ABSTRACT. Contribution: This study explores how to engage and prepare students to learn by integrating AI in teaching activities from a productive failure perspective. Productive failure (PF) has shown in multiple studies to improve conceptual knowledge, while showing no differences in terms of procedural knowledge. Specifically, productive failure is suited towards teaching concepts which build on from previous instruction, which will be the case for most computing engineering courses. Background: Generative AI has disrupted education, providing both novel ways to support deep learning but also reducing student motivation and challenging traditional course assessments. Integration of AI tools on STEM education should provide exciting opportunities to both instructors and students while minimising its misuse and abuse. Research Questions: What is the best way to integrate AI as a support tool in computing courses? Methodology: We have reviewed pedagogical strategies that focus on learning from errors. Based on that review we have identify Productive Failure as a potential framework. Findings: The study has identified two key roles for AI tools to support the PR framework: a guiding role in the exploration phase and an instructor assistant role in the consolidation phase.

ABSTRACT. The objective of this study was to compare the impact of using AI tools versus traditional teamwork evaluating both performance measured in grades and students’ perception of utility and learning experience. A quasi-experimental design was implemented with two groups of first-year engineering students: one group completed an assessment individually with access to AI tools, while the other worked collaboratively in teams without AI support. Quantitative data were collected from exam scores and complemented by perception surveys. We observed that AI did not guarantee correct answers nor deep learning. Our results align with the recommendation of a hybrid and critical use of AI in higher education as well as it reiterates the danger of overreliance on AI tools.

ABSTRACT. In the industrial domain, three primary types of models are commonly employed: white-box, gray-box, and black-box models. These models serve as essential tools for predicting, detecting, and classifying conditions, as well as assessing performance in manufacturing processes and devices. The emergence of artificial intelligence has introduced a powerful paradigm for modeling through data-driven black-box approaches. While these approaches have enabled the development of numerous industrial models, their effectiveness is often compromised by noisy or incomplete data, leading to reduced accuracy and reliability. To address these limitations, informed neural networks have been proposed. This hybrid methodology integrates experimental data with first-principles knowledge, thereby enhancing model robustness and predictive capability. The dissemination of such advanced methodologies is crucial for the education and training of future manufacturing engineers. In this context, the Fiber Extrusion Device (FrED) represents a user-friendly and versatile educational platform for teaching engineering concepts. This paper proposes the use of FrED as a pedagogical tool to demonstrate the application of informed neural networks in modeling manufacturing systems, offering learners an effective and accessible way to understand the integration of physics-based and data-driven approaches.

ABSTRACT. This research-to-practice study introduces a framework that integrates Challenge-Based Learning (CBL) with Experiential Learning (EL) to enhance Authentic Assessment (AA) in higher education. The key contribution is the structured alignment of Kolb's experiential cycle and AA principles (realism, cognitive challenge, and evaluative judgment) with the stages of CBL, emphasizing real-world challenges that mirror professional practice and support the development of core competencies in engineering students. A single case study implemented across six interdisciplinary modules in industrial engineering education at a private university in Mexico City illustrates the framework’s use. Students executed process improvement projects in six small and medium-sized enterprises (SMEs). The findings suggest that the framework enhanced students' problem-solving and communication skills while meeting the intended learning goals. Nonetheless, limitations associated with the methodology and pedagogical integration restrict the broader application of these results. Future studies should explore cross-disciplinary comparisons and longitudinal tracking of competency development.

ABSTRACT. This paper presents a training framework using the Fiber Extrusion Device (FrED) from MIT to enhance manufacturing process education and strengthen the connection between control theory and practical implementation. A common challenge in engineering education is that students often struggle to relate theoretical concepts to real-world systems. This work addresses that gap through an integrated experimental approach. The framework links academic learning with industrial practice by guiding participants through data acquisition, system identification, and PID controller design using FrED’s actuator. Experimental data are modeled with the least squares method to obtain a second order transfer function, allowing learners to observe how parameter estimation and gain tuning influence system behavior. Using MATLAB and Python for analysis and implementation, the methodology improves both conceptual understanding and practical competence, contributing to workforce development in smart manufacturing.

ABSTRACT. Preparing engineers for an era shaped by artificial intelligence requires more than traditional, disciplinary and technical expertise, it requires creativity, adaptability, and the ability to work across disciplines. Yet, students rarely encounter professional-grade projects that integrate technical rigor, artistic creativity, and public impact into a cohesive learning experience. To address this gap, this work presents the multidisciplinary revamp of Jean Luc, an animatronic robot featured in the "Winter Lights" musical at the Discovery Cube in Santa Ana, California. Guided by faculty mentorship, undergraduate students collaborated across mechanical design, electronics, and programming to redesign the robot’s internal systems. Structured through Challenge-Based Learning (CBL) and embodying UC Irvine’s Engineering-plus (e+) framework, the initiative provided students with authentic teamwork experience under real-world constraints while contributing to a live STEAM performance. The results show how projects like Jean Luc serve as unique learning platforms that can bridge the classroom and the stage, to transform public-facing initiatives into powerful platforms for preparing future engineers with the technical, creative, and collaborative skills demanded by the AI-driven future.

ABSTRACT. The education of technology professionals increasingly depends on the integration of diverse fields and bodies of knowledge. In higher education, the adoption of project-based methodologies for training these professionals has become a growing trend. However, this approach requires effective coordination across different domains, whether from an interdisciplinary or transdisciplinary perspective. This paper discusses qualitatively how such integration was achieved between the Design and Leadership axes to support students in developing a sensory mat using the Project-Based Learning (PjBL) model. It concludes by emphasizing the importance of establishing deliberate routines to foster collaboration across knowledge areas and provides suggestions for their implementation, drawing on a specific case study in Brazil. If this kind of initiative is institutionally supported—rather than relying solely on spontaneous collaboration—it has the potential to scale up, leading to pedagogical experiences that are more closely aligned with the educational principles of inter- and transdisciplinarity.

ABSTRACT. Project-Based Learning (PBL) oriented toward sustainability, particularly in electromobility and energy efficiency, provides undergraduate engineering students with a hands-on framework to strengthen problem-solving and collaborative skills. As part of the learning experience, students work in teams to investigate how the technologies explored in these disciplinary areas can positively impact the environment and the management of natural resources. To support their design processes, students are encouraged to use artificial intelligence (AI) tools to discuss design alternatives, refine concepts, and evaluate potential solutions. The development of functional prototypes serves as an effective strategy to connect theoretical knowledge with real-world challenges. Using the Vallaeys Social Responsibility Management Model and a pre-test/post-test statistical analysis, we evaluated the impact of this PBL intervention on a group of Mechatronics Engineering undergraduates. Results indicate that prototype-centered PBL, supported by guided AI use, enhances motivation, creativity, and ethical awareness, demonstrating strong potential for adaptation across engineering disciplines.

