Search Results (452)

The rapid emergence of generative AI tools is transforming software development. Consequently, software engineering education must adapt to ensure that students not only learn traditional development methods but also understand how to use these new technologies effectively and responsibly. In particular, project-based courses provide an effective setting in which to explore and evaluate the integration of AI assistance into real-world development practices. This paper presents our approach and a user study conducted in the context of a university programming project in which students collaboratively developed computer games. The study investigates how participants used generative AI tools across different phases of the software development process, identifies the tasks for which these tools were perceived as most useful, and analyzes the challenges students encountered. Building on these insights, we further examine a repository-aware, locally deployed large language model (LLM) assistant designed to provide project-contextualized support. The system employs retrieval-augmented generation (RAG) to ground its responses in relevant documentation and source code, thereby enabling a qualitative analysis of model behavior, parameter sensitivity, and common failure modes. These findings deepen our understanding of context-aware AI support in educational software projects and inform the future integration of AI-based assistance into software engineering curricula. Full article

Petri nets represent a highly versatile mathematical formalism for modeling discrete event and hybrid systems. For the development of modern complex production processes for Industry 4.0, integrating these formal models with industrial communication standards is an appropriate and effective option. The main aim of the proposed article is to design a new web-based software tool for the modeling, simulation, and control of mechatronic systems with OPC Unified Architecture support. To accomplish this task, an original software solution called PetriLink is proposed. This platform leverages an intuitive graphical interface and significantly expands the formalism by combining hybrid Petri nets with Colored Petri Nets (CPN) data extensions and a reactive OPC UA subscription model. These new features greatly expand the area of systems that can be modeled and controlled, bridging the gap between theoretical academic tools and practical industrial automation. Furthermore, the structural flexibility of the implemented Petri net models enables the explicit representation of symmetric cyber-physical architectures, as well as the design of asymmetric, event-driven control strategies (e.g., using inhibitor and reset arcs) for enhanced system robustness. The platform was evaluated on a reference net of 5000 places and 2500 transitions, where an incremental dirty-flag evaluation mechanism keeps the per-step engine cost below 1 ms for sparse industrial markings and at about 350 µs for a moderate workload of one hundred concurrent tokens, yielding a speed-up of up to roughly three orders of magnitude over naive full re-evaluation and confirming consistent soft real-time behavior on commodity hardware. Offering a graphical environment for the design of discrete event and hybrid system control algorithms, it can be used for education, research and practice in cyber-physical systems (Industry 4.0). Full article

Hepatobiliary surgery is a technically complex subspecialty within general surgery, which requires a comprehensive understanding of complex liver and liver tumour anatomy. The current body of literature highlights the use of three-dimensional-printed liver models (3DPLMs) reconstructed from medical imaging datasets may improve clinician comprehension of patient-specific liver anatomy thus creating a useful tool for hepatobiliary surgical planning and clinician training. The purpose of this systematic review was to examine the clinical utility and feasibility of 3DPLMs in hepatobiliary surgical planning and clinical education and investigate whether these applications influence patient outcomes. Studies were retrieved from three electronic databases (ProQuest, PubMed and Scopus) according to predetermined eligibility criteria. In total, 25 eligible articles were identified, including 18 original research articles and seven case reports. An inductive content analysis approach suitable for heterogeneous bodies of literature was used to synthesise key concepts in this review. There are significant case report and descriptive evidence to support the use of 3DPLMs in clinical education, preoperative planning and intraoperative guidance of patient liver and tumour anatomy to improve hepatobiliary surgical decision making. The studies presented display a large variance in cost and times necessary for the production of 3DPLMs, as studies did not include the software, equipment and full expense of materials used. Additionally, studies concentrated on different aspects of the 3DPLMs production process making them not comparable. This review demonstrates the potential value of 3DPLMs in clinical education, preoperative planning and intraoperative guidance in hepatobiliary anatomy and surgery. Future studies, in particular, randomised controlled trials and experimental research are required to investigate the relationship between 3DPLMs and clinical education and surgical planning outcomes. Full article

