Higher education programs are embracing curricular models that incorporate active teaching-learning methodologies to foster students’ active participation in knowledge building, based on competency-based training. Research-based learning (RBL) stands out as an active methodology that can be used at various academic levels, including undergraduate. In this context, the present research explores the contribution of undergraduate research training to the process of developing generic competencies in engineering students. The study focuses on the perceptions of a group of employers and graduates from a Peruvian higher education institution. A mixed-methods approach was adopted, as interviews and surveys were applied. The study had a descriptive purpose with a concurrent design. The findings suggest that undergraduate research training is perceived as supporting the development of generic competencies such as research, teamwork, effective communication, analytical and critical thinking, adaptability, digital literacy, intellectual curiosity, and lifelong learning (LL). Overall, the results highlight undergraduate research training, implemented through RBL, as an effective strategy associated with the development and strengthening of generic competencies and the alignment of Science, Technology, Engineering, and Mathematics (STEM) education with labor market demands, while underscoring the importance of consolidating an institutional research culture within competency-based curricula.
This paper examines the strategic importance of cybersecurity education as a pedagogical and managerial mechanism for strengthening organisational resilience. Responding to the need for practice-orientated engineering pedagogy, the paper formulates a guiding research question: How can role-specific cybersecurity education be selected, justified, and evaluated so that it supports resilient behaviour across organisational levels? The study combines a structured narrative review of cybersecurity education and awareness approaches with a transparent SWOT-based analytical framework. SWOT is used as a managerial self-assessment tool for identifying strengths, weaknesses, opportunities, and threats in both the general cybersecurity environment and the human-factor dimension. The paper distinguishes gamification from games, explains the rationale for selecting educational approaches, and proposes operational criteria for translating SWOT findings into training requirements. The contribution is intended for educators, cybersecurity managers, and organisational leaders who design or evaluate cybersecurity awareness programmes in engineering-orientated, technical, and socio-technical environments.
This study aimed to develop and evaluate a flipped, microlearning, and augmented reality model to enhance undergraduate engineering students’ conceptual understanding and problem‑solving skills in an electrical circuit laboratory course. The participants consisted of 43 first‑year engi‑ neering students, divided into an experimental group (n = 21) and a control group (n = 22). The experimental group was instructed using the integrated model, while the control group received traditional instruction. Research instruments included a learning achievement test, a problem‑solving skills rubric, and a satisfaction questionnaire, all of which demonstrated acceptable validity and reliability. Data were analyzed using multivariate analysis of variance (MANOVA) and mixed ANOVA. The findings indicated that students in the experimental group significantly outperformed those in the control group in both learning achievement (p < .05) and problem‑solving skills (p < .01). In addition, the experimental group demonstrated greater learn‑ ing retention at two weeks after instruction. These results suggest that integrating augmented reality and microlearning within a flipped classroom framework effectively supports conceptual understanding and enhances problem‑solving skills in engineering education. The integrated model provides a practical instructional approach for improving laboratory‑based learning in science and engineering contexts. This study extends existing research by providing an instruc‑ tional framework that supports both cognitive and experiential learning in higher education.
Engineering students struggle with abstract concepts and theory-practice integration, yet few studies systematically examine how to design virtual reality (VR) learning environments that sustain engagement beyond initial novelty effects. Multi-modal approaches that strategically integrate different technological affordances offer promising but underexplored solutions. This design case study demonstrates the development and implementation of a multi-modal VR learning environment integrating immersive Cave Automatic Virtual Environments (CAVE), non-immersive MATLAB simulations, and artificial intelligence (AI) motion tracking within project-based learning (PjBL). We investigate how different technological modalities can be strategically orchestrated to support situational interest development in undergraduate engineering education. We designed and implemented a four-week “Biomechanics of a Pitch” project in a Strength of Materials course, engaging 20 undergraduate mechanical engineering students. Using an exploratory case study approach with convergent mixed-methods data collection, we measured presence, representation fidelity, and situational interest through validated instruments (α = 0.92–0.93) and focus groups. Data was analyzed through reliability analyses, descriptive statistics, and thematic analysis to understand design effectiveness and student experiences. The multi-modal design enabled distinct complementary technological affordances: CAVE excelled in spatial presence (α = 0.93) and immersive understanding (M = 4.2, SD = 0.8), MATLAB supported analytical thinking and pedagogical usefulness, while AI motion tracking created personalized connections. Student interest evolved from pre-experience curiosity through maintained engagement during multi-modal experiences to post-experience learning motivation. Qualitative data revealed how strategic modality transitions sustained engagement beyond novelty effects. We present an integrated framework synthesizing interest development theory with multi-modal VR design principles, demonstrating how theoretically grounded VR implementations move beyond novelty effects through strategic technological orchestration. The study contributes a design framework, evidence-based design checklist, and practical implementation guidelines for engineering educators seeking to create effective VR-enhanced learning experiences.