ABSTRACT. Teaching abstract concepts such as cubic crystal structures (SCC, BCC, FCC) remains a challenge in science education, requiring strategies that enhance spatial visualization and conceptual clarity. This study evaluates the educational impact of 3D-printed models compared to large-scale shared models in an undergraduate chemistry course. Two conditions were tested: (i) a control group manipulating a single large model collectively, and (ii) an experimental group using small 3D-printed kits in teams. Students also performed literature searches using an AI-based tool (ResearchRabbit) to link theory with real-world applications. A total of 162 students participated under the TEC21 educational model. Evaluation instruments included a knowledge test, an engagement survey, and an impact and usefulness questionnaire. Results showed no significant differences in knowledge acquisition (p = 0.398). However, engagement and usefulness scores were significantly higher in the control group (p < 0.001). Findings highlight that the scale of didactic resources and their use dynamics affect motivation and perceived value, underscoring the need for technological innovation to be accompanied by intentional pedagogical design.

ABSTRACT. The convergence of artificial intelligence and educational innovation is redefining how knowledge is constructed and assessed in higher education. Within this transformation, ANECAIQuim emerges as an intelligent chatbot designed to enhance academic performance, student engagement, and conceptual understanding in chemistry. This study presents the theoretical framework and empirical findings derived from the implementation of ANECAI, a narrative and collaborative learning methodology supported by artificial intelligence. The intervention combined structured pedagogical strategies with technological tools such as ANECAI and ANECAIQuim to promote active, technology-enhanced learning. The experimental design involved 71 engineering students, who participated in four instructional video phases followed by pair-based analytical tasks evaluated by the ANECAI chatbot. Statistical analysis using a paired-sample t-test revealed a significant improvement in academic performance, with the mean grade increasing from 85.26 to 93.52 (p < 0.05) and a reduction in variance from 64.01 to 25.73, indicating more homogeneous learning outcomes. These findings demonstrate the pedagogical potential of AI-driven systems to foster equitable, reflective, and data-informed learning experiences aligned with the vision of Education 6.0.

ABSTRACT. This study evaluates the effectiveness of ADAI, an interactive android-based teaching assistant, in enhancing trust and reducing mistrust among engineering students during micro- classes. ADAI integrates computer vision, natural language pro- cessing, and speech synthesis to provide adaptive explanations of course material, detect raised hands, and administer assessments through QR codes. Students were registered using a facial recognition system and participated in dynamically generated instructional sessions that interspersed questions to monitor comprehension. A mixed-methods approach was employed to measure students’ levels of trust and mistrust after exposure to ADAI. Validated instruments captured perceptions of reliability, safety, and potential harmful outcomes. Statistical analyses in- cluded Shapiro–Wilk tests for normality, Pearson’s correlation coefficients, and paired-samples t-tests. Results indicated that most variables met normality assumptions, enabling the use of parametric tests. Significant correlations emerged between Mistrust and Trust (r = .36, p = .008), NR Mistrust and NR Trust (r = .43, p = .008), and F Mistrust and F Trust (r = .49, p = .037), suggesting interdependence between trust- related perceptions. Paired-samples t-tests revealed that trust scores were significantly higher than mistrust scores (M = −0.66, SE = 0.22, t(54) = −3.02, p = .004), reflecting an overall positive reception of the system. Although exposure to robotics temporarily increased perceived mistrust compared to non-exposure, trust perceptions remained stable across groups. No significant gender-based differences were observed. These findings demonstrate that ADAI can support interactive learning while maintaining a generally positive perception of reliability and safety, offering insights for the development of android-based educational tools that foster engagement and trust in engineering education settings.

ABSTRACT. Hiring a newly graduated software engineer represents a challenge for any organization due to the existing gap between industry and academia. Internship programs are a proven strategy to help address this issue. However, although there are documented cases, they are not abundant, and each organization has its own particularities. This article presents the internship program implemented by a Mexican company five years ago and recently redesigned. The described experience spans the entire process, from participant recruitment to their integration as regular employees within the organization. Program results are shared along with the lessons learned.

ABSTRACT. This paper reports a binational, challenge-based capstone jointly run by ASU and XX in the Sonora–Arizona Megaregion (2021–2025), engaging 50 students across three partner firms. Mixed US–Mexico teams worked through a two-phase workflow—Diagnosis & Definition and Implementation & Control—under faculty oversight and industry mentorship. Charters pre-specified firm-owned KPIs, feasibility gates, and validation windows. A convergent mixed-methods evaluation combined pre/post competency surveys, KPI tracking, and interviews. We observed consistent gains in technical problem solving and transversal competencies (systems thinking, intercultural communication, leadership, distributed project management), plus firm-side validation of relevance and feasibility. Where immediate deployment was impractical, simulation-backed business cases offered credible routes to adoption. The contribution lies in coupling binational teaming with KPI-anchored validation in authentic settings, offering a replicable pathway from learning to implementation. We discuss boundary conditions and future work to extend follow-up and harmonize cross-project metrics.

ABSTRACT. This paper examines the design and early implementation of Challenge-Based Learning (CBL) across the Erasmus+ EU-BEGP consortium in Bolivia, Ecuador, Guatemala, and Peru. Building on the EXPLORE Energy Digital Academy (EEDA) and the Learnify platform, partners co-created modular, Creative Commons resources to support flipped classrooms, remote labs, and peer-reviewed quality assurance. We integrate CBL with Design Thinking, SCAMPER, and Six Thinking Hats to guide students from empathetic problem framing to prototyping and dissemination. Nine locally grounded challenges, spanning electric last-mile logistics, self-sustaining communities, renewable energy design, and sustainable ephemeral construction, connect curricula to real stakeholders and the SDGs. We report consortium-level outputs (shared modules, planners, and QIP reviews) and four illustrative cases that demonstrate gains in technical competence, teamwork, and reflective practice. Findings highlight how a lightweight, modular ecosystem can align transnational actors while preserving local relevance, offering a replicable pathway for modernizing energy-and-sustainability education in Latin America, with measurable learning improvements.

ABSTRACT. This work-in-progress describes a study that aims to compare assessment formats developed using multiple-choice questions (MCQs) with open-ended assessments assisted by chatbots using a custom AI chat tool. Multiple-choice questions (MCQs) have been widely used in various technologies throughout engineering education for several reasons, such as their scalability and efficiency. However, it is argued that they merely allow for answer recognition rather than knowledge construction. Recent advances in artificial intelligence (AI) are revolutionizing reasoning-centered assessment formats with an adaptive feedback approach. In this context, this work seeks to provide an initial contribution by establishing a comparative analysis of Software Engineering students' preferences for traditional multiple-choice questions versus open-ended assessments assisted by AI chatbots. In developing the tool, questions (both in multiple-choice and chatbot formats) are randomly presented to students to assess their knowledge. In a first experiment using chatbot mode, students reported that second-person structured hints guided them toward the correct answers through iterative refinement. A survey of students in an introductory Software Engineering course found that they valued both approaches, but in different ways. The results revealed differences in student preferences: multiple-choice questions were favoured for their ease of use and reduced effort, while chatbot-based assessments were preferred for their support for learning, stimulation of interest, and encouragement of participation. These contrasting preferences highlight how each assessment method aligns with students' different priorities and experiences.

