Developing learning-conducive environments and high-quality teaching is closely linked to faculty well-being and emotional exhaustion. Identifying factors that facilitate or hinder these psychological states is essential to fostering a healthy and productive work environment for educators. This study utilized a convergent parallel mixed-methods approach involving 281 engineering faculty members from a private university in Mexico. Data were collected using a scale for psychological well-being (with Likert-type responses from 0 to 5), a scale for emotional exhaustion (with Likert-type responses from 0 to 4), and two open-ended questions that allowed participants to describe key activities or situations related to these constructs. Quantitative data were analyzed with descriptive statistics, ANOVA, and post-hoc tests with bias-corrected accelerated bootstrap sampling, while qualitative data were examined through content analysis based on primacy principles. The qualitative analysis identified ten categories: five contributing to well-being and five related to emotional exhaustion. The categories contributing to well-being included growth opportunities, institutional support, and positive interpersonal relationships, while categories related to exhaustion involved excessive workload, administrative demands, and curricular changes. Quantitatively, the findings indicate that faculty perceive a moderate level of psychological well-being$(\mathrm{m}= 3.18, SD =0.40)$with no statistically significant differences across well-being categories$(\mathrm{F}=0.08,\ \mathrm{p}=0.989,\ \eta^{2}=0.001)$, Conversely, emotional exhaustion levels were also moderate$(\mathrm{m}=1.89, SD = 1.04)$, with statistically significant differences observed among exhaustion categories$(\mathrm{F}=18.80, \mathrm{p} < 0.001, \eta^{2}$= 0.21). The study concludes that while no single factor overwhelmingly promotes well-being, excessive workload and abrupt changes in educational models are significant contributors to emotional exhaustion. Recognizing these factors enables educational institutions to implement timely preventive measures and faculty development programs encouraging human potential and fostering a positive and healthy work environment. This study contributes to understanding the complex dynamics of academic well-being and burnout. It provides insights for educational institutions to create healthier and more sustainable work environments for their faculty.

Design thinking (DT), widely recognized as a structured method for fostering creativity and innovation, has gained significant traction in research and practice across various disciplines. However, with the rise of disruptive technologies like artificial intelligence (AI), DT practices are gradually evolving, reshaping the innovation process. This study investigates the effectiveness of integrating generative AI into a product design thinking activity, employing a single-group pretest-posttest design (i.e., without a control group). The time interval between pretest and posttest measurements was four hours, coinciding with the duration of the DT activity. Conducted in a chemical engineering course at a private university in central Mexico, the research tasked nine students of average academic performance with designing a new beverage. Over a four-hour session, students used AI tools-ChatGPT, Perplexity, and Gemini-at various stages of the design process, including empathy mapping, need statements, idea classification, hills writing, and storyboarding. Several multidimensional constructs were measured using self-report questionnaires to assess the key attributes that DT stimulates: perceptions of creative self-efficacy, design thinking mindset, and empathy. Additionally, the study explored students' views on the usefulness of generative AI and their intention to use such tools. It was hypothesized that post-test scores for each construct would increase. The analysis involved two phases: first, psychometric indicators (alpha reliability) were obtained; second, a statistical approach for assessing individual change was applied, precisely the standardized individual difference (SID). The SID was set at a nominal level of 0.80 (right tail of the normal distribution). Scores with unacceptable measurement error (alpha <. 60) were excluded from the primary analysis. The results revealed a significant increase in students' perceived usefulness of AI between pre- and post-experiment measurements, with a moderate improvement in affective empathy. Other constructs also showed consistent, though modest, post-test score increases. However, only a few participants exceeded the SID threshold, indicating individual variations in response to the intervention. These preliminary findings highlight AI's potential to enhance student creativity through idea generation and expand their consideration of the end user in product design. The results provide valuable insights and recommendations for integrating AI into innovation-driven projects using the design thinking approach and implementing a single-group pretest-posttest design with short time intervals.

