Pub Date : 2025-09-29DOI: 10.1109/RITA.2025.3615512
Laura Icela González-Pérez;Francisco José García-Peñalvo;Amadeo José Argüelles-Cruz
The responsible integration of Artificial Intelligence in Education (AIED) offers a strategic opportunity to align learning environments with the principles of Society 5.0, fostering human–technology synergy in support of quality education and social well-being. This study presents a systematic review of 36 peer-reviewed articles (2021–2025) focused on educational applications that employ learning analytics (LA) through data-driven approaches and integrate machine learning (ML) models as part of their empirical evidence. Each study was analyzed according to three key dimensions: the context of AIED application, the data-driven approach adopted, and the ML model implemented. The findings reveal a persistent disconnect between the AI models employed and the available educational data, which in many cases are limited to access logs or manually recorded grades that fail to capture deeper cognitive processes. This limitation constrains both the effective training of ML models and their pedagogical utility for delivering meaningful interventions such as personalized learning pathways, real-time feedback, early detection of learning difficulties, and monitoring and visualization tools. Another significant finding is the absence of psychopedagogical frameworks integrated with quality standards and data governance, which are essential for advancing prescriptive and ethical approaches aligned with learning goals. It is therefore recommended that educational leaders foster AIED applications grounded in data governance and ethics frameworks, ensuring valid and reliable metrics that can drive a more equitable and inclusive education.
{"title":"Data-Driven Learning Analytics and Artificial Intelligence in Higher Education: A Systematic Review","authors":"Laura Icela González-Pérez;Francisco José García-Peñalvo;Amadeo José Argüelles-Cruz","doi":"10.1109/RITA.2025.3615512","DOIUrl":"https://doi.org/10.1109/RITA.2025.3615512","url":null,"abstract":"The responsible integration of Artificial Intelligence in Education (AIED) offers a strategic opportunity to align learning environments with the principles of Society 5.0, fostering human–technology synergy in support of quality education and social well-being. This study presents a systematic review of 36 peer-reviewed articles (2021–2025) focused on educational applications that employ learning analytics (LA) through data-driven approaches and integrate machine learning (ML) models as part of their empirical evidence. Each study was analyzed according to three key dimensions: the context of AIED application, the data-driven approach adopted, and the ML model implemented. The findings reveal a persistent disconnect between the AI models employed and the available educational data, which in many cases are limited to access logs or manually recorded grades that fail to capture deeper cognitive processes. This limitation constrains both the effective training of ML models and their pedagogical utility for delivering meaningful interventions such as personalized learning pathways, real-time feedback, early detection of learning difficulties, and monitoring and visualization tools. Another significant finding is the absence of psychopedagogical frameworks integrated with quality standards and data governance, which are essential for advancing prescriptive and ethical approaches aligned with learning goals. It is therefore recommended that educational leaders foster AIED applications grounded in data governance and ethics frameworks, ensuring valid and reliable metrics that can drive a more equitable and inclusive education.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"20 ","pages":"440-451"},"PeriodicalIF":1.0,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11184165","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-19DOI: 10.1109/RITA.2025.3612280
Rasikh Tariq;Diego Gutiérrez Vargas;Farhan Ali;Miguel Gonzalez-Mendoza;Cristina Sofia Torres-Castillo
Employability is vital for graduates to succeed in competitive job markets and reflects higher education institutions’ effectiveness. It is essential to investigate which specific traits contribute to a higher success rate of employability, as understanding these factors can help optimize targeted interventions and improve employment outcomes. The objective of this research is to identify and analyze the key traits that influence student employability using educational data mining techniques integrated with machine learning and deep learning models while providing an explainable framework to inform targeted interventions and enhance job market readiness among graduates. Addressing gaps in existing research, this study integrates a wide range of variables and employs advanced Artificial Intelligence (AI) techniques, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), to develop a predictive framework for understanding employability over time. Using data from mock job interviews, the study applies Shapley Additive exPlanations (SHAP) values to assess the impact of traits like Self-Confidence and Ability to Present Ideas. Hyperparameter tuning through Grid Search and k-fold cross-validation is employed to optimize model performance. The LSTM model, configured with three layers, achieved an accuracy of 91.46%, and demonstrated the highest performance among the evaluated models. Its robustness was further supported by a 90.48% accuracy obtained through 3-fold cross-validation. The current findings highlight the importance of soft skills, such as Self-Confidence, Ability to Present Ideas, and General Appearance, identified by SHAP analysis as critical predictors of employability, emphasizing the need for educational institutions to actively integrate soft skills development into their curricula to ensure students are both academically prepared and professionally equipped.
