This study examines the effects of an immersive virtual reality scenario on participants' risk perception and flood-related self-efficacy as well as its ef-fects on the act of taking precautionary measures for floods. In the short term, a significant increase in risk perception and flood-related self-efficacy was observed following the intervention, which is likely explained by cogni-tive biases, such as the availability heuristic. Participants perceived the risk of flooding more realistically, supported by the increased availability of in-formation in their memory. The risk perception and self-efficacy measures did not decline significantly after a three-month interval with participants still perceiving an enhanced ability to cope with flood events. These results suggest that the VR scenario has a sustainable impact on both risk percep-tion and self-efficacy. Moreover, mixed effects were observed regarding spe-cific precautionary measures, indicating limitations in the data collection methodology. Overall, the study demonstrates the potential of VR as an ef-fective tool in disaster training and highlights the importance of immersive, gamified learning methods for disaster preparedness.

Training new employees on high-tech, often one of a kind, manufacturing systems is a challenging task to achieve, especially without impeding the regular operational processes, as this might bring the production to a halt. Offsite training often lacks hands-on interaction with the machines and can impart advanced concepts and workflows only in a limited way. If XR is employed during such offsite trainings, the learning experience can be improved. However, such scenarios are costly to build and often limited to only the training phase but not the actual operation of the machines in the manufacturing environment. Similar challenges are also present at engineering education facilities where students are frequently trained to operate manufacturing equipment.

Combining XR technologies, digital twins and bi-directional data access enables the use of XR technologies not just for training but also the actual control and monitoring of industry grade machines, both on-site and remote. The aim of this work therefore is to devise and build such a dual-use XR application and evaluate its cost-to-build and its impact on the operators’ training and handling of the machines. Based on user studies, the intention is to derive recommendations and guidelines for the setup and deployment of such dual-use XR systems in educational as well as industrial settings.

A novel manufacturing machine for polymer injection moulding was selected as an exemplary machine. For this machine, a comprehensive XR learning and operation environment was developed to aid during all steps of training, setup and operation, thus supporting the operator throughout all stages of interaction with the machine.

CONTEXT

The monitoring of particulate matter present in cities with high population density, or with industries close to Monitoring "particulate matter" (e.g. pollutants or toxins) is essential in cities with high population density or industries close to urban centres and is a necessary response to the increasing rates of diseases associated with air quality. In this context, and framed within the study of Wireless Sensor Networks (WSN), the idea of implementing a sensor network capable of constant monitoring of "particulate matter" arises, which, through the use of AI, can generate predictions of the behaviour of this material, to generate a work plan with the corresponding authorities.

PURPOSE OR GOAL

This research aims to propose a distributed architecture design for a WSN, where the efficient use of sensor node resources (energy, messages, computation, among others) of the “particulate matter” of the environment of study. A distributed leader selection algorithm will send the data from WSN to Edge Computing to process the collected data and then send the processed information to cloud computing.

APPROACH

A simulation using OMNET++ software will present two scenarios with different behavioural algorithms. In both cases, the following parameters will be measured: correct and error measurements, messages sent, messages skipped (penalty), energy consumed, and leader selection behaviour.

ACTUAL OR ANTICIPATED OUTCOMES

The system performance is expected to improve regarding the number of valid messages sent and the energy consumption of the nodes involved.

CONCLUSIONS/RECOMMENDATIONS/SUMMARY

By implementing the correct communication algorithm, we can extend the lifespan of nodes in the WSN, enabling the network to transmit a larger volume of data. This will significantly enhance AI training and empower us to take steps toward improving air quality.

This paper presents a modular framework for enhancing the digital product passport (DPP) by incorporating a detailed account of the carbon footprint throughout the production process. Using IoT sensor technology, the system gathers time-series data on resource consumption during production. A work-flow engine coordinates the creation of the DPP, allowing for dynamic control and documentation of each step’s environmental impact. The enriched DPP is publicly accessible, providing a transparent step-by-step record of resource use and CO2 emissions, which is available to consumers via web services and QR code use. The findings demonstrate the scalability and flexibility of this system, emphasizing its applicability across various industries, such as flow production or the construction sector. The presented approach enables detailed traceability of resource consumption, allowing manufacturers to monitor usage dynamical-ly and independently of production workflows. By establishing a real-time tracking mechanism, the system ensures that each production step’s resource demand is recorded, regardless of process variations.

The paper starts with an introduction and then reviews the state of the art on product passports, Asset Administration Shell (AAS), and Cyber-Physical Pro-duction Systems (CPPS), outlines the conceptual framework, and describes the implementation and demonstrator. The conclusion summarizes the findings and provides an outlook on future advancements.

This paper presents the concept and implementation of a Virtual Reality (VR) application designed to generate interest in mechanical engineering among pre-university students and engineering freshmen. The presented application com-bines physical demonstrators with virtual interactions to create an engaging hy-brid learning experience. The focus is on creating interest rather than technical depth while maintaining correct terminology, presentation, and function. Through two progressive game modes of increasing difficulty, fundamental en-gineering concepts are experienced, particularly three-panel views and technical drawings. Implemented using the Unity 3D engine for autonomous VR head-sets, the application features multilingual support and intuitive interactions. Ini-tial testing with over 200 participants demonstrates high engagement levels and positive feedback, particularly when isometric views supplement the three-panel views. The application serves as an accessible entry point for students to engage with technical drawings and spatial thinking, contributing to the grow-ing field of gamified engineering education.

This paper is the third in a series extending our previous work (anonymous, 2023; anonymous, 2024) on modeling heating system processes. It compares three neural network approaches for process identification: two physics-informed models and a traditional neural network-based model. Raissi et al. (2019) introduced Physics-Informed Neural Networks (PINNs), where physical laws are integrated into the neural network's optimization process. More recently, Natale et al. (2022) proposed Physically Consistent Neural Networks (PCNNs), which incorporate a parallel network structure to enforce consistency with physical laws. Building on the PCNN framework, we developed a novel physics-based neural network combined with a feedforward neural network (PBNN).

CONTEXT

Welding has long been critical in manufacturing, providing strong, durable joints across industries. Traditional inspection methods like x-ray and ultrasonic testing detect defects post-welding, resulting in time, cost, and resource waste. To overcome these limitations, real-time monitoring systems have emerged, integrating current and voltage sensors with AI, machine learning, and big data analysis. Our research aims to transform the welding industry by capturing high-speed data for immediate defect detection and prevention, enhancing both quality and efficiency.

PURPOSE OR GOAL

The goal of this research is to minimize welding defects and increase process efficiency. By integrating sensors, a data acquisition (DAQ) system, and AI/ML models, combined with big data analysis, we have developed a real-time defect identification and prediction system. The system monitors critical welding parameters like current and voltage at high sampling rates (4K-8K samples/second), allowing for immediate detection of anomalies like porosity or distortion. AI models continuously refine predictions by analyzing vast amounts of data, adjusting parameters dynamically to prevent defects. This approach reduces material waste, rework, and production costs, making it ideal for industries seeking high-quality output.

APPROACH

Our system collects data in real-time using sensors connected to a DAQ, which samples at up to 8K rates for precision. Data undergoes pre-analysis, where noise is filtered out, ensuring accuracy for real-time analysis. Advanced algorithms, powered by big data analytics, detect parameter deviations, adjusting settings to prevent defects. AI models, trained on historical data, identify patterns associated with defects, while ML algorithms improve accuracy with every session. A Mini PC handles data storage and processing in real-time, using LabVIEW for monitoring and Power BI for deeper analysis, enabling users to visualize trends and optimize the welding process.

ACTUAL OR ANTICIPATED OUTCOMES

The implementation of real-time monitoring, enhanced by big data analysis, has shown significant reductions in common weld defects such as cracks, porosity, and lack of fusion. High-speed data acquisition (2K, 4K, and 8K sample rates) ensures immediate response to parameter changes, preventing defects. AI and ML models provide predictive insights, identifying irregularities that could lead to defects and improving over time. This approach lowers rework, reduces waste, and boosts overall efficiency. The system is adaptable to future AI advancements and various welding methods, making it scalable for different industrial applications.

CONCLUSIONS/RECOMMENDATIONS/SUMMARY

To further enhance this system, we recommend automating sensor calibration, adding redundancy in data storage, and integrating IoT connectivity for remote monitoring and predictive maintenance. Overall, the real-time use of AI, ML, and big data analysis revolutionizes welding quality control, reducing costs, improving efficiency, and ensuring defect-free welds. As the industry continues to evolve toward automation and data-driven processes, this technology offers a scalable, future-proof solution.

This research presents a comprehensive framework for integrating digital twin technology with autonomous control in a scale-model backhoe, addressing challenges in precision, adaptability, and real-time synchronization. The proposed system combines advanced trajectory planning using the A* algorithm, Ackermann kinematics for vehicle motion, and forward kinematics modeling for a 3DOF robotic manipulator. These components are seamlessly integrated with a digital twin to enable accurate real-time simulation and validation of navigation and control algorithms.

The digital twin acts as a bridge between the physical system and virtual environment, utilizing Firebase for real-time communication and data synchronization. This ensures that sensor data, control commands, and simulations are aligned, enabling adaptability to changing environmental conditions and validating the robustness of the framework. The trajectory planning system demonstrated high accuracy, with error metrics quantified through simulations, while the manipulator model achieved precise positioning, validating its mathematical foundation.

This work contributes to the field of autonomous heavy machinery by presenting a scalable and modular system that aligns with the principles of Industry 5.0, emphasizing sustainability, efficiency, and human-machine collaboration. The proposed methodology not only validates the feasibility of autonomous control for scaled prototypes but also sets the foundation for expanding the framework to full-scale machinery. Future directions include improving adaptability to dynamic environments, integrating IoT and cloud systems for distributed control, and enhancing energy efficiency to align with global sustainability goals.

This paper presents the design and implementation of a full-stack web application for enhancing predictive maintenance and fleet performance monitoring within smart city initiatives. The application combines real-time data collection, predictive analytics, and a user-friendly interface, allowing fleet managers to optimize maintenance schedules and improve operational efficiency. Leveraging 5G technology for high-speed data transmission, the system enhances connectivity and facilitates seamless communication between fleets and infrastructure. The platform delivers actionable insights into fleet performance trends and maintenance requirements. This study addresses challenges in data extraction from diverse fleet types and outlines future integration plans, including IoT devices and virtual reality training modules for emergency response scenarios. The findings highlight the potential of advanced technologies to enhance fleet management practices, contributing to sustainable urban mobility and the advancement of smart cities.

This paper initially explores applying Digital Twin (DT) strategies to advanced technological training for engineering students while integrating the latest devel-opments in Industry 4.0 into research on Solid State Lighting (SSL) technologies. Modules based on AlGaN far-UVC LEDs will serve as the foundation for the development of new products aimed at photonic disinfection and disease preven-tion. The development of these modules requires a preliminary stage of basic re-search, which includes the design and fabrication of far-UVC LED crystals. Thermal simulation inputs are provided by an infrared camera that measures the p-contact temperature of the far-UVC LEDs in Japan. This data is then used to inform the simulation system in Chile, enabling precise thermal modeling. This study emphasizes the importance of individual LED performance, as it underpins the functionality of the final device. The following stage, currently under devel-opment, extends the DT to analyze the complete thermal performance of the final far-UVC LED module, focusing on heat generation and dissipation within the system. This approach supports the development of photonic disinfection pro-cesses, addressing the demand for efficient and reliable far-UVC LED modules for HealthTech applications. 11 mw light output power in a single far-UVC LED has been achieved at Riken, which paw way to the development of 500 mW far-UVC LED module using 50 pieces of LEDs for Healthcare (surgical theater). The ultimate goal is to further improve irradiance distribution, enhance optical ef-ficiency, and extend the LEDs' lifetime through optimized thermal management in healthcare environments.

