Gamification is a viable strategy used to enhance motivation and engagement in programming classes among students. However, automated evaluation capabilities, which are crucial for giving students fast and correct feedback, are frequently lacking in currently available gamification technologies. This study proposes a novel web-based application that combines automated programming assessment features with gamification concepts; the aim is to provide students taking a programming course with an engaging learning environment. A survey conducted with 215 undergraduate students assessed how the tool affected the motivation and engagement of students. The findings show that the tool had a beneficial impact on students’ willingness to participate in class, study, increase their self-confidence, engage in healthy competition with peers, and learn from their mistakes. The qualitative feedback that students offered regarding the features of the tool that they liked best is also covered in the study. This paper contributes to the field of programming education by presenting a detailed gamified tool, incorporating automated evaluation and gamification in a web-based platform.

In the realm of programming education, enhancing student commitment is pivotal for both academic success and practical application. Traditional methodologies often fall short in catering to the evolving needs of today’s learners. Addressing this gap, this paper introduces a web-based gamified tool designed to transform the pedagogical approach in programming courses. The study’s theoretical contribution lies in its exploration of gamification as a strategy to augment student commitment, bridging the divide between conventional teaching and contemporary learning needs. From a practical standpoint, empirical data encompassing performance metrics, dropout rates, and student feedback demonstrate the tool’s significant positive impact. Notably, students exhibited improved pass rates and reduced dropout tendencies, underscoring the tool’s practical efficacy. Furthermore, feedback highlighted the tool’s usability and the enhanced satisfaction derived from its engagement. Aligned with the existing literature, the findings advocate for the broader adoption of gamification in programming education. This research substantiates the potential of gamification as both a theoretical model and a practical strategy, enriching the discourse on modern programming education.

The precise placement of antennas is essential to ensure effective coverage, service quality, and network capacity in wireless communications, particularly given the exponential growth of mobile connectivity. The antenna positioning problem (APP) has evolved from theoretical approaches to practical solutions employing advanced algorithms, such as evolutionary algorithms. This study focuses on developing innovative web tools harnessing genetic algorithms to optimize antenna positioning, starting from propagation loss calculations. To achieve this, seven empirical models were reviewed and integrated into an antenna positioning web tool. Results demonstrate that, with minimal configuration and careful model selection, a detailed analysis of antenna positioning in any area is feasible. The tool was developed using Java 17 and TypeScript 5.1.6, utilizing the JMetal framework to apply genetic algorithms, and features a React-based web interface facilitating application integration. For future research, consideration is given to implementing a server capable of analyzing the environment based on specific area selection, thereby enhancing the precision and objectivity of antenna positioning analysis.

This work presents the mathematical definition and programming considerations of an efficient geometric algorithm used to add roofs to polyhedral 3D building models obtained from OpenStreetMap. The algorithm covers numerous roof shapes, including some well-defined shapes that lack an explicit reconstruction theory. These shapes include gabled, hipped, pyramidal, skillion, half-hipped, gambrel, and mansard. The input data for the developed code consist of latitude and longitude coordinates defining the target area. Geospatial data necessary for the algorithm are obtained through a request to the overpass-turbo service. The findings showcase outstanding performance for buildings with straightforward footprints, but they have limitations for the ones with intricate footprints. In future work, further refinement is necessary to solve the mentioned limitation.

The educational value of artificial intelligence (AI) depends on technological capabilities and human–AI interaction. This is particularly relevant in interpreter training, which involves high cognitive demands, contextual decisions, and time-sensitive activities. This study examines how Human-Centred Artificial Intelligence (HCAI) principles can guide AI-assisted educational environments for interpreter training. Using Design Science Research (DSR), an environment integrating AI capabilities across the training cycle was developed and evaluated. Drawing on literature concerning Computer-Assisted Interpreting, HCAI, human–AI interaction, technology acceptance, and interpreting cognition, five design principles were derived: learner agency and human control, transparency and calibrated trust, protection of cognitive resources, contextualised assistance, and AI as a support for learning. A technology acceptance study involving 42 professional interpreters provided contextual evidence on technology use, adoption barriers, and professional practice, informing the specification of these principles. The principles were operationalised in the artefact, and its interaction design was examined through formative heuristic evaluation. Professional evidence therefore informs the design without establishing learner-centred validation or educational effectiveness. The contribution is a human-centred, professionally informed framework linking literature, contextual evidence, design requirements, and implementation to align AI capabilities with educational objectives and the cognitive and interactional demands of interpreter training through explicit and traceable design decisions.

The increasing integration of artificial intelligence (AI) into education creates opportunities to support learning, but its educational value depends not only on technological capabilities but also on human–AI interaction. This issue is particularly relevant in interpreter training, where learning involves high cognitive demands, contextual decision-making, and time-sensitive activities. This study investigates how Human-Centred Artificial Intelligence (HCAI) principles can guide the design of AI-assisted educational environments for interpreter training. A Design Science Research (DSR) methodology was adopted to develop and evaluate an AI-assisted learning environment integrating AI capabilities across the interpreter training cycle. Drawing on literature on Computer-Assisted Interpreting, HCAI, human–AI interaction, technology acceptance, and interpreting cognition, five design principles were derived: learner agency and human control, transparency and calibrated trust, protection of cognitive resources, contextualised assistance, and AI as a support for learning. These principles were operationalised in the artefact and evaluated through heuristic evaluation and a technology acceptance study involving 42 professional interpreters. Results show that perceived usefulness alone is insufficient to explain technology acceptance, with trust, reliability, ease of use, latency, workflow integration, and cognitive demands also shaping adoption. The study contributes a human-centred framework for aligning AI capabilities with learners’ needs and educational objectives.