This workshop equips STEM laboratory educators with the tools to design and evaluate effective Intended Learning Outcomes (ILOs) aligned with the principles of Constructive Alignment (CA) and structured through the SOLO taxonomy (Structure of Observed Learning Outcomes). Participants will learn to critically assess and reformulate their laboratory ILOs to reflect progressive levels of student understanding, from surface to deep learning, thus promoting student-centered learning. In a hands-on format, attendees will first (re)develop ILOs for their own (digital) laboratories and then design corresponding pre- and post-tests to systematically assess learning progress. The workshop emphasizes how CA connects ILOs, Teaching and Learning Activities (TLAs), and Assessment Tasks (ATs) to ensure coherence and transparency in laboratory pedagogy. Through a guided example of a cross-reality chemical extraction laboratory, participants will explore how to construct meaningful assessments that capture changes in students’ declarative and functioning understanding. By the end of the session, educators will be able to design laboratory experiences that not only enhance student motivation and autonomy but also provide measurable evidence of learning gains, fostering deeper engagement and reflective teaching practices in STEM laboratory education.

Focusing on mental health, equity and inclusion, cultural diversity, and quality learning outcomes has led to the realisation that respect and supportive university environments are important. Any improvements will not only benefit equity groups but all students, as learning is also about social interaction.

Respect fosters inclusive and supportive learning environments that support students and staff in learning and academic discourses. This is particularly relevant in the current context of polarised public and social media interactions. In universities' quest for knowledge, respect is essential to ensure well-being, diversity of thought and meaningful discourse.

The purpose of this workshop is to develop a shared understanding of what respect looks and feels like in a modern educational context.

Participants will work in small groups to unpack key concepts, such as what respect is. How is respect given and received? And must respect be earned, and can be lost?

It is anticipated that this will provide participants with opportunities to reflect on their own understanding, experiences, and perceptions of respect, and how these influence their interactions with peers and students.

Virtual Reality (VR) is increasingly recognized as a powerful medium for learning, capable of transforming how complex spatial and scientific phenomena are communicated. In higher education, project-based learning and research-integrated teaching approaches enable students to acquire both disciplinary knowledge and transversal skills through authentic design challenges. In parallel, VR is gaining momentum in science communication for public engagement, helping audiences visualize invisible or abstract processes such as geological or energy-system dynamics. While both strands - educational VR and public-facing VR - are well studied independently, little is known about how they can be connected to form a continuous learning structure that benefits multiple audiences.

This paper presents such a connection through a case study of a university VR course linked to public science communication events on geothermal energy. A virtual “elevator into the underground,” developed by students and refined for exhibition use, served as a shared artefact across formal and informal learning contexts. Qualitative evaluation following Kirkpatrick’s model showed high motivation and significant learning gains among students, as well as strong engagement and knowledge transfer among public participants. The results highlight how authentic sustainability contexts and the reuse of student-created artefacts can foster linked learning ecosystems that bridge education, research, and public engagement.

Capture the Flag (CTF) exercises are widely used in cybersecurity education for developing problem-solving and technical skills, but traditional formats often lack realism, teamwork, and situational context. This study, conducted within the ATHENA project at Tallinn University of Technology, explores the use of Virtual Reality (VR) to enhance CTF challenges. Two pilot scenarios were tested: Tower Cipher, a 3D exploration puzzle designed for secondary and vocational students, and Control Room Mission, an industrial control room simulation tested with international cyber talents in both computer-based and VR modes.

A mixed-methods evaluation combined surveys, reflections, and task performance to assess usability, engagement, teamwork, and perceived skill development. Results show that VR tasks significantly improved motivation, collaboration, and situational awareness, especially in operational technology contexts, though technical skill acquisition remained stronger in traditional screen-based CTFs. Challenges included navigation, VR comfort, and scalability.

The findings suggest that VR-enhanced CTFs are not a replacement but a valuable complement to conventional cybersecurity training. They offer promising opportunities for experiential learning, helping bridge the gap between technical exercises and realistic, collaborative practice.

Picking services are essential in logistics and supply chain management. Among these service providers, manual packaging workstations are vital in situations where full automation is not feasible due to economic or operational reasons. This paper presents an overview of A/B testing in VR design projects and affordable ergonomics measurement techniques, which can be used in education as well as in industry. The concept develops and assesses a virtual reality A/B-testing framework that facilitates an iterative, data-driven approach to workstation design, helping to avoid expensive physical prototyping and which allows students to create said scenarios with limited resources available. The modular pipeline includes a virtual prototype stand with standardized task logic, a controlled experimental protocol across six workstation variants, automated collection of head- and hand-trajectory data, automatically computed key performance indicators related to efficiency and ergonomics, and subsequent inferential analyses. Following the implementation, 20 participants empirically test various configurations multiple times to investigate learning effects on times, movement distances, and strain. In addition to the empirical findings, the work provides a reusable infrastructure for VR-based workstation engineering, facilitating rapid evidence-based iteration and a reduction of real-world prototyping.

Virtual reality and metaverse technologies hold strong potential for engineering education, particularly for improving spatial understanding, experiential learning, and collaboration. Yet most implementations remain confined to isolated pilot projects that lack institutional alignment and long-term sustainability. Existing research focuses mainly on single-course case studies, leaving a clear gap in scalable approaches for faculty-wide integration. This paper addresses that gap by presenting a structured, top-down methodology for adopting immersive technologies across an engineering faculty. The proposed Faculty-Level Integration Methodology (FLIM) follows an iterative three-phase process of analysis, design, and validation. The framework is supported by low-cost infrastructure and reusable open-source applications. Results indicate strong motivation among faculty and students, reveal key barriers, and identify opportunities for sustainable institutional adoption, including future university-wide scaling.

Artificial intelligence (AI) is becoming an integral part of daily life, shaping how people access information, consume media, and interact with technology. This study explores how hands-on robotics workshops enhance children's AI literacy in an informal learning environment. A mixed-method approach was used to analyze self-reports on enjoyment and difficulty, along with behavioral observations related to initiative, perseverance, and frustration management. The findings indicate that parental participation influences learning experiences, either by fostering independent problem solving or, in some cases, unintentionally hindering a child's participation. The findings emphasize the need for early AI education that clearly differentiates between automated behaviors and genuine machine learning processes.

Software development projects increasingly require project managers

who can interpret complex data, anticipate risks early, and make informed decisions

in rapidly changing Agile environments. Yet traditional project management

education—both university-based and vocational—often lacks realistic,

data-rich learning contexts where learners can analyze project behavior, explore

risk scenarios, and understand how estimation errors propagate. This paper presents an AI-based dashboard that supports early risk detection in Agile software

projects while simultaneously serving as a training platform in formal project

management (PM) education. The dashboard integrates ML models to predict

task duration deviations, detect sprint-level risks, and provide interpretable analytics.