ABSTRACT. This study evaluates the impact of a brief mindfulness intervention on the emotional well-being and academic performance of engineering students enrolled in the course Mathematical Reasoning in the Built Environment (MA1047) at Tecnológico de Monterrey. A quasi-experimental mixed-method design was employed, comparing two cohorts: a control group (2024) and an experimental group (2025), comprising a total of 31 participants. The intervention consisted of deep-breathing routines, mindful stretching, and guided visualization exercises implemented at the beginning of each class over six weeks. Results revealed a significant improvement in well-being, concentration, and academic performance in the experimental group (p < 0.05; d = 0.82), with a 5.2% increase in the final mean score. Positive correlations between well-being and performance (r = 0.46) suggest that mindfulness fosters emotional self-regulation and cognitive readiness for mathematical learning. The qualitative analysis confirmed a transformation in the classroom climate, characterized by greater calmness, motivation, and collaboration. The findings demonstrate that integrating brief mindfulness practices into STEM courses enhances both well-being and learning outcomes. These results are being considered within the ongoing curricular development processes at Tecnológico de Monterrey for the 2026 academic programs

ABSTRACT. Teaching Data Science to engineering students involves challenges associated with the rapid acquisition of skills in programming, statistical analysis, and predictive modeling. This paper examines how the integration of Artificial Intelligence (AI) and tools such as Python and Power BI supports the learning process of third-semester students at Tecnológico de Monterrey, enabling them to effectively understand, process, and analyze data. The study is based on an intensive 40-hour course delivered over five weeks, covering data exploration, cleaning, transformation, visualization, and the implementation of predictive models, including simple and multiple linear regression and logistic regression. The results show that incorporating AI enhances data comprehension, encourages the development of more structured code, and leads to more accurate predictions, even among students with limited programming experience or low initial interest in coding. Overall, students demonstrate improved performance in descriptive and predictive analytics while developing essential competencies and practical proficiency with industry-relevant Data Science tools.

ABSTRACT. This research article assesses the effectiveness of an augmented reality video game with serious game features named Andromeda, designed for learning physics concepts. It illustrates how the fusion of serious games, video games, and augmented reality offers students a highly immersive and practical learning experience, enabling dynamic interaction with physics concepts. The game's effectiveness in teaching energy and momentum conservation principles is evaluated by implementing both pre-tests and post-tests for experimental and control groups, where differences in learning gains are reported. Students also administered a perception questionnaire regarding using mobile learning resources. With a sample of N = 118 engineering students, it was found that the experimental group achieved an average learning gain 18 points higher (on a scale of 0 to 100) than the control group, which statistically suggests Andromeda's high effectiveness through Student's t-tests (p = 0.04). Additionally, student perception questionnaires indicate that they found the implementation of Andromeda Adventure very helpful. The increment in the learning gain by contrasting the control and the experimental groups suggests that the implementation of Andromeda Adventure positively impacts understanding concepts and problem-solving skills at undergraduate-level physics courses.

ABSTRACT. This paper presents the design, construction, and educational implementation of a low-cost impedance tube for teaching sound absorption in an undergraduate Acoustics course. The tube, built from carbon steel and integrated with MATLAB-based analysis, follows ASTM E1050 and ISO 10534-2 standards for normal-incidence absorption measurements. Technical validation showed absorption curves consistent with literature and manufacturer data, with uncertainties below 1% in amplitude and 0.6° in phase. Tests with foams, carpets, and fibrous panels confirmed accuracy and repeatability, demonstrating suitability for instructional use. Educational evaluation involved 52 students divided into control and experimental groups. The experimental group, which conducted hands-on measurements, achieved higher learning gains (normalized gain 0.56 vs. 0.27), reported greater motivation (90% vs. 55%), and demonstrated stronger analytical skills in laboratory reports. The results indicate that the impedance tube functions not only as a reliable measurement device but also as a pedagogical tool that improves conceptual understanding, engagement, and skill development. This approach offers a scalable pathway to strengthen acoustics education in contexts with limited access to specialized laboratories.

ABSTRACT. First-year engineering students often struggle to connect concepts across foundational science courses such as physics, mathematics, and chemistry, leading to fragmented understanding and reduced motivation. This paper presents FIMAQUI, an interdisciplinary AI-based chatbot designed to support students during the exploratory phase of engineering education. Implemented across three university campuses, FIMAQUI integrates content from core scientific disciplines and provides 24/7 tutoring via natural language interaction. The chatbot was evaluated using a custom Technology Acceptance Model (TAM) survey and the validated Utrecht Work Engagement Scale (UWES-9). Results from 162 participants indicate high levels of satisfaction, clarity, and motivation, with 88.3% recommending the experience. Statistically significant improvements were observed in the Absorption dimension of academic engagement (p = 0.048), suggesting deeper immersion in learning activities. These findings highlight the potential of AI tools to foster interdisciplinary thinking, enhance student engagement, and support autonomous learning in engineering education. The study contributes to the growing body of research on educational chatbots and proposes future directions for scalable, adaptive, and pedagogically integrated AI interventions.

ABSTRACT. The paper examines how the integration of Artificial Intelligence (AI) is reshaping work practices, elevating performance expectations, and redefining competencies in engineering and technology management. Drawing on interviews with industry professionals, it identifies a cognitive shift toward human–AI co-thinking as the mechanism driving these transformations. The study links empirical findings to a conceptual framing that explains why and how AI alters practices, performance benchmarks, and skill demands within hybrid socio-technical systems, and translates these insights into recommendations for curricula, pedagogy, and assessment. By exposing the widening gap between industry requirements and educational provision, it underscores the urgency of reform. The paper calls for systemic change to prepare graduates not only to function effectively in AI-augmented workplaces but also to lead innovation and socio-technical transformation in the AI era.

ABSTRACT. In today’s globalized world, developing global competence through international internships has gained increasing popularity for engineering students. Developing global competence yields benefits for engineering students in terms of enhancing their adaptability, intercultural communication, and professional growth. However, its significance remains underexplored in current literature. To fill in this gap, this qualitative study aims to explore the development of global competence of Thai engineering students before they depart for their international internships. The research sheds new light on the development of open-mindedness and effective intercultural communication skills—specifically empathy, awareness of cultural differences, and the ability to engage with others successfully. This development enhances students’ readiness for intercultural engagement, supports students’ adaptation in new countries, and enriches their internship experiences. The study has a range of implications for including intercultural communication training for engineering students before their departure, to maximise their international internship experience.

