In this workshop we will discuss with invited and interested participants the actual status of the initiative.

This work presents AR FLOW, an Augmented Reality (AR)-supported laboratory environment developed in Unity Engine, that complements conventional teaching of fluid mechanics by making multi-scale flow phenomena interactive. It aims to promote functioning understanding through a modular, level-based learning design that integrates digital lab scripts, AR visualizations, and context-specific learning tasks.

The resulting environment aims to combine script-based learning and interactive laboratories in quest-based learning, which, using Constructive Alignment and the SOLO-taxonomy, can guide students from superficial learning to a relational and extended abstract understanding (deep learning) of the concepts of fluid mechanics.

The rapid diffusion of generative artificial intelligence (AI) and large language models (LLMs) has intensified the need to foster AI literacy in higher education. Although students frequently use AI tools, their ability to apply them critically and purposefully remains limited, underscoring the relevance of instructional formats that integrate structured AI use. This study examines the rede-sign of a bachelor-level wind channel laboratory in chemical engineering. The traditional cook-book-style experiment was converted into a problem-based learning (PBL) format aligned with the SOLO taxonomy and the European Commission/OECD AI literacy framework. In the revised structure, students autonomously engage with theoretical content, select one of two problem scenarios, and use LLMs for planning, conceptual understanding, data interpretation, and reflec-tion. Pre- and post-tests, motivation and technology acceptance questionnaires, and an instrument on generative AI use were employed. Qualitative results indicate increased student autonomy, deeper engagement with aerodynamic concepts, and improved efficiency compared to the in the years 2024 implementation. Students used LLMs across the first three AI literacy domains engag-ing, creating, and managing consistent with the laboratory’s intended learning outcomes. Supervi-sors observed higher motivation and more purposeful inquiry, while the restructured script re-duced time pressure and improved clarity.

The integration of artificial intelligence (AI) into remote laboratories presents sig-nificant pedagogical challenges that require empirically grounded design frame-works. This study is part of research conducted within the framework of a mas-ter's degree project, and the objective is to develop seven design principles for the use of AI-assisted remote laboratories in chemistry education, derived from a multiple triangulation methodology with an inductive approach. Three comple-mentary studies were conducted: a focus group with teachers, a student experi-ence survey, and an analysis of student-AI interactions in a remote acid-base titra-tion II laboratory implemented at the University of Buenos Aires. Seven princi-ples emerged from a systematic triangulation of data from previous studies. The conceptual structure of the principles, based on generalisable teaching design ra-ther than specific disciplinary content, allows for their transferability to remote la-boratories in various fields, including physics, biology, and engineering, contrib-uting to the democratisation of science education in contexts where traditional re-sources are limited.

Mixed Reality (MR) technologies are increasingly used in engineering education to enable, e.g., virtual laboratory training. Yet, most applications support only individual learning, offering limited opportunities for teamwork and communication. This work-in-progress introduces an MR application that allows both local and remote collaboration, enabling two users to interact with the same virtual setup - either co-located or connected online - while sharing synchronized device states and interaction awareness.

The system integrates technical, linguistic, and cooperative dimensions within one immersive environment. In a joint bilingual course, German and U.S. students engage in problem-based laboratory tasks, alternating between English and German. The multi-user architecture embeds collaboration and language use directly into technical training, fostering engagement, co-presence, and teamwork. By shifting MR from isolated practice toward shared laboratory experiences, this approach provides a transferable model for communicative, collaborative learning in engineering education.

Students in the Biochemical and Chemical Engineering programs at TU Dortmund University often struggle to connect knowledge from individual courses into a coherent understanding. These challenges can be derived from limited constructive alignment and high informational load. For example, the first-semester course Introduction to Biochemical

and Chemical Engineering, which provides an overview of the curriculum, illustrates how integrating content from multiple courses can lead to students feeling overwhelmed. To support constructive alignment, the course has been redesigned with clear intended learning outcomes and active learning strategies, including student-generated exam questions and one-minute papers. Complementing this, ChemSPARK, a virtual reality model of a chemical plant representing an industrial process, provides an immersive, process-oriented framework. By contextualizing content in a realistic industrial setting, it is intended to foster cognitive map development, engagement, and long-term knowledge retention.

This study develops the idea that by visualizing an artistic performance through motion and sound graphs, it is possible to identify errors or tenden-cies that negatively affect the sound and the exact moments when they occur. This provides students with a potential way to reduce practice time and maintain a more balanced and healthier schedule. For this study, recordings from the side-positioned camera were utilized, focusing on the arm handling the bow. The video recordings were analyzed using a video analysis software called Kinovea, commonly used by athletes to evaluate their techniques and movements to enhance performance. However, this software also has appli-cations in biomechanics, as it allows for a detailed analysis of body kinemat-ics to correct movement errors and prevent injuries or medical conditions. The goal was to extract the x and y coordinates of the shoulder, elbow, and wrist throughout the entire movement. This was achieved by placing markers on the respective areas. After obtaining the data, it will be processed in MATLAB to generate the graphs. MATLAB is a widely used programming and numerical computation software, commonly applied in engineering as well as other fields such as economics. The graphs were designed to provide as much relevant information as possible to help achieve the study's objective.