{"title":"What Determines Student Employability? Educational Data Mining Through Machine and Deep Learning Approach","authors":"Rasikh Tariq;Diego Gutiérrez Vargas;Farhan Ali;Miguel Gonzalez-Mendoza;Cristina Sofia Torres-Castillo","doi":"10.1109/RITA.2025.3612280","DOIUrl":"https://doi.org/10.1109/RITA.2025.3612280","url":null,"abstract":"Employability is vital for graduates to succeed in competitive job markets and reflects higher education institutions’ effectiveness. It is essential to investigate which specific traits contribute to a higher success rate of employability, as understanding these factors can help optimize targeted interventions and improve employment outcomes. The objective of this research is to identify and analyze the key traits that influence student employability using educational data mining techniques integrated with machine learning and deep learning models while providing an explainable framework to inform targeted interventions and enhance job market readiness among graduates. Addressing gaps in existing research, this study integrates a wide range of variables and employs advanced Artificial Intelligence (AI) techniques, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), to develop a predictive framework for understanding employability over time. Using data from mock job interviews, the study applies Shapley Additive exPlanations (SHAP) values to assess the impact of traits like Self-Confidence and Ability to Present Ideas. Hyperparameter tuning through Grid Search and k-fold cross-validation is employed to optimize model performance. The LSTM model, configured with three layers, achieved an accuracy of 91.46%, and demonstrated the highest performance among the evaluated models. Its robustness was further supported by a 90.48% accuracy obtained through 3-fold cross-validation. The current findings highlight the importance of soft skills, such as Self-Confidence, Ability to Present Ideas, and General Appearance, identified by SHAP analysis as critical predictors of employability, emphasizing the need for educational institutions to actively integrate soft skills development into their curricula to ensure students are both academically prepared and professionally equipped.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"20 ","pages":"271-289"},"PeriodicalIF":1.0,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-08DOI: 10.1109/RITA.2025.3607479
Mihaela I. Chidean;Ana Arboleya;Maria Cerezo-Magaña;Antonio J. Caamaño;Eduardo del Arco;David Cortés-Polo
Actual and future society requires more and more technology-related knowledge. One of the goals of the educational system is to prepare current students and future workers for the different challenges and jobs that they might encounter, even if most future jobs are largely unknown. Although administrations gradually modify education policies that will affect future generations, nowadays, there are students enrolled in non-STEM degrees who require the same opportunities. There are multiple approaches to this issue, such as double major art-engineering degrees or specific technological courses offered for students enrolled in non-STEM degrees. In this work, we present a case study conducted in a mandatory course for an undergraduate design degree in the art and humanities field. The course objective is to teach students basic electronic design and programming with the Arduino platform. To evaluate the previous knowledge and attitude of the students with regard to technology, initial tests were conducted. To evaluate their acquired knowledge, the students’ final projects developed during the course were assessed. The present study analyzes the benefits for this student profile, showing that besides acquiring new expertise they have also broadened their options and opportunities in the labour market.
