Conference Agenda

This study analyzes the perceptions and evaluations of Industrial Engineering students regarding the alternating online and face to face teaching modalities they received during the same semester. Through a survey and analysis of the results, the students' experiences in both environments are explored, identifying the variables that influence their academic satisfaction. The results reveal a marked preference for in-person instruction, highlighting the importance of direct contact with instructors, social interaction among peers, and the ease of developing practical engineering skills. On the other hand, although online modality was valued for its flexibility, students reported significant difficulties, such as decreased sustained attention and technical limitations experienced by both them and the instructors.

This study addresses the critical issue of fragmented learning in engineering education by framing sensor network design not as a collection of isolated technical problems, but as a multidimensional process. Grounded in Edgar Morin’s principles of Complex Thinking, the research implements an interactive simulation experience where students navigate decisions with systemic implications across energy, communications, and data processing.
The pedagogical intervention targeted final-year telematics engineering students, transitioning them from high-level Python abstractions to the friction points of resource-constrained systems using the Contiki-NG operating system and Cooja simulator. The curriculum was structured across four distinct dimensions: infrastructure (C/IPv6), physical context (spatial emulation), communications (DODAG collective intelligence), and data processing through cloud-integrated protocols.
Quantitative results revealed a non-linear learning curve; student performance dropped significantly from a mean of 83.68 to 62.47 points upon encountering the non-unidimensional reality of the simulator, before recovering to 71.57 points as students synthesized the complexity. Qualitative analysis indicated that while 56\% found the tasks difficult and 11\% considered them very complex in terms of simulator usage, 83\% felt the pace was adequate, and the majority agreed the simulation helped bridge the gap between theory and practice. Correlation analysis suggests that student interest and lecturing clarity are primary drivers of successful theoretical integration. Ultimately, simulated environments offer a sustainable, high-fidelity alternative to hardware for mastering real-world network constraints.

The growing ubiquity of artificial intelligence (AI) in critical sociotechnical systems imposes an inescapable responsibility on engineers, who make design decisions with profound ethical implications at the micro level. This article presents a critical review of the literature on teaching AI microethics in engineering education programs. Through a systematic analysis of academic publications, institutional guidelines, and corporate ethical frameworks, a significant gap is evidenced between the emerging consensus on key ethical principles—such as fairness, transparency, explainability, and accountability—and their effective pedagogical implementation in engineering curricula. The results reveal a lack of concrete educational strategies that transcend theoretical discussion and equip future engineers with practical competencies to address ethical dilemmas throughout the AI development lifecycle. It is concluded that it is imperative to reformulate curricula to incorporate applied ethics not as a supplement, but as a fundamental pillar of engineering education. This involves adopting responsible-by-design methodologies, the use of case studies, ethical impact matrices, and bias audit tools, to cultivate the critical judgment and moral sensitivity necessary for the development of trustworthy and human-centered AI.

In the context of the ongoing transformation of engineering education driven by artificial intelligence, this study examines the impact of digital storytelling co-created with generative AI as an innovative pedagogical strategy to enhance learning, motivation, and competency development in STEM environments. The intervention was implemented in an undergraduate course on Analysis of Electromagnetic Systems, involving 170 university students divided into an experimental group (n = 90) and a control group (n = 80). The central challenge required students in the experimental group to explain a complex STEM concept to a non-expert audience through scientific narratives supported by generative AI tools such as ChatGPT, Midjourney, and Runway, integrating textual, visual, and audiovisual resources. In contrast, the control group addressed the same technical content using traditional instructional strategies based on conventional technical reports. Results show statistically significant improvements in the experimental group across technical competencies, including mathematical modeling, programming, and visualization, as well as transversal competencies such as scientific communication, critical thinking, and collaboration. A strong correlation was also identified between student motivation and academic performance (r = 0.79, p < 0.001). Inferential analyses using t-tests, ANOVA, and regression models confirmed a medium-to-high effect size. Overall, the findings indicate that the intentional pedagogical integration of artificial intelligence with digital storytelling fosters dual coding, meaningful learning, deeper conceptual understanding, and sustained student engagement. Aligned with the Tec21 educational model and Sustainable Development Goal 4 (Quality Education), this study provides robust empirical evidence.

The This paper invites reflection on the use of new technologies in the discipline of Architecture, primarily the implementation of Artificial Intelligence, questioning its ethics and what might constitute appropriate use. To this end, it explores the implementation of AI through a critical review of the literature and classroom experience, promoting its role in academic practice while emphasizing its effectiveness as a tool for learning design. This essay seeks to challenge the premise that using AI tools in design disciplines is cheating and invites an exploration of its educational and creative potential.

This article presents an analysis of the implementation of gamification and role-playing as teaching and learning strategies for professional ethics in first-year engineering students. The research design is descriptive and cross-sectional, combining an analysis of student performance in a group-based assessed activity with a survey administered to participating students.

The activity consisted of a simulation game where teams had to design and build a bridge with a limited budget and fluctuating material prices, a scenario designed to generate ethical conflicts. Students were required to identify and justify potential ethical violations, relating their decisions to the professional code of ethics for engineers.

A 19-item Likert-scale questionnaire was administered to 219 students to assess their perceptions of the activity. The analysis of the results indicates the relevance of the applied strategies for first-year students, showing ease in identifying ethical actions and principles, but not in justifying them. Demonstrating that gamification and role-playing promote experiential learning and contribute to the development of ethical awareness in the training of engineers from their earliest levels of education.

This paper presents TecDrone, an innovative educational strategy that integrates Virtual Reality (VR) and Artificial Intelligence (AI) to enable authentic assessment of engineering competencies in first-year students. The intervention was implemented in the “Electromagnetic Systems Analysis” course at Tecnológico de Monterrey, where students designed, assembled, and controlled drones in a virtual environment, justifying their technical decisions through interaction with an AI-powered avatar. Three institutional competencies were assessed: decision-making, demonstration of system functionality, and execution of technical actions. The methodology combined quantitative analysis (TAM survey and performance rubric) with qualitative analysis (thematic coding of argumentative responses). Results show significant improvements in students’ understanding of system functionality and their ability to implement technical actions, along with high levels of perceived usefulness, ease of use, and autonomy. The experience proved to be replicable, engaging, and aligned with principles of active learning and authentic assessment. TecDrone emerges as an effective tool for transforming engineering education by bridging theory and practice through simulated environments. Beyond reporting learning outcomes, this study contributes an assessment-oriented VR–AI framework that operationalizes competency-based evaluation through observable performance, decision justification, and system-level reasoning in early-stage engineering education.