Beyond real-time predictive insights, the dashboard functions as a learning

simulator for PM training programs. Students can upload or simulate project datasets, test planning assumptions, adjust risk thresholds, visualize correlations,

and observe the consequences of decision-making in a controlled environment,

The contributions of this work are: an interactive dashboard for risk-oriented project

analytics and a pedagogical model for integrating AI supported dashboards

into PM education.

Deep Neural Networks (DNNs) achieve strong performance

in semantic segmentation for robotic perception but remain vulnerable

to adversarial attacks, threatening safety-critical applications. While ro-

bustness has been studied for image classification, semantic segmenta-

tion in robotic contexts requires specialized architectures and detection

strategies.

We propose a framework for detecting adversarial attacks using pre-

trained ResNet-18 and ResNet-50 models. Our method leverages ad-

vanced feature extraction and statistical metrics to distinguish clean

from adversarial inputs. Experiments demonstrate its effectiveness across

various attacks, offering insights into model robustness. Additionally, we

compare network architectures to identify factors that enhance resilience.

This work supports the development of secure autonomous systems by

providing practical detection tools and guidance for selecting robust seg-

mentation models.

Children in today's world are used to studying through pictures, and the use of visual media in the classroom frequently improves their learning. This article describes a system that helps preschoolers learn the Latin alphabet's letters.

Given the advantages of using the idea of gamification in the classroom, an entertaining and captivating game was developed to assist students in drawing connections between letters and pictures of animals. Children can use the application, which is built on convolutional neural networks, to learn animal names more quickly by connecting dots on the screen to draw letters.

Through the use of different programming languages, the effective training of a letter recognition

model and the development of an intuitive graphical user interface, it has been demonstrated that a practical and attractive application has been created, which has been well received by children.

It has been shown that a useful and appealing application has been developed, which has been well-received by children, through the use of various programming languages, the efficient training of a letter recognition model, and the creation of an intuitive graphical user interface.

In order to make the application more kid-friendly and portable, a Raspberry BI was utilized together with a display in order to show letters generated from the application and an audio system for instructions.

This paper describes the change in teaching practice when delivering Medical Electronics course to the students from two faculties: students enrolled to Applied Electronics specialization from Faculty of Electrical Engineering and students enrolled to Biomedical Engineering from Faculty of Product Design and Environment at Transilvania University Romania. In previous years there was a lack of interaction between the groups of student from these two faculties so the teaching staff has decided to introduce the problem-oriented project work as summative assessment tool alongside the written exam in a safe learning environment which encouraged creativity. The activities included in the group projects followed the KASH model (Knowledge, Attitude, Skills, Habits) because employability represents a combination of knowledge, skills, attitudes, and personal attributes of graduates enabling them to contribute effectively to the workplace and society progress in general.

CRISPR/Cas-based biocomputation has enabled the design of advanced logic circuits analogous to CPUs. However, the scalability of these systems is limited by the lack of formal verification frameworks. Synthetic genetic circuits (SGCs) fail unpredictably due to resource competition (metabolic load), off-target effects, and crosstalk. The design-build-test cycle is slow and incapable of guaranteeing robustness. This article explores the use of Evolutionary (NEP) and Genetic (NGP) Processor Networks as a unified Bio-CAD (Biological Computer-Aided Design) framework. A conceptual mapping is presented where NGP processors model logic gates, strings model gRNAs and mRNA, and formal filters (rc_s, rc_w) model binding specificity. We argue that the inherently parallel and distributed architecture of NGP, and its demonstrated ability to solve NP-complete problems in linear time, directly reflects the massive parallelism of molecular computation in vivo. This approach would allow for the formal design and verification of fidelity, robustness, and scalability of biocomputers before their costly wetware synthesis.

The purpose of the exercise is truly multifaceted. We’d like to teach the students about digital twin technology, stepper motors, H-bridge, limit-switches, hardware hookup, calibration and PLC programming. The pedagogical framework for this student lab activity is grounded in problem-based and student-active learning, with segmentation as a key component. Segmentation involves breaking down complex tasks, concepts, or problems into smaller, manageable units that are logically connected. Each segment targets a specific sub-skill, enabling learners to master one part before progressing to the next.

The authors have used this assignment in their teaching for 3 years, about 150 students/hand-ins. Results are that the students seem to appreciate the modularity of the assignment, even if it is challenging. Furter the students appreciate the joy of seeing the wing physically move as intended from their own coding. As teachers we see that the students seem quite motivated by the assignment. they ask good questions and learn “the hard way” that a software simulator and hardware can be quite different.

In essence, the narrative about the fighter jet wing serves to give students some motivation and context it could be any stepper motor application.

This project addresses the power overload problem faced by a glass bottle manufacturing enterprise that expanded to five automated production lines and a glass-melting furnace. The existing power system, originally designed for smaller operations, could not withstand the transient current surges—five to eight times the normal level—generated when multiple high-power lines started simultaneously. Manual time-based scheduling was used to stagger production start-ups, but it lacked stability and real-time adaptability.

To solve this issue, a multidisciplinary team of instructors and students from Rui De International College collaborated with the company to design and implement a PLC-based intelligent control system for dynamic power load management. The system adopts a three-tier architecture comprising a data acquisition layer, a control layer, and a management layer integrating PLC logic control, sensor data acquisition, and HMI-based visualization. Four core modules were developed: intelligent staggered start, automated control logic, data analysis, and protection and alarm mechanisms.

Practical application demonstrated that the system effectively eliminated overload-induced shutdowns, stabilized current fluctuations, and improved reliability and production efficiency. With a total cost of approximately RMB 80,000–100,000—far below the RMB 500,000 required for transformer replacement—the solution proved both technically and economically advantageous. The project also exemplifies how vocational colleges can collaborate with industry to deliver innovative, low-cost smart manufacturing solutions while enhancing students’ applied automation skills and instructors’ practical teaching competence.