{"title":"Technology Courses for Non-STEM Degrees: A Project-Based Learning Case Study","authors":"Mihaela I. Chidean;Ana Arboleya;Maria Cerezo-Magaña;Antonio J. Caamaño;Eduardo del Arco;David Cortés-Polo","doi":"10.1109/RITA.2025.3607479","DOIUrl":"https://doi.org/10.1109/RITA.2025.3607479","url":null,"abstract":"Actual and future society requires more and more technology-related knowledge. One of the goals of the educational system is to prepare current students and future workers for the different challenges and jobs that they might encounter, even if most future jobs are largely unknown. Although administrations gradually modify education policies that will affect future generations, nowadays, there are students enrolled in non-STEM degrees who require the same opportunities. There are multiple approaches to this issue, such as double major art-engineering degrees or specific technological courses offered for students enrolled in non-STEM degrees. In this work, we present a case study conducted in a mandatory course for an undergraduate design degree in the art and humanities field. The course objective is to teach students basic electronic design and programming with the Arduino platform. To evaluate the previous knowledge and attitude of the students with regard to technology, initial tests were conducted. To evaluate their acquired knowledge, the students’ final projects developed during the course were assessed. The present study analyzes the benefits for this student profile, showing that besides acquiring new expertise they have also broadened their options and opportunities in the labour market.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"20 ","pages":"262-270"},"PeriodicalIF":1.0,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11153631","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-02DOI: 10.1109/RITA.2025.3605317
Williamson Silva;Renato de Souza Garcia;Rodrigo Cargnelutti;Maicon Bernardino
Requirements Engineering (RE) represents a fundamental activity in the software development process. When executed correctly, RE can have a beneficial impact on the final software quality. Given the increasing demand for competent professionals in the software industry, adopting pedagogical strategies that effectively align theory and practice in teaching Requirements Engineering is imperative. This ensures the training of qualified professionals capable of successfully executing software projects. This paper reports our experience designing and enhancing an innovative proposal pedagogy to teach RE through active blended learning. We grounded our proposal on the Problem-Based Learning (PBL) methodology, which enables external community stakeholders to present real-world problems within the classroom environment. Students take on the role of requirements engineers and participate in various RE activities as they design their software solutions. Our pedagogical proposal combines PBL with other methodologies, e.g., Flipped Classroom, Diaries, and Gamification. We also provide evidence from a case study conducted in our course, in which we assess students’ perceptions of our approach. The results indicate increased student engagement, motivation, and performance, as well as improved understanding of RE concepts and their application to real-world problems. Additionally, we improved the active blended learning proposal based on our lessons learned and students’ perceptions. This work concludes that an active blended learning approach can significantly enhance RE education, offering a practical and adaptable strategy to foster both technical and soft skills among software engineering students. The main contributions of this study are (i) the design of a structured and replicable pedagogical framework for teaching RE using blended learning, (ii) the empirical evaluation of this framework through its implementation in two undergraduate cohorts, and (iii) the refinement of the framework based on lessons learned and student feedback.
{"title":"Innovative Active Blended Learning Pedagogy in Software Requirements Engineering Education","authors":"Williamson Silva;Renato de Souza Garcia;Rodrigo Cargnelutti;Maicon Bernardino","doi":"10.1109/RITA.2025.3605317","DOIUrl":"https://doi.org/10.1109/RITA.2025.3605317","url":null,"abstract":"Requirements Engineering (RE) represents a fundamental activity in the software development process. When executed correctly, RE can have a beneficial impact on the final software quality. Given the increasing demand for competent professionals in the software industry, adopting pedagogical strategies that effectively align theory and practice in teaching Requirements Engineering is imperative. This ensures the training of qualified professionals capable of successfully executing software projects. This paper reports our experience designing and enhancing an innovative proposal pedagogy to teach RE through active blended learning. We grounded our proposal on the Problem-Based Learning (PBL) methodology, which enables external community stakeholders to present real-world problems within the classroom environment. Students take on the role of requirements engineers and participate in various RE activities as they design their software solutions. Our pedagogical proposal combines PBL with other methodologies, e.g., Flipped Classroom, Diaries, and Gamification. We also provide evidence from a case study conducted in our course, in which we assess students’ perceptions of our approach. The results indicate increased student engagement, motivation, and performance, as well as improved understanding of RE concepts and their application to real-world problems. Additionally, we improved the active blended learning proposal based on our lessons learned and students’ perceptions. This work concludes that an active blended learning approach can significantly enhance RE education, offering a practical and adaptable strategy to foster both technical and soft skills among software engineering students. The main contributions of this study are (i) the design of a structured and replicable pedagogical framework for teaching RE using blended learning, (ii) the empirical evaluation of this framework through its implementation in two undergraduate cohorts, and (iii) the refinement of the framework based on lessons learned and student feedback.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"20 ","pages":"252-261"},"PeriodicalIF":1.0,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145073306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-22DOI: 10.1109/RITA.2025.3601613
