ABSTRACT. The functional programming paradigm has a long and storied history, with its beginnings in the Lambda Calculus. In recent decades, it has been shown that robust software can be highly effectively produced by pure functional languages such as Haskell, due to its statically typed nature and strong type inference mechanisms built into the language. Desirable functional features are included in almost all programming languages these days. In this paper, a subset of Python is introduced, using which pure functional ideas such as immutability, pure functions with no side effects, and stateless programming can be learned by students. Bugs can be reduced, performance can be improved, and code can be made more maintainable and scalable by this approach. It is strongly felt that familiarity with pure functional programming is needed by students in computing, and it is argued that this can be taught within introductory programming courses that are taught in Python.

ABSTRACT. Engineering mentoring programs promote student participation throughout their academic years, fostering the development and reinforcement of soft skills essential to their personal and professional growth. This skill development through mentoring programs is one of the main strategies to address the high dropout rates in the first semesters and to strengthen students’ sense of institutional belonging. A key mentoring strategy involves student mentors' participation in extracurricular activities. In this context, we posed the following research question (RQ): What are the primary motivations that lead engineering students, who serve as mentors, to voluntarily participate in the organisation of an International Conference on Education? The methodological approach was mixed, combining quantitative and qualitative methods. Initially, semi-structured interviews were conducted with mentors and conference organisers. Thematic analysis was applied to the interview transcripts to establish different descriptive categories of student motivation. From these, a survey was constructed and administered to all mentors who participated in the event. We then sought to interpret the data and delve deeper into the survey results. Regarding the findings, although the motivational aspects are diverse and complex, one of the primary initial insights concerns the role of certification and the sense of institutional belonging. Certification was one of the main factors that mentors cited as key to their participation. They referred to formal institutional recognition and evaluation that could later serve as a professional credential. On the other hand, an interesting contrast emerged between the students’ and organisers’ perspectives: organisers did not necessarily identify certification as a possible motivator. In the interviews and surveys, mentors mentioned the value of collaborating at the institutional level, practising foreign languages, networking, and developing interpersonal and social skills. They also highlighted the value of certification as a driver of participation. For future research, we plan to continue exploring extracurricular activities, further develop the survey, and identify mentoring program activities that strengthen the soft and interpersonal skills essential for future engineering professionals.

ABSTRACT. Student dropout remains a global challenge in higher education, particularly in engineering. Early detection of students at risk of dropping out enables timely institutional interventions, thereby improving retention. Based on recent literature regarding advances in educational data models and explainable AI, this article describes a model that extends a new approach to computational intelligence for modelling student dropout. This research aims to establish an effective and interpretable early warning system to support data-driven retention policies. The research question is: How can we develop a hybrid neural network model that utilises combined academic and socioeconomic data to predict dropout risk for engineering students with higher accuracy and greater transparency than current prediction tools? The proposed methodology for model design involves extending existing open, real-world, and multi-feature datasets of higher education students. The methodology includes steps for data preprocessing, feature selection, and class balancing. This study proposes a hybrid neural network architecture that extends an HLRNN model by integrating a logistic embedding layer, Transformer-based contextual embeddings, and an optional recurrent module to capture linear, nonlinear, and temporal patterns in heterogeneous student data. The model incorporates a fairness regularizer and utilises a Tabular Transformer Generative Adversarial Network (TTGAN) for data augmentation, addressing ethical principles of fairness and privacy. Results indicate that benchmark models, such as CatBoost, achieve promising macro F1 scores. Future work will empirically implement and evaluate the proposed hybrid model, integrating a Mentoring-in-the-Loop approach to complement predictive results with qualitative monitoring.

ABSTRACT. This research investigates how 12th grade students from different social contexts use generative Artificial Intelligence tools in school activities, drawing on Pierre Bourdieu’s theory of cultural capital. It examines whether educational inequalities are reproduced in the use of these technologies and how they shape learning processes. Using a mixed methods approach, with quantitative surveys and semi-structured interviews, the research will conduct a study involving three types of institutions: public schools, philanthropic schools, and elite private schools. This design allows for both analysis of intragroup dynamics and systematic comparison between distinct social strata. Based on this data collection, the study aims to measure the accumulated cultural capital of students and how it manifests related to patterns of use and contexts of application of generative Artificial Intelligence tools. Ultimately, these findings are expected to provide information to promote educational practices that enhance student performance while addressing persistent social inequalities.