The increasing use of wireless personal networks in educational institutions has created significant challenges in ensuring network security and the reliable testing of communication infrastructure. The selection of appropriate software tools for network security testing is a complex decision-making problem due to multiple software quality criteria and operational requirements. This paper proposes a multi-criteria model for evaluating approaches to wireless personal network security testing in educational institutions through the analysis of representative software tools. The evaluation framework is based on the ISO/IEC 25010 software quality criteria: reliability, functional suitability, interoperability, performance efficiency and scalability, compatibility and maintainability. Five widely used tools (Nmap, OpenVAS, Nessus, Wireshark and Wazuh) were analyzed using a structured multi-criteria approach. Criteria weights were determined using the PIPRECIA-S method, while the ranking was verified using the TOPSIS method. The results show that Wazuh achieved the highest overall score (0.3051), followed by Wireshark (0.2315) and Nessus (0.1954), while OpenVAS (0.1443) and Nmap (0.1225) achieved lower ranks. The stability and reliability of the model were confirmed by sensitivity analysis, Pareto analysis, Spearman’s rank correlation and scenario analysis. The model provides a reliable decision-support framework for selecting network security testing approaches in educational and similar organizational environments. Full article

The emergence of artificial intelligence (AI) tools, particularly generative models, in the last five years has fundamentally transformed the framework and methodologies of learning in higher education. Students are integrating AI for producing new ideas, assisted and personalized search, academic writing, advanced data analysis, and personalized learning. For this reason, an update of the theoretical and conceptual framework regarding the adoption of technologies in the educational environment is required. Based on traditional Technology Acceptance Model/Unified Theory of Acceptance and Use of Technology (TAM/UTAUT) models, we propose a new Partial Least Squares Structural Equation Modeling (PLS-SEM) model developed for the context of AI in higher education. The novelty of the model lies in the integration of the mediating relationship through trust (trust in AI outputs, TAIO) between perceived academic integrity risk (PAIR) and behavioral intention to use (BI), while anchoring perceived learning utility (PUL) and perceived effort expectancy (PEE) in AI literacy-specific self-efficacy (AILSE). The model is tested using a sample of 339 higher education students from economics and computer science specializations and validated using the R environment and the SEMinR package as specific software tools. Our proposed research hypotheses consider six reflective latent constructs and a mediating relationship, which we analyze using validated PLS-SEM techniques. All items included in the model constructs are formulated for use in university educational contexts and are adapted to specific AI tools for learning in the university environment. Full article

Elevators exemplify mechatronics by integrating mechanical, electrical, and software systems. This paper discusses a four-story tabletop elevator model developed to demonstrate mechatronics and automation concepts in engineering education. The system utilized an Arduino MEGA microcontroller, 3D-printed components, an integrated servo motor, and standard electronics to replicate commercial elevator logic. The physical design features a ball screw linear actuator for vertical motion. It replicates dual-door systems with one door on the moving car and fixed doors at each floor that open simultaneously upon arrival. Development included designing the physical model, prototyping control algorithms, and integrating hardware and software. The model successfully demonstrated key functions: automatic dual-door operation, safety interlocks, smooth inter-floor motion, responsive floor-selection buttons with LED feedback, and efficient routing algorithms prioritizing requests based on current direction and location. Performance testing confirmed that the model accurately replicates modern elevator behavior and serves as an effective educational tool. Full article

Recent advances in large language models (LLMs) have enabled new forms of software creation through natural-language interaction. However, many AI-assisted coding tools continue to assume familiarity with development environments, programming workflows, and technical conventions, which may limit accessibility for early-stage learners and communities historically underrepresented in digital participation. This challenge is particularly relevant in Aotearoa New Zealand, where Māori and Pacific peoples remain underrepresented across STEM and technology pathways. This paper introduces TechTahi, a browser-based, syntax-free AI-assisted platform designed to support low-barrier digital creation through natural-language prompts and immediate in-browser previews. The study had two aims: to describe the design rationale and workflow of TechTahi and to explore early learner perceptions following initial use of the platform. An exploratory pilot design was employed. Five participants completed a post-use survey after hands-on interaction with TechTahi. Responses were analysed descriptively, with open-ended feedback reviewed for recurring themes. Findings suggested generally positive perceptions of accessibility and ease of use, particularly the ability to create working applications without prior coding knowledge. Participants also identified opportunities for culturally relevant features, including language support and locally meaningful design elements, alongside areas for improvement such as clearer onboarding guidance and reduced information density. These preliminary findings suggest that syntax-free, culturally responsive AI creation tools may offer promising pathways for widening participation in digital learning. Further research with larger and more diverse samples is needed to evaluate longer-term educational impact. Full article