Hypertension remains a highly prevalent cardiovascular condition, strongly influenced by autonomic imbalance and chronic stress exposure. Receptive music therapy has gained recognition as a supportive non-pharmacological strategy capable of modulating autonomic and emotional responses, yet ob-jective personalization remains limited. This study investigated the clinical effects of receptive music therapy in adults with hypertension and applied ar-tificial intelligence–based acoustic analysis to identify musical characteristics associated with cardiovascular improvement.

A total of 134 hypertensive adults were followed for six months, engaging in regular home-based listening, while a highly adherent subgroup additionally participated in supervised sessions with physiological monitoring. Blood pressure and metabolic parameters were assessed longitudinally. Musical stimuli were analyzed using a combined AI framework including acoustic feature extraction, unsupervised clustering, and supervised regression model-ing.

Significant reductions in systolic and diastolic blood pressure, alongside helpful lipid profile changes, were observed exclusively among adherent par-ticipants. Therapeutic musical profiles were defined by slow tempo, harmon-ic consonance, rhythmic stability, and reduced timbral complexity, showing strong associations with parasympathetic activation.

These findings support AI-enhanced receptive music therapy as a viable and scalable adjunct in hypertension management, enabling data-driven person-alization of therapeutic listening strategies.

The paper fits into the new concepts for engineering education in higher and vocational education institutions, including emerging technologies in learning. It approaches interdisciplinary and synergistic elements from nature harmoniously combined with modern technologies to highlight some physical and acoustic characteristics of wood. The novelty of paper consists in the use of electronic systems to highlight the chromatic and acoustic resonance properties of the structures of deciduous wood species (oak (Quercus robur Pall.), field elm (Ulmus minor Mill.), downy ash (Fraxinus pallisiae Wilmott), honey locust (Gleditsia triacanthos L.), ash (Ailanthus altissima (Mill.) Swingle), cherry (Prunus avium L.), plum (Prunus domestica L.). The seven rounds obtained from the listed wood species were mechanically processed and protected against humidity and temperature variations by coatings with transparent varnishes and resins. Their exposure and fixation is achieved using a removable metal structure inspired by biomimetics. The chromaticity of the wood was highlighted with high-power LED-COB light sources designed and executed using 3D printing, using a design inspired by nature. The acoustic resonance of each species is highlighted by playing the audio recording of the seven resonance frequencies corresponding to the mentioned species. These frequencies were extracted by processing the acoustic signals with Fast Fourier Transform (FFT) analysis following experimental modal analysis. From a musical point of view, the sounds obtained represent the pentatonic system frequently used in Afro-American music - bluess. The work highlights this aspect by correlating the sound source with the light indicators placed in the proximity of each woody species. The technologies involved in the paper range from CAD design, CNC machining, finishing and stabilization with advanced materials, 3D printing, testing and signal processing, electrical diagram design and incorporation of electronic lighting and acoustic systems.

This study investigates how AI can act as a pedagogical partner in developing the vocal technique, expressive skills, and interpretative autonomy of opera singers. The research seeks to determine whether conversational AI models can assist in structuring vocal study, providing contextual feedback, and supporting artistic reflection. The motivation arises from practical experience in preparing and performing roles such as Caramello (Eine Nacht in Venedig – Johann Strauss), Ruggero (La Rondine – Giacomo Puccini), and Don Carlo (Don Carlo – Giuseppe Verdi). The study adopts a qualitative approach based on self-observation, reflective dialogue with AI models, and iterative documentation of the vocal study process. Conversations with AI were analyzed to identify patterns of feedback, interpretative guidance, and organizational structuring of practice sessions. The framework also anticipates the use of visual and auditory AI tools, including image and GIF processing, 3D modeling, and audio analysis technologies, to illustrate phonatory mechanisms and to integrate these multimodal resources into future didactic materials.

The paper aims to compare the acoustic and environmental characteristics of anthropogenic and forest areas in order to highlight the parameters that contribute to forest therapy and psychological well-being. Thus, the study was carried out in one of the most famous resorts in Brasov County, with nine measurement points for noise levels, pollution levels, acoustic spectrum and anthropogenic/natural elements in the vicinity being established. All recordings were made on the same day, with weather conditions being approximately constant throughout the study. The results showed that the equivalent noise level decreased by 32% in the forest, compared to the central area of ​​the resort, while the CO2 level was approximately constant, considering that the resort is located in a mountainous area, surrounded by mixed deciduous and coniferous forests. Subsequently, the acoustic recordings were processed in Laboratory Virtual Instrumentation Workbench (LabVIEW) and the frequency spectra were extracted. After listening to the forest acoustic sequences, the level of delta waves increased, and the level of theta and low alpha waves decreased.

Laboratories have played an integral part in science and engineering education for a long time. It makes sense that students have opportunities to put theoretical knowledge in a practical context. Another aspect of laboratory learning is to introduce student to aspects of professional practice, for example in relating to how tasks are completed in an engineering workplace.

The learning outcomes of laboratories have been much discussed, and there are several frameworks to support the analysis of their contribution to the educational objectives, such as the widely cited work of Feisal and Rosa (2005).

As the world is seeing immense change in the way that we interact with laboratory learning activities, and the capabilities of generative AI, it is timely to re-evaluate if our laboratory learning activities are still fit for purpose.

Previous work has shown that laboratory learning activities still have a place in a modern engineering curriculum (Kist et al, 2024).

When traditional laboratory learning activities were developed, it is likely that the learning objectives focused on the procedural nature of the activity. It was assumed that students learned things by completing the activities. As we move to virtual and remote activities, and as AI becomes more capable of performing procedural tasks, it is critical that we regularly review our approach.

