User interface development often faces usability challenges due to discrepancies between developers’ conceptual models and users’ mental models. This paper introduces an enhanced model-driven approach centered on DataForm, a formal language designed to capture, structure, and operationalize users’ mental models in UI design. We extend DataForm to support visual customization, rendering flexibility, and real-time propagation through a modular architecture that separates business logic from interface representation. Integrated into the Task and Data Model-Based User Interface Development (TD-MBUID) framework, DataForm enables model-driven prototyping and dynamic updates. Its abstract syntax is formalized to allow hierarchical structuring of data and interface elements, while propagation mechanisms ensure synchronization between the back-end and the front-end. A case study in a virtual learning environment demonstrates the approach’s potential to enhance interface adaptability, streamline UI evolution, and improve alignment between system behavior and user cognition. While preliminary results are promising, they are based on a structured implementation scenario. Further experimental validation with end users is necessary to empirically assess the extent to which DataForm supports cognitive alignment and usability improvements across diverse interaction contexts.
This study analyses the preservation of traditional art in digital environments from the Mu + Art 2024 exhibition at the University of Pamplona. The research, with a qualitative approach, was based on structured interviews with four participants and was articulated around six key categories: fundamental aspects, evaluation of the event, user experience, digital referents, documentation and preservation, as well as accessibility and cultural value. The results indicate that effective digital preservation requires a balance between applying advanced digital technologies and protecting the authenticity and essence of traditional art. The discussion highlights the importance of maintaining artistic integrity during the digitization process, suggesting that the combination of technological innovation and care for authenticity is essential for the preservation of the cultural value of art in its migration to digital platforms. In conclusion, the research emphasizes that the preservation of traditional art in digital environments requires strategies that integrate technology and authenticity to ensure the preservation of its cultural identity
The Uncanny Valley effect describes the discomfort or eeriness experienced by humans when encountering highly realistic yet subtly imperfect digital characters, has been a long-standing challenge in computer graphics and artificial intelligence. With the recent advancements in generative artificial intelligence, particularly in text-to-video models, the creation of lifelike digital characters has become more accessible and sophisticated. However, these artificial intelligence-generated characters often exhibit subtle artifacts in motion, facial expressions, and realism, potentially amplifying the uncanny valley. This work explores the extent to which the uncanny valley is perceived in videos generated by text-to-video models. We begin by outlining key concepts related to digital character generation, the uncanny valley theory, and the impact of generative artificial intelligence on human perception. We then conduct an experimental evaluation where participants assess videos based on realism, emotional response, and perceived eeriness. The dataset consists of synthetic videos produced by various text-to-video models, including models trained on diverse datasets with differing levels of animation fidelity. Through qualitative and quantitative analyses, our results highlight significant variations in uncanny valley perception across models, revealing common weaknesses in facial consistency, eye movement, and expression dynamics that contribute to the uncanny sensation. Our findings offer insights into the factors exacerbating the uncanny valley in digital characters created by generative artificial intelligence and provide guidelines for improving the naturalness of synthetic characters in text-to-video applications. This research contributes to the ongoing discourse on human-artificial intelligence interaction, shedding light on the psychological barriers to adopting generative artificial intelligence for realistic digital humans.
Innovation is essential in higher education to equip professionals with the skills needed to advance the democratization of artificial intelligence. Building on this premise, we conduct a comparative study examining the impact of education mediated by Generative Artificial Intelligence (GenAI) versus traditional approaches, with critical thinking competence at its core. This article reviews the state of the art on GenAI-mediated training processes and their influence on critical thinking. The review was conducted between September and December 2024, yielding 161 initial results. After applying the inclusion and exclusion criteria, 23 documents relevant to the research questions were identified. Our findings suggest that integrating GenAI into teaching, evaluation, and other academic practices is crucial for delivering effective learning experiences that enhance students’ critical thinking skills. GenAI can enhance critical thinking through structured frameworks and real-time feedback, but it requires strategic pedagogical integration to avoid technological dependence. The teacher’s role is fundamental as a cognitive facilitator, guiding students toward independent reasoning while promoting critical AI literacy.
Usability evaluation on e-commerce platforms, based exclusively on manual heuristic analysis, has critical limitations in terms of subjectivity, time consumption, and lack of scalability that hinder sales optimization. To address these challenges, this study proposes a hybrid model that integrates heuristic evaluations with automated tools, aiming to increase efficiency and objectivity by combining precise metrics with the contextual insight of expert evaluators. Methodologically, a comparative framework was established by applying 16 heuristics and analyzing tools such as Lighthouse, WAVE, and Hotjar to assign improvement scores based on their technical detection capabilities. The results demonstrate that automation significantly optimizes the identification of performance, accessibility, and interaction issues, reducing human bias, although it is acknowledged that design consistency and user perception still require manual interpretation to ensure complete accuracy. This research constitutes a fundamental contribution by establishing the theoretical foundations for scalable hybrid approaches, offering a preliminary proposal with high potential to transform usability evaluation in competitive digital environments, thereby improving the end-user experience.