The 21st century industrial landscape is undergoing an unprecedented transformation, driven by artificial intelligence (AI), sustainable digitaliza-tion, and the rapid evolution of smart building technologies. These devel-opments are redefining how knowledge is generated, transferred, and ap-plied within both industrial and educational environments. This paper ex-plores the synergy between smart industry and education in the era of In-dustry 5.0 — an emerging paradigm emphasizing human-centric, resilient, and sustainable systems. Through an interdisciplinary analysis of European and global case studies, including initiatives in Germany and Ukraine, the study identifies effective models that integrate AI, smart infrastructure, and sustainability principles into educational ecosystems. Empirical evidence from university–industry collaborations demonstrates that such integration enhances technical competence, promotes creativity, and builds resilience within future-ready workforces. Drawing on the works of Koeberlein-Kerler (2021, 2023), who pioneered the concept of hybrid learning ecosys-tems and living laboratories for engineering education, the paper substanti-ates that the merging of smart industry and education is essential to achiev-ing the goals of sustainable innovation, digital inclusion, and professional adaptability.

The rapid emergence of generative AI is reshaping educational practice by requiring teachers to renegotiate professional roles, competencies, and class-room routines. This study examines how Estonian teachers understand and adopt AI, drawing on survey data from 210 teachers, reflections from 40 trainers and IT-education experts, and a stakeholder focus group conducted in 2025. The analysis deliberately centers on teacher readiness rather than student experiences, treating it as a prerequisite for how AI is framed, regu-lated, and enacted in classrooms. Using the Kübler–Ross change curve and the Concerns-Based Adoption Model, the study maps teachers’ emotional readiness and adoption stages across school types. Results show rapid growth in digital and AI competencies but uneven, largely individual-driven adop-tion, with persistent uncertainty around ethics, academic integrity, and im-plementation. The findings indicate that national initiatives must align more closely with teachers’ foundational needs, emotional readiness, and organiza-tional conditions to enable responsible AI integration by 2026/27.

The OSKA 2024 monitoring report forecasts for Estonia that by 2027, Estonia will need at least 40,200 ICT specialists, 1.5 times more than in 2020. Cybersecurity and data analysis (including AI) tasks are often carried out by professionals with other job titles (e.g., IT managers in small firms), so official statistics underestimate their actual numbers. Estonian Vocational Education and Training (VET) curricula were developed in 2014, and discussions for a current update began in 2024, with the aim of implementing changes in 2026. Since 2014, the technology has undergone significant changes. For example, cybersecurity threats have evolved rapidly, and generative artificial intelligence (AI) technology appears to already be impacting how students approach modern learning. The Estonian president also announced a new 2025 program called AI-Leap, whose goal is to implement AI into education. As the school's overall goal is to impact students' knowledge, skills, and values, these topics should be integrated into the official curriculum sooner rather than later. Currently, the goal is to implement the Digital Competency Model in VET education; however, it has been unclear which version to use, as 2.0 does not include AI, and 3.0 has not been announced yet. Additionally, several cybersecurity and AI-related competency models are available, including the UNESCO AI Competency Framework for Students, the European Cybersecurity Skills Framework Role Profiles, and the ACM/IEEE-CS/AAAI Computer Science Curricula 2023, which can be helpful in delivering a higher-level approach to curriculum updates.

This paper explores the European Union’s Artificial Intelligence Act (EU AI Act), which entered into force in August 2024. The Act is examined through the lens of economic com-petitiveness, focusing on its implications for AI companies operating within the EU. The EU AI Act presents a risk-based classification system for AI systems—ranging from minimal to unacceptable risk—and imposes strict obligations for companies regarding high-risk and general-purpose AI models. While the minimal risk is unregulated, the limited risk AI systems are subject to lighter obligations.

From a comparative perspective, the study contrasts the EU’s precautionary, ethics-driven approach with the more innovation-centric models of the United States and China. By exploring national industry cooperation and policy makers response, using the case study of Sweden’s AI innovation ecosystem, this paper analyses how European states can support innovation and counter the effects that the EU AI Act has on the agility, scalability, and global competitiveness of European AI startups and SMEs. It highlights growing concerns from European leaders who warn that the Act’s complexity, vague definitions, and rapid implementation timelines could lower EU’s competitiveness.

Based on a quantitative survey of 413 Romanian respondents, this analysis reveals a significant "AI Readiness Paradox". While individuals feel moderately prepared for AI's integration into their work, an overwhelming 77% believe the nation as a whole is not sufficiently informed. The findings highlight widespread anxiety about job displacement, particularly for office and administrative roles, which are seen as highly exposed. Furthermore, AI is currently valued more for the personal "comfort" it provides (37%) than for productivity gains, which is reflected in a low willingness to pay for AI services, as 67% of users engage only with free versions. A sense of fatalism is also prevalent, with 35% of respondents believing they will have no choice but to accept AI's pervasive integration into their lives.

TThe past decade has marked a decisive transformation in educational environ-ments, driven by rapid advancements in digital technologies and accelerated by the COVID-19 pandemic. Since 2016, broadband infrastructure, learning man-agement systems, and immersive instructional tools such as virtual and augment-ed reality have redefined teaching and learning processes worldwide. The sudden shift to remote instruction in 2020 further intensified reliance on digital platforms, fostering hybrid synchronous–asynchronous learning modes in the post-pandemic period. Within this context, engineering education—and particularly Computer-Aided Design (CAD) instruction—has undergone significant peda-gogical and technological evolution.

This study examines the development of CAD education over the last decade by conducting a bibliometric analysis of literature indexed in the Scopus database be-tween 2016 and 2026. Using a systematic keyword search and RIS-file extrac-tion, the dataset was processed in VOSviewer to generate keyword co-occurrence networks and overlay visualizations. The analysis identified 828 relevant publica-tions and revealed twelve thematic clusters reflecting pedagogical foundations, digital design tools, emerging technologies, artificial intelligence integration, and professional training. Dominant research themes include engineering education, design instruction, 3D modeling, and Industry 4.0-aligned digital competencies. Emerging topics, such as open-source platforms, benchmarking, machine learn-ing, and data-driven design, indicate increasing convergence between CAD edu-cation and advanced computational methods.

The findings highlight a research transition from software-centric approaches to-ward holistic, technology-enhanced pedagogies that promote digital literacy, ex-periential learning, and interdisciplinary design. However, gaps remain in linking CAD competencies to broader educational transformations such as lifelong digital skills, competency-based learning, and sustainability-driven design practices.

This paper presents a modeling environment for global human-centric digi-tal strategies that emphasize technologically enhanced production. Building on Industry 4.0 foundations, it explores how AI, collaborative robots, digi-tal twins, and sensor-driven simulations can be leveraged to optimize ergo-nomics, productivity, and economic efficiency in work processes. The ap-proach integrates automated analysis tools, intuitive drag-and-drop inter-faces, and a comprehensive robot library to enable skill-based human-robot task allocation and realistic, safe simulations. Designed for education, the system fosters exploratory, problem-based learning, aligning cognitive psy-chology principles with hands-on experiences to enhance understanding of complex cyber-physical systems. By combining technological, ergonomic, and didactic perspectives, the method supports the acquisition of Future Skills, promotes active learner engagement, and prepares students for the challenges of the human centered industrial paradigms.