This study designed an AI-integrated collaborative problem-based learning (CPBL) course aimed to bolster programming self-efficacy among undergraduate and to elucidate the underlying psychological mechanisms that drive self-efficacy. A total of 179 Taiwanese undergraduates participated in an 18-week intervention that integrated AI toolkits, CPBL frameworks, and instructional scaffolding to facilitate interdisciplinary application. To achieve our goals, two instruments were developed to measure programming-specific achievement goals and mindsets. The results indicated that the AI-supported CPBL environment effectively fostered a growth mindset, mastery experiences, and self-efficacy in programming. Mediation model analysis revealed that growth mindset and mastery experience functioned as dual mediators linking achievement goal orientation to self-efficacy. Notably, mastery goals exerted a markedly stronger influence on self-efficacy compared to performance goals. The findings highlight the necessity of prioritizing mastery-oriented pedagogy in the design of AI-integrated programming curricula.
Modern industrial and systems engineering curricula excel at teaching students to mitigate physical supply-side disruptions, yet frequently fail to address the intra-supply-chain allocation of financial distress. This theoretical paper proposes a novel, problem-based learning curriculum framework designed to bridge the gap between operational logistics and financial risk-shifting. It critiques the pedagogical reliance on traditional bankruptcy prediction models, demonstrating how they systematically misclassify capital-intensive original equipment manufacturers (OEMs) into distress zones due to the structural liabilities of captive financial divisions. Grounded in systems thinking and Kolb’s experiential learning theory, the proposed framework introduces dynamic industry benchmarks into the classroom, specifically utilising the Strouhal credibility index. By structuring a pedagogical module around the 2020-2022 automotive crisis, and utilising descriptive statistical comparisons rather than complex econometrics, this framework equips engineering educators with an accessible blueprint to teach the financial bullwhip effect. This ensures future engineering managers understand the structural vulnerabilities embedded within hierarchical supply chains and the limits of OEM-driven supply chain finance.
Engineering laboratories play a significant role in facilitating problem-based learning across electrical, environmental, computer, and interdisciplinary engineering programs; however, most lab sessions still rely on physical attendance, delayed grading, limited equipment references, and little visibility into student engagement. This lack of visibility compromises instructors’ ability to identify learning barriers, unsafe conditions, uneven resource use, and at-risk students during lab sessions. An Internet of Things (IoT) smart lab framework is proposed that uses real-time sensor data, edge processing, and cloud storage to support learning analytics for improved operational and pedagogical decisions. The framework integrates data from environmental sensors, smart equipment, workstation logs, access control, and learning platforms to create a continuous data stream. An analytic layer processes this data using structured event learning and sequence modeling to estimate engagement, detect at-risk sessions, identify equipment bottlenecks, and enable timely intervention. A prototype simulated undergraduate engineering lab is developed to evaluate latency, reliability, predictive performance, and interpretability. Results show 93.4% engagement accuracy, 0.914 macro-F1, 0.887 AUROC for early risk detection, and 1.8 seconds median latency, outperforming temporal and non-temporal baselines. These findings demonstrate that multimodal sensing with explainable AI improves operational visibility and educational analytics. The framework defines the engineering lab as a cyber-physical learning environment rather than a collection of devices, supporting safer, more efficient, and data-driven learning across engineering disciplines.