Technological advancements have led to the widespread adoption of voice interaction technologies in smart speakers, Internet of Things (IoT) devices, mobile applications, and other intelligent systems. Despite their growing presence, several usability challenges persist, hindering their effective integration into users’ daily lives. Issues related to natural language understanding, system feedback, error handling, privacy, and user trust negatively affect user experience. Furthermore, the lack of specific heuristics, validated measurement scales, and standardized guidelines limit both the evaluation and systematic development of these technologies, making consistent assessment difficult across different contexts of use. This systematic literature review aims to identify the main knowledge gaps and consolidate recent research on heuristics and usability evaluation methods for voice interaction technologies. Based on an analysis of studies published in recent years, it offers a comprehensive overview of existing approaches, limitations, and trends. The findings contribute to improving the design and evaluation of these systems, while also guiding future research and supporting the development of more user-centered voice interaction solutions.
Computational Thinking (CT) and STEM competencies represent essential skills for the next generation of engineers, crucial to driving technological innovation and addressing the complex problems that define the global challenges of the 21st century. The lack of effective strategies for integrating CT and STEM competencies into undergraduate education makes it difficult for students to achieve the CT skills needed to face the challenges of their professional lives. This article presents a model that integrates CT and STEM competencies in the formative activities of engineering (E) students, called CT4E. The model is evaluated through a comparative case study to determine its contribution to the development of CT skills in the context of two introductory engineering courses. The study considered 60 first-semester students of the Engineering Introduction course at the Electronics and Telecommunications Engineering program of the Universidad del Cauca, divided into experimental and control groups. The results obtained from pretest and post-test questionnaires showed a greater increase in key CT skills development, such as abstraction, decomposition, pattern recognition, and algorithmic design, in the group in which the methodology was applied. These promising results support the CT4E model as an effective and replicable tool for strengthening CT and STEM competencies in engineering education (T E) contexts. Even so, the model requires further refinement cycles, particularly to simplify its application for both teachers and students.
Empathetic technologies transform elderly care by enhancing emotional well-being, autonomy, and social engagement. This paper examines integrating assistive technologies (ATs) with empathy-driven principles to support older adults. We conduct a structured narrative literature review, categorizing existing studies based on key themes such as emotional recognition, adaptive interaction, and user-centered design. The findings highlight the potential benefits of AI-driven virtual agents, wearable health monitoring devices, and accessible user interfaces in mitigating age-related challenges. However, barriers such as usability, digital literacy, ethical concerns, and privacy risks must be addressed for broader adoption. Human-Computer Interaction (HCI) insight provides valuable guidance in designing effective ATs, incorporating user-centered approaches, co-design methodologies, and affective computing techniques. This paper also identifies critical research gaps, including the need for experimental validation, improved emotion recognition models, and strategies to ensure ethical implementation. Future research should focus on refining AI frameworks, enhancing accessibility, and promoting transparency in data usage to foster greater acceptance and effectiveness of empathetic assistive technologies. By addressing these challenges, ATs can better support aging populations, improving their quality of life and social inclusion.
The Internet of Things has significant potential to transform industrial operations. However, its adoption faces challenges related to organizational readiness and limited empirical evidence in Latin America. This research explicitly investigates Argentina’s Buenos Aires Metropolitan Area, where industrial companies are pivotal to the national economy. Maturity models for the Internet of Things often lack contextualization, thus hindering practical assessment in industrial settings. Thus, this study assessed 37 industrial organizations using the ATLANTIS maturity model, which considers technological, organizational, and human dimensions, as well as multiple subcomponents. Data were collected through a structured questionnaire, and statistical analyses were performed to determine the levels of the organizations. The results show a difference in overall Internet of Things maturity between large enterprises and small and medium-sized enterprises, with the former showing higher scores on several dimensions. Large enterprises demonstrated notable strengths in device management and connectivity, while both groups showed higher scores in enterprise integration and relatively low scores in compliance and contextualization. A strong correlation was found between technology and organizational maturity scores, suggesting their interdependence. The study highlights the heterogeneous landscape of Internet of Things adoption, with large enterprises significantly more advanced than small and medium-sized organizations. This highlights the need for tailored strategies to support small and medium-sized enterprises in improving their technological infrastructure and organizational readiness to adopt the Internet of Things in the region successfully.