ABSTRACT. This paper explains how we built a virtual teaching assistant (VTA) powered by a Retrieval-Augmented Generation (RAG) model using OpenAI’s AI chatbot technology and used it to enhance student engagement plus learning in online cybersecurity education at the graduate level. Our AI conversational agent (VTA) was implemented in two graduate-level online courses in cybersecurity, namely Risk Management and Data Privacy. The chatbot answered questions based on course materials and helped explain conceptual topics covered. To our knowledge, this is a first of its kind research case study that applied a RAG-powered VTA chatbot in graduate-level cybersecurity education. We detail the system’s architecture, usage patterns, and student feedback. Students reported that the VTA was helpful, and our findings suggest that AI tools like this can enhance learning in technical online education. Additionally, we collected and analyzed student interactions using Latent Dirichlet Allocation (LDA) to uncover key themes and assess how well student questions aligned with system responses.

ABSTRACT. The article describes the experience of a Peruvian university in Lima in reorienting undergraduate research in Industrial Engineering from pre-investment studies (based on secondary information) toward the solution of real problems in the students’ environments. Initially, most theses focused on pre investment studies (57.05% of 156 thesis plans) and lacked real impact. The reorientation is driven by linking students from the “Beca 18” program—who come from poorer and more remote regions of the country—with problems in their regions of origin. This approach forces a transition toward applied and experimental research using primary data, generating high and direct social and business impact. The instructional design model known as Analysis, Design, Development, Implementation, and Evaluation (ADDIE) is implemented in key courses, supported by the use of appropriate AI prompts and active learning methodologies such as Problem-Based Learning (PBL), Project-Based Learning (PjBL), and Case-Based Learning (CBL). A successful pilot case extended the shelf life of avocado from 7 to 21 days. Expected benefits include an increase (from 3 to 7) in applied research projects developed in the students’ regions of origin under the Beca 18 program, generating tangible benefits for local organizations, as well as an increase (from 4 to 10) in the number of students orienting their research projects toward technological innovation.

ABSTRACT. Alternative grading practices (AGPs) are increasingly popular in undergraduate education, but research on the outcomes of these strategies in terms of skill acquisition and validation is limited. AGP aim to motivate students with learning difficulties by avoiding feedback accompanied by a grade. In this study, the scores achieved in AGP are compared with those achieved using the Standard Evaluation Method (SEM). The subjects of this study were 52 junior students in Electrical Engineering at the Polytechnic School of Montreal. The subjects were initially submitted to the AGPs, then the same students were placed in the context of SEM. Analyses of the results in the context of AGP and SEM are initially performed, and it emerges that the grades obtained in the context of AGP overestimate the students' levels. Compared to the scores achieved in AGP, the average grade of the group is higher than that achieved in SEM. However, the highest grades were not necessarily achieved during AGP. Overall, it appears that the AGPs help to improve the group average grade but not necessarily to ensure that all students achieve the expected competencies. Expected grades, study failure rates, and student satisfaction are also analyzed, along with avenues for future research regarding skills acquisition, validation, and transfer in engineering education.

ABSTRACT. This study analyses the integration of Research-Based Learning (RBL) and Challenge-Based Learning within a Competency-Based Education framework to enhance engineering students’ complex-thinking competencies in the context of Industry 5.0. The research was conducted through an international experience in which engineering students from Mexico collaborated with an additive manufacturing (AM) research group in Spain. Acting as co-researchers in a project on AM for healthcare innovation, students engaged in experiential inquiry under interdisciplinary supervision and competency-based assessment to examine how RBL supports systemic, critical, and scientific thinking. A qualitative case study design was employed, drawing on professors’ rubric-based observations, students’ research deliverables, and institutional course evaluations. The validated eComplex rubric was applied by five professors to assess competency levels, and descriptive statistics summarized performance outcomes. Qualitative analysis of students’ feedback complemented the quantitative results, offering insights into the perceived quality of mentoring and learning support. Findings indicate that the proposed model fosters analytical, reflective, and innovation-oriented capacities, reinforcing human-centred and sustainable learning outcomes consistent with Education 5.0 and the challenges of Industry 5.0.

ABSTRACT. Generative artificial intelligence literacy has become a key competency in higher education. This requires students to use precise technical language. Previous studies have shown that most of the specialized terminology in the field of computing comes from English loanwords. Furthermore, these terms are often preferred over their Spanish equivalents. However, it is unknown whether linguistic familiarity equates to conceptual understanding. Moreover, no studies address this issue in the context of generative artificial intelligence literacy. This paper presents the results of a survey administered to a sample of 40 higher education students in Chile. The instrument assesses the level of knowledge of twenty Anglicisms on generative artificial intelligence. In addition, it explores the respondents' choices regarding the Anglicism and its translation. The findings show an inverse relationship in the choice process. The more familiar a student is with an English term, the less likely they are to prefer its Spanish translation.

ABSTRACT. The integration of Artificial Intelligence (AI), particularly large language models like ChatGPT, is rapidly transforming pedagogical practices in engineering education. This work explores a comprehensive framework of innovative applications where ChatGPT moves beyond a tool to become a foundational asset in the engineering classroom. Different distinct use-cases are listed, including as a personalized teaching assistant, an interactive problem generator, a code explanation and debugging partner, and a simulator of professional engineering scenarios. The findings demonstrate that ChatGPT can significantly enhance personalized learning, foster critical thinking and algorithmic reasoning, and bridge the gap between theoretical knowledge and practical application. While acknowledging challenges such as academic integrity and model hallucinations, this work suggests that the strategic implementation of ChatGPT holds the potential to create more dynamic, efficient, and deeply engaging educational experiences, ultimately preparing students for the complexities of modern engineering practice. This work concludes by outlining promising future research directions and critical areas for further investigation into the application of ChatGPT within engineering education.

ABSTRACT. This paper presents an innovative mentoring program to support engineering students struggling with low motivation or failing grades in the training unit (UF) F1016B: Electrical Systems Analysis. The project integrates cutting-edge technology, including Arduino, Sensors, and Tinkercad, into a gamified learning environment to enhance student engagement and learning outcomes. The mentoring program aims to address the high dropout rate prevalent in engineering programs, particularly during the first academic year, by offering an alternative and interactive learning approach tailored to the learning styles of current generations. The program was developed during the February-June 2024 semester at Tecnológico de Monterrey, involving two groups: an experimental group that took part in the mentoring program and a control group that followed the standard curriculum. The mentoring program was specifically designed for students at risk of failure, identified after their first two exams. It offered them the chance to engage in hands-on activities using technological tools. The mentoring activities, which included building circuits with resistors and capacitors on a Protoboard and measuring the angular velocity of a bicycle wheel using a Hall effect sensor, were a key part of the program. Students were required to present their designs and engage in oral argumentation to demonstrate their understanding of the topics. A quantitative approach was employed to measure the program's impact, comparing the grades of students in the experimental group who participated in mentoring with similarly at-risk students in the control group. The results revealed statistically significant improvements in four of six activities and the final course grades for mentored students. Additionally, the experimental group had a 0% dropout rate compared to a 7.7% dropout rate in the control group, suggesting that the mentoring program enhanced academic performance and improved student retention. These findings highlight the potential of integrating technological tools and gamification methodologies into mentoring programs to foster deeper learning and engagement in engineering education. However, further iterations of the program with larger student cohorts are necessary to validate the generalizability of these results. The study concludes that early identification of at-risk students and targeted interventions utilizing innovative educational technologies can be a promising strategy for combating high dropout rates in engineering programs, while also improving academic success and retention.