In this paper, a framework will be introduced to assist educators in a review of existing laboratory activities, and the development of new laboratory learning activities, to ensure that the learning objectives focus on the real intent of the activity. This will include clearly identifying the purpose of the learning activities and also identifying where AI can be used within the activity.

The goal is to provide a tool to support regular review of learning activities to ensure that the integrity of the learning activities is maintained while fostering the requisite AI capabilities in students.

The development of artificial intelligence (AI) technologies has made it possible to build systems that can continuously evolve, in a way comparable to human education. This type of system can be trained to solve tasks in various fields, gradually advancing from a beginner to an advanced level of performance, autonomously adjusting to their complexity and requirements. The concept AI Virtual Student/Worker (AIV-S/W) promises not only the automation and optimization of processes, the elimination of human errors, the substitution of the human factor, respectively the improvement and personalization of education, offering innovative solutions and high-performance standards in various fields.

On the one hand it is impossible to underestimate the influence of generative artificial intelligence(AI) tools on the educational process. Students and learners by nature will always try to use techniques to limit their efforts to reach a goal. So educators are facing the problem of modifying the pedagogical approaches, so that they could include implementation of AI tools in an ethic way, helping student to reach learning outcomes.

On the other hand, project based learning (PBL) is a well-known didactic format in engineering education as it allows to provide students a reality based project environment. PBL is a student-centered model of teaching and learning by doing (student)projects. Projects are to be understood as complex tasks, based on challenging questions or problems. Projects typically run from 2 weeks to one semester. It involves learners collaboratively, in planning, problem solving, decision making and/or research activities. Students acquire autonomy and responsibility, develop overarching skills and apply knowledge. At the end of the projects students report and/or present their results. Combining the positive outcomes of the use of AI with well-defined project cases, will augment the learning experience, without suffering from the loss of original work for the students.

In the work authors research the possibilities of the retrieval augmented generation for the implementation in project based learning approach for the model based system engineering. This allows to increase the field of application of the existing equipment COCO and improve students motivation.

Although Artificial Intelligence (AI) is transforming the industry, many univer-sity programs prioritize generic AI principles above domain-specific applica-tions. This paper investigates an adaptive course format to overcome the gap to real-world use cases. The suggested concept expands on a two-semester pro-gramming course and introduces students to current AI approaches used in re-search and industry in mechanical engineering. The authors aim to address bu-reaucratic limitations in curriculum reform by focusing on modules that pro-mote flexible, problem-based learning. The course structure will include a lec-ture series with a high-level overview and in-depth workshops on specific ap-plications led by active AI researchers. Students will work on semester-long projects with instructors as clients, which specify the technical requirements to be fulfilled. This dual position requires learners to deal with not only AI tech-niques, but also project management, client communication, and interdiscipli-nary interaction.

The broad representation of AI applications is highlighted by providing a brief overview over the starting topics and goals of challenges set in different me-chanical engineering domains such as product development, control systems and thermodynamics.

This paper explores how smart technologies and artificial intelligence (AI) can contribute to building resilience and professional preparedness among social care workers responding to disasters. Drawing on the theoretical framework of resilience and the “help the helpers” vision, it investigates the ethical and pedagogical implications of integrating AI tools into disaster-related social work education. The study builds on doctoral research on Romanian frontline social care workers’ psychosocial responses to crises, emphasizing the shift from reactive coping to proactive learning in technology-mediated environments. The paper aims to discuss how digital training platforms and intelligent systems can support ethical decision-making, reflective practice, and emotional regulation in high-stress contexts. It proposes a framework for socially sustainable and ethically informed workforce development in the era of smart technologies.

Generative Artificial Intelligence (GenAI) is reshaping the educational landscape. A single GenAI prompt can generate a carbon footprint several times higher than that of a standard internet search. Yet, discussions of environmental sustainability remain limited within GenAI-in-education research, which primarily focuses on usability and ethics. This study examines the perspectives of K–12 students and teachers on the environmentally responsible use of GenAI. A questionnaire comprising 12 Likert-scale items was distributed to participants from 63 schools, yielding responses from 729 students and 66 teachers across 80 schools. The results were quantitatively analysed. The presented Structural Equation Modelling model demonstrated strong performance. Students and teachers generally expressed positive views toward environmentally responsible GenAI practices, although their perspectives differed in six aspects, primarily regarding understanding of computational power demands. The differences were statistically significant, as determined by t-tests.

Integrating symbolic AI planning with robotic execution remains a central challenge in cognitive robotics and robotics education. While automated planning provides powerful tools for high-level reasoning, students often struggle to connect symbolic models with concrete robot behavior. This pa-per presents the design, implementation, and evaluation of an educational workshop that bridges AI-based task planning and robotic manipulation through simulation in NVIDIA Isaac Sim. The workshop was developed as part of an advanced Cognitive Robotics course and combines symbolic plan-ning using the Unified Planning Framework (UPF) and PDDL with execu-tion of manipulation plans by a simulated Franka Emika Panda robotic arm. A Robot Manipulation Language (RML) was introduced to abstract gripper orientations and model pick-and-place actions symbolically, enabling stu-dents to reason about spatial affordances while maintaining executability. The workshop follows a two-stage workflow: modeling and planning manip-ulation tasks, followed by execution and validation in a high-fidelity simula-tion environment. Evaluation results based on student performance, reports, and questionnaires indicate that the workshop effectively supported under-standing of AI planning, robot affordances, and the planning–execution loop. The study demonstrates the educational potential of modern robotic simulation tools for making complex cognitive robotics concepts accessible and actionable.