Usability is an important factor for cross reality laboratories. However, after conducting a literature survey on the ACM Digital Library and IEEE Xplore, we were only able to identify 15 papers discussing the usability of cross reality labs, which in itself reveals a lack of focus on that topic. Further analysis on the papers showed room for improvement, e.g., most papers only used questionnaires, which do not allow for deep usability analyses. From the surveyed papers, we were able to identify a total of 26 factors which play an important role in usability for cross reality labs, categorised into technology, laboratory operation, and learning. Based on our results, we can conclude that usability research for cross reality labs is an under-researched area at the moment and would benefit from more focus.

This paper presents a booking system for federated cross reality systems realised in the CrossLab architecture. The system itself consists of four micro services communicating through the Advanced Message Queuing Protocol (AMQP). The system can handle both devices as well as device groups (where multiple similar devices are collected and the booking system can choose one of them). Federated bookings are handled based on eventual consistency, which means that the booking stays in a ‘pending’ state until all involved booking systems confirm the booking.

This article describes the development of an ultra-concurrent remote optical microscope laboratory that emulates the traditional experimental process. Its design encompasses all stages of the practical experimental experience, ensuring rigor in the technical aspects involved. The innovation lies in the creation of an interactive, adaptive, and scalable platform that provides an educational resource in biology. The laboratory was developed in four stages: experimental design, data recording and capture, web interface development, and assembly. The developed remote laboratory is an innovative educational resource in biology, covering all experimental procedures and adaptable to different levels of teaching and research, while promoting the development of sensorimotor skills through interactivity.

The study explores the usability and onboarding process of a Mixed-Reality (MR) application called PEARL, designed to prepare students for laboratory work. Originally developed for mobile Augmented Reality (mAR), PEARL was adapted for MR to offer a more immersive and intuitive experience through hand and gesture controls. Since many students lack experience with MR devices, a user-friendly onboarding system is essential. The study aims to redesign PEARL’s user interface and onboarding experience, evaluating how intuitive interaction elements impact usability. First, a literature review will identify existing usability guidelines for MR applications, which will guide the redesign of the interface. This new version will then be tested with students through a user study. Feedback will be collected via an online survey to assess the onboarding and user experience, and the findings will be used to refine the design further. The expected outcome is an improved onboarding process and interface, making PEARL accessible even for MR novices, enhancing their ability to interact with 3D objects in a real-world setting. Ultimately, the study aims to provide best practices for developing intuitive MR interfaces and effective onboarding experiences, especially in educational contexts.

CONTEXT:

The role of Intelligent Tutoring Systems (ITS) in engineering education has gained relevance because of their ability to personalize learning [1], [2], [3], [4], [5], [6], [7]. Despite these theoretical benefits, the practical validation of these systems in real educational environments remains limited. This study examined the implementation and validation of the ITS AIDET (Adaptive Intelligent Engineering Digital Tutor) in an electronics course at a university in the Dominican Republic. 

PURPOSE OR GOAL :

The objective of this study was to validate the effectiveness of the AIDET system by comparing the performance of two groups of students: an experimental group that used the AIDET and a control group that received traditional instruction from a teacher. The purpose was to measure the impact of the ITS on student learning, specifically, in terms of participation, retention, and mastery of complex electronics concepts. The questions that guided the study were as follows:

1. Can the implementation of an ITS to manage the educational process benefit students’ academic training compared with traditional teaching methods?

2. Does AIDET meet the teaching standards required for an engineering course at a university or educational center?

APPROACH:

The study involved 22 students divided into two groups: an experimental group of 11 students who used the ITS AIDET and a control group of 11 students who received traditional instruction. Both groups were assessed simultaneously and underwent two midterm exams. The control group developed course activities and assignments proposed by the professor, whereas the experimental group developed the activities proposed by the AIDET. Both the control and experimental groups took the two final exams in two periods, which were developed by a human professor. The 1st period exam was worth 15/45 and the 2nd period exam was worth 15/55 to obtain a final grade (weighted between 0 and 100 points); the rest of the grade corresponded to the activities carried out individually by each group. In the control group, the average final grade was 59.22 and in the experimental group the average final grade was slightly higher, 60.45. In addition, AIDET proved to be effective in providing personalized feedback based on the VARK learning style and in improving the retention of key course concepts, compared to traditional instruction. The AIDET system was developed in a continuous research process that integrates advanced technologies such as Large Scale Language Models (LLM) and Natural Language Processing (NLP), providing immediate feedback and personalized content [7]. Currently, AIDET has its own Learning Management System (LMS) and is working on the development of an API that allows its integration into other traditional LMS, as well as its scalability to other fields of knowledge.

ACTUAL OR ANTICIPATED OUTCOMES:

Preliminary results show that, although both groups had a similar final average performance, the experimental group had a slightly higher performance in the final grade. Even though the students in the experimental group started with a lower average in the first period, their performance improved significantly in the second period, indicating the positive impact of ITS on their learning.

Leakage is the main cause of energy loss in pneumatic systems. In practice, no pneumatic system is perfectly sealed, and some level of leakage is inevitable. In well-maintained industrial facilities, the level of leakage considered acceptable is usually 2% to 10% of total air consumption. These small leaks are often tolerated because the cost of repairing them may outweigh the potential savings. However, in poorly maintained systems, leakage rates can increase significantly. Therefore, implementing active leak detection and a planned remediation program, along with timely repairs, can reduce leakage to less than 10% of total compressed air production.

The main objective of this paper is to develop a system for the detection of compressed air leaks and assessment the associated energy losses. This was accomplished by designing and testing a laboratory prototype under controlled conditions. The current practical application of this system enables quick and straightforward measurements at key points within the system, providing medium reliable detection of leak locations and estimates of associated losses. Future research is planned to test the prototype device in real industrial environments, where multiple sources of noise are present. Following these tests, the final version of the device will be developed, making it suitable for use in industrial production settings.

The use of SMART complexes with artificial intelligence in vocational education institutions in Ukraine improves learning quality and flexibility, addressing challenges of wartime conditions and advancing Vocational Education 4.0. This study evaluates the effectiveness of SMART complexes in optimizing vocational training by comparing remote education in the central-eastern region, often disrupted by air-raid alerts and power outages, with in-person learning in the western region. Surveys and learning outcome analysis highlight the advantages of individualized learning enabled by AI technologies.

Using a mixed-methods approach, the research integrates qualitative and quantitative data to assess the impact of different learning models on student performance. Findings show that SMART complexes facilitate personalized learning, allowing students to progress at their own pace while adapting task complexity to their abilities. Remote education in the central-eastern region ensures continuity, whereas the fixed pace of in-person learning in the western region restricts adaptability.

The study concludes that SMART complexes enhance vocational education by fostering personalized approaches, developing digital skills, and boosting motivation. Their integration effectively adapts educational content to individual needs and labor market demands, providing a robust solution for optimizing education in contemporary conditions.

The article examines the use of SMART complexes with artificial intelligence elements in the vocational education of Ukrainian students, which represents a significant step towards improving educational quality amid modern challenges, particularly during wartime. These complexes facilitate the advancement of Vocational Education 4.0 by allowing flexible adjustment of the learning pace and content to meet students’ individual needs. The study focuses on comparing the effectiveness of flexible and fixed learning paces in distance and in-person educational models. A key aspect is analyzing the use of distance learning in vocational institutions in central-eastern Ukraine, where frequent air alerts and power outages complicate in-person learning, while fixed-paced in-person learning predominates in western regions, often disregarding students’ individualized educational needs.

The research methodology adopts a mixed approach, combining qualitative and quantitative data collection and analysis. Surveys conducted among students and teachers assessed their experience with distance and in-person learning using SMART complexes. The study analyzed students' academic results, satisfaction with the educational process, and the flexibility provided by SMART complexes through artificial intelligence elements that adapt learning to individual educational needs. SMART complexes include tools for interactive content, personalized assignments, and automated assessment, enhancing student motivation and fostering the development of digital skills.

The findings highlight the advantages of SMART complexes for optimizing learning pace. In regions where in-person education is constrained by external factors, distance learning with integrated SMART complexes offers the only viable option for maintaining stable education. In contrast, western regions, with predominantly in-person models, tend to have less flexible approaches. Using SMART complexes enables vocational education institutions to adapt to labor market demands, develop flexible learning pathways, and significantly improve learning outcomes. Analytical tools within SMART complexes allow rapid responses to educational needs, guiding students toward relevant competencies.

This study examines the impact of a teacher’s disability on learning and teaching effectiveness in a problem-based remote laboratory setting. It seeks to determine if the teacher’s disability influences students' perceived learning gains, the efficiency of the teaching process, and whether the remote lab format makes the course more appealing.The research was conducted in a master's course called “Mathematical and Numerical Methods for Flow and Transport Processes in Chemical Engineering,” where students worked in groups using computer simulations. The course ran over a week-long period, with remote lab sessions lasting three to four hours, followed by an exam. Surveys were administered at the end of the course in 2023 and 2024. Students were asked if they noticed the teacher’s disability, whether it impacted their learning, and if it required extra time to reach learning goals. They were not informed about the teacher's visual impairment before starting the course.Survey results revealed that students did not perceive any negative impact on their learning gains due to the teacher’s disability, nor did they need extra time to achieve their learning objectives. A key difference between the 2023 and 2024 results was that students in 2024 were less aware of the teacher's disability. This change is attributed to the teacher’s improved preparation and experience, which made the teaching more effective and efficient.In conclusion, the study shows that a teacher’s disability does not negatively affect students’ learning outcomes if the teacher is well-prepared. Moreover, the remote lab format was generally well-received by students, even those accustomed to in-person courses.

This study, conducted within the Chemistry Department of the Common Basic Cycle (CBC) at the University of Buenos Aires (UBA), examines student perceptions of Remote Laboratories (RL). The CBC serves over 60,000 students annually, many of whom are enrolled in biomedical science programs, such as medicine, pharmacy, and dentistry, and must complete Chemistry among their six required courses. With class sizes averaging 100 students, this research responds to the challenges posed by large, heterogeneous student groups through the implementation of RL technologies. These laboratories allow students and professors to remotely conduct real experimental activities, offering flexibility in time and place, and enabling repeated practice sessions.

The primary objective of the study was to analyze student perceptions regarding the use of RLs in the Chemistry course, focusing on usability, self-perception of learning, and overall satisfaction. By exploring the impact of RLs, the research sought to determine how these tools can enhance the educational experience in a context characterized by large class sizes and varied student backgrounds.