The integration of artificial intelligence (AI) into vocational education presents both significant opportunities and complex ethical challenges. This study examines the use of AI among learners attending secondary technical and vocational schools, with a particular focus on the boundary between legitimate learning support and academic dishonesty. A quantitative questionnaire survey was conducted with 1,745 learners from Slovakia, the Czech Republic, Hungary and Poland. The collected data were analysed using descriptive statistics and a thematic analysis of open-ended responses. The results indicate widespread use of AI tools among the surveyed learners. Most respondents reported regular use of AI to understand learning materials, generate texts, complete assignments and support project work. Many respondents reported confidence in evaluating AI-generated content, suggesting a perceived level of digital literacy and critical awareness. Nevertheless, open-ended responses revealed several forms of academically dishonest AI use, particularly for generating code, writing essays, preparing presentations and completing tasks without meaningful cognitive engagement. Thematic analysis identified key motivations such as time efficiency and pragmatic task management, alongside a nuanced awareness of the ethical boundary between legitimate assistance and misconduct. The findings highlight the urgent need for pedagogical approaches that promote responsible and ethical use of AI. Moreover, the study reinforces the view that, in an increasingly technology-driven educational landscape, competencies in interacting with AI, such as formulating effective prompts, verifying outputs, and engaging in critical evaluation, are becoming essential skills for vocational school graduates.
Computational thinking (CT) has become a fundamental skill in the 21st century, especially within secondary education. However, integrating CT into teaching and learning requires the participation of teachers specialized in this area. This systematic review identifies and analyzes the most effective methods, strategies, and activities used to teach and assess secondary- level CT skills. To ensure broad and rigorous coverage of relevant research, a literature search was conducted in Scopus, Web of Science, and ScienceDirect databases, covering studies published between 2020 and 2024. The review synthesizes current educational practices, highlighting approaches such as Game-Based Learning, gamification, and assessment activities such as the Bebras Challenge. Findings reveal diverse pedagogical strategies and ongoing challenges, particularly regarding validating assessment instruments and teacher professional development. Besides, this review underscores the need for more robust, standardized assessment methods and further research to advance CT education. The insights provided are valuable for educators, policymakers, and researchers seeking to enhance CT education and its integration into engineering pedagogy.
The purpose of this study is to identify the main determinants of entrepreneurial intention among engineering and business students. A bibliometric meta-analysis was performed with 1215 articles published in Scopus between 2009 and 2023, which spans 15 years of scientific production on entrepreneurial intention. Sources, authors, affiliations, countries, most cited papers, keywords, trending topics, co-occurrence network, thematic evolution, and collaborative networks were analyzed. Results showed that key related themes include entrepreneurial education, contextual factors, and personality traits. An average annual growth of 30.26% in annual scientific publications was observed. 2023 was the year with the highest scientific production, and 2009 had the highest average number of citations. It was shown that students from developing countries tend to have a higher entrepreneurial intention. Only 1% of all studies were conducted in Central America. The most used theory was that of planned behavior. The main determinants include entrepreneurial self-efficacy, attitude toward entrepreneurship, personality traits, contextual conditions, and entrepreneurial education. The practical implication of the present study suggests that higher education institutions can foster an environment that promotes the development of entrepreneurial intention among students.
This qualitative study provides an in-depth overview of requirements engineering (RE) education in Swiss higher education institutions, comparing the offerings at universities and universities of applied sciences. The findings reveal major diversity in course formats, content, and institutional contexts, with a strong emphasis on industry relevance. Many programmes incorporate practical learning, project-based assessments, and alignment with professional standards like the Certificate of Professional Requirements Engineering of the International Requirements Engineering Board. While this research is specific to Switzerland, its insights have broader implications for RE education in Europe, particularly in dual higher education systems. The study highlights the growing importance of emerging themes such as artificial intelligence, stressing the need for curricula that remain responsive and future-oriented. To deepen understanding and strengthen the validity of findings, ongoing research will triangulate educator insights with student and industry perspectives, enabling a systematic comparison of needs, expectations, and perceived challenges in RE education. This triangulated approach will support the development of effective, inclusive, and practice-oriented RE curricula.