This article presents nine years of initiatives dedicated to develop Bachelor-level students’ skills for the Industry of the Future. The work highlights how emerging industrial expectations—driven by automation, artificial intelli-gence, IoT, robotics, cybersecurity, and data processing—require new peda-gogical strategies centered on Project-Based Learning (PBL). Since joining the EduNet network in 2017, the University of Orléans has strengthened in-ternational collaboration and integrated professional-grade tools into its cur-riculum. Several major achievements illustrate the impact of this approach: finalist positions in the 2018 Xplore competition, a national victory in 2019, the creation of the world’s first “PLCnext for Bachelor Level” certification in 2021, and a third-place award in the Xplore 2023 contest for an AI-based food-waste reduction project. In 2026, the certification will be updated to re-flect evolving industrial needs and new partnerships. Emerging technologies such as Augmented Reality further enrich future pedagogical developments.

Deep Neural Networks (DNNs) are deep learning models inspired by biological neural networks, used for complex

pattern recognition, visual and auditory data processing, and multidimensional signal interpretation. They have

become fundamental in areas such as image recognition, natural language processing, time series prediction, and

industrial process optimization. Denoising represents a central application of DNNs, with the role of highlighting

hidden structures and increasing the accuracy of analysis. Noise can be introduced by external factors (lighting

conditions, mechanical vibrations, electromagnetic variations) or internal (measurement errors, instrumentation

limitations). Its reduction allows: identifying defects at a microscopic scale, improving the reliability of

communications, ensuring data security, and optimizing visual processing. The goal of using DNNs in denoising is to

exploit their generalization and adaptability to achieve high performance with low data collection costs.

The 4th Industrial Revolution encouraged the emergence of new robotic solutions and advancements to assist human operators and increase efficiency and safety within all industrial environments, such as energy sectors, automotive, aerospace, indoor agriculture and so on. Therefore, research in this direction is fueled by the constant need of intelligent systems that can fulfill specific needs throughout industries, such as real-time assessment and report concerning equipment and environment. For example, the indoor-agriculture industry needs robotic systems able to provide data regarding the crop growth environment and plant healthcare in real-time remotely and on-site. In addition to this, manufacturing and energy industries are in need of assessing in real-time equipment, such as manufacture equipment, control and electrical panels, turbines, power lines to ensure the safety and efficiency of the industrial environment. Thus, the proposed solution is a compact and mobile sensorial robotic platform that can accommodate a wide range of devices, such as RGB, IR, multispectral and stereoscopic imaging cameras, and sensors, such as air-quality, environmental, smoke, Geiger and so on. Another key-aspect is the possibility of remote operation via Internet. In order to accommodate the wide range of integrated sensors an open-source and modular software framework was used, ROS (Robot Operating System). This approach allows multiple sensors and devices to be integrated within a unitary software framework in order to obtain valuable real-time data of the operating environment. Also, sensors can be swapped with minimal software modifications, offering a modular character to the hardware and software system. The proposed solution is suitable in industrial environments, such as manufacturing, automotive, aerospace, indoor agriculture, energy and adjacent environments.

The large-scale deployment of electric buses and trolleybuses in urban public transport requires advanced energy management strategies that consider operational constraints, power-grid interactions, and battery longevity. Digital Twin (DT) technology offers a promising framework to support such strategies, but it depends critically on high-quality real-time data from the physi-cal system. In this regard, this paper proposes a mobile data acquisition (mDAQ) system installed on electric vehicles as a key enabling layer for a Holistic Energy Management System (HEMS). This system collects syn-chronized multiparameter data in electrical, thermal, positional, and operational streams through IoT communication towards DT models that opti-mize charging schedules, energy flows, and preventive maintenance. Its ar-chitecture combines multiplexed sensing, floating differential measurement, Geographic Positioning System (GPS) synchronization, MQTT-based com-munication, and driver-oriented user interfaces. The proposed solution is po-sitioned within an integrated DT-based framework for urban e-mobility and can be deployed in a real public transport operator network.

Accurate estimation of forest structural parameters is essential for precision forestry, carbon stock modelling, and sustainable management. Traditional manual inventories are labor-intensive and often limited by sampling intensity. This paper evaluates the application of the Forest Structural Complexity Tool (FSCT), a robust automated pipeline, for the segmentation and extraction of dendrometric metrics from high-density LiDAR point clouds. We processed data from four distinct plots located in the Postăvaru and Barzava regions using UAV and Mobile Laser Scanning (MLS) data. The study validates the tool's capability to process datasets from varying sensor sources while highlighting current limitations regarding low-resolution Aerial Laser Scanning (ALS). Results indicate successful segmentation across varying stem densities, with a specific focus on the comparative analysis of geometric volume modeling methods.

The Ball and Plate Systems aim to fix a ball at the midpoint of the flat plate by rolling or pitching the plate in the direction of the x and y axes when it moves in any direction on that flat plate due to gravity or an external disturbance source. This study aims to redesign the ball and plate system with a new design and transform it into a laboratory set for use in educational activities.

Ordinary ball and plate systems perform this function using different mechanical methods. The most popular of these is the version driven by ordinary hobby-use servo motors attached to two sides of the flat plate. The primary reason for constructing these training sets in miniature is the use of touch-sensitive plates to detect the ball, and these are not manufactured in any size.