Alejandro Moreno-Cruz;Jaime Muñoz-Arteaga;Julio C. Ponce-Gallegos;Francisco L. Gutiérrez-Vela
Students with disabilities at the upper secondary level often encounter significant barriers to accessing and engaging with education. This study examines the use of the Design-Based Research (DBR) methodology to develop a virtual reality (VR) application prototype designed to address these challenges. Conducted within the Care Centers for Students with Disabilities (CAED) in Aguascalientes, Mexico, the project involved 18 students with different types of disabilities and their educators in an iterative design process. By utilizing VR technologies, the study aimed to enhance accessibility, student engagement, and academic outcomes for students with disabilities. The DBR approach facilitated continuous refinement of the prototype through iterative feedback cycles, promoting collaboration among researchers, educators, and students to ensure alignment with user needs. Results indicated increased motivation and academic performance for most participants, although significant visual impairments limited the tool’s effectiveness for two students. This paper details the design, implementation, and evaluation of the VR application, emphasizing the integration principles of inclusive education with advanced technologies. The findings highlight the potential of VR to create immersive, adaptive, and inclusive learning environments, providing valuable guidance for future advancements in educational technology.
{"title":"Agile Production of Inclusive Learning Environments With Virtual Reality to Support Bachelor Students With Disabilities","authors":"Alejandro Moreno-Cruz;Jaime Muñoz-Arteaga;Julio C. Ponce-Gallegos;Francisco L. Gutiérrez-Vela","doi":"10.1109/RITA.2025.3601613","DOIUrl":"https://doi.org/10.1109/RITA.2025.3601613","url":null,"abstract":"Students with disabilities at the upper secondary level often encounter significant barriers to accessing and engaging with education. This study examines the use of the Design-Based Research (DBR) methodology to develop a virtual reality (VR) application prototype designed to address these challenges. Conducted within the Care Centers for Students with Disabilities (CAED) in Aguascalientes, Mexico, the project involved 18 students with different types of disabilities and their educators in an iterative design process. By utilizing VR technologies, the study aimed to enhance accessibility, student engagement, and academic outcomes for students with disabilities. The DBR approach facilitated continuous refinement of the prototype through iterative feedback cycles, promoting collaboration among researchers, educators, and students to ensure alignment with user needs. Results indicated increased motivation and academic performance for most participants, although significant visual impairments limited the tool’s effectiveness for two students. This paper details the design, implementation, and evaluation of the VR application, emphasizing the integration principles of inclusive education with advanced technologies. The findings highlight the potential of VR to create immersive, adaptive, and inclusive learning environments, providing valuable guidance for future advancements in educational technology.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"20 ","pages":"227-236"},"PeriodicalIF":1.0,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144914135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-21DOI: 10.1109/RITA.2025.3599123
Jhonattan Miranda;Johannes Freudenreich;Marie Schneider;Leonardo D. Glasserman-Morales
This study addresses the need to ensure the long-term sustainability of Open Educational Resources (OER) in educational environments shaped by Artificial Intelligence (AI). To respond to this challenge, we present the Design for Sustainability of Open Educational Resources (DfS-OER) model. The model is informed by sustainability literature and educational design theory, and it is structured to support practical application. It incorporates five sustainability dimensions: social, economic, environmental, pedagogical, and technological, each connected to a set of design principles. The model was applied in the design of a low-cost, AI-enhanced university course and validated through empirical implementation. Findings from the case study demonstrate that: (a) the model enabled consistent alignment between sustainability objectives and instructional design decisions; (b) the use of AI tools significantly reduced development time, particularly in translation and content generation; (c) inclusive design elements improved learner engagement and accessibility; and (d) the model supports the vision of Education 5.0 by enabling human-centered, scalable, and adaptive learning environments. The DfS-OER model offers a validated pathway for integrating sustainable design practices into digital education systems at scale.
