This study aimed to explore students’ perspectives on integrating artificial intelligence (AI) into scientific collaboration, specifically on writing academic articles and creating scientific posters. The research employed open-ended interviews conducted among 61 civil and military students. Opinions were labelled, coded, and gathered into the following categories: positive impact on collaboration, challenges faced, and educational impact. Among the positives were improving efficiency, enhancing the quality of work, and generating new ideas. The challenges concerned experiencing technical difficulties with AI tools, inconsistency in AI outputs, and AI dependence, which may lead to behaviours on the verge of addiction. Regarding educational impact, students noticed that AI helps improve learning new skills, increases engagement in the task, and enhances critical thinking. As one researcher performed the thematic analyses, Cohen’s Kappa statistic was used to ensure intra-coder reliability. This study highlights the need for further research to optimize the use of AI in scientific collaboration while addressing ethical concerns related to students’ motivations for using AI tools, promoting responsible use, and researching students’ emotions, cognitive processes, and behaviours resulting from their interactions with AI tools. The research provides valuable insights for educators and policymakers to integrate AI effectively into academic practice.

Combating chemical, biological, radiological, and nuclear (CBRN) threats with novel solutions reduces disasters and accident risks and, at the same time, aids sustainability. This research aims to identify the innovations in combating CBRN threats and verify how they fall within the scope of the Sustainable Development Goals (SDG). The study relies on mixed-method research, including bibliometrics and text-mining used to identify clusters, most frequent words, and codes. The material for the research was 156 publications from the Scopus database from 2017 to April 2022. From the analytical process, textual data served as the indicators of the innovations’ directions driven by SDGs. The research results indicated that innovations for combating CBRN threats support sustainability mainly in health and environmental areas, specifically in: SDG 3: Good Health and Well-being; SDG 6: Clean Water and Sanitation; SDG 14: Life Below Water; SDG 15: Life On Land. The research contributes to the landscape of innovations serving sustainable development. Further studies may focus on narrower perspectives, such as environmental protection and health innovations. There can also be concerns about the dark side of CBRN innovations and technologies of dual use.

Energy security education explores various issues, such as a secure and competitive economy and nuclear safety. In the context of energy transition and sustainable development, it also addresses the world’s reliance on nonrenewable and renewable energy sources. The aim of this study was to identify research trends pertaining to energy security education, paying particular attention to renewable and nonrenewable sources. This was accomplished with the use of mixed-method research in two steps. The first step was a text-mining and content analysis of publications on energy security education published on the Web of Science platform between 2016 and 2021. From 660 publications on energy security education, titles, abstracts, and keywords were extracted and analysed with NVivo software to identify the most frequent concepts on energy sources in publications. The concepts were associated with nonrenewable energy sources (coal, natural gas, uranium, petroleum, and fossil fuels), nuclear power, and renewable energy sources (hydro, geothermal, solar, tide/wave/ocean, wind, solid biofuels, biogases, liquid biofuels, and renewable municipal waste). The second step was conducting detailed searches with Boolean operators, where “energy security education” was juxtaposed with the distinguished keywords. All searches on energy security education showed that publication activity tended to decrease, while citations increased. The most explored topics concerned: “fossil fuels”, “oil, petroleum”, “renewable” energy, and “solar” energy sources. An increasing trend was observed for all renewable energy sources as well as selected nonrenewable sources: “oil, petroleum”, “nonrenewable”, and “coal”. Additionally, R-squared values were calculated to indicate the fit of the trendline to the model. Due to the technologically enhanced energy transition and didactic innovations, education focussing on energy sources is expected to remain in demand. Curricula will need to be revised in the future to better reflect this reality.

Chemical reconnaissance, defined as hazards detection, identification, and monitoring, requires tools and solutions which provide reliable and precise data. In this field, the advances of artificial intelligence can be applied. This article aims to propose a novel approach for developing a chemical reconnaissance system that relies on machine learning, modelling algorithms, as well as the contaminant dispersion model to combine signals from different sensors and reduce false alarm rates. A case study of the European Union Horizon 2020 project–EU-SENSE is used and the main features of the system are analysed: heterogeneous sensor nodes components, chemical agents to be detected, and system architecture design. Through the proposed approach, chemical reconnaissance capabilities are improved, resulting in more effective crisis management. The idea for the system design can be used and developed in other areas, namely, in biological or radiological threat reconnaissance.