This descriptive, quantitative study involved 225 Chemistry students from the CBC at UBA during the first semester of 2024. The participants completed a questionnaire, which used a 4-point Likert scale ranging from “strongly disagree” to “strongly agree,” to measure their perceptions of learning, satisfaction, and usability related to RLs. The questionnaire, adapted from previous studies (Idoyaga et al., 2020; Capuya et al., 2022), included six statements addressing self-perception of learning, four statements about satisfaction, and five statements assessing usability, each accompanied by control statements. The data were analyzed through absolute and relative frequencies and measures of central tendency using IBM SPSS Statistics, version 29. All participants’ personal information was anonymized to ensure confidentiality.

The results of the study revealed that RLs were well-received by students. Specifically, 84% of participants found RLs helpful in improving their understanding of key course concepts, while 91% felt that the experience brought them closer to experimental work. Furthermore, 74% expressed interest in the variability of experimental results, and 50% found the RL experience beneficial for engaging in other departmental activities. In terms of satisfaction, 74% of respondents indicated that RLs met their expectations. Usability ratings were similarly positive, with 80% of students finding RLs easy to use, and 70% reporting that they were able to complete tasks without difficulty.

The preliminary analysis suggests that RLs contribute significantly to students’ understanding of the material. However, the findings also indicate a need for further refinement to ensure that RLs integrate seamlessly with other teaching materials. Future studies should focus on improving the cohesion between RLs and other pedagogical elements to maximize their educational potential.

Real experiments imply measurements taken with real test & measurement instruments. This paper builds upon previous work comparing the VISIR remote laboratory against electronic simulators, highlighting the differences between real (remote) measurements and values obtained from mathematical (computer-based) models. It compares measurements taken at the VISIR system and the new Hive system, recently introduced by LabsLand as an updated version of VISIR. It also comprises a simple, direct comparison of the user interfaces of both systems.

This paper presents a novel approach to creating shareable, peer-to-peer remote labs using solely browser technologies, eliminating the need for dedicated servers. Modern web browsers, equipped with APIs for hardware interaction and real-time peer-to-peer capabilities, enable educators to construct labs that operate entirely through decentralized connectivity. Utilizing the Edrys-Lite application, a lightweight reimplementation of the original Edrys classroom system, remote labs can be easily configured using human-readable formats like JSON or YAML and shared through simple URLs. This serverless model leverages WebRTC and WebTorrent for user connectivity, while Yjs CRDTs ensure real-time synchronization and conflict resolution. The result is a scalable, cost-effective solution for running open educational resources as remote labs, providing accessible, reusable, and modifiable labs stored openly on GitHub. Testing has demonstrated successful application across various fields, from Arduino programming to large-scale chemical simulations. This approach empowers educators to deploy lab environments swiftly, enhancing accessibility and reducing setup complexity.

Engineering education in electronics faces significant challenges, particularly in practical environments. Within this context, RLC circuits are essential for the design and analysis of electrical systems. However, the limited access to conventional laboratories has led to the search for solutions based on remote laboratories. VISIR remote laboratory, available at our university, allows for experiments with RLC circuits. Nonetheless, the extensive use of coil-based circuits caused damage to its equipment, with replacements proving highly expensive. To address this issue, a final-year project proposed the implementation of an alternative laboratory with similar functionality. The aim of this project is to develop a remote laboratory for experimenting with RLC circuits in the "Circuit Theory I" course of the Electronic Engineering program. Its capabilities are designed to be comparable to those of VISIR, offering students accessible experimentation to complement theoretical knowledge. Additionally, the project seeks to evaluate the effectiveness of remote laboratories in strengthening students' understanding of concepts such as resonance, impedance, and circuit analysis, while promoting self-learning and experimentation in a safe environment. Students access the laboratory through a web interface without the need to install additional software. They can assemble circuits by selecting components and configuring input parameters, such as signal type, frequency, and amplitude, while observing responses through an oscilloscope. The system is controlled by a Raspberry Pi 400, which selects the circuit, configures the signal generator, and captures measurements from a digital oscilloscope. In this initial phase, the development of a functional first version of the laboratory is expected.

Over the years, the use of SCADA (Supervisory Control and Data Acquisition) systems has gained great relevance

in the industrial and energy sector. Technological evolution has facilitated the implementation of these systems,

which are essential for monitoring, control and storage of historical information. In the educational field, the

understanding of SCADA systems becomes fundamental for the training of engineers in the energy sector, given

their increasing use in the industry. The present study focuses on the implementation of an IEC 61850 compliant

SCADA system in the Electrical Power Systems (SEP) laboratory of the Universidad de Concepción, contributing

to improve the practical training of engineering students.

Education has been heavily affected by the introduction of Generative Artificial Intelligence (GAI), such as ChatGPT and Gemini. Due to its recency, it is important to understand the public perspective on GAI and to assess its benefits and drawbacks on education. A number of studies have been conducted, but they mostly rely on academic literature reviews. While such studies are reliable, they might be less timely than those relying on social media or news. The findings might not be holistic. Therefore, we employ three different content sources to capture the holistic perspective of GAI: social media (X/Twitter), news (Google News), and academic literature (Google Scholar). A total of 3932 contents are analysed via sentiment and thematic analyses. We found that GAI has promising benefits like improved work productivity and access to vast amounts of information. The use might be encouraged in education. However, some concerns have been raised, including misuse and over-reliance. Penalty and identification mechanisms might be needed. It is also expected to prohibit the use on some assessments to prevent over-reliance. Educational stakeholders could integrate GAI in their learning environments with clear policies.

The modern development of artificial intelligence (AI) resembles scenes from science fiction movies, which are now becoming a reality. Each of us has the ability to generate informational content with just a word, symbol, or even sound, and by enhancing text prompts with images, we can achieve the desired results more effectively. This paper explores the generative capabilities of AI and their application in optimizing project actions for the training of engineer-designers. The primary focus of the research is the analysis of key technologies, such as neural networks and deep learning algorithms, which underlie generative models like GPT, DALL·E, and others.

The study highlights the use of generative AI in fields such as creative industries, design, web development, and programming, showcasing its ability to improve the processes of creating new ideas and products. A separate section discusses the optimization of business processes through generative AI, where models can automatically generate new scenarios, projects, or even proposals to enhance productivity. Equally important is the review of AI-based optimization, which can contribute to reducing costs and development time while improving the quality of the final product.

The paper also examines the challenges faced by generative AI, including ethical concerns and questions of responsibility. Overall, the research demonstrates the significant potential of generative AI as a powerful tool for optimizing complex systems and processes, offering new opportunities for innovation and resource savings, and becoming an effective instrument for modern professionals.

CONTEXT

The growing popularization of artificial intelligence (AI) has generated debate about its impact on higher education. AI tools such as ChatGPT and Bard offer new possibilities for teaching and learning, but they also raise concerns regarding technology dependency, lack of human interaction, and the potential for plagiarism.

This study will analyze the use of AI tools in higher education, exploring students' perceptions about their benefits and risks, their purposes for use and their influence on the teaching-learning process. For this study, a questionnaire was used with a sample of higher education students, which collected information about the AI tools they use, their frequency of use, their levels of satisfaction and their expectations regarding the future.

PURPOSE OR GOAL

This study aims to analyze the use of AI tools in higher education, based on student perceptions, with the following intents: Identifying the most popular AI tools among students; Understanding the primary purposes of using AI tools; Assessing the benefits and risks students perceive about using AI tools; Analyzing the relationship between the use of AI tools and academic performance; Investigate students’ expectations regarding the future of AI in teaching.

APPROACH

An online questionnaire was used to collect data and was applied to a group of higher education students. The questionnaire covered topics such as the frequency of use of AI tools, their intended use, perceived benefits and risks, satisfaction levels and expectations regarding the future of AI. The collected data was analyzed using quantitative and qualitative methods. Quantitative analysis allowed the identification of patterns and trends in using AI tools. Qualitative analysis allowed the exploration of students' perceptions and experiences.

ACTUAL OR ANTICIPATED OUTCOMES

The results of the analysis of the collected data revealed some key points. Firstly, the most popular AI tools among students are ChatGPT, Gemini, and Bard. Students said that the main purposes of using AI tools are searching for information, clarifying doubts, carrying out academic work, creating texts, translating, generating ideas, solving programming problems, and entertaining. The benefits most frequently cited by students are ease of research, speed in obtaining information, ability to solve problems and time savings. They also highlighted some risks. The risks most frequently cited by students were the lack of human interaction, technological dependence, plagiarism and limited creativity. However, students express positive expectations regarding the future of AI in teaching.

CONCLUSIONS/RECOMMENDATIONS

The results of this study suggest that AI tools are already being used significantly in higher education. Students recognize the benefits of these tools but are also aware of their risks. The acceptance of AI in higher education is, in general, positive, but it is essential that the use of these tools be discussed and regulated in a responsible and ethical way.

Regulation of the use of AI tools in higher education must consider the need to guarantee the quality of learning, student autonomy, the ethics of using AI, and meaningful human interaction in the teaching-learning process.

Artificial Intelligence (AI) has been shown to perpetuate biases, leading to the marginalisation of minority groups. In the United States, anti-discrimination laws mandate affirmative measures and European legisla-tion, such as the European anti-discrimination law, the Race Directive and the Framework Convention for the Protection of National Minorities, pro-hibit discriminatory practices and encourages institutions to implement positive actions. Ensuring that AI Tools in Education (AITED) does not replicate discrimination is a legal requirement at both the European Union (EU) and in the US.

This paper examines approaches to developing affirmative tools in educa-tion, focusing on the European context. The research question is: How can an educational tool for coding be designed to promote equality, equity, and legality.

Initially, we review existing research indicating that factors such as sex, ethnicity, and race influence students’ learning progress. We then explore the possibility of creating an affirmative educational tool for teaching cod-ing fundamentals, concentrating on minority groups defined by sex, race/ethnicity, and religion, as recognised in European anti-discrimination Law. Educational institutions are legally obligated to address the needs of these groups, ensuring equal learning opportunities to prevent indirect dis-crimination.

Utilizing Political Economy Analysis (PEA) and Critical Race Theory (CRT), we propose a design approach for an educational tool offering three distinct learning variants. The first variant, the contextualized path, sup-ports learning within a humanistic framework. The second variant, the con-firming path, is designed to be sensitive and supportive. The third variant, the conventional path, serves as a traditional approach for comparative pur-poses. The tool development process involves engaging the target groups.

By designing an educational tool with varied learning paths, we aim to cre-ate a more affirmative, inclusive, equitable, and modern AITED that com-plies with European anti-discrimination Law and related regulations.

This study explores the integration of Artificial Intelligence

(AI) and remote experimentation in STEM education. AI-assisted instruction offers personalized feedback to foster critical thinking, while remote experimentation provides hands-on learning experience remotely. The research focuses on the combined use of AI-assisted instruction and remote experimentation in remote laboratories utilizing real hardware.

A mixed-methods approach will be used to assess the system’s impact

on student engagement, learning outcomes, and problem-solving abilities. Quantitative data, including task completion times, error rates, and

AI intervention frequency, will be paired with qualitative insights from

student surveys and interviews. The anticipated outcomes include improved student performance, reduced errors, and faster task completion,

alongside enhanced confidence and critical thinking. The findings will

highlight the potential of integrating AI and remote experimentation to

enhance STEM education and guide future system development.