The aim of this study is to provide a more visually appealing training set and to more effectively incorporate servo motors into the theoretical content of the training set. For this reason, the study was conducted with a plate scale of 1000x1000mm. The purpose of the study is to balance the ball in the centre of the plate with different control methods.

Thanks to this study, the ball and plate system was created in a more visual and user-friendly manner. Because the resulting system includes a next-generation controller like PLCnext, it also allows users to test different control algorithms.

In the last decade, the use of automation in the workplace has experienced an exponential rise, enabled by technological improvements, which improve accessibility and enhance adaptability. This shift towards automated workspaces has been made possible by advancements regarding organizational solutions that, simultaneously, facilitate integration into corporate spaces.

The main objective of this project is to improve the performance of organizations, by implementing an internal platform that manages document transfers, both in physical and digital format, maintains the stock of supplies and provides alerts for their unavailability. In order for the application to be efficient and sustainable over an extended period of time, factors such as cost, long-term impact and compatibility with existing processes and practices are carefully analyzed.

A series of algorithms was used, that manages essential functions, such as robot navigation (A*), task customization (Decision Tree) and predictive maintenance (ARIMA). Following the identification of existing deficiencies, an integrated system (InternBot) can be provided as a solution. The InternBot structure has two versions. The exclusively digital version, Digital-InternBot (D-IB), with a reduced cost, offers functionalities such as: personalized task management, priority-based scheduling, anticipation of consumable needs with alerts and automatic orders. The second version, Social-InternBot (S-IB), has a robotic component that takes over time-consuming and repetitive physical tasks, in addition to the digital part.

The results from implementing this complex system, with dual functionality, digital and physical, consist of quick access to information, reduced workload for staff and optimised document flow. Although the system is designed to be efficient, errors such as incorrect document location, confusion between files with similar names or data synchronization problems may occur. These situations are anticipated and will be managed through continuous monitoring and immediate intervention by monitoring staff, thus ensuring the correct and continuous functioning of the proposed smart platform.

The KICK 4.0 research project focuses on the approach of effectively using large language model-based (LLM) generative AI technologies in laboratory-based engineering education. The aim of the project is to enable students and instructors to explore the benefits and limitations of generative AI systems in higher education.

Using a customized Design-Based Research (DBR) approach, an LLM-based AI tool is integrated into an ongoing course, evaluated iteratively, and refined. To this end, criteria for determining the effectiveness of an AI tool in terms of the quality of feedback were developed based on a systematic literature review and a survey of students and instructors. Given the small number of participants, the surveys were evaluated using qualitative empirical educational research methods. The results of this requirements analysis show that these technologies are considered to have great potential in supporting the individual learning process and that feedback quality is central in this.

This article describes the implementation of a teaching and learning scenario with the aim of integrating a Retrieval-Augmented Generation (RAG) chatbot into a laboratory-based course in fluid mechanics. In the Computational Fluid Dynamics (CFD) course, students learn how to use OpenFOAM, a software tool for solving complex fluid mechanics problems. Classes are held in a computer lab. Students work at individual workstations in front of a PC and the instructor demonstrates the relevant calculations and answers students' questions. In this setting, students are provided with a customized RAG chatbot as a “digital AI assistant.” The chatbot is trained to give students feedback that is comprehensible and conducive to learning. The results of this work will highlight factors for instructional design with regard to the effective use of LLM-based AI tools in instructional settings.

As part of the CrossLab project, LabCAR (Lab Constructive Alignment Recommender) was developed as an innovative tool that supports both students and teachers in the targeted design and implementation of learning processes. The system has a modular structure and enables the analysis and optimisation of learning outcomes (LOs), the coordination of learning and teaching activities, and the generation of well-founded recommendations for monitoring learning success. Students benefit from personalised suggestions for preparing for the defined learning outcomes and receive references to suitable learning resources, like digital laboratories. Teachers receive support in developing and adapting teaching methods and assessment strategies to promote the achievement of LOs. As part of the evaluation, various large language models (LLMs) were analysed in terms of their performance in the context of LabCAR. The study showed that all LLMs tested are fundamentally capable of fulfilling the defined objectives. However, significant differences in the quality and precision of the recommendations generated were found. The results underscore the importance of carefully selecting and adapting LLMs to meet the specific requirements of the education sector and ensure effective support for teaching and learning processes.

Cross-reality (XR) laboratories are becoming increasingly relevant in STEM education, enabling students to engage in experimentation beyond the re-strictions of traditional physical laboratory spaces. At the same time, large lan-guage models (LLMs) such as ChatGPT are rapidly entering higher education and raise new questions regarding competence development, learning practices, and educational integrity. The KICK 4.0 project addresses these developments by integrating AI-based natural language processing (NLP) tools into XR-based laboratory scenarios in fluid mechanics, with the aim of fostering both technical and reflexive competencies for a transforming world of work. This paper pre-sents a brief narrative literature review of the use of LLM/NLP tools in STEM learning contexts and analyses these findings through the lens of McLuhan’s Laws of Media (LOM; Enhance, Obsolesce, Retrieve, Reverse). This review suggests that LLMs may enhance creativity, reflection, and problem-solving; obsolesce highly scripted, instruction-driven laboratory formats; and retrieve di-alogical and inquiry-based learning practices. At the same time, risks of reversal become visible, including deskilling, bias, dependency, and ethical concerns. By combining the KICK 4.0 project with a LOM-guided literature analysis, this paper provides a structured reflection on how human–AI collaboration may re-shape laboratory education. The findings underline the need for critical AI liter-acy and balanced integration of LLMs in STEM learning environments.

