Buele, Jorge

Preferred name

Buele, Jorge

Main Affiliation

Facultad de Ingenierías

Ambato

jorgebuele@uti.edu.ec

ORCID

0000-0002-7556-0286

Scopus Author ID

57200380202

Now showing 1 - 10 of 96

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User Experience in Virtual Reality (VR) Applications for Elderly People with Cognitive Impairment and Dementia: A Scoping Review
(2024)
Buele, Jorge
;
Avilés-Castillo, Fátima
;
Guillermo Palacios-Navarro
Background: In recent years, Virtual Reality (VR) has emerged as a promising tool to improve the well-being and functional capabilities of older adults. Although VR applications have shown positive results, their impact on user experience and therapeutic outcomes still needs to be evaluated. Objective: This scoping review aims to analyze existing studies on VR use in older adults with neurodegenerative disorders, focusing on the factors that influence usability, satisfaction, and immersion, as well as the effects on emotional and cognitive well-being. Materials and Methods: Empirical studies in English were included on VR applications applied to older adults with cognitive impairment without study design restrictions. The search was conducted in IEEE Xplore, PubMed, Scopus, and Web of Science, identifying a total of 650 initial results. After screening, 14 studies met the inclusion criteria. Results: Immersive VR tends to generate a greater sense of presence, which contributes to improving emotional well-being and reducing neuropsychiatric symptoms, such as apathy and depression. However, its impact on cognitive functions, including memory and executive skills, varied depending on the level of immersion and participant characteristics. Despite these positive findings, significant heterogeneity was evident in study designs, measurement instruments, and user experience indicators. Conclusion: Virtual environments have great potential as a therapeutic tool for older adults, but their success depends on the personalization of applications and the adaptation of technology to the specific needs of this population. Future research should focus on developing standardized protocols, incorporating adaptive technologies such as artificial intelligence, and evaluating the long-term effects of VR to maximize its benefits and minimize its risks. This review was registered in Open Science Framework (OSF). Registration Number: 10.17605/OSF.IO/PNU36
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Ethical Use of Generative Artificial Intelligence Among Ecuadorian University Students
(2025)
Buele, Jorge
;
Ángel Ramón Sabando-García
;
Bosco Javier Sabando-García
;
Yánez Rueda, Hugo
Artificial intelligence has transformed educational environments by facilitating processes such as information retrieval, assisted writing, automated feedback, and personalized tutoring. Within university settings, the adoption of technologies capable of autonomously generating content has increased rapidly, becoming a common academic resource for students. However, this accelerated integration poses ethical challenges, particularly when such tools are used without a clear understanding of their implications. This study aimed to examine how students’ emotional attitudes (affective), understanding (cognitive), and practical use (behavioral) of AI relate to their ethical engagement with these technologies. A structured questionnaire was administered to 833 university students in Ecuador. The instrument showed excellent internal consistency (α = 0.992; Ω = 0.992), and the validity analyses confirmed that the dimensions measured distinct but related constructs. ChatGPT was reported as the most used tool (62.2%), followed by Gemini and Siri. The structural model indicated that emotional and cognitive dimensions substantially influenced ethical behavior (β = 0.413 and β = 0.567, respectively), whereas frequent use alone exhibited no significant effect (β = −0.128; p = 0.058). These results suggest that ethical engagement with AI is primarily driven by reflection and knowledge rather than habit. This study contributes to the literature by modeling how different learning dimensions shape ethical behavior in AI use and underscores the relevance of aligning academic practices with socially responsible uses of emerging technologies.
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Robotics in higher education and its impact on digital learning
(2025)
Zamora Lascano, Patrick
;
Lozada, Alan
;
Buele, Jorge
;
Avilés-Castillo, Fátima
In recent years, robotics has transformed various industrial processes but has also influenced teaching methodologies. Although there are literature reviews on its application in professional training, many are outdated or lack a current focus on its impact in higher education. Addressing this gap, the present mini review examines the impact, challenges, and opportunities of this technology in the university setting. To this end, a search was conducted in the PubMed, Scopus, IEEE Xplore, APA PsycNet, and Web of Science databases, selecting 11 studies that addressed diverse applications of robotics, including educational robotics, robotic telepresence, human-robot interaction, and artificial intelligence applications. Their effects on teaching, the factors influencing their adoption, and the strategies used to optimize their implementation were analyzed. The findings show that educational robotics enhances student motivation and engagement, with prediction models reaching an accuracy of 98.78% in assessing academic engagement. Additionally, robotic telepresence emerges as an effective alternative for hybrid education, and social robots and AI-based tutors demonstrated their potential to personalize learning. However, methodological and structural challenges persist, such as the need to develop more accurate evaluation metrics and ensure accessibility and educational equity. Future research should focus on improving these aspects, enabling more efficient integration to enhance teaching processes. This study was registered in the Open Science Framework under the code: 10.17605/OSF.IO/KHDTU.