This short article deals with the implementation and evaluation of the effectiveness of a STEM learning programme for women. The one-week programme consists of workshops on various topics in the field of new technologies, industry 4.0, sustainability or study orientation. One of the assessment methods was the Word Association Test (WAT). The test is used to assess students' knowledge before and after the intervention by means of a pre-test and a post-test. The number of terms associated with the nine key words increased by approximately 27% overall in the post-test. This indicates

positive changes in the knowledge structures. However, it can be observed that the rate of change for the individual keywords is between -3% and 76%, so that different variables are discussed for each keyword.

The 13 Fundamental Learning Objectives (FLOs) from 2002/2005 is the most cited taxonomy for Learning Objectives in the undergraduate engineering laboratory. However, as early as 2003 a revision was suggested. A previous analysis of the objectives using the SOLO-Taxonomy as framework showed significant shortcomings regarding incorrect language, comprehensibility as well as some FLOs only addressing surface learning. Based on both works, this contribution presents a revised version of the 13 FLOs in which overlapping sub-objectives were cut, complex wording and ambiguous terms and operators (verbs describing what students are supposed to do) were improved. Furthermore, every FLO was reformulated to facilitate deep learning. The new FLOs were validated communicatively with researches an to ensure comprehensibility and the continuity of the fundamental engineering content. Issues of reformulating the FLOs to fit the framework of the SOLO-Taxonomy are also discussed. An initial A communicative validation with student assistants in which they compared the two versions of the FLOs, suggested that the reformulated FLOs were clearer to understand, making it easier for students to assess what is expected and what their assessment will be based on. Thus, the reformulated FLOs can not only be directly employed in engineering laboratories but can also be used as a stepping stone with the SOLO-Taxonomy to formulate more specific learning objectives tailored to a certain laboratory.

This research investigates how secondary school students engage with robotics activities within their technology and engineering education programs. The study focuses on project-based experiential learning, involving design, construction, and programming of robotic systems. The sample includes four groups of 10th and 11th graders from two high schools: high-achieving students from a comprehensive school (School A) and newcomer students from a boarding school (School B).

The researchers aimed to identify and compare learning engagement (LE) features typical for each group, using the engagement structures methodology adapted from mathematics education. This methodology examines students’ immediate desires and behaviors during learning activities, identifying patterns of engagement or disengagement.

Observations were conducted over an academic year, and engagement structures were identified and quantified based on their frequency among students. Qualitative explanations for these behaviors were derived from post-course questionnaires.

The study found that engagement structures related to excitement and interest in activities were prominent across all groups. However, the desire to impress classmates was more common among students from School A than School B. Additionally, a sense of obligation to follow teacher instructions was more frequent among 10th graders than 11th graders in School A.

The research highlights that learning engagement in robotics varies with curricula, learning environments, and student backgrounds. The identified engagement structures can help educators better understand and foster student engagement in robotics education.

Next year’s one robust grow (around 17% per annum) of IoT sector is expected, this growth is fueled by an increase in connected assets and corresponding investments in AI and cybersecurity.

The sensor market has grown significantly in recent years. A primary factor contributing to this growth is the increasing integration of sensors in consumer electronics, industrial applications, and automotive. The future of innovation, technology, and industrial advancement is significantly influenced by engineering education.

Engineering schools, like “Transylvania” University with Centre for Valorization and Transfer of Competences CVTC, will continue to mold the next generation of engineers, greatly enhancing society and technology because CVTC recognized like sustaining collaboration between academia and industry. This paper promotes and sustains all these ideas by building and test new IoT flexible monitoring systems for students training and involvement in future research activities.

This work aims to show that learning science, mathematics, and technology in engineering is deeply interconnected and synergistic. The Integrative Learning theoretical framework is used to show that a teaching approach that encourages the understanding of this relationship, helps students appreciate the relevance of their studies and encourages them to engage in interdisciplinary projects that combine scientific experimentation, mathematical reasoning, and the use of technological tools. While mathematics provides the foundational skills necessary for understanding scientific concepts and offers tools for modeling, analyzing data, and interpreting results crucial in scientific research, science relies heavily on mathematical principles. Whether in physics, chemistry, or biology, quantitative methods, statistical and numerical analyses, and other mathematical tools are essential for designing experiments, making predictions, and validating hypotheses. On the other hand, technology serves as a bridge between engineering, science and mathematics. It allows for the practical application of mathematical concepts and scientific theories. Many technological advancements arise from scientific discoveries and mathematical modeling. In other words, it is shown that the learning of science, mathematics, and technology is interdependent, and by using an interdisciplinary and integrative approach to learning students' knowledge is enriched, equipping them with essential skills for the future. This interconnected approach fosters a holistic, deeper comprehension of the world and encourages innovative and critical thinking in students.

This study introduces a RAG-based intelligent tutoring system enhanced to process multimodal data for higher education, addressing the limitations of traditional text-based LLM tutoring tools. While the previous version of the system focused on text responses, feedback from other International Confer-ences has highlighted the need for integrating additional data types like images and videos to foster a richer and more interactive learning experience. The pro-posed system combines LangChain, Large Vision Language Models (LVLMs), and the BridgeTower embedding model, creating unified representations of text, image, and video content. Educational resources are stored in high-dimensional VectorStores, enabling efficient retrieval and contextually relevant responses to students’ diverse queries. By expanding the Intelligent Agent’s capabilities to interpret visual content, this system aims to improve engagement, comprehension, and retention of complex academic material. Early testing has shown encouraging results in text processing, and similar success is anticipated with multimodal integration. This research demonstrates the potential for RAG-based multimodal tutoring to significantly enhance learning in higher education and recommends continued development of these capabilities for broader educational applications.

There is a consensus that technology is shaping the immediate future on Higher Education. Providing a reference textbook for every agent involved in tertiary education, compiling solutions, examples, and experiences on the use of these technologies, becomes a need to give support to lecturers and students, to administrative personnel and people in charge of university degrees.

The use of technology in higher education, with the irruption of AI, LLMs, the Metaverse or gamification techniques, has generated an increasing interest in academicians and researchers. Although there are a lot of resources (internet blog and sites, podcasts…) devoted to this topic, there’s still the need of having a reference text that compiles specific research approaches on the use and application of technology in education. In a dynamic world where news and advances are mixed and raised via social networks and other trans-media channels, the need of reference textbooks is a must so stakeholders can stop and realize on research topics and approaches, providing a deeper discussion and a larger extension.

This book can provide a common framework, compiling solutions and evidences across different sections that summarizes how technology can be applied to shape the learning experience but also the organization of the higher education institution, in the way to the university of the future, providing ideas to foster collaboration and enhance research.

This book responds to the central role of education in all spheres of human life and to the growing interest in its professionalization from different disciplines and areas of knowledge. Indeed, educational actions take place even in industry and business, to assure training the workforce. The never-ending technological developments and their implementation in education impose new methodological demands to monitor the fulfillment of the proposed goals. Assuming research as a mechanism for knowledge production, this book presents methodological orientations to scientifically address educational problems, which are constant and varied.

This comprehensive book, divided into seven sections, showcases groundbreaking research findings that blend new experiences from the COVID-19 pandemic with long-term research on online laboratories and virtual experimentation. Providing an adequate learning experience in the laboratory has long been a major challenge in science, engineering, and technology education. Recent years have further revealed the complexities of offering distance or remotely accessible educational settings, particularly for laboratory-based courses. In response, many academic institutions have innovated by transitioning their laboratory classes into online laboratories or providing laboratory kits for at-home use. This unprecedented situation has sparked numerous new developments, approaches, and activities, revolutionizing the field.

With contributions from leading researchers and practitioners across diverse disciplines, this book delves into current trends, addresses critical challenges, and uncovers future opportunities for laboratory-based education in the context of online learning.

Whether readers are educators seeking innovative teaching strategies, researchers exploring the latest advancements, or academic leaders looking to enhance remote learning experiences, this book provides valuable insights and practical solutions. It explores how online laboratories are transforming education and discovers the potential they hold for the future.

University classrooms (whether virtual or in-person) often cling persistently to a teaching approach defined by the linear, progressive sequence of explanation-application-verification. First, explain (theory); then apply through some practical activity; and finally, verify what has been learned with some form of evaluation. Altering this sequence initially involves experiencing a certain pedagogical discomfort: the desire to move away from teaching in the same way we were taught, understanding that learning methods have changed, and recognizing that cultural and digital trends have influenced the ways our students (and we ourselves) construct knowledge. These explorations often lead to a form of transformation (such as the flipped classroom), which allows for settling into a new pedagogical comfort zone.

This book, however, reflects a restless pursuit. Uriel Cukierman invites us to dive deeply into our teaching practices through his own journey, history, experiences, questions, and answers. Four decades during which he has intertwined teaching, management, and research, generating those meaningful horizons necessary in today’s world to critically examine teaching practices.

In 2006, the Signal Processing Department at Blekinge Institute of Technology and Axiom EduTECH in Sweden worked with National Instruments Corporation in Texas, USA, to set up the Virtual Instrument Systems in Reality (VISIR) Project, which operates as a remote laboratory for electric and electronic circuits.

The VISIR remote laboratory is currently the only system that delivers practical experiments with electronics without the need to go to a traditional lab. This is of increasing importance given the expansion of online education. There is a mass of scientific literature that collects results on the use of VISIR remote laboratory, however, there are few reference works that provide an in-depth exploration of the laboratory's performance and potential.

VISIR Handbook acts as a guide for users, demonstrating many of the real (remote) experiments that can be achieved and replicated with this laboratory. Most importantly, this book demonstrates how VISIR can be used as a learning tool for students. The approach of the book is designed on two levels, with an administrator/researcher approach and a teacher/student approach

IAOE ist publisher of 8 international scientific journals.

The CrossLab architecture allows for the configuration of distributed experiments by connecting the services offered by laboratory devices. The remote laboratory GOLDi will be adapted to use this new architecture. An integral component of GOLDi is WIDE which allows students to program within the context of an experiment. By redesigning WIDE to support the new underlying CrossLab architecture new features have been added like debugging and testing capabilities as well as real time collaboration.

Remote labs have gained popularity for their flexibility, accessibility, and cost-effectiveness. They come in two main types: real-time remote labs, where learners operate equipment remotely, mimicking in-person experiences, and ultra-concurrent labs, which use pre-recorded experiments to support multiple users simultaneously. While real-time labs provide hands-on interaction, they face challenges like scheduling conflicts and environmental dependencies, which ultra-concurrent labs overcome by removing logistical barriers.This paper introduces a web-based ultra-concurrent remote lab builder platform that aims turn real-time remote labs into ultra-concurrent remote labs. Our goal is to attenuate the limitations associated with real-time lab access, especially for long or condition-dependent experiments. We also present a case study focused on an existing real-time Photovoltaic (PV) Solar Remote Lab, which faced challenges when used in real-world settings, due to weather dependencies, long running experiments, and high student demand. The lab was turned into an ultra-concurrent version, enabling learners to study photovoltaic efficiency across different altitudes and conditions without requiring real-time access. The proposed platform effectively addresses limitations in existing remote lab models by providing a user-friendly solution for creating ultra-concurrent labs.

