A virtual-reality-based welding training method for workers in the industry is proposed in this paper. The structural integrity and safety of welds and welded items can only be guaranteed through technical skills. Furthermore, the work of professionally trained individuals in various industrial sectors is necessary due to workplace safety rules in the context of Industry 5.0. Although effective, the traditional welding training methods can be costly, time-consuming, and expose operators and apprentices to a range of hazards, including smoke, heat, and UV radiation. Teaching welding with Virtual Reality (VR) is an alternative tool that offers immersive, repeatable, and modular practice environments that efficiently use resources and time. The purpose of this work is to develop SMAW welding training software that can replicate realistic scenarios in a simulated environment using VR. To evaluate the effectiveness and usability of the software, a pair of reputable instruments were utilized: the NASA Task Load Index (NASA-TLX) to gauge cognitive load and the System Usability Scale (SUS) to evaluate perceived usability. Results were favorable, yielding 47.10 points for NASA-TLX and 85.38 points for SUS. Moreover, the questionnaires concerning the experience of participants with virtual reality welding were crucial to the research using 68 students. VR software can be a viable alternative to conventional training methods, as indicated by the results of tests conducted with the participants. In this regard, the software is highly usable and practical, which reinforces its potential for use in teaching students and training programs for staff in industry before traditional welding training. In this sense, the proposed method as a training tool prepares staff for SMAW operations.
Background: Large Language Models (LLMs) have significant potential for Human-Computer Interaction (HCI), presenting opportunities to assist several stages of design. However, challenges remain regarding contextual understanding, user-aligned suggestions, and the reliability and transparency of AI-driven decision-making. Purpose: This study examines ChatGPT’s potential in supporting user interface design during requirements gathering, focusing on idea generation and its implications for HCI. Methods: An exploratory study was conducted within the context of a chatbot for detecting depressive signs in university students. ChatGPT-4 generated design artifacts for requirements elicitation using four HCI techniques: Questionnaires, Interviews, Personas, and Scenarios. Neutral prompts were used, without prompt-engineering strategies. The outputs were evaluated through a literature-based comparison with established HCI references Preece et al. [2013], Cooper [2004], Barbosa et al. [2021], and by four academic experts using a 5 point Likert scale across six criteria: pertinence, comprehensiveness, clarity, depth, applicability, and coverage. Results: Findings indicate that generative AI produces well-structured outputs that assist in identifying user needs. Pertinence achieved the highest ratings across all techniques, and experts emphasized ChatGPT’s utility as a “starting point” for design activities. However, limitations were observed regarding depth, comprehensiveness, and coverage. In complex domains such as mental health, outputs tended to be generic, while human-led focus groups captured these aspects more effectively. Conclusion: The results show that generative AI tools like ChatGPT are valuable preliminary resources for supporting early HCI design. However, their limitations point to the continued necessity of human expertise to review AI-generated content in context-sensitive domains. This calls for hybrid workflows where human creativity and judgment are complemented, but not replaced, by AI capabilities.
Gender inequality remains pronounced in management and leadership positions within the field of Information Technology. This study aims to understand the phenomenon of women’s leadership in IT by examining the career trajectories, challenges, strategies for overcoming barriers, and motivations of women who occupy management and leadership roles. Adopting a qualitative phenomenological approach, the study draws on in-depth interviews with five women leaders to explore the essence of their lived experiences. The findings reveal common experiential patterns related to pathways to leadership, challenges encountered both along the career trajectory and while occupying leadership positions, and the strategies employed to navigate these contexts. By providing an experience-centered understanding of women’s leadership in IT, this study contributes to the literature on gender and leadership while also offering insights into the organizational and human factors that shape technology organizations and their socio-technical environments. These findings may inform organizational practices aimed at fostering more inclusive and sustainable leadership in the IT sector.