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Predicting Academic Performance in Mathematics Using Machine Learning Algorithms
(2022)
Espinosa Pinos, Carlos Alberto
;
Ayala-Chauvin, Manuel Ignacio
;
Buele, Jorge
Several factors, directly and indirectly, influence students’ performance in their various activities. Children and adolescents in the education process generate enormous data that could be analyzed to promote changes in current educational models. Therefore, this study proposes using machine learning algorithms to evaluate the variables influencing mathematics achievement. Three models were developed to identify behavioral patterns such as passing or failing achievement. On the one hand, numerical variables such as grades in exams of other subjects or entrance to higher education and categorical variables such as institution financing, student’s ethnicity, and gender, among others, are analyzed. The methodology applied was based on CRISP-DM, starting with the debugging of the database with the support of the Python library, Sklearn. The algorithms used are Decision Tree (DT), Naive Bayes (NB), and Random Forest (RF), the last one being the best, with 92% accuracy, 98% recall, and 97% recovery. As mentioned above, the attributes that best contribute to the model are the entrance exam score for higher education, grade exam, and achievement scores in linguistic, scientific, and social studies domains. This confirms the existence of data that help to develop models that can be used to improve curricula and regional education regulations. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Perceptions and Dispositions of Parents and Teachers Toward Technological Learning Communities
(2023)
Núñez-Hernández C.
;
Buele, Jorge
The search for strategies to improve students’ academic performance is increasingly common in education. Parents’ participation and teachers’ attention are key factors in forming a learning community. This article presents the results of a survey in which relevant statistical data was obtained. A significant association (p = 0.571) was found between the frequency with which parents help their children understand school content and their willingness to be part of a learning community, representing 40.65% of the population. In addition, it was found that 66.67% of parents frequently meet with their children’s teachers because they often solve the children’s doubts. Regarding the attention of teachers, it was evidenced that 64.71% of parents who meet very frequently with teachers do so with those who very frequently resolve children’s doubts effectively, which shows a correlation significant (p = 0.467). Finally, the relationship between the degree of mastery of teachers in learning management platforms and their participation in forming learning communities was analyzed. A significant correlation (phi = 0.321) was found between the degree of mastery of teachers in these platforms and their participation in the formation of learning communities, with 28.24% of teachers having sufficient mastery and 20.61% of those with a moderate domain. These findings highlight the importance of parent-teacher collaboration in fostering a culture of learning in school and improving student academic achievement. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.
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Analysis of Ergonomic Risks Based on Physical and Postural Characteristics in the Food Industry
(2026)
Buele, Jorge
Musculoskeletal disorders are one of the leading causes of occupational disability worldwide, affecting workers across various industries, particularly in the food industry. These disorders are caused by factors such as improper postures, repetitive movements, and manual handling of loads. Despite efforts to mitigate these risks, ergonomic assessment in many work environments remains insufficient, as it is often limited to posture observation and does not include precise anthropometric measurements. This study aims to integrate anthropometric measurements with the Rapid Entire Body Assessment method for a more accurate and personalized evaluation of ergonomic risks in the food industry. Through a quantitative approach, anthropometric measurements of workers in a food production plant were analyzed and the Rapid Entire Body Assessment method was applied to assess working postures. The data obtained were statistically analyzed to identify correlations between the physical characteristics of employees and musculoskeletal discomfort. The results showed a significant correlation between anthropometric dimensions, such as elbow height and functional reach, with discomfort in areas such as the elbow, knees, and lower back. These findings emphasize the need to adapt workstations to the physical characteristics of employees to prevent injuries. The integration of anthropometric measurements and the Rapid Entire Body Assessment method provides a more accurate tool for assessing ergonomic risks and designing personalized interventions that improve occupational health and productivity in the food industry. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
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System for Monitoring and Warning of the Ultraviolet Radiation Index: A Study Case in Ecuador Elementary Schools
(2020)
Buele, Jorge
;
Chango F.I.
;
Chango M.L.Á.
;
Santamaría M.
;
Varela Aldas, José
