Conference Agenda

Artificial intelligence (AI), and chatbots in particular, have gained considerable attention as transformative but controversial tools for improving academic writing. Numerous articles have been written on the use of AI in teaching and learning processes, particularly as it relates to writing. This systematic review aims to establish the most common uses of AI linked to academic writing, the most frequent problems or drawbacks that have been identified, and what remains to be done to better exploit these new applications. The findings reveal that AI offers a wide range of applications and advantages in academic writing, which can help students improve their writing skills, increase their efficiency, and produce higher quality work, but it presents significant challenges regarding academic integrity, development of student capabilities, reliability of information, and ethical aspects. Finally, there are tasks to be accomplished by educational institutions, teachers, and students to achieve an effective integration of AI in academic writing, maximizing its benefits and minimizing its risks

The 21st century presents a context where the humanistic training of professionals is essential to address the needs of a diverse and constantly changing society. This work proposes a training approach centered on universal design and accessibility, which aims to strengthen this perspective in the education of engineers. The research conducted has a mixed approach, is descriptive, and is structured in several stages: theoretical, diagnostic, propositional, and evaluative. Various methods are used, such as literature review, documentary analysis, content analysis, structural systemic modeling, focus group techniques, and expert criteria. The sample groups respond to the specific needs of the study in the context of two Ecuadorian universities. The main contribution of this research is the creation of a workshop course that promotes the humanistic training of engineers through interdisciplinary collaboration. This course focuses on equipping students with the tools and knowledge necessary to integrate universal design and accessibility into their engineering projects, particularly benefiting students in fields such as Industrial Engineering, Intelligent Systems, Computer Science, and Civil Engineering, and being highly recommended for the Architecture program, among others. The course modeling is developed considering various practical situations, becoming a valuable curricular option that promotes inclusion and social responsibility in professional practice. In this way, the goal is to train engineers who are more aware and capable of contributing to a more equitable society.

Design Thinking has emerged as a key methodology in engineering education, fostering creativity, problem-solving, and learning through experimentation. This study presents a bibliometric analysis of the scientific production on Design Thinking in the context of engineering education over the past decade (2014–2024), using data extracted from the SCOPUS database.
Bibliometrix in R was employed to analyze key indicators, including the evolution of the number of publications, the most influential authors, and keyword co-occurrence. The results indicate a significant increase in scientific production, with a notable rise in recent publications, highlighting the contributions of leading authors and universities in the field. Additionally, emerging trends were identified in the application of Design Thinking in innovative teaching strategies, such as project-based learning and competency-based approaches.
This study provides a quantitative perspective on the development of research in this area, emphasizing opportunities for future studies and applications in engineering education.

The advancement of Artificial Intelligence (AI) has led to the development of large language models (LLMs), which are currently used to streamline processes in various fields. It is essential to understand their potential for implementation in education. This study evaluates the quality and integration of the STEM methodology in learning activities generated by ChatGPT-4o (OpenAI) and DeepSeek-V3 (DeepSeekAI) for the course C¸ omputer Tools II¨ın the Industrial Engineering program at Universidad Fidelitas, Costa Rica, in 2025. A quantitative research approach was used, employing ´ rubrics to assess the AI-generated responses. The results indicate that both LLMs successfully incorporate the STEM methodology, with ChatGPT-4o excelling in interdisciplinary integration and the promotion of critical thinking, while DeepSeek-V3 demonstrated strengths in source utilization and applicability. This research provides insights into the use of generative AI in education, highlighting its benefits and limitations in the design of educational activities.