ABSTRACT. With the increasing scarcity of teaching staff and their unavailability to provide personalized approaches, eyes are on technology. Could semi-autonomous robots, as teaching assistants, take over some of the burden from the shoulders of teachers? In this paper, we present a pilot study on the use of a robot as a teaching assistant in a higher education laboratory setting to support students’ learning path by allowing them to accumulate knowledge through question-answering interaction with a robot teaching assistant. This robot teaching assistant is a companion of a teacher to provide support in teaching large groups of students, where a single lecturer may struggle to respond to all student inquiries within the allotted time. The robot teaching assistant was pre-trained with course-specific material, enabling it to answer topic-related questions directly. The findings from the pilot study highlight the potential of robot assistants to provide accessible, on-demand academic support and to complement teaching in practical learning environments. This study contributes to the growing body of research on robot-assisted education and demonstrates the applicability of robots as teaching assistants in higher education.

ABSTRACT. This study analyzes upper secondary students’ acceptance of immersive environments using the Spatial platform in algebra learning. The intervention integrated virtual escape rooms to encourage teamwork, mathematical problem-solving, and exploration of three-dimensional scenarios. Conducted over two weeks, the experience used computers, mobile phones, tablets, and VR headsets to compare levels of technological immersion. A quantitative approach was applied, and a validated questionnaire for adolescents was administered at the end. The instrument measured perceived usefulness, ease of use, enjoyment, and behavioral intention to use. The sample included 27 students from a private institution in Mexico. Results showed high acceptance of the immersive environment, particularly in enjoyment and future intention to use. These findings align with the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology. Although limited by sample size and descriptive analysis, the study offers initial evidence supporting the feasibility of immersive technologies in school contexts.

ABSTRACT. In a world that demands adaptability and vision, leadership competencies are essential for navigating the complexities of globalized business environments. These skills are particularly critical for women, who often face structural and cultural barriers such as gender stereotypes, limited access to role models and support networks, and greater risk aversion. This study examined the leadership competencies of male and female postgraduate students in a Mexican business school. Using a quantitative design, leadership skills were assessed according to Yukl’s taxonomy: giving and seeking information, decision-making, influencing others, and building relationships. The sample included 74 students (27 women and 47 men), and the instrument showed high reliability (Cronbach’s α = .960). Women scored slightly higher across all dimensions, but Mann–Whitney U tests showed no statistically significant gender differences. These findings suggest similar competence levels across genders, though broader studies are needed to validate these results.

ABSTRACT. This work-in-progress paper reports on the early implementation of a blended and flipped learning approach in a first-year Electronic Engineering course, introduced to modernize the learning experience and increase student engagement. Core concepts are delivered through pre-recorded videos, while workshop sessions incorporate active learning through multi-level problem sets and real-time interactive activities that promote discussion, participation, and peer engagement. These activities aim to make problem solving more dynamic by encouraging students to share their reasoning. The course uses a continuous assessment model comprising quizzes and laboratory activities, removing the traditional final exam. Laboratories provide hands-on opportunities to reinforce weekly theoretical content. An emerging AI agent was incorporated to offer content support and respond to course-related queries. Preliminary survey responses indicate that students find the developing structure more engaging than previous formats. Ongoing work aims to refine the intervention, examine patterns of AI adoption, and evaluate its educational impact as implementation continues.

ABSTRACT. This classroom-embedded study investigates whether low-cost, paper/cardboard manipulatives can strengthen students’ perceived competence on core three-dimensional topics in Multivariable Calculus. We implemented a foldable, L-shaped modeling base with tangible surface pieces to support hands-on exploration of surfaces, accumulation for multiple integrals, and coordinate transformations, coupled with a standardized lesson sequence (conceptual activation, guided construction and exploration, representation bridge, exit ticket, and reuse). Four course sections (≈100 students) used the materials; inferential analyses focus on one representative section with anonymous pre (n=29) and post (n=26) surveys. Two self-efficacy items targeted translations of planar curves and the representation of 2D equations as 3D surfaces. The share selecting the top two confidence categories increased from 0.707 to 0.923 (Δ=0.216; Cohen’s h=0.58; two-sided p=0.0040; 95% CI [0.078, 0.354]; normalized gain g=0.74). Item-level contrasts were 69.0% to 88.5% for translations (z=1.75, p=0.0805) and 72.4% to 96.2% for 2D to 3D representation (z=2.38,p=0.0175). Results suggest that haptic-visual coupling, when explicitly bridged to sketches and symbolic expressions, is a scalable way to enhance students’ readiness to engage surfaces, solids, and coordinate changes. Limitations include self-report outcomes, unpaired samples, and a single-site context.