ABSTRACT. This paper presents an educational innovation implemented at Tecnológico de Monterrey aimed at enhancing STEM motivation, engagement, and foundational engineering skills among high school and early engineering students. The intervention employed a progressive training sequence using the Drone Racing League (DRL) Simulator, transitioning from keyboard input to gamepad controllers and ultimately to professional-grade radio transmitters, culminating in real-flight practice with FPV drones. Two validated motivational instruments—the MUSIC® Model of Motivation Inventory and the MDI-EE (Motivational Diagnosis Instrument for Engineering Education)—were administered before and after the intervention. A pre-experimental one-group pre/post design was used due to institutional constraints that prevented the formation of a control group. Results showed significant increases across all motivational dimensions, including autonomy, perceived usefulness, interest, self-efficacy, and study predisposition. The revised version of this work also incorporates a structured review of related literature, clarification of the research gap, expanded sample and methodological justification, and theoretical grounding based on Self-Determination Theory and Flow Theory, along with explicit study limitations and directions for future research. Overall, the findings suggest that progressive simulator-based drone training is an effective, replicable, and scalable strategy for fostering STEM engagement and strengthening the connection between theoretical concepts and practical engineering experiences.

ABSTRACT. This research designs and preliminarily validates a concise professional development (PD) plan to integrate responsible AI into higher education teaching while strengthening teacher digital competency. Our measure of faculty self-efficacy with a 12-item scale (n = 100; Cronbach’s α = 0.959) and found moderate to high readiness, with higher scores in resource curation and communication and lower scores in accessibility/ privacy and learning analytics. The validity of nine content PD modules, judged by an expert panel (k = 5), was strong (S-CVI/Ave = 0.956 for relevance; 0.911 for clarity). Semistructured interviews with five experts converged on promptinformed design, authentic assessment with rubrics, privacy/data protection, transparency/authorship, and bias verification. We interpret these findings into deployable micro courses (12–16 h) with implementation resources (prompt guides, accessibility and privacy checklists, verification workflows, rubric packs, analytics starter dashboards). The study provides a feasible, evidenceinformed path to align PD with trustworthy AI principles and identifies priority areas for institutional support and future evaluation.

ABSTRACT. The accelerating addition of artificial intelligence (AI) into engineering practice and education demands more than technical proficiency—it requires a literate, critical mindset capable of navigating algorithmic systems, ethical dilemmas, and socio-technical transformations. This paper presents a conceptual framework for Critical AI Literacy (CAL) tailored to engineering education. Based on contemporary research in AI literacy, data ethics, and engineering education, the framework delineates four interrelated dimensions: Awareness, Technical Fluency, Ethical Agency, and Critical Reflection, and includes them in educational pathways for addition into undergraduate engineering programs. By shifting from an instrumental view of AI competence (i.e., “how to use” AI) to a reflective, agency-centered orientation (i.e., “how to understand and question” AI), the proposed CAL framework offers educators and program designers a structured lens for developing engineers who are not only technically capable but also ethically and critically aware. Implications for curriculum design, faculty development, and future research are discussed.

ABSTRACT. The Felder and Silverman model is widely used in education to characterize students’ learning preferences; thus educators recognize that learners do not follow a single learning style. While the model defines four discrete dimensions of learning styles [13], [14], students often do not fit neatly into a single category along each dimension. This does not represent the reality of a learner and the authors suggest not labeling a learner in this form. To provide a more nuanced understanding of learners, this work proposes a novel method based on the Felder and Silverman model that emphasizes educational data analysis rather than instructional outcomes. The results include a comparison between discrete and fuzzy classification data, with the fuzzy information providing richer input for further educational analysis and strat- egy development. Compared to other fuzzy methods [5], [6], [8], we combined group clustering with fuzzification of information to convert four discrete dimensions to eight dimensions with continuous values using the original questionnaire. In the results, we demonstrate their applicability at various educational levels, including elementary, high school, and university.Our results also compare discrete and fuzzy classification data. The fuzzy classification provides additional insights that can support the analysis educational strategies.

ABSTRACT. This paper presents an evidence-based instructional methodology for mathematics education that combines traditional instruction with AI-assisted problem solving using ChatGPT integrated with Wolfram Alpha. The approach was implemented in two first-year engineering topics, integral calculus and graphical analysis, to evaluate its effectiveness across different mathematical contexts. The instructional design included a traditional session and an AI-assisted session. Students’ perceptions were measured through a Likert-scale survey applied to 49 participants, assessing perceived difficulty, attention, confidence, and ease of error correction. Statistical analysis showed higher perception scores for the AI-assisted activity, with significant improvement in the first implementation (p=0.024, 95% confidence) and a positive trend in the second (p=0.053, 90% confidence). Additionally, 90% of students rated the AI feedback as helpful, and 84% reported increased confidence. Future work will focus on analyzing whether this perception translates into measurable learning gains. These results suggest that AI-assisted activities can effectively complement traditional instruction.

ABSTRACT. The objective of this study was to diagnose teaching competencies within the framework of the TPACK model among university professors in Ecuador. A validated questionnaire was administered that measures the dimensions of technological knowledge (TK), pedagogical knowledge (PK), and content knowledge (CK), as well as their intersections (PCK, TCK, TPK, and TPACK). The research was conducted using a quantitative, non-experimental, cross-sectional approach with a descriptive and inferential scope. The sample consisted of 391 professors, and the data were analyzed using descriptive statistics and nonparametric tests (Kruskal–Wallis and Mann–Whitney U tests). The results showed high levels of CK and PK, while the dimensions related to TK and their combinations presented greater weaknesses. Significant differences were identified by gender and age, but not by type of institution or in the overall level of TPACK. It is concluded that, although there is a solid pedagogical and disciplinary foundation, it is necessary to strengthen technological integration through continuing education strategies that address the deficiencies of university professors in Ecuador.