Museums, as key institutions of non-formal learning, are experiencing rapid digital transformation shaped by emerging technologies. Among these, telepresence robots (TPRs) have attracted growing interest for their capacity to overcome physical and geographical barriers to participation. While immersive tools such as Virtual Reality (VR) and Augmented Reality (AR) are already widely employed to enhance engagement and visualization, the pedagogical and accessibility potential of TPRs remains underexplored.

This study investigates the opportunities and limitations of TPRs in museum education and assesses institutional readiness for adopting emerging technologies. Four research questions guided the work: (1) What is the level of readiness among museum educators and information professionals to integrate digital technologies? (2) How can technologies such as Artificial Intelligence (AI), VR, AR, and TPRs enhance learning processes? (3) What are the key challenges and opportunities for implementing TPRs in museum learning contexts? (4) What is the current technological capacity of museums to deploy robotic and immersive solutions for improved accessibility and inclusion?

A mixed-methods design was applied. First, a survey examined the readiness, experiences, and perceptions of museum educators concerning emerging technologies. Second, on-site observations and semi-structured interviews explored contextual factors affecting implementation practices, especially for TPRs. The survey, which included both closed and open-ended questions, covered digital competences, institutional support, infrastructure, perceived benefits and barriers, and ethical issues. Twenty-one professionals responded, and observations were conducted in twenty-two Estonian museums within the MUIS network.

Findings show high interest among educators in AI, VR, AR, and related tools. Most museums already employ digital solutions such as interactive screens, audio guides, and QR codes. While 81% of respondents expressed readiness to integrate TPRs, only 52% had prior awareness of their educational application. VR/AR tools were more familiar but often limited to temporary exhibitions. TPRs, by enabling remote real-time participation, were perceived as particularly inclusive for individuals with mobility or sensory impairments. Nevertheless, barriers included infrastructural constraints, insufficient funding, and limited staff competences. The study highlights the need for a sector-specific competence framework incorporating AI literacy, immersive design, and digital ethics.

By comparing TPRs with other emerging technologies, the study advances understanding of how museums can support educational objectives, foster inclusion, and drive digital innovation in non-formal learning.

Conclusions and recommendations emphasize that enhancing readiness for TPRs and related technologies requires infrastructural, organizational, and educational measures. Accessibility should be improved through ramps, elevators, barrier-free layouts, and optimized exhibition areas to facilitate robot navigation and visibility. Institutional capacity can be strengthened through designated charging zones, systematic staff training, and awareness-raising workshops. Additional practical skills, such as creating QR codes and audio guides, are needed. Pilot programs, teacher materials, and innovation funding should be developed to support sustainable and inclusive implementation of TPR-based and other technology-enhanced learning solutions.

This study contributes to the discourse on digital transformation in museums by clarifying the educational, accessibility, and organizational implications of telepresence robots and related technologies.

This paper presents the integration of Large Language Models (LLMs) and Physics-Informed Neural Networks (PINNs) into a Computational Fluid Dy-namics course based on deep learning and constructive alignment. The course included a lecture and a computer laboratory, conducted via Jupyter-Hub.NRW, enabling students to access remote Python environments and hardware. A survey of 14 students revealed that LLMs, primarily ChatGPT, were used mostly for programming assistance and debugging, while support for mathematical and physical understanding or generating examples was moderate, and planning activities was rare. Exam results indicate that per-formance differences were driven by cognitive levels as defined by the SOLO taxonomy rather than by AI versus traditional content, suggesting lim-ited intrinsic motivation toward AI. Students actively engaged with three AI Literacy Framework domains—Engaging with AI, Creating with AI, and Managing AI—while the fourth, Designing AI, was addressed through hands-on PINN tasks in lectures and laboratories. These activities exceed secondary education expectations of the AI Framework and represent a tertiary-level deepening of AI Literacy competencies. The findings highlight how AI can support numerics education, enhance learning outcomes, and provide a framework for developing advanced competencies in designing and critically evaluating AI systems in higher education.

Recent transformations in music education highlight the need to balance technical proficiency with the development of 21st-century skills such as creativity, collaboration, critical thinking, and problem solving. Although digital tools have proven effective in supporting engagement and interactive learning, their systematic integration into Romanian higher music education remains limited. This study proposes innovative, technology-enhanced activities for music theory, solfège, and dictation courses, using software such as EarMaster, Auralia, and Tenuto. Adopting a descriptive and design-based approach, the paper outlines strategies that combine traditional musical competencies with transversal skills promoted by contemporary educational frameworks. The anticipated outcome is increased student motivation, creativity, and autonomy through the meaningful use of technology. Overall, the study advocates for a more dynamic and digitally literate approach to music education, aligning with current global trends and promoting pedagogical innovation in the university environment.