ABSTRACT. This study presents an innovative educational model that integrates Artificial Intelligence (AI) into the teaching of differential equations through experimental validation in a school wind tunnel. Conducted with three student groups from the MA1033 course at Tecnológico de Monterrey (Campus Puebla), the project explored the aerodynamic drag and lift governed by nonlinear differential equations derived from Newton’s Second Law and Bernoulli’s principle. Each group interacted with AI at a different cognitive level: analytical regression, predictive modeling, and explainable deep learning. The results demonstrated that the progressive integration of AI significantly improved both modeling accuracy (average R^2>0.97) and cognitive engagement. Beyond data processing, AI acted as a cognitive mediator that enabled students to interpret residuals, identify anomalies, and reason about the limitations of theoretical models. The combination of experimental observation, mathematical modeling, and algorithmic interpretation fostered the development of adaptive reasoning, data ethics, and scientific communication skills. This hybrid AI–experimental learning framework redefines engineering education by transforming AI from a computational tool into an epistemic collaborator, enhancing conceptual understanding, reflective thinking, and metacognitive awareness in scientific learning

ABSTRACT. The teaching of differential equations continues to pose a persistent challenge in engineering education, as many students master procedural steps mechanically without consolidating deep conceptual understanding. This study examines the impact of a generative AI tutor, Tu Tutor de Ecuaciones Diferenciales, integrated with Mathematica, on conceptual comprehension and problem-solving efficiency. A quasi-experimental design involved 135 students distributed in five groups: three experimental (n = 83) and two control (n = 52). Experimental groups interacted with the AI tutor for step-by-step guidance, while control groups followed traditional instruction. Data collection combined entrance and exit surveys, performance tests, rubrics for mathematical justification, time tracking, and prompting analysis. Results show that experimental groups achieved significantly higher accuracy (82.4% vs. 74.1%), reduced solution times (18.6 vs. 24.4 minutes), and stronger justifications (M = 4.1 vs. 3.3). Longitudinal analysis revealed a cumulative effect across the ten weeks, with widening performance gaps over time. Furthermore, prompting quality correlated strongly with reduced error (r = –0.58, p < 0.001), positioning prompting as an emergent competency in AI-mediated mathematics education. These findings indicate that generative tutors, when responsibly integrated, not only improve efficiency but also foster higher-order engineering competencies such as critical decision-making and rigorous methodological implementation

ABSTRACT. Advanced Human Computer Interaction (HCI) and visualization technologies have the potential to improve learning outcomes for architecture students in difficult subject areas where they have historically underperformed. This paper describes a proposal for an immersive learning system for Structural Design. This is critical for the Architecture, Engineering and Construction (AEC) sector: a shortfall of professionals competent in structural design suggests improvements to education in this area are needed. SpARC Learning proposes a combination of technologies to improve learning: Spatial Augmented Reality (SAR), a type of AR that overlays dynamic graphic information onto objects in the learning environment, Tangible User Interfaces (TUI), object-based HCIs allowing users to interact with software, and Artificial Intelligence (AI) that supports verbal interaction with the learning system. This immersive learning system facilitates self-guided experiential learning by small groups of students. This project is in the early stages of development; results of initial testing are forthcoming.

ABSTRACT. Urarina is an endangered language spoken by a small indigenous community in Northern Peru. In 1987, a research team from the University of San Marcos in Lima, Peru, conducted a documentation study to help preserve the language, producing thousands of handwritten lexicon cards and cassette recordings of native speakers. Decades later, these materials had deteriorated significantly, making them difficult to use in contemporary linguistic or educational research. Therefore, this project aims to digitize and restore these artifacts to inform both modern linguistic analysis and future educational initiatives. By converting handwritten notes into a structured database and enhancing analog audio recordings, the project provides invaluable data that can support national educational program proposals and lay the groundwork for integrating cultural knowledge with emerging AI technologies. Beyond safeguarding the materials themselves, this work highlights the role of engineering, particularly AI, in revitalizing pre-digital studies and expanding around endangered-language preservation. The methodology and results presented here aim to encourage similar restoration efforts and to contribute to the conservation and potential pedagogical use of endangered languages.

ABSTRACT. Introductory transportation courses are commonly offered in any civil engineering program to educate students about the various aspects of transportation systems. It is important to assess whether these courses effectively deliver knowledge and promote sustainable transportation literacy among students. The purpose of this work is to evaluate the effectiveness of an introductory transportation course in developing sustainable transportation literacy among college students. The study assesses the baseline transportation literacy of students at the beginning of the semester, evaluates the impact of the course on students' knowledge of transportation systems, and identifies any areas of improvement. The pre-course survey indicated a baseline of limited awareness across environmental, social, economic, and technological aspects of transportation sustainability. However, the post-course survey shows a different picture. The students showed an increase in their awareness of all areas. The observed changes affirm the effectiveness of the course in increasing students’ knowledge of sustainable transportation.

ABSTRACT. Traditional approaches in transportation engineering education are insufficient to address the complex, interconnected challenges of climate change, resource depletion, equity, and technological disruption. This work suggests that the path forward lies in multidisciplinary integration. Different novel undergraduate course concepts that deliberately fuse transportation engineering with diverse fields such as data science, psychology, law, ecology, and business are presented. These courses are structured into thematic clusters, including technology and data integration, human-centered design and behavior, policy, economics, and justice, and resilience and circular systems. The proposed courses aim to produce a generation of engineers who are not only technical experts but also systems thinkers, ethical problem-solvers, and collaborative leaders, equipped to design and manage transportation infrastructure that is sustainable, resilient, and equitable.