This study presents Kaboom, a pervasive game for mobiles devices designed to promote physical activity by integrating real-world movement into its core gameplay mechanics. The objective of the research is to explore how geolocation and step counting sensors can be used to create a responsive and engaging user experience that supports health promotion. Using a using a three-layered architecture, the system collects contextual data via mobile sensors, processes it locally, and stores it in the cloud. A technical feasibility study was conducted on five different smartphones with varying hardware specifications to evaluate sensor calibration consistency, data transmission reliability, and user experience performance. The results showed accurate step counting and route tracking across different android devices, confirming the system’s robustness and compatibility. The study also identified challenges such as battery consumption and the need for improved data protection through encryption. Future work will address these issues while incorporating additional sensors and conducting usability tests in real-world conditions.
The Industry 4.0 paradigm means a revolution in production processes. Companies’ adoption of technologies depends on how they assimilate and incorporate new technologies into current processes with the users who operate them in the real-life context of use. This paper presents a study about the state of technology adoption in Small and Medium-sized Enterprises (SMEs) in Latin America, analyzing the ICT products implemented in each of the company’s functional areas that interact with the people who operate them and identifying the knowledge and capabilities of actual users for each functional area. For this purpose, the InTIC’s® index was used and allowed the measure of a total of 106 companies in different countries and production contexts, from 2020 to 2024. The analysis reveals that 64
This study investigates the impact of different interaction devices—keyboard, joystick, and electronic skateboard—on usability and player experience in a snowboarding game developed for PC. The research examines the influence of the device on the player’s experience, the impact of performance on the experience, and possible gender differences in relation to device preference and gaming experience. 49 university students, aged between 18 and 59, participated in the study. Participants tested each interaction device for 3 min, with the order of device usage alternated. After each test session, participants responded to the Game Experience Questionnaire (GEQ) to evaluate aspects such as Positive Experience, Negative Experience, Tiredness, and Return to Reality. Performance was measured in terms of score and number of lives lost. The results indicated that device choice significantly influenced negative experience and return to reality (Q1). Player performance had a direct impact on the experience, with better performance leading to a more positive experience (Q2). No significant gender differences were found in device preference or gaming experience, including perception of challenge or overall experience (Q3, Q4).
Hamstring injuries are among the most frequent musculoskeletal problems in sports, and the literature indicates that multiple factors contribute to their occurrence, with muscle fatigue being one of the most relevant. In this context, IoT-based wearable technologies have emerged as promising tools for continuous monitoring of muscle activity, enabling early identification of fatigue and supporting injury prevention strategies. This study presents the development of a real-time monitoring system that uses a wearable device to assess hamstring muscle activity and detect signs of fatigue. The proposed prototype captures surface electromyographic signals and performs fatigue detection through frequency-domain analysis, in which a reduction in median frequency relative to a baseline is interpreted as the onset of fatigue. Experimental evaluations conducted during hip lift exercises demonstrated the system’s ability to identify fatigue-related patterns in muscle activation. Furthermore, the system architecture was designed for scalability, allowing the simultaneous monitoring of multiple users. This characteristic makes the solution applicable in different contexts and suitable for both professional athletes and recreational users.
This paper explores the integration of User-Centered Design (UCD), Soft Skills and sustainability principles into Human-Computer Interaction (HCI) education, emphasizing the need to prepare future technologists for inclusive, and environmentally conscious innovation. As computational systems increasingly shape societal interactions, the paper argues that HCI must adopt an interdisciplinary framework—bridging computer science, psychology, design, and soft skills — to address complex challenges like accessibility, cultural diversity, and climate change. Through a case study of undergraduate students in São Paulo, Brazil, the research examines how active learning methodologies (e.g., Project-Based Learning) and UCD practices—such as iterative prototyping, usability testing, and user feedback—equip students to design solutions aligned with the UN Sustainable Development Goals (SDGs). By prioritizing social responsibility and environmental conscience, this approach not only enhances technical proficiency but also cultivates a generation of designers and engineers capable of creating technologies that respect human dignity, promote equity, and mitigate ecological harm. The paper concludes that embedding UCD and sustainability into HCI curriculum is essential for nurturing professionals who can balance innovation with ethical accountability in an increasingly digitized world.
