This study presents the results obtained in a technological and applied research for robotics disciplines, using the Internet of Things with the device Arduino Uno to automate the lights of a residence, to have the ease of control during the day to day, the panel of control is accessed by a browser software on a mobile phone or computer, providing greater convenience for people. During the research were analyzed the different devices available in the national market, the results allowed to conclude the development of a simple solution that could contribute to robotics classes, the main interest related to the results involves the application experience, which can be expanded according to with the need of the user and its environment, proposing new applications in disciplines that involve robotics, database, internet, programming and electronics.

As an emerging technology, the Internet of Things is still a challenge due to its type of structure, as well as its multidisciplinary nature, which involves a great effort in terms of knowledge and understanding of the different types of resources used for the development of this type of project. In the years 2020 to 2022, due to the pandemic, face-to-face classes were compromised, allowing the use of this type of emerging technology. This study presents the results of experiments carried out in virtual courses for the development of a project for the internet of things carried out at the Instituto Tecnologico y de Estudios Superiores de Monterrey. Opinion polls were carried out with students during virtual classes with the Wokwi Circuits tool, the results showed the feasibility of using this tool in an integrated way with others necessary for the construction of IoT projects, the contribution of these results should enable the development of new research applied in more robust and advanced solutions according to the need of the scientific research project, collaborating for the distribution of information and knowledge sharing between different areas of investigation.

In IoT-based supply chain transportation, low rates for data loss, minimizing time to destination, and optimizing energy consumption are paramount. These factors can be influenced by variable parameters, data volume, logging procedures, positioning complexities, and communication hiccups during transit. This study introduces an adaptive data logging algorithm for a cost-effective IoT node, addressing these challenges. This innovation enables real-time data acquisition and remote display via a web interface. Experimental tests demonstrate the prototype's reliability in both controlled indoor and dynamic outdoor environments, particularly in environmental and GPS data collection. Results reveal 5.24% data loss indoors and 23.24% via the web interface. Outdoors, data loss peaks at 55.34%, increasing to 82.76% with the web interface. However, the obtained information is adequate for prototype validation. The algorithm reduces data by 74%, leading to lower data processing and power transmission needs. Moreover, determining the distance from GPS coordinates is essential for predicting travel times and monitoring vehicle velocity to maximize efficiency. The results from this prototype are expected to enhance the development of advanced models, thus enriching future scientific research initiatives that aim to incorporate IoT technology into transportation systems.

This study presents the results obtained in experimental and comparative research, between the Nextion and TFT devices ILI9341, carried out in 2017, focusing mainly on the positive and negative points presented by the different devices when using a touch screen display for Arduino. The methodology used to obtain the data was experimental research with the devices, applying for the systematic review, considering personal experience when using these types of equipment. After obtaining the data was proceeded to consolidate, categorize and analyze the quantitative and qualitative results. There was a perception about the results that point out the pros and cons on the different technologies, among these areas: the ease of creating interfaces to the screen; the ease of configuration and installation, as well as the quality of the products and their prices. In counterpoint, the Nextion device presented a higher cost compared to the TFT devices ILI9341, the main difference between the two devices is in: their prices; ease of solutions creation. In addition to the use of Arduino board resources, in these requirements, the devices Nextion presented better results, as presented in this study.

This paper examines the pedagogical impact of Artificial Intelligence-assisted tools in a university software construction course. Through a 10-week case study with 25 engineering students, the study captures qualitative insights into how students interact with AI tools, the challenges they face, and the learning strategies that emerge. While students reported perceived time savings, the core contribution lies in understanding the educational implications, both positive and challenging of integrating AI into project-based learning environments. The findings reveal that Artificial Intelligence tools enhance prototyping and debugging skills but require structured integration to mitigate over-reliance. The study contributes a framework for balancing Artificial Intelligence use with traditional pedagogy, supporting Sustainable Development Goals 4’s goal of equitable education, while the opinion survey carried out with students shows a result of 96% satisfaction with the use of assisted artificial intelligence tools during the development of the project during the studies.