Educational remote laboratories augmented with digital twinning technologies have the potential to revolutionize learning in Electrical and Computer Engineering (ECE) and Computer Science (CS) education by bridging the gap between theoretical concepts and practical application. Traditional access to advanced laboratory equipment is often hindered by geographic and institutional limitations, reducing opportunities for hands-on experiences. By combining remote laboratories with digital twins—virtual representations of real-world systems—students can engage with immersive, interactive environments that enhance their understanding of complex engineering concepts. The REDTAIL project seeks to develop an ecosystem that leverages these tools to improve laboratory experiences, fostering deeper learning and preparing students for practical applications in engineering fields. This paper seeks to introduce REDTAIL at a high level and outline the current progress in its development.

As a result of the serious natural changes we are experiencing, the subject around ecology has gained interest, and the need for environmental preservation is driving significant efforts to reduce ecological footprints. This has resulted in a considerable trend toward the handling of eco-friendly devices that use resources in a sustainable manner. The concept of SmartEnvi, which includes SmartFlora Technology (SFT), is an innovative and complex system for the future of urban gardening and microclimates that integrates smart technologies and sustainable practices to develop optimal life care solutions and transform urban areas into green smart environments (GSEs). The integration of Linear Programming (LP), Graph Theory (GT), and Discrete-Time PID Controllers (DT-PID) should lead to enhanced plant growth and reduced resource consumption. Based on anticipated results, SmartEnvi has the potential to significantly expand smart plant care by innovatively merging technology and sustainability.

Air quality monitoring is critical for protecting public health and supporting environmental policy, particularly in urban areas of developing countries. This paper presents the design and implementation of a cost-effective M-IoT system for real-time monitoring and mapping of air quality, with a case study conducted in the micro-downtown of San Salvador, El Salvador. The system integrates portable IoT nodes equipped with MEMS and MOx sensors capable of detecting TVOCs, eCO2, and environmental parameters such as temperature and humidity. Data are collected in 30-second intervals and transmitted via GSM (2G) to a cloud-based IoT platform for processing and visualization. The system uses GIS heatmaps to provide a dynamic spatial representation of air quality across the urban landscape. The field test results revealed Good to Excellent air quality in pedestrian areas, with Moderate pollution levels near high-traffic zones. The system performed reliably throughout the test, demonstrating its potential as a scalable, low-cost solution for air quality monitoring in resource-constrained environments. Future work will focus on expanding the monitoring scope, integrating additional pollutants, and exploring advanced communication technologies to enhance system performance.

With the ongoing digital transformation in education, enhancing Artificial Intelligence Generated Content (AIGC) literacy among pre-service foreign language teachers has become essential for adapting to the challenges of the AI-driven era. This study investigates the relationship among Chinese pre-service foreign language teachers' AIGC literacy beliefs, practices, and emotions. Using an explanatory sequential mixed method, the study employs questionnaire surveys, classroom observations, and in-depth interviews to gain a comprehensive understanding of pre-service foreign language teachers' AIGC literacy beliefs, practices, and emotions. The questionnaire covers five dimensions related to AIGC literacy, including Beliefs and Ethics, Thinking and Language Competence, Knowledge and Skills, Application, and Emotional Dynamics. Quantitative data from a large sample of pre-service teachers will be analyzed using SPSS, while qualitative data from classroom observations and interviews will be analyzed using Nvivo. The findings reveal how pre-service teachers integrate AIGC into their teaching practices and the emotional dynamics they experience during this process. This study is expected to provide insights into pre-service language teachers’ views on using AI technologies for language education, which helps prepare them for an AI-driven educational environment and enhance the integration of foreign language education with AI technologies, ultimately improving education quality.

CONTEXT

The World Health Organization states that over 5% of the global population—approximately 430 million people—experience some degree of deafness and muteness. The lack of sign language proficiency among the general population creates communication barriers, impacting the emotional well-being of both deaf-mute and hearing individuals. Developing accessible tools for self-guided sign language learning is crucial for fostering mental health and mutual understanding across diverse groups.

PURPOSE OR GOAL

This study aims to enhance sign language proficiency for deaf-mute and hearing individuals by utilizing computer vision technology to create an interactive, inclusive sign language dictionary. This tool will facilitate independent learning, improve communication accuracy, and promote inclusive social interactions. We hypothesize that computer vision integration will make sign language learning more accessible and efficient, thereby fostering a more inclusive society and improving psychological well-being.

APPROACH

Our approach included analyzing current sign language learning methods, exploring open databases like WLASL with educational video content, and developing a gesture recognition algorithm using computer vision. We programmed algorithms for real-time gesture recognition via webcam, enabling users to compare their gestures with standard models and receive immediate feedback. An interactive user interface supports various sign languages, allowing users to practice specific gestures. The application was tested with a target group to assess usability and gesture recognition accuracy.

OUTCOMES

The dictionary application achieved an 85% accuracy rate in gesture recognition, allowing users to practice gestures in real-time and facilitating smoother self-study. Feedback from test users indicated that the tool effectively supported learning new gestures and improving proficiency.

CONCLUSIONS

This project advances accessible tools for sign language learning, enhancing communication between deaf-mute and hearing communities. Findings confirm that computer vision effectively improves gesture recognition and communication skills. Future work will expand the gesture database and enhance feedback mechanisms for greater accuracy.

The study is devoted to computer gamification of balance boards deploying Arduino microcontrollers and computer games with open source software. The re-search investigates 62 computer games with topics related to Olympic sports from the open Scratch-repository of programs. To evaluate computer gamification, the authors proposed recommendations to create a sequence for using games that employs technical limitations of the Rocker and Wobble balance boards; a description of game design based on the “Mechanics-Dynamics-Aesthetics” model and the “GamePlay-Bricks” model. This paper proposes to evaluate games by the following properties: informational proximity of boards to the game scenario, proximity of signals from computerized boards and signals of keyboard keys or computer mouse movements in games; proximity between the movements of the game character and the movements of the player on the board, complexity of the game scenario, methods of evaluating player results; level of game mechanics coverage, level of balance between mechanics, dynamics of game characters. Based on the recommendations, 42% of games have been selected without reprogramming each game by creating the C-program of the Arduino Leonardo microcontroller, which simulates keystrokes and computer mouse movement. The study investigated the mechanics and dynamics of games while analyzing the impact of game aesthetics on the effectiveness of board gamification was beyond the scope of this study, as was the impact of Exergame design on improving the player’s mental health, which will be the topic of further research.

Thanks to the continuous evolution of computational technologies, emotion recognition and identity protection systems have become vital components in artificial intelligence (AI), especially in domains involving human-machine interaction (HMI) and affective computing (AC). These systems are crucial for capturing and analyzing real-time emotional responses, providing significant advancements in personalized user experiences. However, due to cyber-attacks growth (such as spoofing attempts, malware, ransomware, phishing, brute force attack, denial of service), robust systems are needed not only to ensure accurate emotion recognition but also to provide secure identity management and optimized data storage. This project addresses these challenges by integrating advanced detection algorithms and hardware solutions to enhance both the reliability and security of emotion recognition platforms.

Project MäRI intends to bring forth evidence-based information regarding the experience when a social, humanoid robot, a care recipient and nursing students meet. To collect this information an application, Flossa, needs to be developed for the Arcada robot Alf, a commercial robot from Sanbot called Elf. Information for app development on social, humanoid robots is limited and to make sure Flossa can bring forth the appropriate information for the MäRI project Alf and the application must be serviceable. This in turns means that Alf and Flossa must be able to communicate in Swedish through speech synthesis and speech recognition.

This work describes the development process for the application Flossa and its 4 software. A server implementation that uses speech synthesis and speech recognition services from Google to make the robot Alf communicates in Swedish. A web application that becomes Flossa’s graphical user interface, based on a nod-tree that enables the interaction between user and robot. And finally, a streaming server and an Android application, developed from the included tools provided by Sanbot, that together bring life and movements to the robot during the interaction with the user and the graphical user interface.

CONTEXT

The impact of Artificial Intelligence (AI) on education, is a matter of discussion across a wide range of fields since the widespread public dissemination of the generative AI-based tools, such as ChatGPT, designed to simulate a real conversation using natural language based on the "Generative Pre-trained Transformer" technique [1]. Even before the advent of "ChatGPT," there were numerous precedents for the application of AI techniques in education, such as "Learning Analytics" and "Adaptive Learning" [2], as well as others for proctoring, plagiarism detection, automated tutoring, etc.

As with previous technological advancements such as radio, television, electronic calculators, computers, and the Internet, the introduction of new technological tools consistently generates debate about their role in education. The application of AI in education is not the exception to this rule. This article describes a study aiming to gather evidence regarding the opportunities and challenges for its effective use in Engineering Education courses by analysing Argentine teachers’ perceptions. Conducting rigorous research and experimentation we will identify effective approaches for applying AI to benefit both teachers and students, with the aim of enhancing education, facilitating learning, and promoting greater inclusion.

Perspectives on this issue are diverse, some studies have dealt with other challenges that AI poses to education and Engineering Education. Some [3] make recommendations regarding the ethical implications of using generative AI-based tools in Higher Education. Others explore the perceptions and attitudes of teachers and students towards these technologies and their impact on teaching and learning practices [4] [5].

PURPOSE OR GOAL

This paper constitutes a segment of an ongoing Research Project developed at the National Technological University (Buenos Aires School) and the Center for Educational Research and Innovation (CIIE) in Argentina. The primary purpose of this paper is to disseminate the initial findings of an in-depth analysis conducted among Argentine engineering educators, with the aim of identifying their perceptions about the use of Artificial Intelligence applications (AIA) in undergraduate courses. To this end, the main objective of this research is to analyse educators' opinions and concerns regarding the use of AIA in their courses, while also identifying critical areas for upskilling to facilitate the effective integration of these tools, ultimately enhancing student learning outcomes.

This study provides a succinct description of the impact of AIA at the National Technological University (Buenos Aires School) and a compilation of AIA survey studies run at Latin American universities aiming to investigate the faculty’s opinions about the educational use of these applications in STEM undergraduate courses. It delineates the research methodology employed, elucidates the instruments crafted specifically for this study, and describes the research process used to collect and organize data. Furthermore, this study highlights preliminary evidence regarding the opportunities and challenges faced by faculty in incorporating these applications. This paper aspires to serve as a reference for future investigations addressing similar topics and to provide insights that will inform subsequent research within the broader framework of the ongoing project.

This paper presents the perspective of chemistry students of the CBC of the University of Buenos Aires, using a virtual assistant integrated in the Remote Laboratory of Acid-Base Titration II. The integration of this artificial intelligence tool arose due to the lack of teaching support for learning in virtual environments such as Remote Laboratories. The aim of this study is to investigate students' perceptions of their experience with a virtual assistant integrated into a remote acid-base titration laboratory. A questionnaire was designed and administered for data collection. It consisted of 11 Likert-type statements, the statements were linked to an agreement level of 1 to 4, where 1 indicated strongly disagree and 4 strongly agree. These statements focused on assessing the usability of the virtual assistant and students' perceptions of their own learning experience when using the tool. In addition, three open-ended questions were included to collect qualitative information aimed at identifying areas for future improvement.