The incorporation of digital tools into the traditional singing studio is a con-troversial topic, especially in the context of classical singing. Classical vocal pedagogy requires the direct contact between teacher and student, particular-ly in the early phases of vocal development, to ensure that proper technique is acquired by the student and healthy vocal production is established. Fur-thermore, aspects of vocal emission such as colour, brilliance, and dynamics are strongly influenced by the performance venue, since classical singing does not typically use microphones to augment sound. Moreover, there is al-so the issue of preserving a centuries-old legacy, as attested by vocal treatises dating back to the 17th century. The present study was motivated by the in-creased use of online platforms in the process of vocal education. This raised the question: to what extent can the teaching process benefit from the use of video conferencing tools that require the use of microphones, given that the quality of sung tones is altered when sound waves are converted into electri-cal signals? Furthermore, the authors observed that certain digital tools have been specifically designed to help singers warm up or improve their intona-tion, prompting inquiry into how these emerging tools might impact classical vocal pedagogy.

Context - The integration of AI into music therapy enables the creation of personalized playlists, generation of new music, providing real-time biofeedback and even creation of responsive and adaptive clinical, home-based, and digital therapeutic musical environments. Integration process need to consider hardware and software aspects related to advanced sensing, efficient computing, robust security purposes.

Purpose - The authors perform a critical review of existing publications related to therapeutic use of music for the relaxation purposes by implementing biofeedback loops with consumer-grade biosensing wearables coupled with other devices. Also it is emphasised the requirement for BSc, MSc and PhD students in music therapy to learn about the potential offered by Edge AI in the design and development of emotion-responsive music generation and other therapeutic tools which contain personalised real-time feedback systems.

Approach - Integration process need to consider hardware and software aspects related to advanced sensing, efficient computing and robust security purposes.

Advanced Sensing - Smart wearable devices based on real-time contextual analysis aim to provide a more personalised user experience by analysing environmental data alongside personal biometrics. The precise data acquisition and analysis in real-time is based on the integration of novel multi-sensory fusion techniques that leverage Edge AI so the decision making process is fast and responsive.

Efficient Computing – sustainable AI of Things (AIoT) requires substantial electricity consumption leading to significant carbon emissions. Real-time feedback and improved gesture recognition accuracy are provided by integration of wearable sensors with advanced data processing techniques. Music therapy has an important role in the management of stress and anxiety because the enhanced vascularization of various mesocorticolimbic structures and activation of dopaminergic neurotransmitters regulate the autonomic system, emotion and cognitive function.

Robust Security –The scalable and ethically grounded AI-wearable integration could consider federated learning for privacy, deep learning for noise filtering in EEG data, Generative Adversarial Networks (GAN) for enhanced performance in emotion recognition accuracy, temporal smoothness, and perceptual coherence of emotion-aware music generation

Anticipated Outcomes - The paper aims to provide useful technical information to music therapists, researchers and undergraduate and postgraduate students studying music therapy courses on how edge AI can enhance traditional methods (i.e. lyric analysis, music-assisted relaxation, Guided Imagery and Music, music assisted relaxation etc) by offering new creative possibilities, increasing efficiency, and addressing challenges like personalization, cost, ethical implications of AI use in music therapy.

Conclusions - The latest developments in sensing, computing and security aspects related to the use of edge AI in music therapy enhance the capacity of therapists, students, researchers and other users to generate personalised music, perform real time emotion recognition using long term comfort and ergonomic wearable devices, develop intelligent assistive tools for improvisation, music composition and music theory. etc. This paper provides a foundation for understanding current state of the art for wearable devices used in music therapy, efficient computing solutions and robust security methods. Also it emphasises areas for future research and development related to technological aspects of the interdisciplinary field of music therapy.

Nowadays, artificial intelligence (AI) has a heightened interaction with music, being known to even transform music creation. The implementation of AI is discussed to have both, positive and negative outcome: if on one hand, AI helps musicians to overcome creative blocks, to create new musical experiences and to be an aide in gathering and analysing data, on the other hand, it leads to copyright issues and to the idea of a probable substitution of human being musicians.

No matter the source of music the emotional effect is existent but variable as a function of cultural context, listeners’ background, and musical preference.

The aim of this paper is to use artificial intelligence to study the perception and reception of electroacoustic music through analysing listener data (mainly listening habits) in order to identify patterns and preferences. Audio features, neurological measurements (e.g. EEG Mindwave set), psychological tests and sentiment analysis are used to determine the feelings/emotions led by music.

A look at how Artificial Intelligence reshapes work in Europe shows both hope and unease coexist. Although most people see advantages, nearly two out of three think their jobs could vanish because of AI. A clear majority (84%) insist humans must stay in control. Based on data from Special Eurobarometer 554, this paper explores what lies behind such anxieties through the sociological lenses of thinkers like Marx and Weber. Findings point clearly that unequal risk shapes nervousness: those who feel economically challenged or lack tech confidence report higher stress levels. Still, fear of losing control shows up everywhere, even among those fluent in digital tools, echoing Weber’s unease with the hidden rules of bureaucratic systems. In response, our approach leans on a “Trust by Design” model shaped around the EU AI Act, where openness and shared decision-making help shift attitudes from pushback toward collaboration.

The Future of Jobs Report 2025, published by the World Economic Forum, ranks Artificial Intelligence and Big Data as leading transformative technologies shaping the global labor market, with remote and XR-enhanced technologies implicitly included under “AI and information processing.” yet their distinctive experiential potential deserves explicit attention in engineering education. Considering their growing adoption across industries for modeling, testing, and training - and their potential to complement traditional methods by fostering experiential and collaborative learning - these technologies play an important role in preparing future engineers for emerging roles in the future of jobs. This work explores how remote and XR-enhanced technologies can be integrated as complementary tools that naturally engage students’ curiosity, enhance learning, support inclusive and future-oriented learning. Selected examples from applied projects in mechanical engineering demonstrate practical strategies for integration, and show how these technologies, by improving motivation and conceptual understanding, can expand opportunities for their use while promoting ethical principles such as collaboration, equity, autonomy, accessibility, and lifelong learning, and offering scalability and safety. All these features are strengthened when integrated with AI and Big Data.