ABSTRACT. Reading fluency is a critical skill for academic and social development, particularly in early elementary education. Yet, many students struggle with speed and accuracy, hindering comprehension and motivation. This study implements an educational intervention using Information and Communication Technologies (ICT) to improve reading fluency among third-grade students. Adopting a qualitative design, the research evaluates how digital tools influence students’ reading pace, motivation, and confidence. Results reveal significant fluency improvements and high levels of student engagement with ICT, highlighting their potential as effective pedagogical tools in primary education.

ABSTRACT. This paper describes an approach to addressing a common issue in engineering education: students often do not follow structured methodologies when tackling projects, either due to a lack of awareness or because they perceive these methodologies as impractical. As a result, their solutions frequently fail to fully meet customer needs, leading to inefficiencies in problem-solving and product development. This misalignment negatively impacts the university-industry relationship, resulting in poor communication and ineffective collaboration. Companies struggle to find graduates with the necessary skills, while students face difficulties applying academic knowledge to real-world contexts. In response to this situation, it is proposed to integrate Design Thinking with structured Product Development Tools, exemplified through a research project developed for a solar energy company. This combination enables students’ creativity to connect with methodologies relevant to industry, offering a more effective framework for engineering education. The implementation of this approach led to the development of a minimum viable product that met the company’s stated needs, thus validating its effectiveness in fostering students’ capacity for innovation and product development.

ABSTRACT. This study investigated the relationship between physiological activation and performance perception in immer- sive and non-immersive educational environments. A gamified escape-room task was used to compare two technological condi- tions—virtual reality (VR) and desktop computing (PC)—among university students. Physiological signals, including electrodermal activity (EDA), blood volume pulse (BVP), and skin tempera- ture, were continuously monitored using medical-grade wearable devices. In parallel, self-report instruments were employed to capture students perceptions of physical state, performance, and task engagement. The results revealed that immersive environ- ments elicited stronger cardiovascular and thermal responses, which were negatively correlated with self-assessed performance. In contrast, EDA emerged as a dominant indicator of cognitive fatigue in non-immersive settings, particularly during early task stages. Temporal analyses also indicated cumulative stress responses in VR and early sympathetic activation in PC. These findings support the integration of biometric monitoring to assess user experience in digital learning and highlight the need for instructional designs that balance cognitive load, technological immersion, and user well-being. Ethical considerations related to biometric data use in educational contexts were also addressed

ABSTRACT. Anticipatory thinking, defined as the cognitive ability to project future scenarios based on previous experiences, represents a key competency in the current educational environment. This research analyzes how anticipatory thinking manifests itself in undergraduate architecture students during the design of future scenarios, and how it relates to their physiological responses in an immersive learning environment. For this purpose, electrodermal activity and emotional reactions were measured. A quantitative experimental design with data collection using physiological sensors and emotional self-assessment instruments was used. Seventy-three fourth semester students participated, divided into three groups, who developed future scenarios in two phases: a theoretical phase and a practical phase in virtual reality. The results showed a correlation between electrodermal activity and moments of high cognitive load, especially during the analysis and adaptation of scenarios. A high perception of motivation and concentration was also observed during the virtual reality phase. These findings suggest that anticipatory thinking generates measurable emotional involvement, and that its development can be enhanced through immersive pedagogical experiences. It is recommended to include control groups and standardize methodological variables, such as additional physiological and emotional elements, to strengthen internal validity and replicability in future research

ABSTRACT. The curriculum, as the articulating element of the educational process, faces challenges and questions in the face of a constantly evolving, digital, and interdisciplinary society. In this sense, unlike traditional curricular perspectives that prioritize the transmission of content or the achievement of learning outcomes in disciplinary domains, the diflexive curriculum is introduced as a dynamic and conflictive proposal for the postgraduate level, which proposes an educational itinerary that creates a bridge between the dynamics and challenges of the real world and a body of interdisciplinary knowledge specific to a subject, where the role of the educator is to “provoke and guide” while the student “reflects and acts”. The article presents the application of this proposal in graduate courses on security and control issues during the first semester of 2025, detailing its results and challenges, as well as practical recommendations for both educators and higher education institutions seeking to explore different curricular approaches.

ABSTRACT. Most universities still rely on manual identity verification for examinations and class tests. While adequate for small classes, such checks are slow, error-prone, and vulnerable to impersonation in larger cohorts. This paper investigates whether these shortcomings can be addressed through a low-cost privacy-preserving system that integrates with existing institutional records. We propose an architecture that captures student ID data and photographs at the point of entry using a barcode scanner and a laptop with a webcam, with subsequent offline analysis against institutional records. A proof-of-concept implementation demonstrates that the system can operate quickly, store data locally and securely, and detect cases of impersonation. Preliminary trials suggest that the approach is both practical and promising, though larger-scale evaluation is needed to validate its effectiveness under real examination conditions.