Students indicated that the virtual assistant provided valuable support during the experimental activity, facilitating the immediate resolution of any queries they had. The study reveals a consensus among participants that tools such as the one investigated could be beneficial in improving student performance. This article provides a clear overview of the use of an Artificial Intelligence tool to support work in an ultra-concurrent laboratory in the field of chemistry. In addition, the virtual assistant's ability to provide assistance to students in resolving specific queries prevents difficulties that might impede the progression of experimental activities.

This article is published as part of the KICK 4.0 project, which aims to successfully integrate NLP AI into university engineering education. The focus is on acquiring new skills required for dealing with AI and recognizing the potentials and limitations of AI technologies.

The aim of the study is to investigate the creation of feedback from NLP AI that is conducive to learning. It examines how feedback can be generated that is correct in terms of content and motivating to the right extent, and how this feedback can be generated in a comprehensible and transparent manner.

This work presents a tool that allows a course instructor to design adaptive learning scenarios for developing STEM competencies. Additionally, this environment features an integrated intelligent tutor, represented by a Conversational Agent, which is capable of guiding the student through a personalized learning path tailored to their individual skills and needs

CONTEXT

Artificial Intelligence (AI) has been widely discussed in the technological and research context for a long time. With the rising of applications like ChatGPT it has become more and more relevant for the educational sector as well and is often named as one of the key enabling technologies of our time (Ramahandry et al., 2021).

PURPOSE

Because of its various applications and broad disruptive potential, it seems pivotal to integrate AI into teacher education. The project “LehrKraft voraus!” (“Teacher Ahead!”) aims to foster AI-related and self-reflectional competencies of pre-service technology teachers regarding the use of AI.

APPROACH

In order to achieve this objective, an investigation is first conducted to ascertain the understanding of pre-service technology teachers regarding AI, their attitudes toward the technology and their need for support in using AI in an educational context. To this end, an open-ended questionnaire is employed, utilising adaptations of both the Technology Acceptance Model 3 (Venkatesh & Bala, 2008) and the PANAS scales (Leue & Beauducel, 2011) with a sample of pre-service technology teachers (n=17). This process provides the basis for the development of a practical and holistic seminar, whereby pre-service technology teachers develop learning situations for vocational schools and put these into practice at a cooperating school. The seminar's structure is following the DigCompEdu framework and its extensions towards AI (Bekiaridis & Attwell, 2024; European Commission: Joint Research Centre, Redecker & Punie, 2017) regarding digitalization and AI related competencies. Additionally, we draw upon the work of Dewey (1910) to foster reflection and critical thinking concerning the use of AI in an educational context.

OUTCOMES

The survey of students shows that they mostly have a basic understanding of AI at the application level. This is combined with misconceptions on AI. Despite these, all participants display a positive attitude towards using AI tools in a school context. A large proportion of respondents are already using AI tools, with a particular focus on chatbots but report having problems using them. There is also a clear desire for more support from the university.

In the resulting seminar, student teachers are required to utilise AI tools to support them in the development of the learning situations. Secondly, the content of the learning situations developed also encompasses applications of AI. The learning situations are then implemented at an actual vocational school. The usage of AI is then critically reflected on in the seminar from a subject-specific and a didactical point of view. This approach facilitates the promotion of both the digitalisation and AI-related skills of the prospective teachers and those of the students at the vocational schools.

CONCLUSIONS

The resulting seminar poses a suitable way of integrating AI in vocational teacher education. As the preliminary study indicates teacher students currently possess only a rudimentary understanding of AI and require further assistance in order to utilise it effectively in their teaching profession. The incorporation of AI into the process of developing and enacting learning situations enables the addressing of AI-related competencies, particularly through critical reflection from multiple perspectives.

Learning processes in Computer Science programs require the development of transversal competencies that are often excluded from official curricula. The infusion of storytelling in teaching strategies can be used to approach these competencies, while supporting meaningful learning. Previous studies have shown the multifaceted role of storytelling in bridging technical knowledge, cultural context, and user engagement across diverse fields. This paper reports on our experience using storytelling in an undergraduate database course. Namely, storytelling was used to evaluate the physical organization of files and indexes. The purpose of this strategy was addressing subject-specific contents while fostering students’ creativity and communication competencies. In this paper we describe the design and implementation of our storytelling-infused teaching strategy, and provide samples of students’ learning results as well as lessons learned from the authors. An anonymous student survey was used to assess this strategy. Overall, results from the survey show that most of the students considered that the teaching strategy helped them develop their creativty and communication skills, and allowed further integration in teamwork dynamics. Indeed, students were able to clearly express these competencies through the creation and presentation of complex characters and conflicts in entertaining stories. Most students also found the strategy to be fun and enjoyable, but nevertheless stressful, due to the short time they had to create the story, while dealing with work overloads from other courses. Furthermore, some students expressed their frustration with the strategy, as they failed to see the value of nurturing creativity through storytelling in the field of computer science. As future work, we plan on experimenting with storytelling-infused teaching strategies in other computer science courses, to ascertain at what level of the computer science program these strategies may yield the best results for student learning and motivation.

Digital Twin (DT) technology has gained popularity in the science and tech industry. This research explores how Digital twin(DT) technology combined with anomaly detection can enhance the reliability of the Electric Vehicle (EV). In the paper, the authors provide an analysis of the methods and tools that are implemented in existing DT for EV, which has shown that anomaly detection could improve functionality of the DT and robustness of the EV. A modular approach and Model based design techniques were used by the authors. For the anomaly detection Failure Mode Effect Analysis was used. The anomaly detection algorithm for Open Modular Experimental Electrical Vehicle (OMAX EV) was developed, which allows to reach an accuracy 83%.

This work considers the potential of band linear system solvers

for improving the scalability of the Extreme Learning Machine method

at large model sizes. The model is tested on the MNIST dataset with

a range of solvers provided by the SciPy Python library. The results

present an overall performance, the performance impact of band solvers

across different matrix bandwidths, and the performance versus runtime

analysis. The findings show potential in applying the proposed method

to very large ELM models with narrow band matrices.

A key challenge in Industry 4.0 is integrating advanced technologies to enhance overall system efficiency. While collaborative robots (cobots) and deep learning-based object detection models have advanced, their deployment for vision-based tasks with robotic arms remains understudied. In this research, a vision-set mounted on a robotic arm is tested for sorting the mechanical fasteners. Three object detection models i.e., YOLO, SSD, and Faster R-CNN have been trained on over 2500 images and their sorting performance is evaluated for static and real-time object detection using vision-set. The trained models were validated through deployment with robotic arm. YOLO has proven to be the most effective algorithm considering training, speed and accuracy while the other models lacked in certain aspects one way or the other.

This paper focuses on the development of an innovative ap

plication that uses artificial intelligence (AI) and augmented reality (AR)

to create an authenticity detection system (ADS). It uses technologies

to analyze micro-expressions, transforming them into emotions based on

which it will indicate the type of sincerity of a person with whom one

is having a dialogue. Using AR, real-time feedback will be superimposed

on the user’s screen, and this technology will also help us offer live mon

itoring of facial movements during interviews or interactions. The ADS

purposes are diverse, from law enforcement (interrogations and state

ments verification), to human resources (hiring processes and honesty of

candidates, social interactions), providing valuable information in nego

tiations, counseling, and therapy. Using Haar Cascade Classifiers (HCC)

combined with Convolutional Neural Networks (CNN) allowed the de

velopment of a new algorithm for the ADS.

The Tec21 educational model is based on challenges posed to students through a training partner. Since Tecnologico de Monterrey introduced this new model in 2020, the importance of having more robust simulators or training partners was detected so that they could provide the necessary information for the students' projects. This was the reason for transforming CAETEC, which was born as an experimental agricultural field, into a living lab and data farm, where it generates information from different operations, such as the voluntary automatic milking system based on robots and intensive agriculture (greenhouse) and precision agriculture in open fields. The design and development of virtual and augmented reality lessons, as well as the use of metaverse, has allowed us to build a distance learning ecosystem for different training units at the Tecnológico de Monterrey and to be a training partner for the development of challenges of the students.

CONTEXT

The COVID-19 pandemic had a profound impact on higher education, resulting in a rapid global shift to remote learning. This transition posed challenges for curriculum design, technological infrastructure, and student and teacher preparation. While some institutions struggled, others seized the opportunity to explore new ways of teaching and responding to future educational challenges. Despite inadequate infrastructure and training, the urgent need for mass online teaching highlighted the importance of rethinking higher education's digital capacity. The pandemic exposed weaknesses but also revealed new opportunities for innovation and the integration of new teaching methodologies. As higher education moves forward, it faces a pivotal moment of adaptation, incorporating lessons from remote learning while preparing for future challenges.

PROPOSE/GOAL

This article investigates the transition to remote/hybrid teaching in Portugal during the COVID-19 pandemic, employing Actor-Network Theory (ANT) to examine the interaction of social, technical, and pedagogical factors. It focuses on determining how various actors—students, teachers, institutions, technologies, policies, and social norms—influence the success or failure of remote learning.

The research primarily investigates students' and technological perspectives, examining factors influencing students' preferences for remote learning, such as security, access to technology, and study conditions. It also assesses the impact on academic performance, digital skills, and learning perceptions, as well as how institutional policies and social conditions shaped the remote learning experience. Additionally, it analyzes the role of digital technologies in adapting to remote teaching and the associated challenges and opportunities.

APPROACH

The research is a case study focused on students' experiences in a Bachelor’s Degree in Computer Engineering in Portugal during the transition to remote learning amid the pandemic. ANT, it examines the complex relationships between human and non-human actors shaping distance learning. The methodology includes qualitative data collection through online questionnaires, gathering students' perceptions and experiences. The data is analyzed using content analysis to identify participant narratives' patterns and themes. ANT is applied to explore the interactions, translation of interests, and resource mobilization involved in implementing remote teaching.

OUTCOMES

Using Actor-Network Theory (ANT), the research aims to highlight the theory's effectiveness in analyzing the complex networks of remote teaching. ANT will be applied to: Map interaction networks, identifying human and non-human actors and their interdependencies; Understand translation dynamics, showing how interests are negotiated and meanings evolve as networks adapt; Identify crucial convergence points (OPP) necessary for remote education participation; Analyze enrollment and mobilization processes, observing how actors align to strengthen the network; Investigate how practices, technologies, and norms shape the remote education system. The research aims to enhance understanding of remote teaching complexities and support future education strategies.

CONCLUSIONS

The research underscores the need for a holistic view of remote learning, emphasizing the complex interplay between human and non-human actors. It highlights the importance of diverse perspectives—students, teachers, institutions—and technical aspects. The study suggests hybrid models can provide flexibility and meet varied student needs, especially during crises, while also stressing the influence of social, cultural, and socioeconomic factors, access to technology, and government policies on remote learning.

In today’s fast-evolving educational landscape, it is increasingly essential for entrepreneurship to be molded into STEAM. This expansion to "STE2AM" seeks to equip learner-students with the skills to innovate, solve sophisticated problems, and build sustainable solutions. Unfortunately, the absence of commonly defined frameworks on the teaching of entrepreneurship as a subject in the STEAM curricula has resulted in a gap in skills acquisition and knowledge transfer.