Teaching science through graphs is an essential strategy to enhance students' understanding of kinematics concepts, yet many learners struggle with interpreting graphs due to specific difficulties, including confusion between intervals, slopes and heights, and graphical representations. This study explores how modifications to visual elements in graphs-specifically color changes to axes and graphic areasaffect students' gaze behavior and engagement with key concepts. Using an eye-tracking device (Tobii Pro X2-60, 60 Hz), two conceptual problems from the widely used Test of Understanding of Graphics in Kinematics were presented to two balanced samples of 12 students each in both original and modified formats. The experimental design measured visit counts (VC) and total fixation duration (TFD) in four areas of interest (AOI): the problem, axes, graph, and response areas. Results indicate that modified visuals significantly increased visual attention on critical elements such as axes and graphic areas. For the slope-related exercise, modified graphics elicited higher visits and fixation times, suggesting that color changes influenced students' problem-solving strategies. Similarly, for the area-under-the-curve exercise, modified visuals directed students' attention more effectively to relevant graph elements. These findings underscore the importance of thoughtful visual design in guiding student focus and understanding, providing actionable insights for developing more effective educational materials and teaching strategies in STEAM education.

The rapid advancement of artificial intelligence (AI) has significantly impacted education by providing personalized and efficient learning experiences. AI tools like chatbots are increasingly used to support student learning, simplifying complex concepts and offering real-time assistance. Despite widespread research on AI in education, its application within Bloom's taxonomy remains underexplored, particularly in STEM fields. This study aims to address this gap by examining the effectiveness of ChatGPT in developing higher-order cognitive skills among engineering students. Specifically, the research investigates ChatGPT's use as a support tool in a chemical engineering course at a private university in central Mexico. The activity was designed to analyze the intention to use ChatGPT and the perception of utility through a didactic strategy aligned with the higher levels of Bloom's taxonomy. The plan involved applying sustainability criteria to improve chemical processes. The study employed a single-group pretest-posttest design, with assessments conducted one week apart. Group-level changes were evaluated using measures of central tendency (mean and median) and variability. In contrast, individual-level changes were assessed using the standardized individual difference and the reliable change index at a nominal alpha level of 0.85, adjusted for practice effects. Results revealed that ChatGPT effectively supports students' understanding at lower and higher levels of Bloom's taxonomy, including skills such as analyzing, creating, and optimizing. The group-level results suggest an overall rise in the measured constructs and consistency in student responses. Between 10% and 19% of participants individually demonstrated improvements in the measured constructs. Additionally, the psychometric properties of the assessments showed satisfactory reliability (with scores equal to or greater than 0.85) and dimensionality. This study highlights AI's potential to enhance learning, particularly in promoting skills aligned with Bloom's higher cognitive levels. However, further empirical research is needed to validate AI's effectiveness in fostering these skills, especially in STEM education. Future studies should also explore the ethical implications of AI in educational contexts and its role in supporting responsible, critical engagement among learners.

The emergence of Generative AI (GenAI) has posed significant challenges to various institutions since its launch. The education sector, in particular, requires an urgent regulatory framework. This work provides guidelines for the incorporation of GenAI into the teaching-learning process of undergraduate students, in which generative tools are promising to revolutionize both their academic and professional trajectories. Despite ongoing debates among Higher Education Institutions (HEIs) decision-makers on whether GenAI should be used for education, students are at odds on the prohibition of its use. Instead, they promptly provided specific recommendations for their peers, after a workshop on AI basics, GenAI applications, and analysis. Five student groups of divergent engineering fields collaboratively proposed usage guidelines for ChatGPT in the learning-teaching process. The authors categorized, compared, and consolidated these into a Decalogue of Use. Employing a qualitative methodology, these guidelines were contrasted with those issued by four institutions: UNESCO, Universidad de los Andes, University of Wisconsin–Madison, and Universidad Iberoamericana. Notably, these institutions concurred with students in only five out of the ten guidelines. These guidelines are of broad nature and therefore applicable across various sectors within HEIs. HEIs, as other governmental and international institutions, have been lagging in developing and implementing regulatory frameworks for GenAI, in contrast to the rapid advancement of this technology. This work offers valuable insights into students’ perceptions of GenAI use. Such evidence is instrumental for HEIs in making informed decisions regarding the regulations of these tools. Our findings encourage not only HEIs but also organizations and decision-makers to propagate the debate about ethical GenAI usage to different societal sectors. Promisingly, working as a community will empower us to make the best and pertinent decisions regarding the adoption of GenAI and other future emergent technologies.