This qualitative study investigates the attitudes and behaviors of students in Bucharest, Romania, toward the increasing use of Artificial Intelligence in academia. Conducted through semi-structured interviews and guided by the theory of planned behavior, the research explores how students from diverse fields like sociology, computer science, and medicine perceive and utilize AI as a learning tool. The study addresses the technology's influence on evaluation processes, academic integrity issues such as plagiarism, and the broader ethical implications of its integration. The thematic analysis of the interview data reveals a complex and dualistic perception of AI's role in the modern educational landscape.

The findings highlight a significant divide in how AI is viewed. On one hand, students widely acknowledge its positive impact, primarily valuing it as a powerful tool for efficiency and enhanced learning. AI tools like ChatGPT are consistently used to save time by summarizing long texts, generating project outlines, and quickly locating specific information. This allows students to better manage demanding schedules, which often balance academic responsibilities with employment. Furthermore, AI functions as a personalized tutor, offering alternative explanations for complex concepts and assisting with technical tasks, which is particularly beneficial for students who need supplementary support outside of lectures. It also serves as a source of inspiration, helping to overcome creative blocks by providing a foundation of ideas upon which students can build.

On the other hand, the study uncovers deep-seated concerns about the negative consequences of AI reliance. A primary risk identified is the potential erosion of critical thinking and the promotion of intellectual laziness. The ease of generating complete assignments encourages a "copy-paste" mentality, which circumvents the fundamental learning process and is detrimental to long-term knowledge retention. This dependency raises significant issues of academic integrity, making plagiarism more accessible and widespread. Moreover, students are aware of the technology's limitations, including its tendency to "hallucinate" or produce inaccurate information and fabricated sources, which poses a threat to the quality of academic work.

The research concludes that the impact of AI is not inherent to the technology itself but is contingent on the user's intent and method of application. While responsible use can augment learning, its abuse leads to superficial understanding. A notable gap exists between prohibitive or nonexistent formal academic policies and the widespread, informal use of AI by students. This disconnect points to a need for universities to move beyond simple bans and instead develop clear guidelines for responsible use. Students advocate for pragmatic solutions, including formal training on AI ethics and the adaptation of evaluation methods to test genuine comprehension over rote memorization. Ultimately, the study suggests that AI's integration into professional and academic life is inevitable. The academic environment must therefore evolve from a position of resistance to one of proactive integration, preparing students for a future where human-AI collaboration is the norm and the ability to leverage these tools effectively is a critical skill.

Telecommunications degree programs should transition from "demonstrative" laboratories to development environments in which students design, implement and operate real systems. Integrating edge platforms (Raspberry Pi/ NVIDIA Jetson), Software-Defined Radio (SDR) and digital signal processing tools (GNU Radio/ MATLAB) enables students to transition from theoretical concepts to functional prototypes early in their studies. However, employers increasingly report that graduates have practical shortcomings, such as difficulty integrating theoretical concepts into real-world applications, gaps in hardware and software troubleshooting, inconsistent use of modern tools, unsafe RF operation and inability to reproduce experiments. The proposed approach addresses these issues by constantly emphasizing the connection between theory and practice through incremental spiral projects and clear maturity criteria.

Cybersecurity trainings are based on static, pre-defined "capture the flag" (CTF) tasks and attack blueprints. Although good learning exercises, these practices cannot mimic the spontaneity and worldliness of real cyber threats to Industry 4.0 and cyber-physical systems. The possible disruption brought by artificial intelligence (AI), especially large language models (LLMs) and reinforcement learning (RL), will enable the creation of dynamic and context-based adversarial plans.

The goal of this paper is to determine if an academic framework for training on AI-crafted attacks, instead of training on fixed exercise sets is feasible or not. Our specific interest lies in large language model (LLM)-enhanced reinforcement learning for designing unpredictable adversarial activity (e.g., phishing emails, SQL injection, variations on malware) that adapts continuously against student defences.

We propose a multi-level learning platform, based on LLMs, which constructs attacks uniquely designed for the student's skill level and defence tactics. The framework consists of:

1. Content generation using LLM for natural-language attacks such as phishing.

2. RL-driven adversarial modelling for evolving technical exploits such as injection or malware evasion.

3. Student defence platforms comprise intrusion detection laboratories, secure coding assignments, and network monitoring simulations.

The data gathering will obtain pre/post-competency assessments, defence success rates, and student perceptions. Comparison analysis will measure the disparity between the learning outcome of the AI-based group and a comparison group based on static CTF challenges.

It is anticipated that learners subjected to adaptive AI-generated assaults will exhibit enhanced critical thinking capabilities, quicker detection and response times, and greater retention of defensive techniques. Furthermore, it is expected that they will develop heightened confidence when confronting unpredictable adversaries, indicative of skills that are more applicable to real-world contexts within Industry 4.0. Additionally, the framework offers educators access to scalable, perpetually updated training resources, eliminating the necessity for manually created challenges.