Background: In the rapidly changing industrial environment and job market, engineering profession requires a vast body of skills, one of them being decision making under uncertainty. Knowing that misunderstanding of probability concepts can lead to wrong decisions, the main objective of this study is to investigate the presence of probability misconceptions among undergraduate students of electrical engineering. Five misconceptions were investigated:insensitivity to sample size,base rate neglected,misconception of chance,illusory correlation, andbiases in the evaluation of conjunctive and disjunctive events.The study was conducted with 587 students who attended bachelor schools of electrical engineering at two universities in Serbia. The presence of misconceptions was tested using multiple-choice tasks. This study also introduces a novel perspective, which is reflected in examination of the correlation between students’ explanations of given answers and their test scores.Results: The results of this study show that electrical engineering students are, susceptible to misconceptions in probability reasoning. Although future engineers from the sample population were most successful in avoiding misconceptions of chance, only 35% of examinees were able to provide a meaningful explanation. Analysis of students’ explanations, revealed that in many cases majority of students were prone to common misconceptions. Among the sample population, significant percentage of students were unable to justify their own answers even when they selected the correct option. The results also indicate that formal education in probability and statistics did not significantly influence the test score.Conclusions: Results of the present study indicate a need for further development of students’ deep understanding of probability concepts, as well as the need for the development of competencies that enable students to validate their answers. The study emphasizes the importance of answer explanations, since they allow us to discover whether students who mark the correct answer have some misconceptions or may be prone to some other kind of error. We found that the examinees who failed to explain their choices had much lower test scores than those who provided some explanation.

The use of infographics for presenting data in the media and on the Internet has become a widespread phenomenon. This study examines how well undergraduates interpret the mean when presented in infographic, their attitudes towards the misuse of statistics, and their perceptions of their knowledge and use of statistics. The infographic was intended for the general public. The questionnaire was developed in a pilot study and then it was presented to a sample of 270 students from universities in Serbia and the USA. The study showed that STEM undergraduates and those who attended a course in statistics interpreted the infographic better. However, between 46% and 65% of those misinterpreted the infographic which indicates that changes are needed in statistics education to improve statistical literacy. Concerning demographic characteristics, somewhat better results were achieved by the American students, while gender had weak significance. Students whose interpretations were more successful perceived their knowledge and use of statistics as better. An extremely low number of students, 7.5%, disagreed that statistics is often misused, while only 14.2% trusted the research results presented in the media. The results from this study can be useful for curriculum developers, teachers, and researchers on statistical education. Keywords: infographics, mean interpretation, non-STEM students, statistical literacy, STEM students, students’ attitudes

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.