Girls from socially vulnerable contexts often face educational and symbolic barriers that restrict their access to Computing. This study examines how Hackathon 360 constitutes a situated formative Computing experience for these girls, extending the investigation conducted in its First Edition by broadening both the empirical basis and analytical scope. The research adopted a comparative mixed-methods design involving distinct cohorts from the 2024 and 2025 editions, both organized as multi-day formative processes, with a short-term longitudinal analysis conducted in the Second Edition. To address the research objective, the analysis articulated evidence from questionnaires, CTScale data collected before each Hackathon, the intersectional characterization of participants, the pedagogical redesign, and HCI artifacts. The findings indicate that the experiential categories identified in the First Edition also recurred in the Second Edition, characterizing the Hackathon as meaningful, collaborative, and socially relevant. The intersectional analysis situated this experience within racial and socioeconomic inequalities, restricted access to computing devices, and limited prior technical experience. Participant feedback informed pedagogical adaptations that expanded hands-on programming opportunities, while the CTScale characterized the Computational Thinking dispositions. The HCI artifacts showed how identities, experiences, and educational and psychosocial barriers were translated into personas, scenarios, interaction flows, and prototypes. Taken together, these findings characterize Hackathon 360 as a collaborative and situated formative Computing experience grounded in access, participation, pedagogical responsiveness, representation, and technical authorship.
Deepfake detection has advanced rapidly in recent years, yet its practical effectiveness remains limited when models are exposed to real-world conditions that differ from controlled benchmarks. This gap is particularly critical in high-stakes scenarios such as electoral processes, where synthetic media can influence public perception at scale. This paper investigates the generalization limitations of current deepfake detection approaches, with emphasis on their applicability in the Brazilian context. Rather than focusing solely on algorithmic performance, we frame deepfake detection as a socio-technical problem, where issues of trust, interpretability, and user response are central to system effectiveness. Through a critical analysis of existing datasets, evaluation protocols, and detection strategies, we show that current research paradigms often fail to account for distribution shifts, cultural variability, and adversarial adaptation. By bridging detection research with policy-oriented perspectives, this work contributes to a more actionable understanding of how synthetic media challenges can be addressed in Brazil and similar socio-political contexts. Finally, we explicitly derive implications for public policy in the Brazilian electoral context, connecting observed technical limitations to challenges in regulation, platform governance, and the use of automated systems in institutional decision-making.
Brainstorming is a fundamental stage in game development, supporting the generation of ideas and the early articulation of design decisions. However, designers may face difficulties in selecting appropriate tools and in integrating player-centered concerns, such as motivation and emotion, into ideation in a structured way. This study investigates the tools and methods used during brainstorming sessions in game design, focusing on practitioners' preferences, challenges, and perceptions regarding psychological aspects in concept formation. An exploratory questionnaire-based study was conducted with participants involved in game development and prior brainstorming experience. The results indicate the use of diverse supports, including organizational platforms, visual collaboration tools, analog resources, and prototyping practices. They also suggest that, although participants recognize the importance of motivation, emotion, and player behavior, these dimensions are not yet consistently incorporated into brainstorming through structured methods or tools. The study offers an empirical overview of current brainstorming practices and points to opportunities for more structured and player-centered ideation supports.
Digital games have become increasingly relevant not only as entertainment products but also as cultural and interactive media experiences, reinforcing the need for reliable instruments to assess player experience and satisfaction. Despite the growing Brazilian gaming market, there is still a lack of validated instruments in Brazilian Portuguese for measuring game user experience. This study aimed to translate, culturally adapt, and preliminarily validate the Game User Experience Satisfaction Scale (GUESS-18) for use in the Brazilian context. Using a sample of 259 Brazilian secondary school students, the study included translation procedures, cultural adaptation through comprehension testing and item reformulation, item sensitivity analysis, exploratory and confirmatory factor analyses, and reliability assessment of the scale’s nine dimensions. Exploratory Factor Analysis (EFA) indicated acceptable sample adequacy (KMO = 0.584), confirmed by Bartlett’s Test of Sphericity, χ²(153) = 1281.494, p < 0.001. The analysis identified nine factors with loadings ranging from 0.605 to 0.967, conceptually consistent with the original GUESS-18 theoretical structure and subscales. Although the Confirmatory Factor Analysis (CFA) fit indices were below the reference values commonly recommended in the literature, the EFA results support the structural consistency of the Brazilian version. Reliability analysis showed Cronbach’s alpha values ranging from acceptable to very good across the nine factors and the overall scale. The findings suggest that GUESS-18-BR is a promising instrument for assessing player experience and satisfaction among Brazilian players, contributing to research and development in digital games and user experience evaluation.
