Authors:
Andrea Lazarte-Aguirre, Rafael Fernández-Concha, Nicolás Núñez
Abstract:
In a highly competitive context where generative artificial intelligence (GAI) tools are gaining increasing relevance in educational learning environments, it is essential to understand the motivations and factors driving graduate students to adopt these technologies. This study systematically identifies the factors influencing graduate students’ intentions to use GAI tools. Students and alumni from a graduate business school in Peru were surveyed to assess their intentions regarding GAI technology usage. The study builds on the Unified Theory of Acceptance and Use of Technology (UTAUT) by incorporating GAI literacy as a variable. In late 2024, 251 participants from diverse backgrounds completed a questionnaire, which was then analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS 4.1.0.2. This analysis aimed to uncover key factors influencing GAI adoption in higher education. The findings reveal that performance expectancy (PE), effort expectancy (EE), and perceived risk (PR) significantly influence the intention to use GAI, whereas facilitating conditions (FC) and social influence (SI) do not. Furthermore, prior experience with GAI moderates the relationships between FC, SI, and the intention to use GAI. These insights into the factors shaping GAI adoption intentions are vital for informing strategies to ethically leverage artificial intelligence (AI) in business and academia. By understanding user motivations, organizations can develop targeted policies and training programs to ensure responsible AI integration and maximize its potential benefits.
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Authors:
Eduardo Vázquez Bonilla, Anilú Franco-Árcega, Virgilio López-Morales, Manuel Alejandro Ojeda-Misses
Abstract:
This study explores the impact of ludoeducational robotics on second language learning through the implementation of two interactive applications developed for the Ludibot robot. The aim was to assess whether these applications, based on constructivist and interactionist principles, could enhance learner motivation, improve vocabulary and grammar acquisition, and support meaningful engagement in French language learning. A total of 82 postgraduate students in Mexico participated in individual 45-minute sessions guided by a French instructor. During these sessions, students interacted with Ludibot using voice and computer peripherals to engage with the educational games. Data was collected through a 20-item post-session questionnaire exploring perceptions of educational robotics. The results showed high levels of user satisfaction, particularly regarding usability and motivation. Quantitative data confirmed that students found the experience enjoyable and valuable, though some expressed concerns about overreliance on technology. The system’s technical foundation involves two main architectures employing specialized components, a custom nonlinear control law for locomotion, and the integration of external sensors like the Kinect. The study contributes to the growing field of ludoeducational robotics by offering a pedagogically grounded model that integrates robotics, gamification, and language didactics. Key implementation challenges included ensuring precise Kinect sensor calibration and developing robust communication protocols for the real-time interaction between multiple subsystems. It demonstrates that non-adaptive robots can still provide effective learning experiences when carefully designed. The findings support the inclusion of robotics in language education and point to the need for further research on adaptive systems, diverse learner populations, and long-term learning outcomes.
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(¿Qué determina la empleabilidad de los estudiantes?)
(O que determina a empregabilidade dos estudantes?)
Authors:
Test Author 1, Test Author 2, Test Author 3
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.
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Authors:
Steven Van Vaerenbergh, Irene Polo-Blanco, Álvaro García Gómez
Abstract:
Mathematical applets have become essential tools in the teaching and learning of mathematics, offering interactive and dynamic representations of mathematical concepts. For students with autism spectrum disorder (ASD), however, these applets must be designed to address their specific cognitive and learning needs to be truly effective. Despite increased efforts to support students with ASD in digital learning environments, no validated set of guidelines exist for designing mathematical applets tailored to these students. This study fills this gap by developing and validating a set of guidelines, operationalized for dynamic mathematical environments such as GeoGebra. In particular, the study advances beyond prior work by following a rigorous three-phase validation methodology involving two rounds of expert review with quantitative agreement analysis, and by explicitly grounding each guideline in the cognitive traits characteristic of ASD and their documented impact on mathematical learning. The multi-phase process starts with a literature review that incorporates cognitive traits of ASD, their impact on mathematical learning, and interface design principles. The guidelines are then refined through two rounds of expert validation, first by specialists in ASD and mathematics education, then by experts in GeoGebra applet development. Expert feedback is systematically integrated, ensuring both theoretical soundness and practical feasibility. The final guidelines provide a structured approach for designing mathematical applets tailored to the needs of students with ASD, while also serving as a foundation for further research in this field.
