics in Poland, as well as point out factors influencing their intention to use this tool. The study motivation was related to an ongoing academic discussion mainly focusing on the disadvantages of AI solutions used in scientific work and the willingness to fill the gap by showing teachers’ attitudes toward AI. The data was collected online by inviting academic teachers from Polish public universities to complete the prepared survey. The survey was prepared using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model extended with Personal Innovativeness. It revealed the acceptance level of ChatGPT usage in Polish universities by teachers and researchers and the antecedents influencing willingness to use this technology in academic work. The paper contributes to the theory of AI usage by structuring the studies regarding ChatGPT application for teaching and research, and provides practical recommendations on ChatGPT adoption in the work of academics.

Digital and media literacies refer to a specific set of skills and abilities. The range of these skills as they concern the educational process has been broadly discussed. In this paper, we analyzed the Polish educational system to determine the scope of the sorts of digital skills young people and students should achieve in order to be considered digitally and media literate. We compared sets of recommendations from the last ten years issued by different national governmental and nonprofit organizations for the Polish education system. We identified a set of skills that should be expected to be possessed by young people and students during their education. Additionally, we discussed results regarding the situation caused by the COVID-19 pandemic and the shift from regular education to distance learning.

This study verifies the ability of large language models (LLMs) to generate a curriculum and develop syllabi for specific courses. We prompted four models to generate two sets of curricula for a bachelor's degree in Economics and Management. We also generated syllabi for the courses included in the curriculum. We chose five Polish public economics universities offering those degree programs for comparison. Four LLMs were used in this experiment: ChatGPT-3.5, ChatGPT-4, Google Bard, and Gemini. Two of them are multimodal models. The study used an iterative approach, increasing the detail of the prompt in each iteration. The results show that the more specific prompt is given to the LLM, the less accurate the results are. Moreover, the experiment shows that none of the LLMs developed a complete curriculum at a level comparable to that generated by humans. However, LLMs can significantly help create a curriculum and develop syllabi by humans, provided that there is close human-AI collaboration. The results obtained from the AI-assisted curriculum design differ depending on the model. By analyzing the differences between the tools and the real degree programs and syllabi, we determined that multimodal models are better suited for this task than older models.

The COVID-19 pandemic made higher education institutions switch to distance learning in a very short period of time. The situation was challenging not only for universities themselves but also for the students and teachers. Some universities did not have the means, in terms of infrastructure, for a smooth transition to distance learning. Some teachers were not prepared for the extensive usage of ICT in their work. The pandemic developed dynamically, and it made it extremely difficult for both governments and universities to plan and implement firm solutions on how to conduct the teaching process. The presented paper focuses on the situation of Polish higher education institutions between March 2020 and March 2022. It reviews legal acts and ordinances introduced in the stated period, which focused on the sustainability of the teaching process, countermeasures for the spread of COVID-19 and the implementation of distance learning. The case of the University of Economics in Katowice, Poland, is used to show the correlation between governmental legal acts and those introduced by the university as part of the COVID -19 spread prevention and teaching process support.

This study examines the possibility of correlation between the data on human mobility restrictions and the COVID-19 infection rates in two European countries: Poland and Portugal. The aim of this study is to verify the correlation and causation between mobility changes and the infection spread as well as to investigate the impact of the introduced restrictions on changes in human mobility. The data were obtained from Google Community Mobility Reports, Apple Mobility Trends Reports, and The Humanitarian Data Exchange along with other reports published online. All the data were organized in one dataset, and three groups of variables were distinguished: restrictions, mobility, and intensity of the disease. The causal-comparative research design method is used for this study. The results show that in both countries the state restrictions reduced human mobility, with the strongest impact in places related to retail and recreation, grocery, pharmacy, and transit stations. At the same time, the data show that the increase in restrictions had strong positive correlation with stays in residential places both in Poland and Portugal.