This innovative practice WIP paper discusses the importance of deploying Artificial Intelligence (AI) in Latin American Higher Education Institutions (HEIs). It presents the preliminary findings of a workshop in Mexico that brought together nineteen Latin American universities' academic and IT leaders to define strategies for AI deployment. AI can shape higher education (HE) by advancing skills and technologies, generating and sharing knowledge, and supporting people adjusting to technological changes. However, AI integration in HEIs is limited, and there are significant global gaps, particularly in resource-constrained contexts and countries without guaranteed infrastructure. Thus, the intended outcomes of this work are the dynamic involvement of participants, who were encouraged to introduce novel topics that contribute to the overall AI discourse to yield a comprehensive and adaptable global strategy for AI implementation and deployment in HEIs through collective wisdom. Under this rationale, we designed and conducted a workshop sequence. A survey was conducted before the workshop to obtain leaders' opinions about AI deployment in HE. Later, participants were distributed in teams, and a unique restriction was considered for their proposals to generate a deployment strategy. As findings, academic institutions have been slow to adopt AI, but the new generation patterns have forced them to embrace it. They are considering automating their operations to improve student services and attract more students. However, the primary challenge is the lack of strategy. Conclusive remarks emphasized the collaborative efforts among universities to fortify and enhance the implementation and deployment of AI solutions within HE. In the future, we aim to establish a foundation for future collaborations on innovation projects related to AI in education among the workshop attendants.

This study examined the potential of the W-STEM mobile app to inspire and motivate more young women to pursue careers in science, technology, engineering, and mathematics (STEM). Developed within the Erasmus+ project "Building the future of Latin America: engaging women into STEM," the W-STEM application collects testimonies and life trajectories of outstanding and successful women in various STEM areas, presenting them as role models of easy access. Through in-depth interviews, invaluable insights were gained into the motivations, achievements, recommendations, and experiences these outstanding STEM women shared. Analysis of these testimonials revealed inspiring messages and critical lessons that can encourage the involvement of the next generation of women in traditionally male-dominated STEM disciplines. By leveraging the ubiquity of mobile devices, the W-STEM app provides unprecedented access to female role models in STEM, demystifying stereotypes and encouraging more young women to pursue paths in these fields. With its focus on providing inspiration, guidance, and resources in an accessible and engaging way, W-STEM represents a powerful technological tool that promotes gender equity and female participation in STEM fields. As of May 17, 2024, the application has garnered 16,275 views across 344 videos, averaging 47.28 views per video. These metrics highlight the app’s significant reach and engagement, underscoring its potential impact in providing accessible female STEM role models to a broad audience and encouraging more young women to explore and succeed in STEM careers.

The Covid-19 pandemic forced Higher Education classes to shift to online education. This sudden change required universities to respond rapidly, adapting their classes to remote courses maintaining quality educational delivery. In this context, our university implemented the Digital Flexible Model (DFM) to continue with its academic offer. The challenge involved moving 55,000 classes per week to the DFM, attending 90,000 students with 9,400 professors, all in one week. At the end of the semester, a strategic survey was applied to a statistical sample of professors and students from the Engineering School to get feedback about their experience with the DFM. The results highlight takeaways to improve the instructional model and offer interesting reflections by gender from the experience of faculty and students.

This contribution presents the design of a course, called a block, for first-year engineering students using challenge-based learning as an educational strategy. The design is competency-based instead of learning objective-based and is based on the How People Learn Framework and the Legacy Cycle to integrate three different disciplines that are important to solve the engineering-context challenge: physics, mathematics, and computer tools.