Large language models (LLMs) are increasingly integrated into interactive systems across domains such as software development, robotics, and education. As these systems evolve from simple chat interfaces to autonomous, tool-using agents, the design of human–LLM interaction becomes critical. This paper presents a structured review of interaction with LLM-based systems, focusing on how prompting and interaction design mediate system behaviour and autonomy in practice. We analysed 87 studies from 2021–2025, identifying key interaction mechanisms and application-specific challenges. Based on this synthesis, we propose a two-dimensional taxonomy that classifies systems by interaction mechanism (conversational exploration, task-oriented assistance, tool-mediated interaction, and agentic workflows) and level of autonomy (advisory systems, guided execution, delegated execution, and high-autonomy execution). The taxonomy is supported by decision rules, worked examples, and a human-centred lens, emphasizing user control, transparency, error handling, and learning. Our review highlights a shift from single-turn prompting to structured multi-step workflows and the need for evaluation that considers both process and outcomes, particularly in safety-critical settings. This work provides a framework for analysing, comparing, and informing the design of human-centred interaction with LLM-based systems. Full article

This paper presents a novel platform for automated self-paced learning in Computer-Aided Design (CAD) courses within engineering education. The platform fully automates the entire learning cycle, including exercise generation, submission, scheduling, test design, and grading. The central hypothesis posits that complete automation reduces repetitive tasks for instructors, allowing them to dedicate more time to individualized student support. The system also provides key advantages: it generates unique exercises for each student to prevent plagiarism while maintaining comparable complexity; it delivers instantaneous grading and feedback to enhance motivation; and it enables students to work with almost any CAD software, as the evaluation relies on physical properties rather than commercial tools. After several years of successive testing and refinement, the tool can generate and accurately grade frequent activities in large student cohorts, providing abundant data points. Their statistical analysis, via multiple approaches, confirms that the system reliably produces individualized exercises, reduces grading errors, and offers prompt, consistent feedback, thereby supporting a more efficient and engaging learning process. Full article

The increasing adoption of Large Language Models (LLMs) in software development has enabled automatic code generation from natural language, yet the influence of communicative factors such as prompt tone remains underexplored. This work introduces PromptTone, a controlled dataset designed to investigate how variations in prompt politeness affect LLM-based code generation in web development. The dataset is constructed through a structured experimental design combining three variables: programming paradigm (Vue.js Composition API vs. Options API), LLM provider (GPT, Claude, Gemini), and prompt tone (impolite, neutral, polite), resulting in 396 generated components across 22 implementations. Data were collected in an educational setting under a single-prompt constraint to capture first-shot model behavior, and are provided in both hierarchical and CSV formats, including prompts, generated code, and error annotations. Preliminary analysis reveals that prompt tone influences output characteristics such as verbosity, with model-specific patterns: for instance, some models exhibit increased output length with more polite prompts, while others remain stable. Differences also emerge across programming paradigms, suggesting an interaction between tone and code structure. These findings highlight that LLMs are sensitive not only to semantic content but also to pragmatic aspects of input. Overall, the dataset provides a novel benchmark for studying human–LLM interaction in code generation, supporting future research on prompt engineering, model evaluation, and socially-aware Artificial Intelligence (AI)-assisted development tools. Full article

Precision Livestock Farming (PLF) tools are increasingly used in dairy production, but their success depends on farmers’ perceptions, needs and investment capacity. This study explores the current use of digital technologies, satisfaction levels and future expectations among Italian dairy farmers. An online questionnaire with 19 questions collected 53 complete responses between May and November 2025. Most of the farms were free-stall Holstein dairy farms located in the Po Valley and managed by relatively young and well-educated farmers, many of whom had a background in animal production. The adoption of PLF tools was widespread: management software (73.6%), automated total mixed ration (TMR) preparation (66.0%), heat stress mitigation systems (62.3%) and collar sensors (52.8%) were the most adopted technologies. Satisfaction with current tools was high, although installation costs and poor system integration were consistently identified as major constraints. Farmers expressed clear priorities for future devices, particularly early diagnosis of health problems, calving, heat, lameness, and feeding and rumination functions. The results suggest that PLF in Italian dairy systems is moving from the adoption phase to that of consolidation. However, improvements in interoperability, affordability and farmer-centred design remain essential to support a wider and more equitable spread of the technology across the sector. Full article