CONTEXT

Modern military field operations face critical bandwidth constraints due to reliance on satellite uplinks, tactical radio systems, and the need for low electromagnetic signatures. Despite these limitations, legacy network traffic including advertisements, telemetry, background updates, and tracking scripts consume substantial portions of available bandwidth, negatively impacting mission-critical communications. While existing research has explored Software-Defined Networking (SDN) approaches for agile routing and load balancing in constrained military environments, a significant gap exists in solutions targeting traffic filtering at the DNS level to reduce overhead and enhance security posture. This study addresses this gap by examining DNS-based traffic optimization as a complementary approach to existing military networking strategies, building upon proven civilian applications of DNS filtering technologies that have demonstrated bandwidth savings of 10-30% in non-military contexts.

PURPOSE OR GOAL

This research investigates whether deploying Pi-hole—an open-source DNS-level filtering solution—on low-power Raspberry Pi hardware within military field networks can effectively minimize non-essential traffic, reduce latency, and augment security in bandwidth-constrained operational environments. The study seeks to answer the primary research question: Can DNS-based filtering provide measurable improvements in bandwidth utilization, network performance, and security posture when integrated with enterprise-grade routers in simulated military field conditions? The motivation stems from the critical need to maximize available bandwidth for mission-critical communications while simultaneously reducing security vulnerabilities associated with advertising networks, tracking services, and malicious domains. This work aims to demonstrate a scalable, cost-effective solution that complements existing SDN capabilities and can be rapidly deployed in tactical environments where every transmitted byte impacts operational effectiveness.

APPROACH

The study employed a controlled deployment methodology using Raspberry Pi Model 3B hardware (selected for its low power draw of approximately 5W and rugged potential) integrated with an ASUS RT-AX1800U enterprise-grade router in a simulated field environment. The Pi-hole DNS filtering solution was configured as the primary DNS resolver for all client devices, with security enhancements including DNSSEC validation, query rate limiting, and curated military-specific blocklists. The network topology utilized a hub-and-spoke architecture with Cat-6 wired connections, DHCP configuration assigning Pi-hole as primary DNS, and QoS rules prioritizing mission-critical traffic. Data collection focused on four primary metrics: total bandwidth utilization, external DNS query frequency, web page load times, and simultaneous connection counts. Performance was measured by comparing network behavior with Pi-hole active versus inactive states across multiple simulated mission scenarios. Security effectiveness was evaluated by testing detection and blocking capabilities against known malicious domains from military threat listings. The deployment incorporated network segmentation via VLANs to isolate mission traffic, and comprehensive logging enabled detailed analysis of DNS query patterns and potential security incidents.

Integrating advanced technologies into educational environments is essential for training students in industrial automation. In this context, the Polytechnic School of Engineering of Ferrol (EPEF) has incorporated Phoenix Contact PLCnext technology, thanks to its membership in the EduNet - International Education Network. PLCnext technology represents a significant evolution from traditional controllers, expanding their capabilities beyond the languages defined by the IEC 61131 standard. This versatility allows for the integration of modern languages, facilitating the development of more advanced applications adapted to the current challenges of industrial automation. In the educational context, PLCnext offers a hybrid environment that promotes practical learning, experimentation, and the acquisition of cross-cutting skills in programming, control, and data management. The main objective of this work was to design and implement a comprehensive data acquisition and control system for laboratory level plants, using PLCnext controllers as the system's decentralized cores. The aim was to demonstrate the viability of this technology in educational environments by enabling remote control, SCADA visualization, and data logging. In addition, the flexibility of PLCnext to integrate tools such as MATLAB/Simulink in a laboratory environment was demonstrated.

Abstract. This article presents a new framework designed to improve image interpretation in four high school science disciplines through the use of open-weight language models optimized for resource-constrained environments. It also makes a significant contribution to the field of educational AI. While most previous studies focus on proprietary or hybrid models, evaluated using benchmarks such as VisioMath, ScienceQA, SceMQA, or MMMU, our study proposes an alternative

approach. We optimized four open-weight models—Mistral-7B, LLaVA-1.5-7B, Kosmos-2, and Qwen2-VL—selected for their lightweight architecture, low memory footprint, and robustness. The optimization focused on low-rank adaptation (LoRA) and 4-bit quantization in a resourceconstrained environment, specifically to improve the understanding of scientific images. The results of Bench Low, distinguishing between multiple-choice (MC) and free-response (FR) questions, reveal significant variations depending on the type of task, model, and discipline. LLaVA-1.5-7B stood

out in particular for its superior performance, achieving near-perfect accuracy on multiple-choice questions (96.9%) and the highest score on open-ended questions (69.1%), with an overall average of 79.3%. In contrast, Kosmos-2 achieved the lowest results (40.8% overall), with high variability between disciplines, reflecting uneven coverage of training data. For reproducibility purposes, the source codes are fully available on Github: https://github.com/mouazmikail/Bench-Lowv1/tree/master

A novel system is presented that bridges computational fluid dynamics (CFD) simulations with immersive extended reality (XR) environments by enabling tactile perception of flow phenomena through haptic wearables such as gloves. The newly created conversion pipeline from industry standard fluid simulation software to standalone XR devices is described. In the XR environment, users can dynamically adjust visualization styles (e.g., particle tracing) and simulation playback speeds, allowing flexible exploration of flow behavior. Additionally, the system generates haptic feedback by mapping simulation data to per-finger forces on haptic gloves, where each finger independently perceives localized flow data. A key focus is a low-latency rendering pipeline that synchronizes haptic output with user hand movements, ensuring spatial consistency and tactile fidelity across all fingers. By bridging the gap between simulation software, immersive visualization, and high-resolution tactile feedback, this work establishes a scalable framework for making fluid dynamics tangible in XR.

Remote monitoring and control (RMC) operations, built upon IoT and IIoT systems, enable real-time supervision and actuation across smart environments such as industrial facilities and other remote engineering applications. These systems leverage interconnected devices and advanced data analytics to enhance operational efficiency, safety, and decision-making processes. However, their reliance on light-weight communication protocols with limited built-in security features, such as MQTT, exposes them to cyberattacks that can compromise reliability and safety. This paper investigates the operational impact of such attacks on an IoT/IIoT-based RMC system and proposes a hybrid Edge–Fog deployment of machine learning (ML)-based intrusion detection systems (IDS) to strengthen resilience and ensure continuous operation.