Research on teacher training in the field of engineering seeks to advance understanding of teacher training for engineering education. This topic remains essential given the growing demand for technical professionals. This paper summarizes research conducted since 2000 at four higher education institutions, two in Brazil and two in Portugal. Based on the following categories: Initial motivation; training trajectories and the presence or absence of pedagogical training; the need or otherwise for such training; difficulties faced and opportunities envisioned in teaching; and, finally, self-recognition of the “teacher being.” The paper seeks to analyze how engineering teachers are constituted. Interviews were conducted with teachers working in engineering programs, who indicate professional fulfillment as the main factor common to all.
This quasi-experimental study investigated generative AI (GenAI) tools—Copilot for chemistry and GitHub Copilot for mathematics—on academic achievement and sustainable professional development among 160 undergraduates (40 experimental/control per department) at the University of Baghdad’s Ibn Al-Haitham College of Education for Pure Sciences (2024–2025). Non-random assignment controlled for covariates. Pre/post validated tests (α ≥ .85; 15 MCQ + 5 essay items) measured outcomes. ANOVA revealed significant gains for experimental groups (p < .001, η2 = .41, Cohen’s d = 0.72 [95% CI: 0.45–0.98]). Chemistry excelled in affective domains; mathematics in cognitive/skills. Findings affirm GenAI’s domain-specific efficacy, providing datasets for AI-STEM pedagogy.
This study examines teachers’ perceptions of artificial intelligence (AI) in education, focusing on perceived benefits and concerns. The sample comprised 550 teachers from five technical universities in the Czech Republic, and the study employed a quantitative approach. The findings indicate cautious optimism toward AI, with teachers assigning moderate to high importance to its educational role. AI is primarily associated with information access, continuous availability, and instructional support, while more advanced applications such as assessment and creative assistance are viewed as less central. Although many teachers report at least occasional use of AI—most commonly ChatGPT; however—adoption remains moderate and focused on improving instructional efficiency. At the same time, respondents express concerns about misuse, cognitive impact, and ethical governance. Teachers perceive a notable gap between their own ethical awareness and students’ limited consideration of ethical issues when using AI, highlighting the need for clear policies, professional development, and ethical guidance to support responsible AI integration in higher education.
This paper explores the relationship between mathematical resilience, achievement goals, and mathematics performance among engineering students. This study utilized 287 responses from the 945 students in the various engineering programs at Camarines Sur Polytechnic Colleges, Philippines. Questionnaires were adopted from the Mathematics Resilience Scale (MRS) and Achievement Goal Questionnaire-Revised (AGQ-R). The results show varying levels of performance. The study reveals a negative correlation between mathematics performance and resiliency, along with value and struggle. Likewise, there is a positive correlation between academic performance and mathematical resiliency related to growth, indicating that students who believe in their ability to improve will achieve better results and academic success. Findings also reveal that there is no significant correlation between academic performance and achievement goals. These results suggest that these goals shape attitudes but do not directly affect mathematics performance. Based on the findings of the study, there is a need for policies to enhance engineering students’ mathematics resilience in the institution. Relevant educational policies may be crafted, including but not limited to integrating growth mindset principles into the curriculum, promoting mentorship programs for struggling students, and prioritizing resilience and emotional well-being, thereby enhancing academic performance and the overall learning experience.
Linguistic diversity represents a significant challenge in engineering education, particularly in contexts that require international collaboration and access to multilingual technical content. Although commercial machine translation tools are widely available, few solutions are specifically designed to support pedagogical objectives in engineering training environments. This study presents the design and implementation of an artificial intelligence (AI)-based Spanish-Japanese voice translator with a modular architecture tailored for educational use. The system integrates speech recognition, optical character recognition, and machine translation components and was evaluated under controlled conditions using typed text, handwritten input, and voice data. Experimental results show an overall accuracy above 94%, with particularly strong performance in handwritten text recognition (98%) and Spanish audio transcription (96%). Beyond translation functionality, the proposed system supports project-based learning (PBL) by enabling students to interact with and modify AI modules, fostering competencies in natural language processing and intelligent systems design. The findings suggest that AI-driven multilingual tools can enhance digital inclusion and intercultural communication in engineering education contexts.
