Conference Agenda

The mining industry faces new challenges to become more efficient. The ongoing development of AI technologies is not unrelated to this industry. From an educational point of view, it will be necessary to prepare new students who will form, in the short and medium term, work teams in mining operations. In this regard, the role of the university becomes indispensable, given that it must update its curriculum in accordance with industry requirements. This paper presents a preliminary strategy for implementing AI technologies in the curriculum of a mining engineer. The methodology used consisted of identifying stakeholders, forming an internal curriculum committee for the program and an external one (professionals from the sector), and gathering information on the current and future needs of the mining industry. Information was gathered on courses offered by mining schools at prestigious universities. The content was compared, and a preliminary list of courses that should incorporate AI was compiled. Finally, a preliminary budget for implementation was prepared. The program's strengths and weaknesses were also identified. Strengths include ABET accreditation, a continuous improvement plan, and existing IT infrastructure. One of the major weaknesses is the small number of full-time professors and their age range.

In the context of higher education in engineering, there is an increasing need to integrate transversal competencies such as sustainability and critical thinking. Those competencies, combined with technical proficiency in emerging technologies like the Internet of Things (IoT) and Artificial Intelligence (AI), allow future engineers to develop soft skills highly valued in the industry. This paper presents the results of two innovative educational experiences implemented in a challenge-based learning course named "Implementation of the Internet of Things" (TC1004B), located in the 3rd semester of the computer and information technology area. The educational experiences implemented are: (1) replacing a traditional challenge of designing a biometric wearable with a challenge of developing a multipoint IoT system for environmental pollutant monitoring, and (2) implementing a discussion forum ("DebatIA") as a complement to the challenge where students, using generative AI tools, argue for or against the ethical and environmental impact of their solutions. Through a comparative quasi-experimental design, the performances of two cohorts (2023, N=52; 2024, N=48) are analyzed using institutional analytical rubrics. The results show improvements (p < .05) in key sub-competencies related to sustainability, complex data analysis, and user-centered interface design. A slight decline is observed in a specific technical sub-competency, possibly associated with the learning curve of the new environmental domain. It is concluded that the intentional integration of challenges contextualized in real socio-environmental issues, combined with ethically mediated AI reflection spaces, significantly enhances deep learning and the development of transversal competencies in engineering students.

Engineering education faces the challenge of fostering meaningful understanding of thermodynamic principles while simultaneously engaging students in sustainability-oriented learning experiences. This study presents a Project-Based Learning (PBL) intervention focused on the design and construction of a solar water heater, implemented in a first-year engineering course at a higher education institution in Mexico.

The educational experience was conducted over a five-week period and involved 45 engineering students, following a structured methodology that integrated literature research, analytical heat transfer modeling, prototype construction, experimental testing, and computational performance simulation. A mixed-methods research design was employed to evaluate the impact of the intervention, combining quantitative data collected through a 23-item Likert-scale questionnaire with qualitative feedback obtained from open-ended questions. The reliability of the instrument was verified using Cronbach’s alpha (α = 0.925).

The results indicate high levels of student engagement and satisfaction, with more than 90% of responses reflecting agreement or strong agreement regarding the effectiveness of the PBL approach. Students reported improved understanding of thermodynamic concepts, particularly heat transfer mechanisms, as well as the development of key engineering competencies such as problem-solving, teamwork, and technical communication.

These findings suggest that sustainability-focused, hands-on PBL experiences can significantly enhance learning outcomes in early engineering education. The study contributes a replicable pedagogical framework for integrating thermodynamics, sustainability, and active learning strategies within constrained academic timelines, offering practical implications for engineering educators seeking to bridge the gap between theory and real-world application.

Student persistence in engineering programs represents a significant challenge for higher education institutions, particularly during the early semesters, where foundational courses exhibit high repetition and failure rates. In this context, the present study examines the relationship between academic performance, course load, and student persistence through the construction of longitudinal academic trajectories. The analysis is based on an institutional dataset comprising academic records of students enrolled in core courses of a Software Development program between the 2022-1 and 2025-1 academic terms. From these data, a synthetic performance indicator referred to as the approval rate was developed, along with a binary variable representing student persistence. The analytical approach combined descriptive statistics, exploratory visualizations, and a logistic regression model to assess the relationship between cumulative academic performance and the probability of persistence. The results reveal a strong association between approval rate and student persistence, with the proposed model demonstrating high predictive power. Additionally, the individual-level analysis of the relationship between course load and academic performance reveals substantial heterogeneity in students’ responses, enabling the identification of distinct academic profiles. These findings highlight the relevance of longitudinal performance indicators for understanding student trajectories and for supporting institutional strategies aimed at promoting academic persistence.

Problem-framing skills are central to engineering design, yet traditional engineering curricula often emphasize well-structured, algorithmic problems that limit students’ engagement in problem framing. Client-based projects offer a promising means of creating authentic design experiences that support students’ problem-framing skills, but their effective implementation in engineering courses represents several pedagogical and logistical challenges. Consequently, this study asks: How might a client-based project promote students’ problem-framing skills in a fourth-year electronic engineering design course? Using a design-based research approach, we examined an iteration of a redesigned electronic design course that integrated a client-based project. Data included student team artifacts, video-recorded client–student interactions, and interviews with students, the client, and the instructor. Findings indicate that students were able to translate client needs into design requirements while meeting course learning outcomes, and that engagement with multiple stakeholders supported deeper evaluation of solutions and richer understanding of the problem space.