Revista de Investigación en Tecnologías de la Información

Teachers and the use of artificial intelligence (AI) as a learning process

2025-03-15T03:14:06+00:00

Artificial Intelligence (AI) refers to the use of computer systems that perform tasks that require human intelligence to generate learning experiences and optimize processes through software that feeds on information and algorithms that collect, process, apply rules and numerical models that interpret data. This includes natural language processing, robotics, machine learning, personalizing learning, adapting to student needs, and providing solutions to complex problems. This study defines the research objective to identify teachers' perception of the use of artificial intelligence (AI) in the classroom. The research design was non-experimental and quantitative, applied to a sample of 50 professors who teach classes in higher education. The results report that 44% of the teaching staff almost always stays updated on technological trends, 48% know the learning dynamics of the students and 66% their adaptation capacities. In this intervention, the study concludes that the teachers have extensive experience in teaching and are in a transition stage towards the incorporation of (AI) in their pedagogical strategies.

The Impact of artificial intelligence and digital tools on core subjects in higher education

2025-09-01T17:03:44+00:00

The integration of artificial intelligence (AI) and digital tools in higher education has transformed pedagogical methodologies, creating both opportunities and challenges in core subjects. This study examines the differential impact of these technologies on Mathematics, Language and Literature, Natural Sciences, Social Sciences, and Foreign Languages through a sequential explanatory mixed-methods design, combining a systematic review of 312 indexed studies with semi-structured interviews of 15 experts. Results reveal significant performance improvements, particularly in STEM and language disciplines, though with limited effects on critical thinking and creativity. Key paradoxes are identified, such as the tension between learning personalization and outcome homogenization, alongside gaps in teacher training and equitable access. The conclusions emphasize the need for hybrid models integrating AI with traditional pedagogy, ethical protocols to mitigate biases, and discipline-specific strategies, proposing a framework for responsible implementations that balance technological innovation with educational quality.

Design of an Edge IoT sensor node for air pollutant monitoring

2025-03-27T14:55:12+00:00

Air pollution is an environmental and public health issue that requires effective monitoring solutions. This work presents a low-cost Edge IoT sensor node for air pollutant monitoring, designed to expand geographic coverage and improve accessibility to environmental data. The system uses an ESP32 as the processing unit, sensors to measure particulate matter and pollutant gases, and a LoRa module for long-distance data transmission. An exponential moving average filter was implemented to reduce measurement noise, and comparative calibrations were conducted with reference stations. Results indicate that the node provides reliable measurements of PM10, PM2.5, CO, and NO2, although with lower accuracy compared to specialized monitoring stations. However, its integration with robust platforms allows it to complement traditional monitoring networks. This solution represents a scalable and accessible alternative to support decision-making related to air quality and environmental policies.

Web application built with the Laravel Framework integrated with MikroTik routers to manage clients, billing, and generating hotspot vouchers.

2025-06-09T18:51:49+00:00

The use of hotspot networks to provide controlled internet access is common in public and community environments. However, their administration often involves manual processes that hinder efficient user management, sales reporting, and voucher generation. This study demonstrates how applying an agile methodology such as Scrum, combined with modern web tools, enables the development of an effective application to automate these processes. Scrum was adopted as the development framework, organizing functionality into planned sprints based on user stories. The system was built using Laravel, Livewire, MySQL, and other web technologies, integrating features for managing clients, hotspot vouchers, sales reports, and vending machines connected via NodeMCU and MikroTik routers. Functional, integration, and performance testing confirmed the system's stability, efficiency, and alignment with project goals. Operational time improvements of up to 96% were observed in key tasks, along with a significant reduction in human error. These findings suggest that the combination of agile development and specialized automation provides an effective and adaptable solution for the technical administration of hotspot networks.

Online social support as a self-regulation strategy among university students in a virtual bachelor's degree in pedagogy

2025-09-02T03:42:19+00:00

The necessity for students pursuing their studies online to cultivate self-regulation skills, such as peer social support, is emphasized. This study aims to describe the social support strategies employed by undergraduate students in the Pedagogy program at a public virtual university. The research adopts a quantitative, descriptive approach. Information was collected through responses to the online social support subscale of the Self-Regulation for Learning Online (SRL-O) instrument, Spanish version, which was designed by Broadbent et al. [1]. The findings suggest that, in general, students are highly willing to request assistance; however, a group was also identified that still exhibits low participation in this practice. This study underscores the significance of formulating strategies that are oriented towards collaborative endeavors and the establishment of virtual spaces that promote support and mentorship within this student community.

Predictive maintenance of electric motors based on TinyML and motor current signature analysis

2025-08-13T06:38:48+00:00

The operational continuity of electric motors is essential for industrial productivity, as unexpected failures result in economic losses and safety risks. This study proposes a predictive diagnostic system based exclusively on Motor Current Signature Analysis (MCSA) with on-device inference using TinyML, targeting resource-constrained environments. The design includes current signal acquisition through a non-invasive transducer, analog conditioning, preprocessing via root mean square calculation in overlapping windows and normalization, and the training of a lightweight one-dimensional convolutional neural network optimized for microcontroller execution. The prototype was evaluated using a class-balanced dataset, applying standard classification metrics and resource usage profiling. The results show perfect discrimination between normal and abnormal conditions associated with power electronics disturbances, with inference times compatible with real-time monitoring and low memory consumption. It is concluded that MCSA, combined with edge inference, is a viable and low-cost alternative for predictive maintenance, particularly in facilities with infrastructure limitations, and that its integration into multivariable systems could expand coverage to mechanical failure modes.

Faculty evaluation of ChatGPT use in higher education in the health sciences

2025-10-10T16:43:48+00:00

This study analyzes the perception of health sciences faculty regarding the use of ChatGPT in their teaching practices, within the broader context of the expansion of generative artificial intelligence in higher education. Using a quantitative, non-experimental, descriptive, and cross-sectional design, 196 faculty members from Nutrition, Nursing, Psychology, and Dentistry programs at higher education institutions in Sinaloa, Mexico, participated. A validated 20-item questionnaire was applied, comprising three dimensions that assess perceived usefulness, ease of use and teaching support, as well as learning personalization, critical thinking, and assessment, with a reliability of α = .957. The findings reveal a predominantly positive evaluation of ChatGPT, highlighting its capacity to optimize time, generate instructional materials, and complement traditional teaching without replacing it. Faculty members recognize its potential to diversify pedagogical strategies and address student diversity, although they express reservations regarding the accuracy of the information and its impact on the development of critical thinking. It is concluded that ChatGPT is regarded as an effective didactic resource that contributes to improving educational practice, although its full integration requires ongoing training and ethical guidelines to promote responsible use within health education.

Digital circular economy: Leveraging accessible technologies to drive sustainability in MSMEs and local enterprises

2025-08-20T04:10:22+00:00

This study assesses how accessible digital technologies (mobile apps, free software, collaborative platforms) facilitate circular economy adoption in Latin American SMEs. Through systematic documentary review, circular models (recommerce, waste valorization, repair) enhanced by mobile apps, open-source software, and collaborative platforms are analyzed. Results identify benefits such as cost reduction (15-25%) and new revenue streams, though challenges like digital divide and financing access persist. Successful cases demonstrate implementation viability with modest investments. It concludes that Digital Circular Economy represents a promising path for SME sustainability and competitiveness, requiring an ecosystem approach combining technology, training, and policies adapted to regional context to overcome barriers and capitalize on emerging opportunities.