ABSTRACT. The widespread adoption of generative AI poses a direct challenge to academic integrity in programming courses, particularly in take-home contexts without supervision. Based on classroom observations in a third-year front-end web development course---where students performed well on structured tasks but struggled with a seemingly simpler SVG logo reconstruction exercise---this paper conducts a comparative evaluation of several mainstream large language models (LLMs) using the same assignment. The results show that current models can reliably generate functional HTML/CSS and data-driven SVG charts but generally fail at logo reconstruction tasks that require geometric decomposition and reasoning with basic primitives. Some models approximate shapes using dense polygon meshes rather than understanding underlying structure. While supervised assessments remain an effective safeguard, they do not scale to large cohorts. Our findings suggest that designing tasks that foreground structural reasoning, intermediate representations, or explainable design processes may offer a more sustainable path toward AI-resilient programming assessment---at least for a little while.

ABSTRACT. This study presents a deep learning approach for recognizing the Peruvian Sign Language alphabet from video sequences, aiming to promote inclusive and accessible sign language learning. The proposed system combines a Convolutional Neural Network (CNN) with a Long Short Term Memory (LSTM) architecture to capture the spatial and temporal characteristics of static and dynamic gestures. A custom dataset was created with recordings from ten volunteers in both controlled and non-controlled environments, covering all 28 alphabet letters. The preprocessing pipeline included hand detection, cropping, grayscale conversion, and contrast enhancement through computer vision filters to improve gesture clarity. Experimental results show that the integration of filters enhanced mean accuracy across all categories, and the CNN–LSTM architecture achieved robust recognition of alphabet gestures. Beyond improving automatic recognition, this model can serve as an assistive learning tool to help users practice and learn sign language.

ABSTRACT. Understanding the factors that influence undergraduate students’ intent to pursue graduate studies is crucial for developing effective academic pathways and increasing domestic participation in STEM graduate programs. Formal undergraduate research experiences, referred to as REUs, have the potential to enhance student confidence, promote a sense of belonging, and encourage persistence in STEM; however, their impact on intent to apply to graduate studies has not been thoroughly examined. This mixed-methods study investigates whether participation in REUs influences students’ intentions to apply to graduate school by comparing national, Computing Research Association (CRA), and local, Carnegie Mellon University’s Robotics Institute Summer Scholars (RISS), data. Analyses of CRA national surveys show that students with formal research experience are more than twice as likely to report an intent to apply to graduate school. Formal REU programs are highly effective educational interventions that measurably increase intent to apply to graduate studies.

ABSTRACT. The traditional model of higher education is based on faculty and support staff responsible for instruction, research, service, and administration. This model is facing an unprecedented challenge from fast advancements in Artificial Intelligence (AI). This work explores the potential for AI to reduce and replace a wide range of faculty and staff roles within the academic field. Different AI applications across the academic functions are presented, including automated course instruction and curriculum design, AI-powered tutoring and assessment, support administration, and strategic institutional planning. These options demonstrate that AI can transform the faculty and staff from a broad-based workforce into a more specific workforce. While this transition promises significant gains in operational efficiency, scalability, and cost reduction, it also raises profound questions regarding the reduction of faculty and staff positions, educational quality, the preservation of academic mentorship, and the ethical implementation of automated systems. This work concludes that a strategic integration of AI is inevitable. The challenge for university leadership is to manage this transition responsibly and preserve the core values of academia.

ABSTRACT. The transformation of engineering education requires frameworks that integrate artificial intelligence (AI) and sustainability as core components of competency-based learning. This paper examines the evolution from the Tec21 model to Plan 2026 at the School of Engineering and Sciences (EIC) of Tecnológico de Monterrey, highlighting the convergence between AI, sustainable development, and authentic assessment. Using a qualitative and interpretive methodology grounded in institutional documentation and comparative analysis, the study identifies structural and epistemological innovations that redefine engineering education. Plan 2026 establishes a coherent curricular framework centered on three types of learning units, development, process, and integration, supported by intelligent evaluation systems that personalize feedback and trace competency progress. Sustainability becomes a transversal axis embedded in disciplinary and transversal competencies, aligning education with the United Nations Sustainable Development Goals (SDGs). The findings show that AI acts as an epistemic mediator enhancing formative assessment and adaptive learning, while sustainability provides the ethical framework guiding professional practice. Together, these pillars define an Education 6.0 model where human and artificial intelligences co-create ethical, data-driven learning ecosystems, positioning Tecnológico de Monterrey as a regional benchmark for sustainable innovation.