Background: This article examines the application of crowdsourcing solutions in collaborative crisis communication, focusing on the challenges faced by crisis and emergency responders, as well as the collaborative potential offered by digital platforms. Purpose: The primary objective of this study is to explore and categorize the challenges encountered in collaboration among stakeholders during crises and emergencies. The secondary research goal aims to assess how crowdsourcing solutions, such as crowdfunding, contribute to enhancing communication and coordination in crisis management. Methods: To achieve these objectives, a systematic literature review was conducted, categorizing the key challenges faced in crisis scenarios and the system requirements that crowdsourcing solutions must address. Furthermore, an evaluation of User Experience (UX) in crowdfunding campaigns during humanitarian crises was performed, investigating factors that influence donations, donor concerns, and the challenges involved in mobilizing volunteers and resources. Results: The analysis identified key entities in crisis communication: the crowd, agents (authorities and professionals), and the environment (platforms, apps, or maps). Requirements emphasized trust, rapid response, efficient coordination, active engagement, and transparency. Challenges included issues with transparency, authenticity, and security. The reputation of institutions was crucial for donations, and participants valued access to updated information and communication channels with institutions. Conclusion: Crowd collaboration enhances data sharing, transparency, and resource allocation in crisis scenarios through crowdsourcing solutions. Challenges in developing these solutions include ensuring trust, validating data, and coordinating rapid responses. Similarly, crowdfunding platforms face challenges related to concerns about authenticity and transparency, which significantly influence donation decisions.
This study presents a qualitative audit of commercially available facial recognition tools featuring Automated Gender Recognition (AGR), specifically Amazon Rekognition, Face++, and Google's Vision AI, to investigate their inherent intersectional biases and broader ethical and social implications. While Artificial Intelligence offers efficiency, evaluating AI bias using isolated demographic categories overlooks intersectional discrimination, allowing deeper systemic inequalities to persist, disproportionately harming marginalized groups such as Black, transgender, and non-binary individuals. To counteract these biases, we evaluate the tools across four critical dimensions: legal and corporate responsibility, ethical considerations, social implications, and Justice, Equity, Diversity, and Inclusion (JEDI). Our findings reveal a mixed landscape. Google demonstrated a proactive shift toward algorithmic justice by removing gender labels from its Vision AI, acknowledging the problematic nature of inferring gender from appearance. Conversely, Amazon Rekognition and Face++ exhibit a concerning lack of transparency regarding their training datasets and misalignment with inclusive best practices, particularly concerning non-binary gender views and the necessity of gender identification. Ultimately, the findings reinforce that achieving algorithmic justice requires moving beyond mere inclusion to ensure user autonomy, privacy, and systemic change. Relying solely on technical accuracy is insufficient; early integration of frameworks like Design Justice and IEEE Ethically Aligned Design is essential. Furthermore, policy must evolve to prevent AI from being constrained by binary legal definitions. Establishing ethical, human-centric technology demands strong interdisciplinary collaboration to protect vulnerable populations and ensure algorithms genuinely respect the diversity of the communities they serve.