{"title":"Design for Sustainability of Open Education Resources in the Era of AI: A Case Study","authors":"Jhonattan Miranda;Johannes Freudenreich;Marie Schneider;Leonardo D. Glasserman-Morales","doi":"10.1109/RITA.2025.3599123","DOIUrl":"https://doi.org/10.1109/RITA.2025.3599123","url":null,"abstract":"This study addresses the need to ensure the long-term sustainability of Open Educational Resources (OER) in educational environments shaped by Artificial Intelligence (AI). To respond to this challenge, we present the Design for Sustainability of Open Educational Resources (DfS-OER) model. The model is informed by sustainability literature and educational design theory, and it is structured to support practical application. It incorporates five sustainability dimensions: social, economic, environmental, pedagogical, and technological, each connected to a set of design principles. The model was applied in the design of a low-cost, AI-enhanced university course and validated through empirical implementation. Findings from the case study demonstrate that: (a) the model enabled consistent alignment between sustainability objectives and instructional design decisions; (b) the use of AI tools significantly reduced development time, particularly in translation and content generation; (c) inclusive design elements improved learner engagement and accessibility; and (d) the model supports the vision of Education 5.0 by enabling human-centered, scalable, and adaptive learning environments. The DfS-OER model offers a validated pathway for integrating sustainable design practices into digital education systems at scale.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"20 ","pages":"244-251"},"PeriodicalIF":1.0,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-19DOI: 10.1109/RITA.2025.3600114
Karla Bayly-Castaneda;María-Soledad Ramirez-Montoya
Integrating Artificial Intelligence (AI) in education has opened new possibilities for personalized learning, yet its potential to develop higher-order thinking skills in specific domains such as financial literacy remains underexplored. This study investigates the impact of designing and using a personalized AI agent in a mobile application on developing critical thinking, decision-making autonomy, and financial behavior among undergraduate students. A qualitative exploratory case study was conducted with 27 students enrolled in a Personal Finance course at a private university in Mexico. Participants created their own AI-based financial planners using the Poe platform and reflected on the experience in responses to a structured questionnaire. Thematic content analysis using ATLAS.ti revealed that using AI agents produced (a) enhanced critical thinking through scenario-based simulations, (b) greater autonomy in financial planning and decision-making, (c) increased motivation for continued learning in personal finance, and (d) immediate adoption of financial actions to achieve long-term goals. This study contributes to the field by exploring how student-created AI agents can act as instructional tools and perceived catalysts for behavioral change. These findings are relevant for educators, instructional designers, and policymakers aiming to leverage emerging technologies to develop cognitive, and practical competencies in higher education.
{"title":"Empowering Financial Decision-Making With AI Agents: A Case Study on Critical Thinking Development","authors":"Karla Bayly-Castaneda;María-Soledad Ramirez-Montoya","doi":"10.1109/RITA.2025.3600114","DOIUrl":"https://doi.org/10.1109/RITA.2025.3600114","url":null,"abstract":"Integrating Artificial Intelligence (AI) in education has opened new possibilities for personalized learning, yet its potential to develop higher-order thinking skills in specific domains such as financial literacy remains underexplored. This study investigates the impact of designing and using a personalized AI agent in a mobile application on developing critical thinking, decision-making autonomy, and financial behavior among undergraduate students. A qualitative exploratory case study was conducted with 27 students enrolled in a Personal Finance course at a private university in Mexico. Participants created their own AI-based financial planners using the Poe platform and reflected on the experience in responses to a structured questionnaire. Thematic content analysis using ATLAS.ti revealed that using AI agents produced (a) enhanced critical thinking through scenario-based simulations, (b) greater autonomy in financial planning and decision-making, (c) increased motivation for continued learning in personal finance, and (d) immediate adoption of financial actions to achieve long-term goals. This study contributes to the field by exploring how student-created AI agents can act as instructional tools and perceived catalysts for behavioral change. These findings are relevant for educators, instructional designers, and policymakers aiming to leverage emerging technologies to develop cognitive, and practical competencies in higher education.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"20 ","pages":"220-226"},"PeriodicalIF":1.0,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-15DOI: 10.1109/RITA.2025.3599155