ABSTRACT. We introduce an institution-scale educational innovation: a standardized Evaluation Center deployed across four campuses (Cuernavaca, Toluca, Tampico, Sonora Norte) in synchronous hybrid mode, simulating professional recruitment and performance. Multi-campus teams collaborated via Zoom with external evaluators. The study integrates triangulation (students, employers, faculty) and a PRE/POST design to estimate perceived change and convergent validity. Participants included students (PRE n=56; POST n=44), 5 employers, and 11 faculty; internal consistency was high (α=0.96 PRE; 0.93 POST). POST results indicated utility 8.8/10, realism 8.6/10, confidence gained 8.4/10; 84% recommended continuation. Cross-campus deployment with shared governance and calibrated rubrics enabled comparable metrics, detection of local gaps, and immediate formative feedback. Triangulation confirmed alignment with professional competencies: employers rated 9–10/10 on industry relevance, while faculty stressed curricular applicability. The primary contribution is the integration of professional simulation, longitudinal evidence, and inter-campus governance into a replicable mechanism for program-outcomes assurance and accreditation readiness. Novelty lies in combining PRE/POST evidence, actor triangulation, and hybrid synchronous operation, thus underpinning external validity. This work demonstrates that authentic, multi-campus performance assessment can simultaneously scale, maintain fidelity, and enhance employability.

ABSTRACT. This study investigates how students in distance higher education use the chatbot. There is a need to conduct these studies since there is currently extensive use of Generative Artificial Intelligence (GenIA) by students, but data are not being systematically collected. To address this, a chatbot is being developed with the aim of gathering data and understanding metacognitive aspects—particularly spacing as a type of metacognitive strategy—and to classify students into different profiles. Conducted at IPLACEX, Chile’s largest distance-learning institution, the research analyzed 10,490 chatbot conversations from 4,276 students during the first academic term of 2025. For this proposal, two complementary studies were conducted. Study 1 (S1) examined the temporal structure and spacing of chatbot interactions. Study 2 (S2) applied Latent Class Analysis (LCA) and multinomial regression to identify and characterize three user profiles. Results revealed that most conversations were brief and concentrated within short study sessions. Women participated more frequently in extended conversations. The analysis of 30-, 45-, and 60-minute intervals showed limited temporal spacing, indicating that chatbot primarily support immediate, task-oriented needs rather than prolonged learning sequences. In addition, three profiles were identified. The first one included students who used the chatbot mainly for administrative or general clarification purposes; Profile 2 represented those who interacted more intensively with content-related topics; and Profile 3 with lower chatbot interaction. Together, both studies highlight that chatbot serve differentiated functions in distance education, ranging from instrumental support to cognitive regulation. The findings underscore the potential of GenIA tools to foster accessibility and metacognitive awareness among non-traditional students, while also emphasizing the need for pedagogical strategies that guide their effective and reflective use.

ABSTRACT. Reflecting the global commitment to equitable education articulated in the United Nations’ Sustainable Development Goals, this study investigates Students' Evaluations of Teaching (SET), instruments widely used in higher education despite often containing several biased elements. This mini-review and bibliometrics analysis aim to identify major thematic breakthroughs in SET research, particularly regarding scientific productions and collaborations, and to reveal trends related to gender bias and its association with SET. To achieve these objectives, a mini-review and bibliometrics analysis were performed using the Scopus database, focusing on publications from 2020 to 2024. The data from seven papers were analyzed using PRISMA guidelines and Biblioshiny data visualization tools to synthesize the scholarly work. Key findings reveal a declining trend in recent scholarly outputs on SETs. Mexico notably leads the world in global scientific contributions in this area. Critically, the analysis consistently highlights pervasive gender and intersectional biases that influence SET outcomes. These biases often disadvantage specific instructor demographics and demonstrate a tendency towards same-gender affinity among evaluators. This study provides a synthesized understanding of gender bias in SETs through its robust methodological framework. It offers valuable insights for promoting more equitable evaluation practices and fostering inclusive academic environments.

ABSTRACT. Teachers identify the primary motivation for implementing Collaborative Online International Learning (COIL) as the benefits it provides to students. Key challenges include managing differences in time zones, academic calendars, students' motivation, and course topics, with particular emphasis on aligning subjects across disciplines. The icebreaker phase is regarded as a crucial element of the collaboration, receiving a high relevance rating of 4.64 on a five-point Likert scale. Participation in COIL initiatives fosters the development of both pedagogical and personal competencies, notably in leadership, communication, empathy, and flexibility. Overall, COIL collaborations demonstrate the intellectual engagement of educators and their commitment to advancing innovative and globally oriented teaching practices.