Books adapted for Augmented Reality (AR) combine traditional printed pages with digital layers of interactive content such as 3D models, animations, or audio, accessed through mobile devices, creating a hybrid reading experience that merges physical and virtual elements. This paper presents a case study on the use of AR in printed books, focusing on the challenges and lessons learned from human-centered evaluation. Using the children’s book “Alan Turing: His Machines and His Secrets” as a case, we analyzed the feasibility of integrating AR features into works not originally designed for this purpose. The study involved testing two AR Software Development Kits (SDKs), Vuforia and ARCore. Results indicated that Vuforia offered the most efficient image recognition, supporting 71% of page detections when the smartphone was handheld and 76% with device support. A usability assessment with 18 participants, based on the System Usability Scale (SUS), revealed positive acceptance of AR interactions but also highlighted ergonomic limitations when handling books and mobile devices simultaneously. The findings emphasize the importance of experimental design, pilot testing, and consideration of user habits in AR evaluation studies. Overall, this work provides practical insights to guide future illustrators, developers, and researchers in adapting books to AR contexts.
Emotion recognition (ER) is a preliminary step towards endowing emotional intelligence to machines, which learn it from data. Emotion data resources are pivotal resources for studying and building ER models and an important determinant for their advancement. Brazilian Portuguese (BP) is a dialect of the 8th most spoken language in the world and the 7th globally used on the web, yet it is a low-resource language. Handful of reviews exists citing BP-ER data resources but none are based exclusively on BP nor cover all research literature. This exploratory review assesses the current status of the available BP-ER data resources for advancing ER models in BP. We extensively explored emotion studies among all research publication types up to 2024 and provide an overview of the existing ER data resources in speech, facial expression, and text modalities useful for computation purposes. Overall, 59 data resources were discovered and are listed with the details of emotions included, sources employed in their creation, and their availability under respective modality. The reported data resources are smaller in size and less than 60% are available either openly or on request. A unanimous observation in all the works that created and studied these resources highlights their scarcity. As research with adequate resources will be beneficial for advancing ER modeling, we emphasize the need for the larger in-the-wild multimodal ER data resources in BP. We believe our review will be beneficial in guiding future studies on the aspects of building corpora for advancing emotion recognition modeling in BP, as well as for any low-resource language studies.
Introduction: Digital platforms play a central role in mediating major international events, serving as gateways for information, mobilization, and public engagement. Evaluating usability in heterogeneous, cross-cultural contexts such as the Conference of the Parties (COP) poses significant challenges. The emergence of Generative Artificial Intelligence (GAI) offers new opportunities for automated usability assessment, but its alignment with real user experience remains underexplored. Objective: This study critically compares GAI-generated usability evaluations against human perceptions of climate conference websites. GAIs assessed both COP29 and COP30 platforms, while human evaluations focused exclusively on COP30 during the event in Belém, Brazil. The objective is to examine convergences and divergences between automated and human-centered assessments. Methods: Four GAIs (ChatGPT, Gemini, DeepSeek, and Copilot) performed heuristic evaluations based on ISO 9241-210 criteria. GAI outputs were analyzed using Directed Categorical Content Analysis. These findings were complemented by an exploratory evaluation involving ten human participants (n=10) with diverse profiles, whose perceptions were collected via an online questionnaire during COP30. Final analysis involved triangulation of GAI findings with human feedback. Results: Results revealed a notable divergence in overall assessment. Humans reported high satisfaction and usability for critical tasks, contrasting with GAIs, which penalized COP30 for structural deficiencies, particularly low Controllability. However, convergence occurred in Individualization Adequacy, the lowest-scoring criterion for both GAIs and humans, confirming systemic failures in adaptability and clarity. Conclusion: GAIs and users converge on structural issues but diverge on subjective and contextual evaluations. This highlights the value of hybrid models: GAIs provide systematic heuristic data, while human feedback grounds practical and sociocultural interpretations. Future work should include longitudinal evaluations, automated accessibility checkers, and hybrid workflows for robust assessment frameworks.