Solar ultraviolet (UV) radiation has increased in recent years due to ozone layer depletion and in Ecuador in particular, due to its geographical position and the height of its cities. Prolonged sun exposures in childhood increase the risk of causing malignant effects on the skin and eyes, such as squamous cell carcinoma, melanoma and cataracts. For this reason, this document describes the design of a device based on UV optical sensors that allows determining the existing radiation index. As a processing unit there is the Raspberry Pi 3B+ embedded board and to display the data physically there are LED panels. The storage of information is done through a database managed by MySQL and also implemented on the board. The levels of the ultraviolet radiation index (UVI) are presented through a graphical user interface (GUI) in real time, which also allows generating a report in a.csv file. Functional tests were carried out in the central courtyard of two educational units, to raise awareness among parents and authorities on the adoption of preventive measures that avoid possible damage to the skin of children when carrying out outdoor activities. © 2020, Springer Nature Switzerland AG.
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Energy Transition in Industry as a Viable Path to Sustainable Decarbonization
(2025)
Humberto Murillo-Jiménez
;
Marco Centeno-Alarcón
;
Buele, Jorge
The industrial sector, responsible for a substantial share of global greenhouse gas emissions, faces the dual challenge of advancing decarbonization while ensuring long-term competitiveness. Addressing this dilemma requires a transition toward renewable energy sources that not only reduce emissions but also enhance energy security and compliance with increasingly stringent climate regulations. This study examines the integration of renewable energy technologies into industrial processes, highlighting both opportunities and persistent barriers. On the benefits side, renewable adoption has the potential to deliver significant emission reductions, strengthen energy independence, and improve corporate reputation through alignment with sustainability targets. Nevertheless, limitations such as high initial investment costs, intermittency of supply, technological uncertainty, and unstable regulatory frameworks continue to hinder large-scale deployment. Emerging digital technologies, including machine learning for predictive maintenance and blockchain for energy traceability, are identified as enabling tools that improve efficiency, transparency, and integration across supply chains. By employing a narrative review methodology, this analysis synthesizes documented case studies and verifiable performance metrics to provide a structured view of current practices. Findings demonstrate that sector-specific renewable integration, such as solar thermal in manufacturing or green hydrogen in heavy industries is both technically feasible and economically viable under favorable conditions, yielding measurable reductions in carbon intensity. However, success depends on designing tailored strategies that consider local resource availability, fostering stable policy frameworks that reduce investment risk, and promoting cross-sector collaboration. Ultimately, a context-sensitive and adaptive approach emerges as essential to scaling industrial decarbonization without undermining competitiveness, ensuring that sustainability and productivity evolve in tandem.
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Virtual environment application that complements the treatment of dyslexia (VEATD) in children
(2020)
Buele, Jorge
;
López, V.M.
;
Varela Aldas, José
;
Soria, A.
;
Palacios-Navarro, G.
The educational disorders that children present at an early age can cause them to not fully develop throughout their lives. In this research work a 3D virtual system that allows the child who has been diagnosed with dyslexia to complement the exercises performed in a conventional therapy is described. To achieve this an application was developed, the app consists of two games (each with three levels of difficulty), and that are part of the rehabilitation program. In each of these games virtual objects are combined with auditory messages to provide the user with an immersive experience, and to train more than one sense at a time. In the first game task, the activity asks the children to correctly locate the syllables that compose a word and for the second activity the children will listen to a word, after the games asks the children to select the correct word. This tool has been tested by a group of children (eight), with ages ranging from 8 to 12 years old, whose development can be supervised at home by their parents, since it is an intuitive and easy to use interface. The results obtained are stored in a database and in this way the medical specialist can monitor the progress of the child throughout his treatment. For the validation of this proposal the SUS usability test was used. © Springer Nature Switzerland AG 2020.
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Support vector machine as tool for classifying coffee beverages
(2020)
Varela Aldas, José
;
Fuentes, E.M.
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Buele, Jorge
;
Meló, R.G.
;
Barat, J.M.
Classifiers are tools widely used nowadays to process data and obtain prediction models that are trained through supervised learning techniques; there is a wide variety of sensors that acquire the data to be processed, such as the voltammetric electronic tongue, as a device employed to analyze food compounds. This paper presents a normal and decaffeinated coffee beverage classifier using a Support Vector Machine with a linear separation function, detailing the classification function and the model optimization method; to train the model, the data measured by 4 electrodes of a voltammetric tongue that is excited by a predetermined sequence of positive pulses is used. In addition, the results graphically show the measurements obtained, the support vectors and the evaluation data, the values of the classifier parameters are also presented. Finally, the conclusions establish an acceptable error in the classification of coffee drinks according to caffeine presence at the sample analyzed. © Springer Nature Switzerland AG 2020.
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Buele, Jorge

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