ABSTRACT. The European Union co-funded project “Modernising Digital Education in Energy Transition for Circular Economy in Latin America” (EU-BEGP, 2023–2026) brings together seven countries and eleven institutions to rethink how gender balance in STEM education can be achieved. This paper presents how the consortium has advanced women’s participation, paying attention to the regional and cultural factors that shape opportunities in energy transition fields. Data from Bolivia, Ecuador, Guatemala, Perú, Spain, and France reveal encouraging progress: women now account for 44% of participants, a figure that challenges the male-dominated traditions of STEM. The results also show that some institutions have been especially effective in engaging women. These findings suggest that broader results can be achieved when gender-inclusive strategies are intentional and reinforced through international collaboration. By highlighting how different cultural contexts influence participation, EU-BEGP offers practical lessons for educators and policymakers committed to building more diverse and inclusive STEM programs.

ABSTRACT. Society is changing at a remarkable pace, and Artificial Intelligence (AI) has rapidly become one of the most influential forces driving this transformation. Higher education is directly affected: while students tend to adopt AI tools with enthusiasm—drawn by their efficiency, accessibility, and appeal—faculty are still defining how these technologies fit into their teaching and professional identity. This study examines how university faculty perceive their role in the classroom in the context of AI. Using a qualitative survey design across multiple disciplines, it draws on the perspectives of 36 faculty members to explore evolving responsibilities, teaching methods, and student engagement. The results indicate that although AI is already widely adopted in teaching practice, its integration remains uneven, with significant concerns around plagiarism, weakened critical thinking, and insufficient institutional support. The findings underscore the importance of the Faculty as a facilitator and strategic integrator of AI, ensuring that technology complements pedagogical judgment and that education keeps pace with innovation and its broader social impact.

ABSTRACT. This study explores the impact of Google NotebookLM, an artificial intelligence-based tool, on the learning experience of first-year engineering students. In a controlled academic setting, 91 students used NotebookLM to carry out tasks involving the synthesis of technical information and subsequently evaluated their experience using the UEQ-S (User Experience Questionnaire – Short Version), supplemented with open-ended questions. Quantitative results reveal a positive perception across pragmatic dimensions (clarity, efficiency, usefulness) and hedonic dimensions (interest, novelty, motivation). Qualitative comments highlight the tool’s ability to summarize and organize complex content, as well as its potential to foster autonomous learning. Although limitations were identified, such as slow processing and interface design issues, most participants expressed willingness to reuse NotebookLM in future academic projects. This work provides preliminary evidence of the value of integrating AI tools into engineering education, particularly in early stages of training, and suggests future research directions regarding its longitudinal impact and comparison with other educational technologies.

ABSTRACT. It is common to document the teaching-learning processes from the student’s perspective, their experience, and the level of development they achieved through the tools provided by the teacher; however, little is said about the teacher’s experience and workload, as well as the products obtained from the methodology applied to the course, such as the publication of papers at international conferences. In this paper, we aim to present the results from the professor’s perspective of the inquiry-based learning methodology for the mechatronics program within the TEC21 educational model of the Tecnol´ogico de Monterrey. The proposal includes a discussion of the inquiry-based learning methodology, the SCRUM methodology used for managing research projects, external evaluators to provide feedback to students, and the management of international conferences to maintain an external incentive to the classroom. The results include feedback from graduate students, publications obtained from conferences, and the discussion of the constant updating of the material available for the course delivery.

ABSTRACT. This study examines predictors of students’ Behavioral Intention (BI) to enroll and continue in a fully online Computer Engineering program in Chile using a UTAUT2-based survey (N=65). Analyses used descriptive statistics, Spearman correlations, Mann–Whitney U tests, and multiple regression with diagnostic checks and BCa bootstrap confidence intervals (5,000 resamples). Perceptions were generally high. BI correlated most strongly with Habit, Performance Expectancy, and Facilitating Conditions. In the baseline UTAUT model, Performance Expectancy was the only reliable unique predictor of BI. In an alternative specification including Habit and Attitude, model fit improved modestly and Habit showed the largest point estimate, though bootstrap intervals indicated a marginal unique effect. Exploratory subgroup comparisons suggested higher Social Influence and Facilitating Conditions among students financing their studies with their own income. Implications include making task value explicit, providing early visible support, and using predictable course routines.

ABSTRACT. The growing demand for quality assurance in digital learning highlights limitations in traditional instructional design, especially in evaluation and adaptability. This paper presents a formal system that uses semantically-enriched JSON-LD representations to encode pedagogical elements (learning outcomes, assessments, and activities) and their alignment. The system enables automated validation against standards like Quality Matters, fostering consistency and transparency. A case study demonstrates its practical use in curriculum refinement. Key features include extensibility for complex models, integration with virtual learning environments, and compatibility with AI agents. This approach supports scalable, interoperable, and quality-assured digital ecosystems, aligned with educational innovation goals in Latin America.

