Conference Agenda

The rapid integration of Large Language Models (LLMs) into computer science education necessitates understanding student perceptions in diverse contexts like Mexican higher education. This study investigates the experiences of 311 computer science students from public and private Mexican universities. A hybrid methodology was employed, first validating the survey instrument through Confirmatory Factor Analysis and reliability tests. Statistical methods were then combined with machine learning in a comprehensive analytical framework. Results reveal a clear narrative: students overwhelmingly perceive LLMs as transformative, significantly enhancing programming efficiency. This core perception is deeply embedded within an interconnected network of positive attitudes and behaviors. Strong correlations between perceived efficiency, positive attitudes, future use intentions, and increased usage frequency create a virtuous adoption cycle. The study concludes that LLMs are firmly rooted in Mexico's programming education, urging a pedagogical shift from "if" to "how" they are used, thereby informing ethical curriculum design and AI literacy initiatives.

This study investigates the influence of embedding TRIZ (Theory of Inventive Problem Solving) within a Design Thinking framework on engineering students’ problem-solving performance. The study employed a quasi-experimental design with 68 undergraduate engineering students who were assigned to two groups: a control group using Design Thinking (DT) and an experimental group using DT combined with TRIZ. The pedagogical design challenge students tackled was the development of learning tools for neurodiverse children. Final solutions were appraised using a rubric that assessed creativity, originality, technical feasibility, and systematic coherence. Independent samples t-test analysis confirmed no significant differences in creativity and originality between groups; however, statistically significant improvements were observed in technical feasibility and systematic coherence in DT+TRIZ condition (p < 0.05). These results indicate that design thinking and TRIZ can enrich student solutions with high technical quality while not undermining creativity. These findings suggest that hybrid innovation frameworks can help engineering students balance creative ideation with technical rigor in complex problem-solving contexts.

Generative Artificial Intelligence (GenAI) has transformed multiple sectors, with education being one of the most impacted. This research analyzes the use of GenAI in upper secondary students (K-12), a vulnerable and understudied sector where the central problem identifies risky practices such as uncritical use, overestimation of AI capabilities, and absence of critical thinking that generates cognitive dependence and validation of false information. This study aims to promote the development of critical thinking about GenAI use through a didactic intervention with fourteen students of 15-16 years old from the Instituto Politécnico Nacional in Mexico. Using a qualitative approach with a pretest-posttest design, the methodology employed semi-structured interviews organized in five dimensions: perception of GenAI, representation and reasoning, mediated learning, critical interaction, and ethical considerations. The results reveal significant post-intervention transformations: paradigm change from instrumental to critical-reflective use, development of verification protocols, construction of complex and structured prompts, understanding of contextual limitations, and establishment of ethical frameworks. Furthermore, this research concludes that teacher intervention can modify forms of thinking, demonstrating that with adequate pedagogical guidance, students develop competencies for responsible GenAI use, minimizing risks and maximizing benefits, these findings underscore the need for further research on teacher education, who directly influence students' perspectives and practices regarding GenAI use.

The Ephemeral Classroom, developed between 2023 and 2025 by the UTN Mar del Plata Regional Faculty together with TecnoPesca Argentina, was implemented within the framework of the Ship Theory I course, corresponding to the third year of the Naval Engineering degree. This innovative co-curricular device integrates university education with real-life scenarios in the fishing and naval industry, transforming the productive space into a classroom through situated and challenge-based learning (ABR) experiences, guided by the AVIVA teaching model. Over the course of three editions, more than 45 students participated in progressive technical challenges related to the launching, balancing, and structural modifications of ships. The project strengthened the link between university and industry, promoted motivation and the development of professional skills, and consolidated CREA as a space for pedagogical innovation. In short, the Ephemeral Classroom established itself as a replicable practice and a model of disruptive technological education with a territorial impact.

Baseline evidence on student motivation and topic interests can inform the design of research-based learning (RBL) interventions in engineering education. This paper reports baseline findings from a mixed-methods study (survey N = 120; semi-structured interviews n = 10) conducted prior to implementing a longitudinal RBL pathway across three engineering mathematics courses in an Aerospace Engineering degree at Universitat Politècnica de València. Quantitative results indicate a high perceived value of early research-skills development (PVI = 4.70/5) and very high willingness to participate (95.8% rating 4–5), with motivation frequently attributed to the possibility of open-topic selection. Students reported broad interest in aerospace systems, AI/ML, sustainability, and telecommunications. Interview analysis yielded five themes: (1) research as an identity bridge from mathematical procedures to engineering practice; (2) scaffolding as an enabler despite high achievement; (3) time as the primary barrier; (4) traceability as professional practice; and (5) the need for clear boundaries for optional AI use. These findings inform the design of a two-year educational innovation project (PIME) that will implement staged deliverables, analytic rubrics, and postdoc-led training sessions, and they motivate the planned evaluation framework for subsequent phases.