José G. Mercado-Rojas;Rasikh Tariq;Juan A. Marchina-Herrera;Inna Artemova;Jorge Sanabria-Z
Citizen science projects that use Internet of Things (IoT) devices are transforming environmental education by enabling real-time, participatory data collection. However, few initiatives integrate Artificial Intelligence (AI) to support the analysis and prediction of environmental dynamics, as well as their interpretation and deeper learning outcomes. This article presents a framework for incorporating AI into IoT-based citizen science educational systems, exemplified by the SKILIKET project. SKILIKET combines quantitative sensor data (e.g., temperature, CO2, humidity, UV, and noise) with qualitative human observations (e.g., perceived smells, sounds, and visual cues) collected via a mobile app to help participants better understand socioecological phenomena in their environments. Using a Design-Based Research (DBR) approach, the study explores AI functionalities that could support environmental interpretation, predictive analytics for heterogeneous environmental data, and conversational agents for reflective learning. Preliminary tests show that AI-powered predictive models aid pattern recognition and foster participant reflection. The proposed framework outlines principles for modular AI integration, emphasizing user-centered design, ethical data practices, and alignment with STEM education goals. It establishes a foundation for AI-supported citizen science education, aiming to foster critical thinking, civic participation and proactive environmental stewardship.
{"title":"Framework for AI Integration in Citizen Science: Insights From the SKILIKET Project","authors":"José G. Mercado-Rojas;Rasikh Tariq;Juan A. Marchina-Herrera;Inna Artemova;Jorge Sanabria-Z","doi":"10.1109/RITA.2025.3599155","DOIUrl":"https://doi.org/10.1109/RITA.2025.3599155","url":null,"abstract":"Citizen science projects that use Internet of Things (IoT) devices are transforming environmental education by enabling real-time, participatory data collection. However, few initiatives integrate Artificial Intelligence (AI) to support the analysis and prediction of environmental dynamics, as well as their interpretation and deeper learning outcomes. This article presents a framework for incorporating AI into IoT-based citizen science educational systems, exemplified by the SKILIKET project. SKILIKET combines quantitative sensor data (e.g., temperature, CO2, humidity, UV, and noise) with qualitative human observations (e.g., perceived smells, sounds, and visual cues) collected via a mobile app to help participants better understand socioecological phenomena in their environments. Using a Design-Based Research (DBR) approach, the study explores AI functionalities that could support environmental interpretation, predictive analytics for heterogeneous environmental data, and conversational agents for reflective learning. Preliminary tests show that AI-powered predictive models aid pattern recognition and foster participant reflection. The proposed framework outlines principles for modular AI integration, emphasizing user-centered design, ethical data practices, and alignment with STEM education goals. It establishes a foundation for AI-supported citizen science education, aiming to foster critical thinking, civic participation and proactive environmental stewardship.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"20 ","pages":"200-208"},"PeriodicalIF":1.0,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-11DOI: 10.1109/RITA.2025.3597848
Luis Magdiel Oliva-Córdova;Inés Álvarez-Icaza;Carlos Enrique George-Reyes
Critical thinking is a key competency in higher education, particularly in digital environments that demand analysis, judgment, and informed decision-making. Generative Artificial Intelligence (GenAI) represents an emerging opportunity to enhance complex cognitive skills in educational settings. A quasi-experimental research design with a mixed-methods approach was employed and applied to a sample of university students aiming to address the research question: What is the impact of using GenAI tools on the development of critical thinking in university students? The educational intervention consisted of structured workshops that incorporated GenAI tools into activities aimed at developing six dimensions of critical thinking: remembering, understanding, applying, analyzing, evaluating, and creating. Results showed significant improvements in applying (p =.002), analyzing (p =.0008), and evaluating (p =.003), all associated with higher-order thinking skills. Three dimensions -remembering, understanding, and creating- showed statistically significant changes. However, students’ perception showed a notable increase in the value of GenAI use. Additionally, participants highlighted the usefulness of GenAI for generating ideas, decision making, and promoting deeper reflections. The study concludes that the intentional integration of GenAI technologies, when aligned with clear educational goals, can produce a meaningful and positive impact on the development of critical thinking in university contexts. These findings offer empirical support for designing innovative, ethically grounded learning experiences that incorporate GenAI to strengthen cognitive development in higher education.