ABSTRACT. This research presents the design, implementation, and validation of KitMec, a modular educational tool developed to support the teaching and learning of simple machines in engineering and design education. The study examines persistent difficulties students face in understanding and applying mechanical principles, particularly in Latin American contexts where access to didactic materials is limited or dependent on costly imported kits. A user-centered, design-based methodology was applied, structured in three stages: exploration, conceptualization, and validation. Six university instructors from industrial design, mechanical engineering, and mechatronics, along with ten design students, participated in semi-structured interviews to identify pedagogical needs. Subsequently, a digital validation involving 29 participants achieved a 72% acceptance rate. Results indicate that KitMec promotes active learning, improves conceptual understanding, and increases motivation through hands-on experimentation and interdisciplinary collaboration. Its modular, low-cost design enables adaptability across disciplines and educational levels, contributing to inclusive and sustainable STEM/STEAM education in Latin America.

ABSTRACT. This study examines how resubmission opportunities influence student engagement and performance in an undergraduate industrial engineering course. Motivated by the limitations of traditional one-shot assessments, this research explores how iterative submission practices support reflection, responsiveness to feedback, and sustained learning. Using a comparative descriptive design, data were gathered from 40 students who completed ten group-based exercises, some of which allowed resubmission after instructor feedback. Submission frequencies, exercise grades, and individual midterm exam scores were analyzed using descriptive statistics and visualizations to identify observable patterns. Results indicate that groups engaging in more submissions—particularly those with resubmissions —tended to achieve steadier exercise performance and generally stronger exam outcomes. Although not implying causality, these tendencies highlight the value of iterative engagement in fostering persistence and feedback literacy. The findings align with broader literature on formative assessment and underscore the relevance of resubmission practices in engineering education, especially as artificial intelligence reshapes professional workflows that demand continuous refinement and adaptation. The study suggests that resubmission policies can help cultivate the iterative problem-solving habits essential for engineers in AI-driven environments.

ABSTRACT. Generative AI (GenAI) is advancing rapidly, but its methodical integration into engineering education is not yet established. This paper proposes a GenAI-assisted teaching and learning model that seeks to complement and gradually overcome the prevailing "frontal one-to-many theory-and-drill" (FO2MTD) model and thread-based curricula. We articulate four hypotheses addressing classroom-level pedagogy and curriculum-level sequencing, and we report on the design and initial implementation of a pilot that addresses the first hypothesis. We argue that a measured, evolutionary transition can preserve comparability of results while leveraging GenAI's personalized, on-demand guidance and design and analysis support. We conclude with recommendations for standardization and inter-institutional coordination to move from local experiments to mainstream practice. Future work will also examine the role of GenAI in fostering teamwork, developing assessment frameworks, and supporting lifelong learning beyond the degree program.

ABSTRACT. This survey examines isolation techniques for securing autograders against untrusted student code in educational environments. Amid the increasing adoption of online autograders for evaluating submissions (e.g., Jupyter Notebook), robust security is essential to mitigate risks including cheating, unauthorized access, and malicious exploits. We review code isolation methods - system call interposition, kernel primitives, and virtualization - and evaluate their efficacy in educational contexts based on ease of deployment, exploit resistance, performance overhead, and educator usability. Analysis of previous research reveals strengths in kernel - and container based solutions for scalability and efficiency, while virtual machines provide superior isolation for high-risk scenarios. Limitations, such as kernel vulnerabilities in shared environments, are addressed alongside emerging trends such as hybrid models and AI-assisted filtering. This work provides practical guidelines for educators to implement secure autograders, ensuring fair assessment and institutional resource preservation.

ABSTRACT. The emergence of Generative Artificial Intelligence (GAI) and its widespread applications has the potential to widen access and break barriers for individuals who may not possess specialized technical knowledge or familiarity with relevant jargon in cryptographic ciphers. We investigate a solution that considers both the structure of the code and its documentation; thus, it is easy to access the relevant code and its components. We implemented our solution using Knowledge Graphs and Retrieval-Augmented Generation. When assessing our solution, OpenAI offered a higher number of correct responses than Gemini, with both models exhibiting varying reliability for complex technical queries in cryptographic ciphers.

ABSTRACT. Education faces significant challenges in managing disruptive behaviors among primary school students. Gamification and Artificial Intelligence (AI) offer innovative strategies to address these challenges by creating personalized and engaging learning experiences. This study presents a novel approach that integrates AI and gamification to generate adaptive educational content tailored to individual student needs. An experimental design employing pre- and post-tests was implemented to evaluate the system’s effectiveness. Results indicate a significant reduction in disruptive behaviors, improved classroom environment, enhanced student motivation, and increased engagement. Additionally, students demonstrated better emotional regulation and attention. The findings highlight the critical role of teacher training and adaptability in implementing these technologies successfully. Overall, the integration of AI and gamification shows considerable potential for supporting students’ social, emotional, and academic development. This research underscores the practical implications of using AI-driven gamified interventions in education and suggests directions for future studies, emphasizing sustainability, replicability, and the promotion of inclusive learning environments.