The project funded under Erasmus+ titled "Defining Standards in STE2AM Education" will help bridge this gap, working to establish cross-border standards and best practices to the effective integration of entrepreneurship across STEM and Arts disciplines. The project's main aim is to develop clear, adaptable, and inclusive standards for STE2AM education intended to foster the acquisition of relevant, actionable, and transferable entrepreneurial skills by students throughout Europe and beyond. The project also intends to promote a common STE2AM educational approach enabling institutions to develop robust frameworks for the nurture of future-ready entrepreneurial talent in a structured manner.

The current paper explores emerging technologies used in online exam su-pervision. Utilizing a qualitative approach through examining recent litera-ture and detailed case studies of major testing service providers, the paper highlights advances in artificial intelligence (AI), machine learning (ML), deep learning (DL), biometric verification, and blockchain-based meth-ods. These technologies collectively aim to enhance the integrity, fairness, and security of remote examinations by automating identity verification, de-tecting suspicious behaviors, and ensuring tamper-evident data management. Emerging technologies in online exam supervision have reached a high de-gree of sophistication and maturity, and online proctoring providers are con-stantly investing in innovation, regulatory compliance, and improving can-didate experience. While these technologies can offer promising improve-ments in detecting and preventing cheating, they also present particular challenges. These challenges are related to resource requirements, scalability, and ethical considerations. A hybrid approach combining technological solu-tions with human oversight seems to be the most effective strategy current-ly. There is also a need for standardized frameworks along with recommen-dations and guidelines to address privacy concerns and ensure the ethical use of these technologies in online exam settings.

The COVID-19 pandemic and the ongoing energy crisis in Germany have challenged educational institutions in providing hands-on training for computer science and engineering students. In response, the University of Applied Sciences Mittelhessen developed the "MICRO" platform, enabling remote access to embedded systems experiments. MICRO allows students to interact with microcontroller-based assemblies containing sensors and actuators through a web interface, offering real-time feedback and hands-on experience even in a remote setting.

This study aims to enhance the MICRO platform by integrating a low-cost multi-function instrument that extends the system's interactivity. The instrument enables students to remotely generate and analyze analog and digital signals while interacting with embedded system components, such as sensors and actuators. This integration allows for real-time signal processing, improving the overall educational experience by closely replicating physical lab environments.

The architecture of the system is designed for flexibility and scalability. The existing database has been expanded to store granular details of each assembly, from individual pins to entire component connections. A RESTful API developed with a Spring back end in Kotlin supports dynamic retrieval of data and communication between the database and the emulation service. The Python-based emulation service processes user-triggered events from the web-based interface, allowing real-time control over the experimental setups through WebSocket connections. This modular approach ensures that the system can easily adapt to future changes, additional components, and more stations without extensive reconfiguration.

The study's outcomes include improved usability and interactivity within the MICRO platform. The enhanced database provides a comprehensive view of the system, while the emulation service enables students to manipulate components like potentiometers and servo motors remotely. The service also supports communication protocols such as I2C, SPI, CAN, and UART, offering flexibility in experiment design and execution. Anticipated future developments include adding more specialized database entities, creating templates for easier assembly creation, and improving the emulation service to handle a broader range of functionalities.

The integration of the low-cost instrument significantly improves students' ability to perform meaningful remote experiments, offering an immersive experience that parallels traditional lab settings. MICRO's unique capability to emulate hardware interactions in real-time sets it apart from other remote lab platforms. Future improvements, such as real-time data visualization and expanded protocol support, could further enhance the educational experience. Overall, these enhancements position MICRO as a scalable, flexible, and valuable tool for remote learning in embedded systems education.

The integration of Large Language Models (LLMs) holds significant promise for enhancing programming education by providing personalized, immediate feedback and fostering student engagement through adaptive learning. Existing research demonstrates that LLMs can offer meaningful learning support when designed with pedagogical considerations. However, challenges such as hallucinations in feedback and learners' negative attitudes towards automated instructional solutions hinder broader adoption in educational contexts. Many existing solutions fail to promote critical thinking or address diverse learner needs.

This paper proposes design of an interactive story-based environment for teaching programming languages, utilizing LLMs and automated assignment grading through a plug-in for Visual Studio Code (VSC). The goal is to provide contextual, pedagogically relevant tasks and feedback to students, employing Socratic questioning to encourage active participation and critical thinking.

We adapt an existing VSC plug-in framework to support PHP and other common languages, designing middleware that enriches student prompts and redirects them to a custom-tuned curriculum-driven Swedish LLM. This architecture integrates meta-prompts based on pedagogical strategies into LLM interactions, employing a Socratic dialogue approach rather than providing direct answers. Anticipated outcomes include increased student engagement through storyline-based tasks and personalized feedback within the VSC environment, alongside better alignment of LLM interactions with pedagogical objectives.

By presenting the underlying architecture of the prototype, we contribute to the use of generative AI in software engineering education. Our work highlights the potential of AI-powered tools in education to improve learning while addressing ethical considerations and ensuring need for thoughtful implementation to avoid amplifying biases or diminishing the role of teachers. Further studies are recommended to evaluate the impact of LLM interactions on student learning outcomes and to explore adaptability in real-life educational simulations.

Many people are getting interested in bowling, but they tend to stop playing after a few times. One of the most prominent reasons is that they do not have the op-portunity to deepen in their bowling technique and practice accordingly. Thus, they do not feel any improvement in their technique of bowling, so they are disappointed, and they are dropping out. STAYinBowling is a project funded with support from the European Commission, created to help bowling athletes improve and stay engaged with the sport. An online questionnaire was used, answered by both bowling athletes and coaches, to identify their needs and specify the areas that some digital tools might be useful. Based on the participants’ questionnaires a Massive Open Online Course was created. Moreover, an internet of things toolbox was implemented, that consists of two applications for tracking the movement of the hand and the steps during an athlete’s throw. Finally, unified bowlers’ metrics information is supported by a dashboard presenting gathered data by the applications of the IoT toolbox. The purpose of the study is to present the process of collecting the needs, designing and creating the digital tools, as well as to present some of the feedback we got from the experts on a formative evaluation.

CONTEXT

In recent years, addressing the issue of harassment in sports, particularly for children, has gained increased attention. Research shows that children, especially in competitive environments like sports, are vulnerable to different forms of harassment, which can severely impact their well-being, and the incident often goes unreported due to fear or lack of communication channels (Fortier, Parent, & Lessard, 2020).

PURPOSE OR GOAL

This work is presented as an output of HALT4Kidds (Halting Harassment and Abuse in Sports using Learning Technologies for Kids) European Erasmus+ Sport project (https://halt4kids.phed.auth.gr/). HALT4Kids project aims to create digital tools (namely mobile apps, serious games and digital stories) for self-paced personalised education and support of kids in a direct and transparent manner. The digital tools have been designed and developed in a co-creation manner by engaging main stakeholders like Physical Education experts, psychologists, computer designers and engineers.

This paper will present an online and mobile based alert ecosystem developed in the context of HALT4Kids project for Child Safety in Sports.

(NOTE: please find more in the abstract submission file)

The integration of Large Language Models (LLMs) into education, particularly in software engineering and IT, presents opportunities and challenges. While LLMs support problem-solving and code generation following the specifications, their effectiveness often depends on students' ability to formulate precise and effective prompts. To address this, frameworks known as meta-prompts have been proposed. However, the impact of specific frameworks on students' prompt-writing skills and learning outcomes remains underexplored.

This study examines two didactic frameworks—Iterative Feedback and Reflection (IFR) and Adaptive Learning Progression (ALP)—to assess their effectiveness in enhancing prompt-writing skills and learning engagement. We propose complementary metrics within the Overall Prompting Effectiveness (OPE) framework, defined through three key dimensions: Adaptability, Relevance, and Efficiency. These dimensions encapsulate essential components for effective interaction with LLMs in educational contexts.

The design of controlled experiment involves IT-engineering students divided into two groups, each using one of the two different didactical meta-prompt-enhanced frameworks. The IFR group engages in iterative cycles of prompt refinement and self-reflection, while the ALP group utilizes adaptive meta-prompts that dynamically adjust task complexity based on performance. Data collection focuses on OPE-aligned metrics, including the number of prompt iterations, time efficiency, response alignment, and learning progress self-rating, allowing for a comparative analysis of the frameworks' impacts on learning outcomes.

Our work establishes and evaluates these metrics, contributing to research in LLM-assisted learning. It addresses gaps in prompt engineering by showing how IFR and ALP frameworks can be utilized to enhance skill development and offers guidance on integrating LLMs into educational contexts for better interactive learning.

This paper explores the integration of digital competence in teaching and learning research skills within the University 4.0 paradigm, emphasizing its applicability in educational contexts outside technical disciplines. University 4.0, aligned with Education 4.0, frames research as a central element, fostering creativity, innovation, and continuous learning. The study assesses self-perceptions of digital com-petencies among graduate and postgraduate Educational Sciences students in Ar-gentina, focusing on their ability to utilize digital tools essential for research in humanistic fields. Employing a mixed-method approach, data was collected through an online survey, examining self-perceived digital competence and the extent to which it aligns with the requirements of modern research practices. Re-sults reveal gaps in digital proficiency, even in basic computing commands, more notoriously at graduate than postgraduate levels. These findings underline chal-lenges in adapting digital tools in educational careers distant from engineering, stressing the need for targeted interventions to achieve the Student 4.0 model. The study contributes to understanding digital literacies' role in fostering research skills, emphasizing the necessity for university programs to offer transformative opportunities that address digital gaps and align with evolving educational para-digms. It highlights critical implications for curriculum design in fostering digital competence as a cornerstone for future educational leadership and innovation.

Portfolios have long been used to collate professional achievements and serve a purpose in presenting the capabilities of an individual, group or company. In re-cent times, the traditional folder or folio has been replaced to a large extent by an electronic version, or the e-portfolio. In an educational context, the goal of portfo-lios remains the same – to showcase the capabilities and achievements of a stu-dent as they reach the learning outcomes of their course of study, or the graduate capabilities of the profession they seek to enter. As with many examples, conver-sion to electronic format has potential advantages and disadvantages, and creates new opportunities. It is possible that there are missed opportunities in the area of student portfolios, often due to the institutional choice of structure or software platform.

This paper unpacks the key purpose, likely use cases and hence requirements of an undergraduate engineering student portfolio. A portfolio will be used to demonstrate progress towards and achievement of graduate capabilities. A sys-tems engineering approach is applied to the requirements analysis of the e-portfolio in the given context. A quality standard will be developed to assist in the selection of structure and software platforms.

The first milestone is the articulation of the purpose of the portfolio. This will be developed based on literature as well as the requirements of the stakeholders. A requirements analysis follows, taking in the perspective of stakeholders includ-ing students, staff, the institution, future employers and accrediting bodies. The V-Model Systems Engineering design approach is then used to explore the area of e-portfolios, discovering and defining the stakeholder requirements.

This paper considers the design and development of a project-based learning unit within an undergraduate engineering degree. The context of the project is within an engineering curriculum with a strong reliance on project-based learning to support the development and assessment of technical and professional engineering skills which are captured in graduate outcomes.

