The article points out some of the challenges faced by students at the University of Ostrava, Comenius University in Bratislava and Al-Farabi Kazakh National University, who had almost no previous practice in online teaching during the COVID-19 pandemic situation. The students from the mentioned universities were interviewed to share their experiences with online teaching and learning during the COVID-19 lockdown. In this paper, qualitative pedagogical research in the local conditions of the three mentioned universities is used, with the processing of answers of selected students who were interviewed as an available sample. The students were also asked to describe their impressions of this situation from their social and personal points of view. The focus is on the positive and negative aspects, boundaries, and problems of online university teaching during the pandemic situation, as well as changes in the personal life of the students. The aim of this introductory small-scale study is to provide a basis for future research on the impact that the COVID-19 situation has had on the educational process at the above-mentioned universities, as well as to assist educational providers in foreseeing and eliminating the possible problems of lecturers when establishing an online educational environment. Some conclusions are drawn from the interviews that offer potential for further research in educational science, because many difficulties from the students’ point of view are related to their social status, lack of social contact, technical problems with Internet connections, the carrying out of lectures and the exams of students. The students’ answers are categorized, and each category is described.

This paper presents the findings of a pilot study aimed at gaining deeper insights into student errors in solving mathematics tasks from the Czech national school-leaving examination (maturita), while also exploring the potential of artificial intelligence (AI) to support error analysis and provide targeted feedback. The study began with an analysis of publicly available CERMAT data, focusing on tasks that have consistently shown low success rates over the years. Based on this analysis, a subset of tasks was selected and further tested on students preparing for the exam. The results were compared with national statistics to validate the relevance of the identified difficulties. A revised version of the test was then developed and administered to a new cohort of students, enabling the collection of a dataset of real student solutions for qualitative error analysis. The study adopted a nuanced framework for error classification, distinguishing between “slips” (minor, often procedural errors) and “true errors” stemming from a lack of conceptual understanding. Emphasis was placed on understanding the nature and origin of these errors, their recurrence, and implications for learning. Student work was analysed in all phases of the error-handling process, including detection, diagnosis, explanation, and correction. At the same time, the study evaluated selected AI tools, primarily ChatGPT 4.0—for their potential to solve exam-level mathematics tasks at the university level and identify errors in student solutions. Multiple test items were processed through the AI system, and its responses were compared with those of students. Particular attention was given to the AI's behaviour when confronted with incorrect or incomplete answers. The results revealed both the promise and limitations of current AI models in supporting formative assessment, particularly with respect to misinterpretation of task wording, difficulty in recognising alternative valid strategies, and occasional inconsistency in the quality of feedback. The findings contribute to the broader discussion on how AI can be effectively integrated into educational practice—not as a replacement for teacher judgment, but as a supplementary tool to enhance student understanding, develop metacognitive skills, and improve preparation for high-stakes assessments such as the maturita exam.