Background: The Covid‐19 pandemic has created significant challenges for the global higher education community. Understanding of students' perception has important implications for the quality of the learning process, as it affects students' engagement in learning, helps educators rethink the principles of the learning design and further improve the developed programs.Objectives: Understanding of how rapid and necessary changes of learning caused by the pandemic are related to students' intrinsic motivation and awareness.Methods: There were 832 participants in this study. Quantitative and qualitative research methods employing relevant statistical techniques were used to research students' opinions regarding the distance learning process.Results and Conclusions: Our analysis showed that first‐year students were significantly less motivated during the learning process than older students, they saw distance learning as less valuable and less interesting than the others. Our research found several positive consequences of the pandemic: working according to students' own schedule in a relaxed environment, looking at the lecture again if necessary, feeling free to ask questions and communicate with teachers and saving travel time.Implications: Teachers should have more understanding for the first‐year students who are threatened when it comes to developing motivation to learn and help them cope with learning anxieties, encourage their self‐belief and give them extra support during the learning process.

This paper presents SPARK_AI, a prompt-orchestrated system architecture for governing how large language models (LLMs) conduct structured and adaptive reasoning in human–AI interaction. The framework mitigates ad hoc LLM use by replacing direct answer generation with a process-oriented, step-by-step reasoning workflow. We focus on SPARK_AI_MATH, a domain module that supports learners in solving non-routine problem-solving tasks by operationalizing well-established problem-solving phases and guided questioning dialog strategies (Socratic-style prompts), with an optional tool-mediated visualization layer (e.g., GeoGebra). The module implements a five-phase conversational protocol consisting of problem interpretation, analysis of givens, planning, execution, and reflection, together with a controlled hint policy. This design is realized through a stateful system architecture in which each problem instance is maintained as an independent interaction track with a persistent reasoning state. User acceptance was evaluated by first-year mechanical engineering students (N = 108) using an expanded Technology Acceptance Model instrument, and the results were analyzed via PLS-SEM. The findings indicate overall favorable perceptions, with perceived usefulness and learning support emerging as key predictors of intention for continued use. Beyond this specific domain, the SPARK_AI framework enables efficient domain adaptation through localized prompt strategies while preserving a shared cognitive control layer for reasoning-centered human–LLM interaction.

In contemporary society, the ability to understand and interpret data has become a fundamental skill. Regardless of STEM or non-STEM fields, all individuals is expected the minimum level of stochastic literacy. This study examined Non-STEM students’ base-rate reasoning, their attitudes toward statistics, and the potential of artificial intelligence (AI) to support the analysis of students’ explanations. There participated 105 students, from the University of Novi Sad (non-STEM: Law, Economics) during regular classes. They solved a base-rate task, provided written explanations, and completed an attitude questionnaire. Responses were coded into six categories reflecting correctness and explanation type. Overall, 41% of students answered the task correctly, with no significant differences by faculty or gender. However, almost half of the participants chose the same wrong option. Students of the Faculty of Economics were more likely to provide explanations (70%) compared to law students (25%). When considering only responses with explanations, a significant gender difference was found, with male students more likely to provide correct probability based reasoning, while female students more often relied on equiprobability explanations. Attitude measures showed generally positive orientations although self-efficacy was weaker. AI-based (ChatGPT) coding of explanations gives results comparable to human classification and shows its potential for identifying misconceptions, albeit with certain limitations. Findings emphasize the need to strengthen probabilistic reasoning and statistical self-efficacy among Non STEM students, while also pointing to AI’s potential as a research and pedagogical tool.

Teaching mathematics is a challenge in itself. Today, mathematics is often perceived as "unpopular", which makes it particularly demanding to engage students and motivate them to think and learn. Guided by these challenges, we designed the international eTwinning project MathMay, whose main goal was to popularize and apply mathematics through collaborative, creative, and interdisciplinary activities. This paper presents selected tasks and examples of the project's implementation. Elementary school students (aged 11-15) from two schools in Serbia and one in Spain participated in the project. The multidisciplinary nature of the projects fostered the development of multiple competencies - mathematical, digital, scientific, personal, and social. Students reported enjoyment and satisfaction while applying mathematics in real-life and creative contexts. Overall, all project tasks were successfully implemented, and the planned goals were achieved, demonstrating the potential of international collaboration for improving students' motivation and positive attitudes toward mathematics.

The cover image is based on the Special Issue Article Higher education students' experiences and opinion about distance learning during the Covid-19 pandemic by Aleksandra Stevanović et al., https://doi.org/10.1111/jcal.12613.