Project-based learning is an effective pedagogical approach in contemporary computer science education, integrating theory with practical skills through real-world projects that cultivate critical thinking, problem-solving, and collaboration. At the XXXXX, students engage in an Informatics Project after foundational coursework in computer science, programming, and mathematics. Traditionally focused on full-stack software development—including front-end, back-end, databases, and containerized deployment—the project was expanded for the 2025/2026 winter semester to incorporate embedded hardware integration via the XXXX remote laboratory platform. This integration addresses industry demands for hybrid software-hardware competencies essential for cyber-physical systems, IoT, and smart industries.

This paper examines the pedagogical benefits and technological challenges of embedding remote hardware access within a PBL-based full-stack software development course. It investigates how hands-on interaction with embedded microcontrollers enhances students’ mastery of asynchronous software architectures, microservice orchestration, containerization, and communication protocols like MQTT and RESTful APIs. By integrating physical hardware controllers into an otherwise software-centric curriculum, this approach bridges academic preparation and industrial multidisciplinary competencies, preparing students for roles in Industry 4.0 and IoT development.

The Informatics Project’s core task challenges pairs of students to collaboratively develop a multiplayer quiz application spanning multiple technology layers: a Bootstrap and TypeScript web front-end, a Java Vert.x backend, and MariaDB for persistent data. The system supports real-time multiplayer interaction through both web-based and hardware game controllers remotely accessed via the XXXXX platform, which employs ESP32 microcontrollers. Controllers communicate with the backend using scalable, industry-standard asynchronous protocols. The semester begins with a kickoff introducing project constraints and a preconfigured Docker Compose environment containing essential microservices and demonstration setups to establish baseline communication patterns. Students manage source control and collaboration in GitLab using structured branching and issue tracking, mirroring professional workflows. Remote lab access enables continuous hardware testing from any location, supplemented by on-campus openLab sessions for mentoring and technical support.

Preliminary results demonstrate that integrating remote hardware into PBL enhances student engagement and motivation by providing tangible interaction with physical devices absent in many software-centric programs. This setup helps students grasp asynchronous communications, containerized service orchestration, and embedded system programming more deeply. The remote lab mitigates traditional physical accessibility barriers, supports inclusive and hybrid learning modalities, and fosters teamwork, version control discipline, and professional workflow adherence. Early feedback indicates increased confidence in developing complex distributed systems and hardware-software co-design, with a greater appreciation for interdisciplinary engineering.

In conclusion, expanding full-stack education with remote lab hardware access through PBL creates a scalable, adaptable, and effective teaching framework bridging software and embedded systems education. The XXXXX remote lab offers hands-on hardware feedback in a controlled yet remote environment, significantly lowering experiential learning barriers while supporting geographical and educational inclusivity. This model offers promising opportunities for broader adoption to enhance project authenticity in engineering curricula, emphasizing structured project management and continuous mentoring to optimize learning. Future work aims to rigorously quantify learning outcomes, scale to larger cohorts, and refine hybrid curricula aligned with evolving workforce needs in smart industries and connected system development.

This paper evaluates an adapted Double-Diamond learning framework designed to foster AI and XR literacy in higher-education design studios using an immersive metaverse environment. The five-week sequence com-bines analog sketching, prompt-based generative AI, and validation through extended realities, emphasizing human authorship, critical reflec-tion, and responsible creativity. A mixed-methods approach was em-ployed: quantitative measures included an adapted Motivated Strategies for Learning Questionnaire (MSLQ) and the Utrecht Work Engagement Scale—Student version (UWES-S9) administered pre- and post-intervention, while qualitative reflections supported interpretation of be-havioral and attitudinal changes. Results indicate consistent improve-ments in AI/XR-related motivational and learning strategies and in aca-demic engagement. On a 0–10 scale, MSLQ scores showed a substantial increase in self-reported strategic use of AI and XR tools, whereas UWES-S9 indicated stronger patterns of vigor, dedication, and absorp-tion. We discuss implications for evidence-based smart education and propose AI/XR-literacy-by-design as a replicable model for creative dis-ciplines.

The Colegio Nacional de Educación a Distancia (CONED) is a Costa Rican institution that is a pioneer in Latin America in offering secondary education through distance learning. The institution's educational process is predominantly conducted in virtual environments, with face-to-face attendance being reserved for the administration of summative evaluations in various branches throughout the country.Due to its considerable enrolment, CONED lacks sufficient physical space and trained staff to carry out face-to-face experimental practices with large groups. In this context, remote laboratories (RLs) emerge as the appropriate alternative, given their capacity to facilitate uninterrupted access to online experiments. This feature of RLs serves to democratise learning opportunities, catering to a diverse student population in terms of age, ethnicity and geographic location. In order to overcome the limitations of the CONED modality, the incorporation of Remote Laboratories is allowing the execution of real remote experiments. To enhance experimental practice and offer more immersive experiences, a pilot was conducted with the Microscopy LR, developed by the Remote Experimentation Laboratory of the Universidad Estatal a Distancia in conjunction with LabsLand, winner of the GOLC 2025 award. This resource employs a real microscope that gives students the experience of working with biological samples. The aim of the article is to analyse the educational experience of CONED's seventh year science students on the use of this laboratory. The study was developed using a mixed methodology to analyse students' perceptions following utilisation of the Remote Microscopy Laboratory. An experimental activity, which was included in the evaluative components of the subject, was implemented, this activity comprised seven sections, which allowed students to be guided through a process of exploration and progressive analysis, fundamental to the development of scientific skills by encouraging observation, interpretation, and the integration of theory and practice. An expert-validated questionnaire was designed and administered to 35 seventh-year science students for data collection. The first section sought to collect information on the academic profile of the students. The second section comprised nine Likert-type statements. In addition, 5 open-ended questions were included to obtain qualitative information aimed at identifying areas for improvement. The results showed that remote laboratories incorporated into CONED's science classes bring the experimental component of science closer to a heterogeneous population such as the one studying in this modality, which can strengthen the development of scientific skills in this population. It is recommended to integrate artificial intelligence to support the use of remote laboratories in distance education and to facilitate student learning when the teacher is not available.