ABSTRACT. his study evaluates the effectiveness of an instruc- tional methodology in which an android robot acted as the primary instructor, integrating diagnostic assessment and applied learning strategies in the fields of Internet of Medical Things (IoMT), exoskeletons, and medical robotics. A pre- and post- class evaluation design was implemented to measure students’ conceptual understanding and technical competence. Diagnos- tic results indicated moderate prior knowledge, with expected accuracies ranging from 51% to 62%, while post-instruction evaluations—conducted after lessons delivered by the android instructor—showed significant improvement, reaching values be- tween 65% and 87%. The results demonstrate that android- assisted teaching not only enhanced conceptual comprehension but also fostered problem-solving and analytical reasoning in robotics-related contexts. These findings suggest that humanoid robots can serve as effective facilitators of active learning, promoting engagement, motivation, and measurable knowledge gains in engineering education.

ABSTRACT. This innovation article presents the design of an activity based on the constructivist approach, utilizing the Virtual Reality (VR) tool GeoGebra Mixed Reality. The literature reports that VR stands out for generating immersive and interactive experiences for students. Its use in the classroom enhances the understanding of disciplinary concepts by allowing direct interaction with objects and abstract ideas in a three-dimensional environment.

The activity was implemented in the course Intermediate Mathematical Modeling (MMI), involving 75 students from a private university in Mexico. In this course, students work with multivariable calculus, where the main difficulty arises when sketching 3D functions and concepts on a 2D blackboard. Consequently, students often struggle to visualize or locate these concepts in 3D space, having to imagine them making sense of them—an issue that leads to confusion. This challenge motivated the introduction of VR into MMI classes.

The research question guiding this work is: What is the impact of using Virtual Reality in Intermediate Mathematical Modeling classes from the students’ perception? The results show that university students recognized that VR helped them better understand and reinforce multivariable calculus topics. They also considered it a relevant and recommendable tool for their engineering education, suggesting that fields such as architecture and medicine could similarly benefit from VR. Designing the activity under a constructivist approach proved to be an effective strategy, as it fosters the development of spatial reasoning and creative thinking.

ABSTRACT. In the current digital education landscape, metaverses are emerging as innovative tools with the potential to revolutionize learning. Among these, the Spatial platform stands out for its unique features. This virtual environment allows users to interact through avatars within a three-dimensional universe, expanding opportunities for education beyond entertainment and socialization. This study explored the acceptance and usage of the Spatial platform in a private university in Mexico, involving 44 engineering students in a quasi-experimental study. Students used Spatial to supplement their learning, evaluating dimensions such as perceived usefulness, ease of use, enjoyment, and continued usage intention through an adapted questionnaire. The study's results demonstrated a higher acceptance of the Spatial platform among the group that used immersive virtual reality (M=52.63, SD=8.469, V=71.726). However, these differences were not statistically significant compared to those using desktop virtual environments (M=51.08, SD=9.529, V=90.811). Both groups showed good acceptance of Spatial as an educational tool. Future research should expand to encompass larger and more diverse educational settings, thereby improving our understanding of student preferences and perceptions of these emerging technologies and guiding future academic implementations and technological developments.

ABSTRACT. This study explores the implementation of a web-based gamification platform, Fred!, designed to enhance student engagement and motivation in STEM higher education. Grounded in Self-Determination Theory, the platform integrates short-term, medium-term, and long-term achievements, token-based rewards, and peer collaboration. A total of 270 undergraduate students from chemistry courses participated in the study, divided into experimental (gamified) and control (traditional) groups. Two constructs—perceived usefulness and perceived ease of use—were evaluated through adapted Likert-scale items. Results from confirmatory factor analysis supported the two-factor model with high reliability across both groups. A significant difference was found in perceived ease of use, favoring the experimental group. While no global differences emerged for perceived usefulness, local analysis revealed variation at lower score percentiles. Findings suggest that gamification can be implemented effectively and perceived as easy to use when supported by a dedicated platform. Implications for broader applications and future research directions are discussed.

ABSTRACT. The teaching of numerical methods is essential in engineering, yet their complexity and the heterogeneity of learning curves hinder mastery, particularly when comparing the Finite Element Method (FEM) with the Finite Difference Method (FDM). To address this challenge, we integrated generative artificial intelligence (AI) as a pedagogical scaffold in a MATLAB-based course with two equivalent groups: FDM (n=28) and FEM (n=30), both under the same AI usage protocol and assessment criteria. Over five weeks, we measured accuracy (L2 error), efficiency (time), prompting quality, robustness, documentation, transfer, and AI hallucinations. Mixed-model analyses showed significant reductions in error (η²=0.17, p<0.001) and time (η²=0.14, p<0.001); slopes were steeper for FDM, while FEM exhibited consistent improvements under higher cognitive load. Prompting quality emerged as a predictor of performance (ρ=−0.41, p<0.05). The competency radar revealed complementary profiles; heatmaps showed decreasing hallucination frequency through verification and validation; and the forest plot identified prompting (β≈−0.28) and systematic validation (β≈−0.22) as key factors. We conclude that generative AI, when responsibly applied, accelerates, stabilizes, and diversifies numerical learning, strengthens the instructor’s role, and offers a replicable model aligned with Tec21/ABET frameworks and SDG 4: Quality Education.

ABSTRACT. This paper presents a project-based learning (PBL) approach to teaching advanced control concepts through the design and implementation of compliance-based controllers on a two-degree-of-freedom robotic platform. The platform incorporates three different compliance-based control modes: impedance, admittance, and a Maxwell-based impedance model. The system is designed to be simple, compact, and is possible to replicate in a laboratory class. Each control mode is implemented independently, providing a learning opportunity to understand the behavior of pHRI systems with different compliance methods. This project can be applied in graduate and undergraduate robotics or control systems courses to illustrate the practical implementation of compliance based controllers, mechanical design, and the integration of robotic systems.

ABSTRACT. This study presents a hybrid learning approach for teaching complex bioprocess unit operations, specifically protein chromatography, implemented at Tecnológico de Monterrey. The methodology combined hands-on nickel-affinity chromatography with process simulation using SuperPro Designer. Students first purified a recombinant chromoprotein from Escherichia coli experimentally. They then simulated the process to explore scale-up, possible improvements, and techno-economic evaluation. This fostered a cyclical, bidirectional learning process. Quantitative results (mean grades: 92.7 over 100; institutional satisfaction: 9.92 over 10) and qualitative feedback demonstrated that this integration enhanced engagement, conceptual understanding, analytical reasoning, and professional readiness. This scalable methodology offers a robust framework for experiential learning in bioprocess engineering and can be extended to other unit operations, including filtration and fermentation.

ABSTRACT. Engineering students at Tecnológico de Monterrey, Campus Estado de México, frequently encounter difficulties in planning and executing the design and construction of ultralight aircraft prototypes, mainly due to limited project management training. These challenges hinder project completion on time and reduce opportunities for essential flight testing prior to international competitions. This study proposes the integration of project management methodologies—specifically the predictive framework of the Project Management Body of Knowledge (PMBOK) and the agile SCRUM approach—to improve organization, coordination and time management within student teams participating in the SAE Aero Design competition. By applying these methodologies to the design–build–test cycle, teams are expected to improve efficiency, ensure safety, and increase competitiveness in national and international aerospace design events.

