Conference Agenda

Engineering education faces persistent challenges in ensuring coherent competency development within institutional contexts characterized by curricular complexity, disciplinary diversity, and increasing demands for quality assurance. In such settings, traditional assessment models based on the accumulation of fragmented evidence have shown limited capacity to support meaningful continuous improvement or sustainable teaching practices. This paper presents the conceptual–operational design and institutional implementation strategy of a new competency-based education model for engineering programs developed at scale. The proposed model integrates a curriculum architecture structured through differentiated formative units, a competency-based assessment framework articulated via critical evaluation pathways, and a deliberate strategy for assessment evidence simplification. Rather than reducing academic rigor, this approach prioritizes the relevance, interpretability, and decision-making value of assessment evidence, strengthening alignment among curriculum design, evaluation practices, and the student learning experience. The model was developed through a merit-based and participatory governance process involving more than 600 faculty members and encompassing 18 engineering programs. Methodologically, this study adopts an institutional design–based research approach, focusing on the academic design decisions, governance mechanisms, and implementation strategies that underpin the model. Formal implementation will begin in August 2026 and will be accompanied by a structured experimentation phase and a semester-based continuous improvement cycle grounded in collegial analysis and collective decision-making. This work contributes to engineering education by offering a transferable methodological framework for large-scale educational transformation, emphasizing academic governance, competency-based assessment, and faculty sustainability as central dimensions of educational quality.

Entrepreneurial intention is widely used as a central outcome in entrepreneurship education in engineering; however, less attention has been paid to how different predictors operate when ordered according to their proximity to entrepreneurial action under uncertainty. This study proposes and tests a layered activation model in which entrepreneurial intention is progressively explained through variables ranging from distal structural factors to proximal behavioral mechanisms. A questionnaire was administered to 497 third-year engineering students from three departments at a Chilean university. Measures included entrepreneurial intention, experimentation (as an effectuation-related behavior), cognitive flexibility-adaptability, polychronicity, openness, extraversion, demographic variables, work experience, and family entrepreneurship background. Hierarchical regression analyses revealed a progressive increase in explained variance across five theoretically ordered blocks: demographic variables (R² = .045), experiential background (R² = .124), personality traits (R² = .24), cognitive-temporal mechanisms (R² = .307), and, finally, experimentation (R² = .435). Experimentation emerged as the strongest predictor (β = .52, p < .001), while polychronicity maintained significance in the final model. Personality traits, gender, age, and innovation training were not significant once proximal mechanisms were incorporated. The findings support a progressive activation structure in which entrepreneurial intention is primarily associated with mechanisms proximate to behavioral engagement under uncertainty, rather than with traits or distal conditions. These results have direct implications for curriculum design in engineering, suggesting that formative experiences centered on iterative experimentation and parallel coordination of activities may play a decisive role in strengthening entrepreneurial intention.

The growing adoption of reinforcement learning in robotic motion control creates a need for trained engineers working at the intersection of robotics and machine learning. Even though this area is one that requires knowledge of advanced mathematical concepts such as policy optimization and deep neural network algorithms, its effective implementation depends on a system-level framework. Undergraduate students can study this abstraction inside a simulation and implement it using predefined coding libraries and tools. Two case studies are presented: a model of a racing car that learns to navigate three different racetracks and another model of a quadruped robot that learns to walk towards a target. The students develop the framework using a combination of preexisting elements such as physical environments and policy optimizers and encoding abstract concepts such as actions, observables, rewards, and training episode structures. Results show that undergraduate students can create models that exhibit emergent learning behavior by training policies.

This study examines how Project‑Based Learning complements the tackling of challenges through Challenge‑Based Learning in an early engineering course. It synthesizes literature published between 2019 and 2024 and describes a didactic design with authentic assessment, a public product, and engagement with external stakeholders. The analysis considers three consecutive cohorts (2023–2025) and compares academic indicators with previous periods, together with evidence of performance in a Latin‑American competition. Results show high evaluation, success, and performance rates, and suggest that combining projects and challenges favors the transfer of learning and the development of integral competencies. Implications are discussed for the management of early engineering courses and for the improvement of university education.

This paper presents the results of an educational case study conducted during a one-week intensive course on the use of artificial intelligence (AI) to support scientific writing. Pretest and posttest evaluations were administered to assess the students’ level of use and familiarity with AI before and after the course. Participants worked with a base document in the format of a scientific publication and learned to integrate AI ethically and responsibly, identifying which sections of the text could be supported by this tool. The results show an increase in ethical understanding, metacognition, creative self-efficacy, and academic integrity, demonstrating that an intensive course can significantly enhance the use of AI among engineering students for scientific article development. This study provides evidence of effective strategies for teaching AI-assisted scientific writing.

This proposal examines the integration of Building Information Modeling (BIM) through Autodesk Revit as a didactic strategy to foster design thinking in nine academic subjects of civil engineering at Areandina. Regarding [1], a mixed-methods approach was employed, combining qualitative observation of classroom practices with quantitative assessment of student performance. The methodology emphasized collaborative project development, iterative modeling, and reflective analysis of design processes, [2]. Results indicate that Revit-BIM enhances spatial reasoning, interdisciplinary communication, and sustainability awareness, while strengthening technical competencies aligned with industry standards. The findings support the incorporation of BIM-based strategies into curricular design as a means of cultivating innovative and adaptive problem-solving skills in civil engineering education.

Teamwork is a critical competency in construction engineering; however, a persistent gap remains between its development in university education and the demands of professional practice. This study examines how Construction Engineering graduates reinterpret the collaborative dynamics experienced during their capstone portfolio course once they enter the workforce. Using an exploratory qualitative approach, ten semi-structured interviews were conducted with graduates from the 2024 cohort who had up to one year of professional experience. An inductive–deductive thematic analysis identified ten emergent categories: communication, teamwork, conflict resolution, feedback, decision-making, time management, transfer to the professional context, professional identity development, course evaluation, and labor market insertion. Findings indicate that graduates tend to positively reframe their formative experiences after entering professional practice, recognizing the relevance of the collaborative competencies developed during the capstone course. Moreover, these competencies align closely with those most valued by employers in the construction sector.