Cybersecurity training struggles to keep pace with the rapid emergence of new vulnerabilities and attack techniques. Traditional methods rely heavily on manual setup, static exercises, and delayed integration of real-world threats, leading to outdated and ineffective learning experiences. Meanwhile, the accessibility of Large Language Models (LLMs) has enabled attackers to automate vulnerability discovery and exploit generation, emphasizing the urgent need for adaptive and continuously updated defensive training tools. This paper proposes HackEd, an automated system that uses LLMs and Retrieval-Augmented Generation to create and personalize Capture-the-Flag challenges. The system operates in two phases: challenge creation, where data from multiple knowledge bases is processed to generate vulnerability-based exercises packaged as Docker containers, and experience tailoring, where an adaptive LLM tutor monitors user performance and provides real-time hints and feedback according to each trainee’s skill level. A human-in-the-loop evaluation was conducted to assess technical validity and pedagogical value. The generated challenges were reviewed for realism and educational effectiveness, while controlled student experiments measured engagement and learning outcomes. Results show that LLMs, when combined with structured knowledge bases, can generate relevant challenges with minimal human intervention. Moreover, the adaptive tutor significantly improved learners’ comprehension, motivation, and retention by offering individualized guidance during problem-solving. Technically, the system reduces the cost and time required to update cybersecurity training content. Pedagogically, it enhances engagement and inclusivity by dynamically adjusting difficulty and feedback. Overall, HackEd demonstrates the potential of integrating LLM-driven automation into cybersecurity education, providing a scalable, efficient, and continuously evolving framework to prepare the next generation of cybersecurity professionals.

This analysis is based on in-depth interviews with the creators of AI products, including Romanian developers and tech entrepreneurs. A central finding is the "Creator's Conundrum": developers use AI to become hyper-productive but are acutely aware that the same technology could devalue or render their own profession obsolete, especially at junior and mid-levels. They view AI as a sophisticated "meta-tool" for amplifying their skills—a "thinking partner" or a "tutor"—but exhibit a deep technical skepticism regarding its accuracy, leading to a consistent professional practice of verification. The study also identifies the "Creator's Burden," a heightened awareness among developers of the systemic risks their creations might pose, including massive environmental impact and the potential for large-scale disinformation.

Drawing from in-depth interviews with Romanian entrepreneurs and managers across diverse industries like healthcare, beauty, and agricultural technology, this study uncovers a "Leadership Paradox". Leaders are optimistic about using AI as a tool to amplify their own productivity, but they express deep pessimism about its long-term societal effects, such as the deskilling of future generations and a collective loss of human ambition. The primary barriers to AI adoption are identified not as technological, but as fundamentally human and cultural, including a deep-seated resistance to change and a social stigma that frames AI use as "cheating". The findings also point to a complex workforce "reconfiguration" that threatens skilled but procedural white-collar roles, not just manual labor.

In the high-stakes world of motorsports, tire performance is often the deciding factor between victory and defeat. As racing technology continues to evolve, IR matrix sensors and modular software are transforming how teams monitor and manage tire conditions. These technologies provide a more precise and data-driven approach to tire monitoring, allowing teams to make real-time decisions to optimize tire performance and strategically manage tire wear and degradation. Moreover, with the ability to simulate tire profiles before a race, teams can proactively adjust their strategies, further enhancing their ability to outperform competitors.

This paper presents ProtectU, a multi-model cyber threat detection system designed to safeguard vulnerable users (such as children and the elderly) against phishing, romance scams, social engineering attacks, and malicious URLs. The architecture combines artificial intelligence modules deployed via gRPC microservices and served in Docker containers. ProtectU integrates seamlessly with a user-friendly web interface and supports multilingual data processing (English and Romanian), ensuring accessibility and effectiveness in diverse linguistic contexts.

In contemporary times, the advancement of cutting-edge technology leads to a need for more people to understand complex systems and how to work with them, education is therefore required to advance along with the rise of technology. One such technology that enhances education in particular practical ways is Virtual Reality (VR) and Extended Reality (XR), offering a new angle for researchers and educators to pursue in the ultimate goal of creating new education methods that can be used to help modern day persons better interact with the technology they use in their day-to-day lives and careers.

The paper is devoted to the progress of the Romanian School of Fluid Power Systems developed by the authors in the frame of Online Engineering. The main stages of creating a modern patrimony of knowledge, hardware and software in the frame of the Doctoral School of the Power Engineering Faculty of the National Science and Technology University POLYTECHNICA Bucharest are presented. The assessment of the value of the main achievements of the former PhD students is briefly presented. The scientific and technical contributions can be identified in the industrial impact of more of 50 graduated from this valuable doctoral school.

The main purpose of the paper is the assessment of the manner of promotion the concepts and tools offered by the on-line engineering in the complex field of the power systems which use fluids under pressure. The wide interest of an efficient high education system in this field comes from the need to collect and use of wide interdisciplinary knowledges and skills, gathered by different scientific and technical field as fluid dynamics, electrical drives, sensors and transducers, analogue and digital controllers and communications.

The integration of digital laboratories into engineering education offers a transformative approach to bridging theoretical knowledge and practical application. This manuscript presents the "DigiMatLabExam" project, developed at TU Dortmund University, which incorporates ultra-concurrent remote laboratories (UCRLs) into the Fundamentals of Materials Engineering course and its corresponding e-examinations. By simulating key materials testing methods (tensile test, hardness testing and charpy impact testing) and enabling detailed microstructure analyses, the digital laboratory fosters competency-oriented learning for over 350 students annually. Initial evaluations revealed enhanced student motivation, confidence, and practical understanding of material properties. Furthermore, a structured didactic implementation ensures that students develop critical competencies during lectures and exercises to prepare for summative assessments effectively. This innovative methodology aligns educational practices with industry demands, paving the way for scalable advancements in STEM education.

Higher Education Institutions are not only digitalizing their degree programs but also aiming for a more open and inclusive Higher Education. Among these efforts to increase inclusivity lies the accessibility of degree programs, courses and the university campuses to enable students with disabilities and/or chronic illnesses (SwDI) to participate in everyday university life.

However, digitalization and accessibility are often not planned for together and accessibility gets relegated to an afterthought or quick fix applied in the end. This creates barriers for SwDI to participate as intended. This is even more apparent in STEM laboratories (conventional and digital ones) which are normally not accessible.

The project "Lab4All - Access@DigitalLabs" dedicates itself to researching and improving the accessibility of digital laboratories ranging from Desktop VR-Simulations over Ultraconcurrent Remote Laboratories to real-time Remote Laboratories. Besides legal regulations and guidelines of the WCAG, SwDIs are integrated into all processes from assessing needs, requirements, experiences and expectations to testing the accessibility of selected laboratories to evaluating the success of revising the laboratories to be more accessible.

