For the proper sustainable development of Colombia, it is critical to ensure appropriate management of projects that will use these royalties. The GES group has a tool where all technical and legal inspections are managed, and developing and optimizing this tool is fundamental to comply with the objectives of Law 2056 of 2020 and Resolution 271 of 2021 and avoid the process of misuse of resources for sectors such as construction, education, and health. A total of 31,583 projects have been approved since 2012 with investments of COP$ 111,318,490,310,087, from which have been invested COP$ 95,848,713,073,511 from royalties. This evidence the reliance of the country to guide resources to the sector where they are most needed and the commitment of the government and entities to make an efficient investment. This project’s innovative proposal is to integrate triggers with automated workflows with Power Automate and a human-like chatbot that will operate through Power Virtual Agents, technologies that have proven efficient in automating administrative processes and increasing operational efficiency in many other industries. The implementation will be made with the SCRUM methodological approach and change management, which would allow the GES group to make a change that can be improved and adapted. The expected results of the implementation will enable to automation of many processes in different parts, such as alerts and notifications much more accurately and quickly, and the chatbot will assist experts by providing real-time assistance, so it will also increase the quality of the user experience. All of this increased efficiency will grant the experts more time to do more critical tasks, which will improve productivity and quality. A capacity for innovation and improvement will also increase. In conclusion, the implementation of these technologies and methodologies is a pilot test that eases the way for like processes in the public and private sectors. Nevertheless, this implementation can only occur with the commitment of the entire group of GES to make these changes sustainable and help the country find Sustainable Development Goals (ODS) 9 and 16, the promotion of inclusive societies, and resilience infrastructure.

Con el avance de la tecnología, se están abriendo numerosas posibilidades. Por ejemplo, la industria de la lingüística se ve claramente afectada por estas mejoras tecnológicas. Por lo tanto, el estudio tuvo como objetivo analizar el uso de la Inteligencia Artificial- Aumentada para el aprendizaje de 2L inglés. Para el estudio, se recolectaron datos primarios de una muestra de 135 estudiantes y se realizó un análisis cuantitativo y cualitativo, diseño metodológico mixto secuencial. Los resultados mostraron que se pueden ampliar el vocabulario, precisar la pronunciación y como ha mejorado la escritura sobre el uso de la IA.

Palabras claves—Inteligencia artificial, inteligencia aumentada, pronunciación, vocabulario, aprendizaje inglés.

The arrival of artificial intelligence, brought by the fourth Industrial Revolution, marks a significant change in education, as the concept of Education 4.0 emerges. This new educational phenomenon integrates technological developments, such as artificial intelligence and robotics, into present teaching and learning approaches. Studies highlight the positive and negative aspects of implementing language models, like ChatGPT, in education settings. Key benefits include the individualized learning experience it can offer students, while some drawbacks include ethical issues. Through a non-experimental and descriptive approach, including a comprehensive literature review, this study focuses on exploring the impact and applications of ChatGPT in university education and evaluating its contribution to Education 4.0, through surveys to 122 participants involved in the educational setting, like professors and students. Findings revealed diverse perceptions: 44% of professors and 35% of students believed ChatGPT can significantly contribute to Education 4.0. 20.63% of students responded being extremely familiarized with the language model, while only 2.44% of professors felt that way. Notably, 90% of the participants reported to having used ChatGPT in academic activities, including feedback provider, research, tutoring, and class content ideas. These insights bring to light the revolutionary potential artificial intelligence tools have on education. A thoughtful and complementary use of these tools can further improve current educational methods.

Nowadays, companies sustain their permanence in the market with the satisfaction of their customers, and manufacturing companies, specifically those in the metal-mechanic sector, are no strangers to this. This is due to the fact that they have a method of working to order and a high variability of products with low volume, which leads to failure to meet delivery deadlines. Thus, in the field of industrial engineering, there is a percentage indicator that measures this problem. This is the "On Time Delivery" indicator which, with the efforts reviewed in the literature and success cases, is the most accurate for the analysis. The importance of solving this problem lies in the shortcomings of the models applied without a combined structure such as Rapid Response Manufacturing and Lean Manufacturing (TPM-SMED). In this case study, a current value of 50.10% in orders with on-time delivery of the machining line was determined. This integrated model, after validation, managed to increase the percentage of on-time deliveries to a final value of 91.07% (includes: final machine availability of 90.17%; Set-up time of 213.35 minutes and Cpk of 1.1651). This result significantly reduces the existing technical gap compared to the 95.5% indicated by the Tier 1 sector in the Industry Week report. In addition, this model can also be Applied to scenarios

In the competitive market, companies face significant challenges in inventory management due to increasing demand. The implementation of advanced technological tools, such as Python with Artificial Intelligence (AI), offers an innovative solution to optimize these processes. Python, with its ability to automate tasks and manage large volumes of data through specialized libraries, enhances accuracy and efficiency in inventory management. This article focuses on the General Objective (GO) of determining the influence of Python with AI on inventory management in America during 2023. Additionally, Specific Objective 1 (SO1) seeks to ascertain how automation with Python AI influences inventory management, while Specific Objective 2 (SO2) evaluates the impact of using libraries with Python AI in this area. A systematic review of various information sources, including articles, theses, and reports from different databases, primarily in America, was conducted. The results indicate that the integration of Python with technologies such as AI has significantly reduced inventory control times and improved the accuracy of business operations. It is concluded that disseminating this information is crucial to highlight the benefits of implementing Python, as it can encourage the adoption of emerging technologies by more companies. This not only enhances their operational efficiency but also enables them to remain competitive in an ever-evolving market.

Although the application of deep learning in autonomous vehicle systems consolidated over time, its use could entail certain risks. Therefore, this study aimed to identify these risks in autonomous vehicle systems through a literature review. To achieve this, the PRISMA methodology was used for the collection and selection of studies, as well as the PIOC strategy for formulating research questions, in this study that did not use meta-analysis. Based on inclusion and exclusion criteria, 27 open-access articles from the Scopus database were selected. The results showed that the application of deep learning in autonomous vehicle systems encompassed key aspects such as environmental perception, object detection, and route planning. However, significant risks were also identified, such as inaccuracies in perception, vulnerability to attacks, detection errors, and lack of interpretability of the models. To mitigate these risks, detection and evaluation techniques such as cross-validation, sensitivity analysis, and testing in simulated environments were proposed. Additionally, tests were conducted in various scenarios and conditions, such as urban, suburban, rural environments, highways, and adverse weather conditions. The research concluded that although deep learning had the potential to improve vehicle autonomy and safety, it could also present significant risks. It was recommended that future work focus on developing and validating new techniques to address these risks, as well as establishing regulatory frameworks and standards to ensure the safety and reliability of autonomous vehicles powered by deep learning.