ABSTRACT. This research-to-practice paper explores the integration of Generative Artificial Intelligence (GenAI) with live case studies to enhance experiential learning in operations management education. GenAI can transform education through advanced human-like text generation capabilities but raises concerns regarding pedagogy, ethics, and academic integrity. To address these challenges, this study proposes a framework that combines Kolb's experiential learning theory with live case studies, positioning GenAI as an agent-to-learn-with to foster student engagement in real-world scenarios. A case study methodology was used to demonstrate this framework in a postgraduate operations management module. Students used GenAI tools to navigate a live case study to upscale a UK-based ethnic restaurant's operations, illustrating the possibilities of the approach. The results showed that students valued GenAI's role in addressing complex challenges and found the live case study engaging. This study contributes a pedagogical framework for integrating GenAI into experiential learning. However, the limitations include the case study research method, moderate student participation, and the complexity of live case studies. Future research should address these limitations and explore the impact of Gen AI on learning outcomes in greater depth.

ABSTRACT. The adoption of authentic assessment (AA) in engineering education faces persistent challenges: diverse interpretations of AA qualities, limited alignment with accreditation requirements, and new demands introduced by generative AI. This paper proposes the Authentic Assessment–Engineering Competence (AA–EC) Framework, comprising three components: (i) ten consolidated AA qualities structured within a Task–Process–Outcome (TPO) model, (ii) the AA–EC Alignment Model mapping these qualities to AHEP4, ABET, and CEAB accreditation frameworks, and (iii) the Authenticity Progression Scale (APS), a five-level rubric for evaluating coursework authenticity. Developed through a systematic literature review (PRISMA 2020) and grounded theory coding, the framework equips educators with structured tools for coursework design, enables students to track competence development, and provides institutions with verifiable, accreditation-ready evidence. Although developed for engineering, the framework is adaptable across disciplines by defining discipline-specific and context-specific AA qualities and aligning them with professional and accreditation frameworks.

ABSTRACT. The online component in blended learning is well supported with sophisticated learning management systems. However, there is a need to develop and investigate how to implement engaging face-to-face activities in a Blended Learning approach in higher education. The F2F component offers educators opportunities to innovate in teaching methods that integrate well with online learning. We have designed a teaching method for F2F sessions and implemented it in two courses of the mechanical engineering programmes at the Singapore Institute of Technology (SIT) and the University of Glasgow Singapore. In this paper, we propose using modelling diagrams to guide students in solving engineering problems and to connect theory with experimentation, enabling experiential and active learning. Our study aims to obtain student feedback through structured questionnaires to identify student engagement levels. We present findings from the questionnaire on the learners' preferences. In particular, our findings identify that the proposed method improves student engagement and enables learning.

ABSTRACT. This study proposes a hands-on pedagogical approach for teaching Overall Equipment Effectiveness (OEE) within Industrial Engineering courses through the assembly of a chair using LEGO® pieces. The activity simulates a production environment where students collect operational data on availability, performance, and quality to calculate OEE and identify improvement opportunities. Conducted with undergraduate students organized into teams, the exercise included two assembly rounds: an initial baseline and a second after implementing improvement actions. Statistical analysis using paired t-tests showed a significant increase in OEE mean of 39.52% mainly due to reduced downtime and defects. The activity enhanced student engagement and understanding of Operational Excellence principles. These results confirm that LEGO-based simulations are an effective, low-cost tool for teaching process efficiency, data analysis, and continuous improvement in industrial engineering education.

ABSTRACT. When ChatGPT became openly available in November 2022, higher education actors grappled with managing student use of GAI. For instructors seeking guidance on how to manage student use of GAI, there were precious few policy resources on which to draw. Higher education actors were forced to be reactionary, responding, more often than not, independently of other stakeholders. And while faculty waited on the formulation of larger GAI policy frameworks to inform their everyday teaching practice, some implemented localized policies of their own, at the levels of programs and courses. This paper presents the case of course-level policy. It reports on how student use of GAI, effective academic year 2023-2024, is managed in the capstone course of an undergraduate degree program in Electrical and Computer Engineering. The paper outlines the course policy and procedure on student use of GAI; defines responsible use of GAI in the context of the capstone project; and shares the teaching scaffolds used to support responsible use of GAI.

ABSTRACT. The present study evaluates the effects of the pedagogically guided use of ChatGPT on university-level kinematics learning and the development of metacognitive abilities in engineering students. An experimental design with random assignment was applied to two groups (Control and ChatGPT) solely differentiated by access to the artificial intelligence assistant. Both groups were to solve equivalent kinematics problems evaluated by performance tests (pretest and posttest) and a metacognitive rubric based on written reflections. The data was analyzed by Welch's t-tests and an ANCOVA model to control the initial level of familiarity with AI. The results demonstrated a positive and statistically significative effect favoring the ChatGPT group (g = 1.30), demonstrating a conceptual and metacognitive gain. The experimental group reflections revealed deeper planning, monitoring and verification processes. The findings suggest that the structured integration of ChatGPT can function as metacognitive scaffolding, strengthening both conceptual comprehension and scientific self-regulation.

ABSTRACT. This research presents a technical proof of concept for an Intelligent Tutoring System (ITS) that enhances learning interactions through Retrieval-Augmented Generation (RAG). By integrating the qwen2-math model, the system retrieves validated instructional content in real-time to generate context-aware responses for quadratic equation solving. Moving beyond rigid interaction models, this study employs a qualitative analysis of interaction scenarios with a pilot sample (n=2). Findings indicate that the RAG-based architecture ensures pedagogical grounding and higher precision compared to static systems. Designed to foster human-AI collaboration, the platform supports educators by acting as a decision-support tool, shifting their role toward instructional orchestration and mentoring. This work demonstrates the feasibility of using local RAG pipelines to create adaptive, transparent, and responsive educational environments while identifying key targets for future large-scale empirical validation.

ABSTRACT. The use of advanced generative AI, such as ChatGPT, presents a significant challenge to the integrity and effectiveness of traditional engineering assessments. This work responds to this challenge by proposing different innovative assessment methods designed to be resistant to AI use. These methods change the focus from product-based and text-heavy outputs to process-oriented and competency-based evaluations. These methods are built on principles emphasizing hands-on creation, real-time performance, metacognitive reflection, and complex interpersonal skills. Strategies range from in-class practical exams and project-based learning to oral examinations, peer reviews, and real-time simulation. These methods promise that by focusing assessments on uniquely human skills such as hands-on creation, real-time adaptation, and ethical reasoning, engineering instructors can not only maintain academic integrity but also more effectively foster professional competencies required of future engineers.

