Conference Agenda

– This study aimed to systematically analyze the factors that contribute to the underrepresentation of women in Technology, specifically in the field of AI, for a comprehensive understanding of the Challenges and Opportunities they face. A quantitative, cross-sectional study was conducted. The Multiple Regression Analysis technique was used. The study participants were 735 professional women working in different industries linked to technology in Ecuador. The sample of 636 was selected through intentional sampling. A 15-question Likert scale questionnaire was applied. The results reveal that restricted access to technical skills development opportunities negatively affects women's participation in the technology sector; the absence of role models and targeted mentoring programs has a negative impact; negative gender stereotypes and biases towards their capabilities in technological careers represent an important barrier to their integration. On the contrary, government initiatives aimed at promoting their inclusion in the technology sector have had a positive effect. And, a greater presence of women in research and development teams of artificial intelligence solutions is associated with greater innovation, diversity, and ethics in these systems. In conclusion, it is imperative to address various factors such as access to training opportunities, the availability of role models and mentoring, the elimination of gender stereotypes and biases, and the strengthening of government policies and programs.

The integration of advanced automation from Industry 4.0 with the human-centered sustainability of Industry 5.0 marks a critical shift in modern industrial engineering. This research delves into how cognitive digital clones can enable the convergence of these two paradigms, enhancing both operational efficiency and decision-making processes in a more sustainable, human-oriented way. A mixed-methods approach was applied, including a thorough review of relevant literature and an analysis of case studies from companies already adopting these technologies. Quantitative data showed a 20% increase in operational efficiency and a 15% reduction in carbon emissions, underscoring the positive impact of this integration. Additionally, qualitative interviews revealed that human involvement in decision-making significantly improves organizational resilience in the face of unexpected disruptions. These findings suggest that this technological hybridization not only boosts productivity but also fosters more sustainable and adaptable industrial practices, which are essential in addressing global challenges such as climate change and economic instability. In conclusion, effective collaboration between humans and machines, supported by advanced cognitive technologies, offers a key strategy for the future of industrial engineering, allowing companies to remain competitive and sustainable in an increasingly demanding global landscape.

The intersection of human creativity and Artificial Intelligence (AI) in innovation processes is a growing area of exploration. This study investigates the application of Design Thinking (a structured, human-centered methodology) to assess how third-semester engineering students from diverse disciplines (Industrial Engineering, Innovation and Development Engineering, Mechanical Engineering, and Mechatronics Engineering) compare to AI-generated solutions in addressing a real-world educational challenge: designing innovative tools to support children with learning difficulties.

A comparative experimental approach was employed, where 67 students, divided into 13 teams, applied the five Design Thinking phases (Empathize, Define, Ideate, Prototype, and Test). Their solutions were systematically analyzed against those generated by AI tools (ChatGPT, Gemini, and Copilot), which followed the same Design Thinking framework. Quantitative metrics, such as the number of ideas generated and prototyping time, were assessed alongside qualitative variables, including originality, feasibility, scalability, and alignment with user needs. Statistical tests (Mann-Whitney U and Student’s t-test) were applied to determine significant differences between human and AI outputs.

Results indicate that AI excels in originality, user alignment, and scalability, while students demonstrate greater feasibility and contextual adaptability. AI-generated solutions were consistently limited in number (4-5 ideas), whereas student teams produced a broader range. Additionally, AI significantly reduced prototyping time. These findings suggest that a hybrid approach, integrating AI’s computational power with human-centered problem-solving, could optimize innovation processes in engineering education. Future research should explore AI as a collaborative design tool rather than a competing entity.

A learning factory is a concept developed to mimic industrial settings for academic or consulting services. This environment offers the space where Industry 5.0 concepts can be understood through experiential learning. Interest in these settings has increased in recent years because of their potential to offer an enriched setting for engineering education, training, and research. This work aims to present a roadmap towards the development of a learning factory in academia, starting from the conceptualization of the idea, and recommendations for implementation based on previous developments by the authors. The proposed approach is described from planning, development to validation phases, and finishes with a discussion of lessons learned and useful best practices. This document also describes the experiences of developing projects for undergrad courses, capstone projects, research internships and graduate projects. Projects include work on additive manufacturing, collaborative robots, artificial intelligence, augmented and virtual reality, mechatronics, automation, to name a few. Some of the results obtained from these projects include obtaining grants, publications, conference presentations, collaboration with other institutions, and the development of entrepreneurships.

Higher education has changed in recent years. An abrupt change came with the COVID-19 pandemic when education had to be entirely virtual. As a result, we now know from practical experience that virtuality has both benefits and drawbacks. After observing the development of undergraduate classes before and after the pandemic, it is evident that it is necessary to know more about the factors that affect student learning achievement in virtual classes. Once the factors have been identified, the next step consists of designing strategies to reduce negative effects and increase positive effects on learning achievement. Thus, this study presents a systematic literature review using the PRISMA method to identify the relevant factors in virtual higher education in Latin America and the Caribbean after the COVID-19 pandemic. The search was carried out on the Web of Science database. Thirty-five publications were found that meet the search requirements. Additionally, the study included 13 publications identified during the analysis. According to studies, factors that positively affect learning achievement include digital literacy, greater provision of virtual education and training from institutions with high prestige, access to greater information, and satisfaction. On the other hand, negative factors included stress, low self-esteem and discipline, economic situation, absence of motivation, and low confidence.

Higher education institutions are undergoing rapid transformations due to evolving technological advancements, pedagogical shifts, and changing student demographics. The Alpha Generation, born after 2010, has unique learning preferences that include haptic interactions, digital fluency, and a demand for personalized learning. As a response, educational institutions are integrating innovative methodologies to enhance engagement, improve inclusivity, and optimize learning outcomes. This paper explores key aspects of future learning, including pedagogical shifts, technology integration, adaptive learning, and multimodal learning, while examining the LESLIE project as a relevant example of a project that embodies these future learning principles.