The fast evolution of Industry 4.0 and smart factory technologies requires a shift in engineering education toward greater integration of hands-on learning with advanced industrial systems. This paper presents two educational experiments aimed at enhancing student preparedness and engagement in robotics and automation. The first investigates the effect of pre-lab virtual training using digital twin software on student efficiency during robotic programming tasks. Results show that prior virtual preparation reduces completion time and supports more effective problem-solving, although learning transfer varies among individuals. The second experiment evaluates student perceptions of the Beckhoff XPlanar platform as a teaching tool for industrial transport systems. Survey results reveal strong appreciation for the educational value of real hardware, despite challenges in programming difficulty and time constraints. The findings highlight the pedagogical benefits of combining simulation-based preparation with hands-on experience in modern robotics education.

In the context of the digital transformation, the clergy functions as a unique spiritual workforce whose mission is the ethical and psychological guidance of communities. Today, the members of these communities ("parishioners") are deeply embedded in a digital ecosystem that generates new and complex existential challenges: algorithmic addiction, social isolation, ideological disinformation, and anxieties related to the intersection of technology and humanity, such as transhumanism. These issues inevitably enter the pastoral sphere, demanding a new set of competencies from priests.

However, traditional clerical training, specific to seminaries and theological faculties, has been slow to adapt to this new reality. Curricula rarely include dedicated modules on digital ethics, internet sociology, or the pastoral implications of Artificial Intelligence. This discrepancy creates a significant competency gap, leaving many clergy unprepared to effectively address the problems their parishioners face in their daily lives.

The primary purpose of this study is to identify and map the key competencies and strategic frameworks necessary for effective pastoral ministry in the 21st century. The research aims to define a model of the "digital shepherd", a spiritual guide equipped not only with theological knowledge but also with the discernment and skills required to navigate and provide guidance in a novel digital landscape.

This goal is pursued by answering the following research questions, based on the analysis of the perceptions of Orthodox clergy in Romania: 1) What new professional challenges does the digital environment pose to pastoral practice? 2) What specific competencies (technical, ethical, communicational) are necessary for priests to effectively guide their communities? 3) What are the competing institutional strategies the Church can adopt in the online environment (e.g., defensive isolation vs. missionary engagement)?

The study employs a qualitative methodology, focusing exclusively on the in-depth analysis of semi-structured interviews conducted with 8 Orthodox Christian clergy in Romania. The sample is diverse, including parish priests, monastics with technical backgrounds, and academic theologians, to ensure a comprehensive perspective on the professional challenges across different segments of the Church.

Thematic analysis of the transcripts focused on identifying recurrent themes related to the professional practice of the clergy. These include pastoral care strategies in the digital environment, debates on the Church's institutional policy towards technology, the personal difficulties of priests in using new media, and, most importantly, the perceived deficits in their own professional training to face these new realities.

The global discourse on workforce readiness for the AI era focuses almost exclusively on technical upskilling and reskilling. Training programs prioritize teaching employees to operate AI tools, manage data, and adapt to automated workflows, treating the human worker as a component to be updated for a new industrial paradigm. This approach, while necessary, is centered on a purely instrumentalist view of human capital, emphasizing efficiency and productivity above all else. This paper argues that such a focus is critically incomplete. It overlooks the profound impact of advanced digital technologies on the inner lives and moral frameworks of workers, affecting their autonomy, ethical discernment, capacity for deep work, and sense of purpose. Current training prepares workers to operate technology but not to resist its potentially dehumanizing tendencies. This study explores this "inner curriculum" of non-technical skills, drawing on unique insights from a community grappling with the tension between ancient tradition and hyper-modernity.

The primary purpose of this research is to identify and articulate the humanistic and ethical competencies required for workforce resilience in an AI-saturated society. The study is motivated by the need to move beyond an instrumentalist view of AI training and to propose a more holistic model that equips workers not only to use technology but to remain its master. This goal is pursued by analyzing the perceptions of Orthodox Christian clergy and laity in Romania. The key research questions are: 1) What are the primary spiritual and ethical risks AI poses to human agency and well-being? 2) How do these risks manifest as challenges within the workforce, particularly regarding critical thinking and interpersonal relationships? 3) What "spiritual skills" or virtues from Orthodox tradition can be translated into a secular framework for modern ethical workforce training, moving from passive consumption to active, value-driven engagement with technology?

The study employs a qualitative methodology, utilizing in-depth, semi-structured interviews with a sample of 8 Christian Orthodox clergy and 6 lay believers in Romania. This two-tiered sample was chosen to capture both the "pastoral-analytical" perspective of spiritual leaders observing broad societal shifts and the "lived-experience" of laypeople navigating these technologies in their daily lives, from young IT engineers to disenchanted seniors. Interview transcripts were analyzed using thematic analysis to identify recurrent patterns. Key themes that emerged include: the tension between technology as a threat versus a missionary field ; the asymmetry of power between tech architects and users, likened to a game of "Mafia" where the uninformed majority is easily manipulated ; the generational divide in digital adoption and perception; and the imperative to "sanctify" technology through intentional, value-driven use rather than fearful withdrawal.

Generative AI is increasingly being integrated into education, offering transformative possibilities for both teaching and learning and tailoring educational content to the needs of individual students. We have chosen to elaborate on questions lying in the intersection of economics, linguistics and innovative engineering, aiming to benefit from automation achievable using AI. In our previous research we established methodological approaches to testing ability of Large Language Models, in particular Google Gemini and OpenAI ChatGPT, to process figurative speech and tasks in mathematics and physics defined in plain English; the present work is a solution for modelling financial projections of technological startups and competitor search. The said projections are based on an array of variables such as research and development cost, operational expenses, monthly increase of the customer count, cost of customer acquisition and so on; the input data is provided partially in numeric values and partially in the form of linguistic variables, the latter are translated into numbers (or weights) which are configurable, and modelling is done. The output of modelling is processed by a large language model to obtain a textual description in English, or any other supported language. This solution shall be used in the training of students of computer science and automation programs to help them master innovative project management and improve their business development skills. At this stage our solution relies on API-based access to 2 commercially available pretrained language model, namely, Gemini version 2.0 and OpenAI version GPT-4o, at a later stage we are considering development of our own language model. Besides, since our students are not English native speakers, generative capabilities of large language models assist them with the language barrier.