Effective software engineering education today requires tools that adapt to individual learner proficiency and progress, while ensuring positive student engagement. Gamified platforms represent an effective approach to learning and maintaining motivation, but their efficacy depends on a robust underlying architecture. This systematic literature review analyzes state-of-the-art artificial intelligence (AI)-based adaptive architectures designed to support gamified learning tools, highlighting their architectural models (such as intelligent tutoring systems, multi-agent systems, and immersive virtual reality/augmented reality environments), adaptation mechanisms (including Generative AI and chatbots), and personalization strategies. A significant focus is placed on Process Mining and Learning Analytics as methodological approaches to organize learning paths and guide dynamic adaptation based on student behavior. The results of the selected studies demonstrate advantages such as increased engagement, longer-term participation, and personalized learning pace. However, challenges remain, such as common assessment criteria, integrating different technologies, and system scalability. The findings offer concrete insights for designing the next generation of effective gamified learning tools, based on data and software engineering processes. Full article

Nano-aerial vehicles have emerged as pivotal tools in modern robotics research, offering a safe and scalable means to validate complex algorithms in resource-constrained environments. This scoping review synthesizes the extensive body of work on the Crazyflie nano-quadcopter and evaluates its potential for drone application development in research and academia. The Crazyflie quadcopter has emerged as a leading open-source platform for education and research in aerial robotics due to its modularity and low cost. Despite its rapid evolution, there is currently no comprehensive synthesis mapping its diverse applications across hardware configurations and research domains. This evaluation systematically charts existing research on the Crazyflie platform, outlining its development, identifying relevant hardware and software configurations, categorizing major research topics, and identifying knowledge gaps. A systematic search was performed on three major databases, Scopus, Web of Science and Google Scholar, for studies published between 2015 and 2025. The results indicate a rapid growth in scientific production, an involved research community and very diverse thematic approaches. Expansion decks for the Crazyflie have been analyzed together with their relation to specific fields of research. While control systems remain the primary research theme, there is a significant shift toward artificial intelligence and swarm robotics. Full article

The challenge of software vulnerabilities persists globally, despite the widespread availability of advanced security tools and comprehensive developer guidelines. This issue is not the result of professional negligence, but rather the complex and non-intuitive nature of secure coding. This research takes on the massive data silos in the security industry by providing a comprehensive review of best practices drawn from 35 reputable academic and corporate sources. Authentication, cryptography, input validation, and deployment hardening are some of the key development domains into which these technologies are organized. We conduct a comprehensive analysis of each practice, elucidating the specific security issue it addresses, prevalent implementation patterns, and potential hazards, in addition to serving as a checklist. Simple precautions, like not using passwords that are hardcoded, and more involved methods, such correctly encoding output and configuring access controls effectively, are all part of the range of practices. We assert that despite the prevalent usage of tools like as static analyzers, numerous vulnerabilities persist due to developers’ insufficient training in integrating security considerations into their coding practices. This work aspires to serve as a comprehensive, organized resource that supplies developers with the necessary context and guidance to make informed, security-oriented decisions along the software development lifecycle. The aim is to develop a more extensive resource than those presently accessible, which can also assist educators or security teams during code instruction or evaluation. Full article

Research on ChatGPT GPT-4 and GPT-5 in higher education has focused on quantitative adoption models (intention to use and predictors) and fragmented effects (writing, performance, well-being, dependence, or ethics). However, this approach keeps the debate stuck in an outdated phase of debate about the tool’s acceptance, even though ChatGPT is part of the academic ecosystem. The objective of the study is to understand, from students’ voices, how the quality of academic interaction with ChatGPT is configured, and to identify patterns of decision-making, validation, ethical regulation, and communication (transparency/concealment) in university contexts. An interpretive qualitative approach was followed. A total of 418 university students participated, all of whom provided qualitative data through semi-structured virtual interviews. The data were analyzed using reflective thematic analysis in six phases, with the support of ATLAS.ti software for rooting and density calculations. The results revealed ten categories that structure the phenomenon (adoption, attitudes, writing, translation, performance, cross-cutting skills, integrity, well-being, disciplinary use, and institutional integration). A continuum was observed between high-quality interaction (verification, rewriting, appropriation, and responsible authorship) and low-quality interaction (cognitive delegation, overconfidence, dependence, and concealment). The quality of student interaction with ChatGPT requires critical, ethical, and institutional regulation to guide and legitimize the academic process. Full article