ABSTRACT. This paper builds on Krugman’s New Economic Geography (NEG) by introducing a framework that extends traditional geography-only cluster analysis to include individual-level characteristics using clustering algorithms and Kernel Density Estimation (KDE). Rather than depending exclusively on geographic proximity, this multidimensional approach conceptually integrates diverse individual-level attributes to enable a richer and more detailed identification of agglomeration patterns. This paper offers guidance and a conceptual foundation for future empirical research aiming to capture the complex interaction between geographic and non-geographic factors that influence economic clusters. To better illustrate how the method generalizes to social sectors, we apply it conceptually to higher education: institutions are embedded in a joint space of latitude–longitude and characteristics such as research intensity, faculty size, selectivity, disciplinary mix, and collaboration networks. We then relate multidimensional density to outcomes including graduate placement rates, research output (e.g., publications and citations), and external funding. Furthermore, to bridge macro-level patterns with micro-level behaviors, this paper proposes a visionary LLM-driven simulation, utilizing the latest agent-to-agent (A2A) technology. In this framework, each individual in a cluster is represented by an LLM-based “agent” that is aware of its own profile and characteristics. These agents make decisions based on non-linear behaviors, leveraging the current state-of-the-art agent-based modeling (ABM) techniques. Although the agent-to-agent simulation is conceptual at this stage, it sets the foundation for future work that combines spatial econometric models with agent-based methods to gain a deeper understanding of how clusters form and evolve.

ABSTRACT. Adaptive learning platforms are increasingly used to support introductory programming, yet rigorous evidence on their effectiveness remains limited. This mixed-methods study investigated the impact of a mastery-based adaptive platform on student performance and satisfaction in a first-year programming course. Two course sections (N = 124) were assigned to either adaptive or traditional LMS-based practice. Data sources included pre- and post-tests, course grades, log data, a validated satisfaction questionnaire, and semi-structured interviews. ANCOVA and logistic regression were used to analyse performance and pass rates, while thematic analysis was applied to qualitative data. Results indicated significantly higher learning gains, exam scores, and course completion in the adaptive section, alongside greater satisfaction with feedback and self-pacing, though perceived workload increased. The findings provided empirical support for integrating adaptive platforms into programming curricula and offered design recommendations for aligning adaptive practice with assessment and instructor support.

ABSTRACT. Immediate monitoring of student activities and performance is essential for learning. Thus, remote laboratories can become not only a technological tool but also a pedagogical tool when they integrate strategies that ensure learning. This study proposes to implement a novel remote laboratory for engineering education, applying Kolb’s experiential learning theory and the IEEE 1876-2019 standard, which will not only guarantee learning, but also allow it to be expanded to other types of laboratory, as well as make it possible to take advantage of the data collected to determine student engagement, behavior, and performance. The development will be open source, allowing the source code to be altered and adapted to meet the needs of users, thereby ensuring universal access to quality education, a requirement of Sustainable Development Goal 4. Likewise, the impact of remote laboratories on teaching is expected to be evaluated both quantitatively and qualitatively in the future.

ABSTRACT. This work-in-progress paper reports on the implementation of a business intelligence solution in a Peruvian microenterprise, developed by undergraduate systems engineering students at a Peruvian public university. The project adopts agile methodologies (Scrum), the learning by doing pedagogical approach, and tools such as Power BI Pro combined with Google cloud services. This study aims to establish an academic laboratory in which the company, instructor, and students assume Scrum roles to deliver a real-world BI project using production data. Preliminary results are reported through Scrum events, reflecting incremental dashboard development and stakeholder feedback. Early benefits are evident for the firm, which advances its digital transformation, and for students, who strengthen technical and professional competencies aligned with engineering practice

ABSTRACT. This study presents the development and implementation of a mobile application designed to monitor stress in medical students using a biometric wearable device, IoT technologies, and Machine Learning algorithms. The application captures real-time physiological data, including heart rate, heart rate variability, and electrodermal activity, utilizing these parameters to predict stress levels. Data is collected using Empatica E4 wristband to then be processed by a Python-based server using signal processing techniques, which is then fed into a predictive model to classify and provide real-time stress levels into three categories: no stress, moderate stress, and high stress. Two machine learning models were employed: Standard Random Forest and Balanced Random Forest. These models achieved accuracies of 91.18% and 88.57%, respectively, demonstrating reliable classification of stress levels. Additionally, the application offers stress management tools such as articles, podcasts, and meditation guides to enhance the well-being and academic performance of medical students. The system's effectiveness was validated through a trial involving two medical science students during their clinical shifts, showcasing its

ABSTRACT. Innovation is a critical factor for the success and survival of higher education institutions (HEIs). Strategic agility (SA) enables HEIs to anticipate environmental demands and adapt through flexible resource alignment. This study examines the effects of human capabilities (HC) and digital technologies (DT) on SA and the impact of SA on innovation performance (IP). Using a higher order construct model, SA is defined through strategic sensitivity, leadership unity, and resource fluidity. The hypotheses were tested using Partial Least Squares Structural Equation Modelling. Data were collected through an online survey of 157 area coordinators from eight campuses of a Colombian HEI. Results show that HC and DT have significant positive effects on SA, with DT fully mediating the relationship between HC and SA. In addition, SA has a strong and significant effect on IP

ABSTRACT. University-industry certificates are increasingly designed to help employees move into new roles while continuing their work responsibilities. This work in progress describes the evaluation of two early courses in a fully online certificate codeveloped with an aerospace manufacturer. Electric Circuits uses mailed component kits and vendor-aligned digital multimeter training, while Industrial Robotics relies exclusively on offline simulation that mirrors teach-pendant programming when plant access is not possible. New course-specific pre and post surveys capture both confidence ratings and open-ended reflections. Early results suggest that industry-relevant topics and tools closely aligned with day-to-day responsibilities can drive meaningful improvement even in an online format. The analysis applies smallsample appropriate methods and highlights quality-improvement actions that will shape future offerings. Planned additions to the certificate include Thermodynamics, Computer-Aided Manufacturing, Sensors and Actuators, Safety, and Mechatronics, which will allow for certificate-level evaluation after Summer 2026.

ABSTRACT. A critical challenge in engineering fields is the widening gap between academic instruction and the rapidly evolving industry practice. When faculty expertise in theory is not matched by real-life hands-on experience, graduates are often lacking the applied skills and professional fluency demanded by employers. This work details the structure and impact of a successful faculty externship program designed to bridge the critical gap between academic instruction and evolving industry practice. In this work, Utah Valley University's Faculty in Industry and Business (FIB) program is presented as a tool to bridge this gap. The program's goal is to gain critical, firsthand insights into industry competencies, including the importance of soft skills like project management and client communication; the use of modern collaborative and data visualization tools; and the mindset essential for solving open-ended problems. More importantly, the program demonstrates a direct pathway to pedagogical impact, as the experience gained plays a major role in curriculum improvement, including the integration of the latest standards, specifications, and software. This work argues that structured externship programs are not a supplementary benefit but a core component of effective faculty development, serving as an essential tool for transferring real-world expertise into the classroom and empowering educators to prepare students for career success.