Globally, the demand for IT professionals continues to exceed supply, high-lighting the need for early, accessible, and inclusive career guidance. Tradi-tional counseling services often lack the capacity to personalize pathways in-to rapidly evolving fields such as AI, cybersecurity, and sustainable technol-ogies. To address this gap, Tallinn University of Technology (TalTech) de-veloped AI Career Pathfinder (AI-Pathfinder), a conversational AI system that aligns learners' interests with bachelor-level IT programs. Designed for students, adult learners, and high school teachers, the tool uses LLM-powered dialogue to help users identify suitable study options. Based on data from 479 pilot participants, the study shows how playful, conversational in-terfaces can reduce barriers to IT education while revealing hidden biases, age-related preferences, motivational differences, and technical challenges. An unexpected outcome—human-like interaction—illustrates both the promise and risks of AI-based mentoring. The paper contributes to global STEM outreach by demonstrating the dual potential and pitfalls of integrat-ing AI into career guidance.

Educational remote laboratories allow educators and students to perform experiments with real hardware remotely, addressing geographical and financial barriers in engineering education. However, their potential is often limited by the cost and complexity of specialized physical equipment. The REDTAIL project bridges this gap by integrating high-fidelity simulations with real hardware, creating a more immersive and scalable remote lab experience. This paper presents the REDTAIL unified platform and its accompanying software development kit (SDK), which empowers educators to develop custom simulations, utilize a library of default and community-built assets, and deploy them seamlessly within their courses. Development of default simulations shows the tool chain’s capability to create effective simulations with varying levels of complexity and cross-domain communication between hardware and simulations. Furthermore, we report on successful undergraduate classroom deployments, validating the platform's suitability for higher education. Ongoing development and outreach aim to expand the simulation repository and refine the SDK, advancing the platform's role in promoting a more accessible and scalable future for engineering education.

Abstract. Accurate and timely identification of malignant morphology in histopathology slides remains a major challenge and is still largely performed by expert pathologists scanning whole-slide images for nuclear atypia, crowding, and architectural distortion. Conventional deep learning systems can detect cancer-associated patterns but are typically trained in a static “train-once/predict-once” fashion, behave as black boxes, and offer limited support when confidence is low or domain shift occurs. To address these gaps, we present Look, Mark, Explain, a minimal agentic AI framework that combines classical computer vision with large language model (LLM) reasoning to support cancer slide review while keeping the pathologist in the loop. High-resolution H&E images are processed with a contrast-enhanced, marker-controlled watershed pipeline to segment nuclei and extract 27 interpretable features per cell capturing size, shape, chromatin texture, nucleoli, and spatial context. For user-selected nuclei, a multimodal LLM receives a cell close-up, a contextual tissue view, and the feature vector, and returns a cancer-likeness assessment, confidence, and a pathology-style explanation that explicitly links its judgment to the provided measurements and visible cues, without issuing a formal diagnosis. We demonstrate the system on a breast cancer sample with 971 segmented nuclei, showing that population statistics, size-stratified comparisons, clustering metrics, and LLM narratives align with classical cytologic criteria and highlight enlarged, hyperchromatic, irregular cells in crowded, disorganized regions. The prototype uses standard digitized slides without stain normalization and is embedded in an interactive interface for slide exploration and on-demand AI consultation. While not intended for clinical use, it illustrates how a simple, reasoning-first, pathologist-in-the-loop agent can “look, measure, and explain” in a way that complements existing workflows and provides a testbed for future human–AI studies in digital pathology.

In Electrical and Computer Engineering (ECE) curricula, students typically interact with real hardware to gain hands-on experience in dedicated in-person laboratory sections. Remote laboratories are an alternative by allowing students to interact with the real hardware remotely. However, they are often constrained by the cost and space of physical equipment. This paper presents the development of a novel educational remote laboratory for Programmable Logic Controllers (PLCs) that will integrate real hardware with interactive 3D digital twins. Part of Project REDTAIL, this work details the design and implementation of a unified architecture that synchronizes an Arduino Opta PLC with a simulation of a water bottling plant. Our methodology involved instructor interviews to define requirements, followed by the parallel development of the hardware interface, the backend framework, and the 3D simulation. The primary outcome is a functional prototype that validates the technical feasibility of this hybrid approach. By augmenting a single, physical PLC with diverse virtual scenarios, the system directly addresses the scalability limitations of traditional PLC remote laboratories.

In Electrical and Computer Engineering (ECE) curricula, the hands-on laboratory experience is often hindered by issues like limited physical resources and scheduling conflicts; to address these challenges, REDTAIL implements a multi-module approach, which is centralized in the REDTAIL Simulation Repository (RSR). This paper focuses on the RSR, a scalable, open-source centralized hub designed to provide instructors with a dedicated access and deployment point offering lesson plans, exercises, and various other tools. It features a robust web interface that delivers a clear educational experience, connecting the back-end of simulations, and through integration with \LabsLand, seamlessly incorporates access to remote laboratories. Future work will focus on cultivating an active community of educators within the RSR, enabling them to share and customize educational materials, as well as integrating an AI assistant to provide real-time support and facilitate automated lesson customization and scaffolding. The RSR is thus a valuable step toward solving major issues limiting ECE curricula, empowering instructors, and promoting more efficient learning outcomes.

This paper presents the development and pedagogical integration of a Financial and Accounting Education Application (TECBOOKS) designed to bridge the theory-practice gap for industrial engineering students. Students in this field are required to master complex, interconnected concepts across finance, accounting, and production to evaluate real-world projects effectively. Traditional classroom methods often fail to provide the hands-on, interactive experience necessary to internalize the complexities of project evaluation. This critical skill determines the financial viability of investments.

The TECBOOKS application is a modern, modular, single-page application built on React, specifically engineered to function as a powerful, visual, and accessible analytical tool. Its core innovation lies in its capacity for seamless integration with a separate production line/ERP simulator. This connectivity allows the application to automatically retrieve simulated, personalized production data generated by the student’s strategic decisions in the ERP environment.

Once connected, the TECBOOKS dashboard processes this raw data to generate crucial analytical outputs in real-time. These outputs include dynamic financial statements (Income Statement, Balance Sheet), Key Performance Indicators (KPIs), and sophisticated project evaluation metrics such as Net Present Value (NPV) and Internal Rate of Return (IRR), utilizing various statistical forecasting methods. This integrated approach ensures that the calculation of robust metrics, such as NPV, is not merely a theoretical exercise but is grounded in the realistic operational complexities of a simulated production environment.

The modular design also ensures that individual components—like the statistical forecasting or financial statement generators—can be used independently, making the app scalable and applicable to students in diverse academic fields. Although currently in the early stages of institutional adoption, this solution aims to significantly boost student engagement, financial literacy, and overall comprehension of real-world project complexities through a gamified and data-driven learning ecosystem. Future work will focus on empirical evaluation of student outcomes to quantify its effectiveness.