The program in this context is designed so that the student learning outcomes are intentionally scaffolded through the program, and so the requirements and scope of the particular units are well defined.

The paper documents the use of the V-Model systems engineering approach to the design, development and evaluation of a student project in the first instance, and the development of the assessment tasks in a project-based learning unit.

This paper focuses on encouraging and inspiring young women to study engineering by investigating how female students are encouraged and motivated to study in the STEM field. To this end, a theory-based study orientation programme for female secondary school students was designed, implemented and evaluated. The design-based research project aims to provide evidence-based and transferable findings on the successful STEM study choice orientation of women in the context of digital transformation. In addition, the development of self-efficacy, initiative and creative potential of young women will be analysed. The structure and implementation of the study orientation programme will be explained in detail. In particular, the production management workshop, in which female secondary school students were introduced to production processes through game-based learning, will be presented and discussed. It is analysed which workshops did motivate the participants most and in which they point out to have learned most. Self-assessments of the students before and after the workshops are presented. In addition, some outstanding examples of concurrent observation are presented in order to discuss the insights gained and improvements for future interventions.

Previous studies of handgrip strength show that besides maximum force (HGSmax), other relevant descriptors obtained from the handgrip strength time profile (HGS(t)) are associated with muscle function and physical functional performance. Recently, a study has shown that the HGS(t) can be described by linear models of first or second order, with or without time delay.

A Gripwise dynamometer was used to obtain and record in the cloud a larger data set, allowing the identification of models based on adequate continuous-time transfer functions. This work supports the exploration of universal descriptors, other than maximum HGS (HGSmax), that can be related to muscle function and physical functional performance evaluated by universally used tests. Therefore, the purpose of this work is to contribute new insights to characterize the Handgrip strength time profile by applying model identification methods to a large and diverse data set.

The proposed approach can also be useful to motivate students, for example in the area of Biomedical Engineering, to apply model identification methods in real applications and learn about their relevance for describing physiological processes through physics-informed machine learning models and generating features for different purposes.

The authors consider the results promising for future exploitation of the approach to investigate and extract the characteristics of the time-dependent HGS (HGS(t)).

A laboratory course related to the titration of equilibrium states of ternary mixtures for biochemical and chemical engineers is updated concerning its topicality and pedagogical benefit. The existing scripts are updated with Intended Learning Outcomes (ILOs) directly communicated prior to the laboratory activities to the students. Furthermore, pre- and post-laboratory course tests assess the increase in student’s understanding regarding the topic in a structured manner. The previous laboratory involved much outdated manual work, as the main learning activity of the old laboratory was the titration of chemicals, which is a skill already learned in previous laboratories.

Thus, new learning outcomes are introduced based on the newly constructed and erected automated titration apparatus.Using this apparatus, students will be able to apply fundamental concepts of automation and program a controller for titration interactively.

Nowadays, the interaction of the various actors in the electrical distribution networks and especially the increase in the information/data flows that they provide have given rise to smart grids. Our project aims to enhance this volume of data, not only to monitor processes and visualize data, but rather, to use the information provided by telemetry and IoT equipment installed in power grids to optimize their operation. The objective is to implement a tool that allows to efficiently take advantage of the potential of smart grids, and that through automation, using systems like PLCNext and SCADA, it is possible to optimize energy management, including the presence of distributed generation. With this, the use of sustainable technologies will be promoted, contributing to the progress of the communities, maintaining the care of the environment from an energy perspective

It is proposed to implement an energy management platform, integrating automation equipment and software provided by Phoenix Contact. Data from the electrical network present in servers of University of Concepcion (UdeC) are considered to design a system composed of SCADA and PLCNext that monitors, manages, optimizes, forecasts and evaluates the behavior of the electrical network in different operating scenarios. Subsequently, it will be implemented at pilot level in the energy management and efficiency laboratory of the UdeC.

From the study, it can be concluded that automated systems allow a more efficient operation of the electric power distribution systems through demand management. By monitoring operations in real time, it is easier to adapt to new variables that may arise when technologies such as distributed generation systems are to be integrated into the network. It is also feasible to implement a pilot on a larger scale, as in the case of Concepción Campus.

This paper investigates the use of a Stewart platform as an effective case study for parallel robots and offers a valuable educational opportunity. While a physical setup may be costly or challenging to construct, other ways exist to implement this concept through written computer programs such as MATLAB. Parallel robots, like a Stewart platform, serve as useful examples of inverse kinematics. It presents a ready to-use case study for robotics classes. This paper addresses two main topics, the first concerns inverse kinematics, which has already been extensively researched. An overview of the latest developments in inverse kinematics calculations is provided. The second topic is the calculation of the workspace for a Stewart platform. This paper presents a novel and efficient algorithm that utilizes tensors and inverse kinematics to determine the workspace of the robot. Consequently, this algorithm reduces calculation time by a factor of 27.8, compared to a state-of-the-art algorithm. The evaluation of this case study is conducted through the administration of a student feedback survey, which includes both closed ended background questions and questions employing a Likert scale.

This contribution outlines the motivation, objectives, methods, and ongoing findings of a doctoral thesis pursued to obtain a PhD in Education at the National University of Buenos Aires, Argentina. Engineering programs face significant tensions and changes, prompting institutions to design creative and effective responses to evolving conditions and societal expectations. In Argentina, the Open Access initiative has catalyzed transformations in how re-searchers and educators create, preserve, and disseminate knowledge. These challenges have led international engineering schools to examine the openness of their undergraduate programs and its impact on teaching practices by means promoting of Open Educational Practices. Despite extensive international research on these topics, evidence of similar studies in Argentina - particularly in Engineering Education - remains scarce. The thesis investigates whether Open Educational Practices can foster collaborative models, such as Professional Learning Communities among faculty, to facilitate the creation and (re)use of Open Educational Resources in engineering programs. Additionally, it seeks to evaluate their potential to enhance teaching practices and develop new competencies at the National Technological University, Buenos Aires. The study further examines the feasibility and implications of promoting these practices through appropriate policies and strategies. Initial findings, derived from a Systematic Literature Review, indicate that Latin American STEM institutions are exploring openness and gaining insights into its impact on teaching practices and educational programs. The next research phases will focus on deepening qualitative knowledge through selected case studies and implementing and assessing a pilot Open Educational Practices initiative at the National Technological University, Buenos Aires.

This work explores the latest opportunities that machine learning opens up for creating interfaces capable of adapting to users' needs in real-time. Modern machine learning technologies enable developers to create interfaces that respond to user behavior, emotions, gestures, and voice commands. This creates a new level of interactivity for the user interface and an engaging on-site experience, enhancing convenience and effectiveness of interactions.

The article begins with an overview of the basic concepts of machine learning and its role in interface design. It highlights how new algorithms allow developers to train systems based on collected data, making it possible to create intuitive and adaptive UIs. In particular, tools such as Google’s Teachable Machine are discussed, which allow for the quick creation of models to recognize various input data (images, sounds, gestures) without the need for deep programming knowledge.

The article raises the issue of adaptive interfaces' ability to recognize users' emotions. Today, the use of computer vision and natural language processing technologies enables systems to analyze facial expressions and voice. These new capabilities revolutionize the understanding of a classical website, which can operate on fast interactions. Another approach is that in educational platforms, adaptive interfaces can adjust the presentation of material based on the learner's emotional state, significantly increasing the effectiveness of learning. This provides a more engaging experience in a new format of adaptation and interaction. Such adaptive systems can offer personalized content, taking into account users' preferences, making the interaction more individual and meaningful.

The work also examines the advantages of adaptive interfaces for people with disabilities. For example, gesture-responsive interfaces can provide accessibility for users who cannot utilize traditional control methods. This underscores the importance of inclusivity in design, as technologies can help overcome barriers to accessing information and services.

Alongside the powerful possibilities, there are challenges associated with implementing machine learning in adaptive interfaces. Among the main challenges are issues of ethics, data privacy, and model accuracy. Today, there is a need for further research in this area and the development of standards to ensure the safe and effective use of machine learning in adaptive interfaces.

This paper demonstrates the contribution of machine learning algorithms in real-life problem resolutions, taking the example of fraudulent job postings detection. Therefore, the Support Vector Machine (SVM) is associated with a dataset that includes both real job postings and those considered to be fraudulent. Before using this dataset, the content was submitted to preprocessing techniques. Moreover, data mining techniques were relevant to extract essential features from the dataset. The disparity between fraudulent and non-fraudulent classes is addressed by applying the SVM-Synthetic Minority Over-Sampling Technique (SVM-SMOTE) method, considering that synthetic samples from the minority class (fraudulent) are generated to improve the model performance. Also, the model was trained and tuned on processed data to achieve high performance. Additionally, the performance of the model was examined following key factors including accuracy, precision, recall, and F1-score, respectively. The correct or incorrect predictions obtained from the model are exposed by using the confusion matrix.

This paper presents a project (work in progress) where entrepreneurship and higher education in AI (from Master level to postdoc level) are integrated in order to produce a dual effect; helping SMEs to gain insight in how AI can aid in the corporate environment and to expose AI researchers to the real-life situations in the company world. If successful, the companies are made ready for the AI revolution and the researchers more equipped for corporate settings. The project is ongoing, so this paper addresses a work-in-progress project. The paper reflects on the project as well on some aspects that need to be highlighted due to earlier research in the area. There are also some in-sights from the first case study that was presented in December 2024, with 7 more to come during 2025. Finally, some conclusions are drawn and some further research directions is pointed out. The project was funded by Liedon Säästöpankki säätiö and the support is gratefully acknowledged.

Blockchain technology is creating rapid transformations for large-scale fu-ture economies across multiple sectors of finance, health and supply chains. By 2025, the World Economic Forum projects that 10% of the global GDP will be generated by or based on blockchain technology: the demand for an adequately skilled workforce is thus critical. When vocational education and training (VET) programs operate below par or fail to meet the modern-day requirements posited by the blockchain industry, the BCH4VET project should address the gap through the development of an innovative modular curriculum on blockchain technology. This project is set to give three key outputs: A VET framework tailored to blockchain sciences, an AI powered assessment platform and, a gamified e-learning platform. These tools are meant to promote professional development, prepare students for new de-mands in the job market and foster inclusivity through assistance to many underrepresented groups. In these innovations, BCH4VET provides a frame-work for the sustainable incorporation of the emerging technologies into ed-ucation, empowering learners and educators to survive in a digitally oriented world.

In any product development cycle, costs can soar when a project takes longer than anticipated. Because accurately estimating a project’s completion date is not easy. Even in Agile Scrum, where the project is planned and run in short iterations, the risk remains at large. Machine learning can play an essential role in planning and estimating the project schedule to estimate user story efforts. This paper is an effort in that direction, where the effectiveness of Extreme Learning Machines(ELM) in the domain of predicting the effort estimate of user stories (multi-class text classification domain) is studied and compared with some existing techniques like SVM (Support Vector Machine) and LR(Logistic Regression). In this paper, the focus is to highlight the performance of ELM in the field of multi-class text classification, results from other models are studied and analyzed. Some common techniques are studied to improve the accuracy of models, like feature selection and parameter tuning.