{"title":"Evaluation of Generative AI Use to Foster Critical Thinking in Higher Education","authors":"Luis Magdiel Oliva-Córdova;Inés Álvarez-Icaza;Carlos Enrique George-Reyes","doi":"10.1109/RITA.2025.3597848","DOIUrl":"https://doi.org/10.1109/RITA.2025.3597848","url":null,"abstract":"Critical thinking is a key competency in higher education, particularly in digital environments that demand analysis, judgment, and informed decision-making. Generative Artificial Intelligence (GenAI) represents an emerging opportunity to enhance complex cognitive skills in educational settings. A quasi-experimental research design with a mixed-methods approach was employed and applied to a sample of university students aiming to address the research question: What is the impact of using GenAI tools on the development of critical thinking in university students? The educational intervention consisted of structured workshops that incorporated GenAI tools into activities aimed at developing six dimensions of critical thinking: remembering, understanding, applying, analyzing, evaluating, and creating. Results showed significant improvements in applying (p =.002), analyzing (p =.0008), and evaluating (p =.003), all associated with higher-order thinking skills. Three dimensions -remembering, understanding, and creating- showed statistically significant changes. However, students’ perception showed a notable increase in the value of GenAI use. Additionally, participants highlighted the usefulness of GenAI for generating ideas, decision making, and promoting deeper reflections. The study concludes that the intentional integration of GenAI technologies, when aligned with clear educational goals, can produce a meaningful and positive impact on the development of critical thinking in university contexts. These findings offer empirical support for designing innovative, ethically grounded learning experiences that incorporate GenAI to strengthen cognitive development in higher education.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"20 ","pages":"237-243"},"PeriodicalIF":1.0,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145051072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-29DOI: 10.1109/RITA.2025.3593422
Fabián Andrés Jalk Duque;Sonia Valbuena Duarte;Francisco Juan Racedo Niebles
Computational thinking (CT) is recognized as an essential macro-skill that can be developed at any age and in various contexts, being a key component in science, technology, engineering, and mathematics (STEM) disciplines. For this purpose, the design and development of an interactive simulation to measure the charge-mass ratio of the electron, using Helmholtz coils to generate a uniform magnetic field, is presented. Based on Lorentz’s law, it describes how electrons accelerated by an electric field are deflected in circular trajectories under the influence of a magnetic field. The studio uses GeoGebra software to create a virtual lab environment where users can control experimental variables such as acceleration current and voltage. Results obtained from the simulation align with established theoretical and experimental values for the electron charge-mass ratio, suggesting its potential as a supplementary educational resource.
{"title":"Computational Thinking Through a Dynamic Simulation of the Electron Charge-Mass Ratio","authors":"Fabián Andrés Jalk Duque;Sonia Valbuena Duarte;Francisco Juan Racedo Niebles","doi":"10.1109/RITA.2025.3593422","DOIUrl":"https://doi.org/10.1109/RITA.2025.3593422","url":null,"abstract":"Computational thinking (CT) is recognized as an essential macro-skill that can be developed at any age and in various contexts, being a key component in science, technology, engineering, and mathematics (STEM) disciplines. For this purpose, the design and development of an interactive simulation to measure the charge-mass ratio of the electron, using Helmholtz coils to generate a uniform magnetic field, is presented. Based on Lorentz’s law, it describes how electrons accelerated by an electric field are deflected in circular trajectories under the influence of a magnetic field. The studio uses GeoGebra software to create a virtual lab environment where users can control experimental variables such as acceleration current and voltage. Results obtained from the simulation align with established theoretical and experimental values for the electron charge-mass ratio, suggesting its potential as a supplementary educational resource.","PeriodicalId":38963,"journal":{"name":"Revista Iberoamericana de Tecnologias del Aprendizaje","volume":"20 ","pages":"191-199"},"PeriodicalIF":1.0,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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