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Authors:
Grissa Vianney Maturana González, Claudia Elena Durango Vanegas, Carlos Mario Zapata Jaramillo, Carla Maria Zapata Rueda
Abstract:
Competencies are skills, knowledge, and personality traits helping to obtain satisfactory performance in work teams. Software engineering games allow students for learning about computer fields in a safe and controlled environment. Commonly, such games mention skills to perform activities related to a software development team. However, competencies and competency levels are not directly related to the roles of software work teams in such games. In this paper we propose a game for improving the identification of the competencies and competency levels used in Essence for some roles of software development teams. The game allows for teaching strategies for reaching optimal competency levels for such roles.
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Authors:
Benjamín Maraza-Quispe, Juan Abdon Palo-Rosas
Abstract:
This study explores the pedagogical potential of integrating haptic glove technology within immersive virtual reality environments for medical training. A quasi-experimental design was conducted with 50 second-year medical students from a private university in Arequipa, randomly assigned to control and experimental groups. The experimental group participated in three 90-minute sessions over two weeks, using Hi5 Noitom 2.0 gloves and VR platforms (Hand Physics Lab and VR Surgery Simulator) to perform tasks such as suturing, forceps manipulation, and anatomical palpation. Three validated instruments were employed, a psychomotor rubric, a self-assessment questionnaire, and an observation checklist, all demonstrating strong reliability ( $\alpha =0.83$ –0.88). Statistical analyses (independent samples t-tests, ANCOVA, and repeated-measures ANOVA) revealed significant improvements (p
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Authors:
Juan Carlos Suárez Gómez, Mónica Marcela Sánchez Duarte, Andrés Chiappe, Laura Fontán de Bedout
Abstract:
Educational inclusion and addressing diversity are the primary challenges facing the education sector in the 21st century. This article explores the potential of Artificial Intelligence (AI) as a supportive tool for educators working with students with Functional Diversity (FD), aiming to foster the development of autonomy in learning. It examines how AI can facilitate teaching practices in diverse classrooms and assist students with these characteristics, promoting their autonomy. The analysis suggests that AI offers significant opportunities to provide personalized support to this population by delivering immediate feedback, tailored resources, and improving teachers’ classroom management. In this sense, the integration of AI can help create truly inclusive educational spaces, benefiting not only the target population but also educators and their families. However, discussions regarding the security and ethical considerations of AI in handling the personal data of the educational agents involved remain ongoing.
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Authors:
Analía Conde, Carmen Blanco, Nicolás Serrano, Enrique Reina
Abstract:
In recent years, there has been a paradigm shift in educational methodologies within higher education. Traditional lecture-based approaches have become less effective in engaging newer generations of students. As a consequence, there has been an increase in the exploration of innovative pedagogical strategies. Among these, active learning methodologies have gained prominence, positioning students at the center of the teaching and learning process and encouraging their active participation. One such approach is gamification, which integrates game design elements into educational contexts to enhance student motivation and engagement. This study focuses on the implementation of gamification in the “Database Design I” course, which is a mandatory first-year subject in the curricula of Computer Engineering, Telematics, Civil, Industrial, and Data & AI Engineering programs at the Faculty of Engineering of the University of Montevideo (FIUM), Uruguay. Addressing the observed lack of student motivation in the subject, a gamified application named FIUMLand was developed. This platform offers students an alternative and engaging way to review course content. An initial prototype was introduced to the 2024 cohort, featuring games designed to reinforce SQL knowledge covered in class. Students’ feedback is encouraging, indicating increased student motivation toward the subject. Students have reported that the gamified platform has been a valuable tool in consolidating the concepts learned during lectures.
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