This study explores the needs and expectations of Brazilian youth regarding the use of technology to support Financial Education. By adopting a User-Centered Design approach and employing Design Thinking and Co-design methodologies, an educational game prototype was developed to simulate real-life financial challenges and foster learning through gamification. The initial concept was tested during a Design Thinking session involving nine students from different undergraduate programs. The results confirmed that gamification is an effective approach for supporting Financial Education. Based on the insights obtained, low-fidelity prototypes were developed. Subsequently, a Co-design session was conducted with ten volunteer students enrolled in Computing programs, who evaluated the validity of the game’s development progress and provided suggestions for improvements and additional features. The process involved the active engagement of potential users throughout requirement elicitation, prototyping, and iterative validation. As a contribution, this research offers insights to support the development of technological solutions aimed at assisting young individuals in financial management and promoting informed economic decision-making.
Digital accessibility is essential for ensuring inclusive interactive experiences for diverse user profiles, particularly for individuals with Autism Spectrum Disorder (ASD), who often face barriers when interacting with computational systems. However, traditional guidelines, such as the Web Content Accessibility Guidelines (WCAG), do not fully address the specific needs of this audience, highlighting the necessity for more specialized methodologies. In this context, this study proposes and validates a heuristic evaluation model focused on the usability and accessibility of interactive systems for ASD users. The model was developed based on existing guidelines, a systematic literature review, and expert interviews, consolidating a set of heuristics tailored to the cognitive and sensory characteristics of ASD individuals. To validate the methodology, the model was applied in the evaluation of the digital game “Sago Mini Forest Adventure”, which is commonly used in therapy sessions for children with ASD. The results showed that 54.2
Heuristic usability evaluation is essential for identifying interaction issues in digital products, but traditional methods rely heavily on human expertise, making them costly, time-consuming, and often inconsistent across different evaluators. This study presents a comprehensive comparison between AI-driven heuristic evaluations and expert assessments, using Nielsen’s heuristics as a benchmark framework. Controlled experiments were conducted on multiple digital platforms within the WebArena environment, including OpenStreetMap, GitLab, and OneStopShop. Results demonstrate that AI agents powered by Large Language Models can achieve substantial agreement with human evaluators (Cohen’s Kappa values between 0.68–0.74) while significantly reducing subjectivity and evaluation time. The automated approach maintains consistency across evaluations and provides scalable solutions for usability testing. The findings highlight the potential of AI to enhance and complement traditional usability testing methodologies by providing cost-effective, consistent, and rapidly deployable assessments. This research contributes to the integration of AI-driven approaches in usability evaluation practices, optimizing the user experience assessment process for modern digital product development.
Speaking in English has consistently been a challenge for learners, particularly for future teachers regarding oral production. This article examines the relationship between self-efficacy variables and oral communicative competence (OCC) in pre-service English teachers, exploring the potential of immersive virtual environments such as Virtual Reality (VR) and the Metaverse. A literature review identified key factors—perceived English proficiency, language ego, inhibition, language anxiety, and communicative interaction—that influence self-efficacy and OCC. To investigate these relationships, a pilot experiment was conducted with four English-teaching trainees from a teacher training college in Mexico. Participants engaged in discussions using BigScreen, a VR application within the Metaverse, utilizing Meta Quest 2 and Meta Quest 3 headsets. Pre- and post-test questionnaires indicated a direct relationship between self-efficacy and the identified variables, particularly classroom efficacy, instructional efficacy, and efficacy in exemplifying meanings. Statistical analysis was performed using Python, applying linear and multiple correlation techniques to evaluate these relationships.
To achieve adequate user experience in an immersive system, maximizing the sense of presence is directly related to the mechanisms and devices designed to enhance immersion. One example is the inclusion of real elements within a virtual environment that users can naturally manipulate in a mixed-reality system. These systems can be developed by integrating motion capture technologies, ranging from built-in tracking systems in virtual reality head-mounted displays to high-precision tracking laboratories. This paper presents a calibration protocol aimed at integrating two optical tracking systems to work together and enhance the user’s sense of presence within a virtual environment. The first system is a high-precision tracking system for interactive objects. The second system is a low-cost tracking system embedded in a commercial head-mounted display for tracking the user’s fingers and hands. The paper details the stages of the protocol through its application in a virtual environment and discusses the results of the subjective evaluation conducted with users.
Generative Artificial Intelligence (GenAI) has a significant potential to enhance productivity and quality, however, the adoption of Generative AI in real-world scenarios lacks the empirical studies. There is a limited understanding of how Large Language Models (LLMs) can be effectively applied, its impacts, and its potential limitations in software engineering activities. To address these issues, this study introduces a set of AI-human interaction patterns and evaluates it through two exploratory studies involving students and professional software engineers. The results show, as highly effective, the proposed patterns, with 90
