Generative artificial intelligence is currently used for disparate purposes, including education, particularly concerning STEM disciplines, among which Mathematics is prominent. Given the importance of both mathematical and digital skills in the world today, it is useful to inquire about the impact of this novel AI form on the didactics in Mathematics, by considering it in conjunction with accustomed technologies for more effectiveness. A survey directed at more than 60 teachers instructing at military academies and universities in several European Union Member States allows us to investigate the relationship between using generative AI and performing certain teaching choices under the methodological domain. Furthermore, we assessed the tandem between AI and traditional digital tools, both as a feature itself and in terms of repercussions on the methodologies instructors adopted. Quantitative analyses of scaled responses, performed by means of both descriptive and inferential statistics, are considered along with reflections on the qualitative answers, in the spirit of a mixed methods approach. As main outcome, we found that teachers’ use of generative AI is associated with paying more attention to specific mathematical skills during lectures, and with using teaching practices possessing a solid methodological base. Additionally, we detected interesting insights concerning integration with the use of other digital tools, resulting in instructing practices being favored by both kinds of technology, thus confirming AI as a complementary tool, rather than a replacement. The study was conducted within the framework of the DIMAS project, an international initiative aimed at deepening learners’ interest in Mathematics and taking advantage of digital tools to provide students with more manageable mathematical problems and applications.

Mathematical and digital skills are recognized as key competencies for lifelong learning. In Mathematics education, identifying appropriate methodologies and tools to increase motivation and engagement is crucial, particularly in interdisciplinary contexts such as security and defence. Through a survey administered to 2,000 students and teachers at military academies and universities in several European Member States, this study investigates the role of technology in promoting students' motivation to learn Mathematics in this field, including non-STEM majors. The research also examines the perceived effectiveness of various teaching methodologies and digital tools in teaching Mathematics from different perspectives. The study follows a mixed methods approach, combining quantitative and qualitative analyses of the responses to the survey through statistical inference techniques. Findings indicate that digital tools positively impact students’ perception of Mathematics, supporting self-assessment and interactive problem-solving. Additionally, both students and instructors favor practical methodologies such as problem-solving, learning by doing, and collaborative learning, while the integration of gamification and AI-based tools remains limited. The results contribute to a deeper understanding of best practices for teaching and learning Mathematics in security and defence contexts and provide insights for future research in other interdisciplinary and applied fields. Our analysis took place in accordance with international partners from the European project DIMAS, which aims at increasing interest in studying Mathematics in the field of security and defence and at providing more understandable Mathematics problems and Mathematics applications using digital tools.

This paper presents a novel evaluation method of areas affected by natural disasters with the purpose of managing these crisis situations. Since it is necessary to have a real overview of a specific area in the shortest time, our methodology proposes a neural network with backpropagation approach for flood detection from UAV images. For this, the Local Binary Pattern (LBP) texture operator is used for areas classification. The LBP operator labels each pixel of the analyzed image by comparing it with its neighbors, which ends with the computation of a binary number that it is converted to decimal format named LBP code. Thus, based on the generated LBP codes, a histogram type feature is computed and used in both training and testing phases of the proposed neural network. Over 50 images obtained with the aid of UAV technology were tested with the proposed neural network and good results in terms of accuracy for flood areas detection were obtained.