This qualitative study provides an in-depth overview of requirements engineering (RE) education in Swiss higher education institutions, comparing the offerings at universities and uni-versities of applied sciences. The findings reveal major diversity in course formats, content, and institutional contexts, with a strong emphasis on industry relevance. Many programmes incorporate practical learning, project-based assessments, and alignment with professional standards like the Certificate of Professional Requirements Engineering of the International Requirements Engineering Board. While this research is specific to Switzerland, its insights have broader implications for RE education in Europe, particularly in dual higher education systems. The study highlights the growing importance of emerging themes such as artificial intelligence, stressing the need for curricula that remain responsive and future-oriented. To deepen understanding and strengthen the validity of findings, ongoing research will tri-angulate educator insights with student and industry perspectives, enabling a systematic comparison of needs, expectations, and perceived challenges in RE education. This triangulated approach will support the development of effective, inclusive, and practice-oriented RE curricula.
This study examines the awareness of social engineering (SE) and the need for cybersecurity education among undergraduate engineering students at Obuda University in Hungary. A total of 173 participants, primarily from Generation Z and without a formal specialization in cybersecurity, completed a structured questionnaire. The questionnaire assessed familiarity with the SE concept, exposure to manipulation, confidence in detection, and openness to intergenerational mentoring. The results revealed moderate knowledge levels (5.4/10) and high exposure to suspicious messages, primarily through social media and instant messaging platforms. Most participants (92%) expressed a strong need for further cybersecurity education. The preferred formats were practice-oriented and included simulations, expert-led sessions, and hands-on workshops. Students perceived older adults as more vulnerable (61%), yet approximately one-third reported helping or receiving help from other generations regarding digital safety. These results underscore the necessity of a contextualized, participatory approach to cybersecurity education. The study proposes an intergenerational mentoring model that combines the digital fluency of younger learners with the caution and life experience of older users. This approach could bolster cybersecurity awareness in engineering and teacher training programs.
Heat exchange is a key part of the current chemical and mechanical engineering curriculums at the university. However, students often see it as abstract because of the 'black box' nature of commercial heat exchangers in industry and the high cost of laboratory pilot plants for learning. This manuscript introduces an innovative educational project focused on the design, 3D-printing, and experimental testing of a plate heat exchanger (PHE). To overcome the low thermal conductivity of standard PLA for FDM printing, a high-metal-composite filament (>60% bronze-filled) was used, which allowed for much more efficient heat exchange. The project, conducted by a chemical engineering undergraduate student, consisted of an initial screening of open-source 3D models of heat exchangers, followed by fused deposition modelling 3D printing using different filaments and technical validation. The experimental testing was performed using tap water at 60 degrees C and 20 degrees C to determine the overall heat transfer coefficient. Our results indicate that 3D-printed PHEs show a significant thermal performance, providing a low-cost alternative to commercial units or expensive learning units. To the best of authors' knowledge, this approach goes beyond technology. This offers a practical way to update and democratize engineering laboratories while increasing student engagement through hands-on challenges.
This quasi-experimental study investigated generative AI (GenAI) tools-Copilot for chemistry and GitHub Copilot for mathematics-on academic achievement and sustainable professional development among 160 undergraduates (40 experimental/control per department) at the Non-random assignment controlled for covariates. Pre/post validated tests (alpha >= .85; 15 MCQ + 5 essay items) measured outcomes. ANOVA revealed significant gains for experimental groups (p < .001, eta(2) = .41, Cohen's d = 0.72 [95% CI: 0.45-0.98]). Chemistry excelled in affective providing datasets for AI-STEM pedagogy.