User experience (UX) is fundamental for the acceptance and use of information systems. Although there are well-known and widely used UX evaluation techniques for traditional interfaces, a literature review revealed several gaps regarding UX evaluation in non-immersive 3D interaction. One significant gap is the predominant use of pragmatic criteria in assessments, while another is the lack of an approach that evaluates hedonic aspects using facial emotion recognition. This work proposes an approach for automatically evaluating user experience in non-immersive three-dimensional environments, focusing on its hedonic aspects based on facial emotion recognition. An experimental protocol was developed and approved by the Ethics Committee. The experiment was conducted with 52 participants. Throughout the testing period (before, during, and after the interaction), participants' faces were recorded using a low-cost camera. The experiment involved participants playing a game and answering questionnaires, including categorization and mood profile instruments, as well as the UEQ-S and the PLEX Framework. The Face-api.js library was used for facial emotion recognition. The hypothesis that automatic facial emotion recognition can support user experience evaluation was confirmed. This method enabled the estimation of UEQ-S and PLEX questionnaire responses with an average error of approximately ±1 point using only emotion extraction through an artificial intelligence model. Given that UX evaluation is crucial for the acceptance of new software or functionality, this work contributes to improving system quality and acceptance.
Previous studies on tactile guides in VR focused on generic tasks and diverse audiences, leaving gaps in understanding their role in specialized medical simulations. This study investigates how physical properties—weight, shape, texture, thermal sensation, and grip—affect the user experience and Sense of Agency (SoA) in a surgical context. Thirteen participants with prior surgical training manipulated four objects representing virtual scalpels, allowing a controlled evaluation of physical–virtual correspondence and ergonomic comfort. The results suggest that even moderate mismatches between physical and virtual objects can generate discomfort and reduce perceived control, highlighting the critical role of ergonomic and kinesthetic factors in precise tasks. Compared to previous works, these findings suggest that assessing multiple perceptual and motor variables in a specialized audience provides deeper insights into optimizing VR-based surgical training and designing effective tactile guidance strategies.
Background: Serious games are a category of games developed with goals beyond entertainment, such as knowledge transfer and skill development. These games have already been proven as a successful means of knowledge transfer and scientific dissemination. They aim to share knowledge by engaging players through the ludic elements of a game. Despite that, the time, effort, and knowledge required to fully develop a game are not trivial and may limit the possibility of different researchers sharing their research through them. Based on this, it is possible to see benefits in the development of a straightforward tool allowing researchers to create serious games related to their specific research. Purpose: This study aims to evaluate a simple tool for creating simple games based on scientific research from the players’ perspective and provide recommendations, as well as gauge the interest of the players in the research shared through the games. The goal is to provide information on the players’ needs and preferences for games with the purpose of scientific dissemination. We hope with this research to promote interest in scientific content and provide ecommendations for other researchers interested in using serious games as a means of scientific dissemination. Methods: The evaluation was realized through 20 structured interviews with students of a game development undergraduate course. Each participant played three different minigames customized to share three different researches: Article, Data, and Categories. These interviews were recorded and transcribed to be analyzed through the process of thematic analysis. Results: After the realization of thematic analysis, the selected quotes were separated into 83 different codes. These codes were then grouped into one of eight defined themes: Game type, Positive perception, Negative perception, Scientific dissemination, Suggestions for the games, Design accomplishments, Design flaws, and Theme impact. Participants reacted positively to the games format of emulating simple daily games and the purpose of sharing research results, while providing valuable insight into how to improve the games. Results also showed that the games did promote interest and curiosity from the participants about the shared studies. Conclusion: The interviews showed that there is interest from the players in this type of serious game. They also provided evidence that these types of games can promote curiosity in scientific content and even the active pursuit of knowledge. This research shows that this approach to scientific dissemination can stimulate interest while broadening the reach to different audiences by lowering the technical barriers that researchers would have to go through to make a game about their scientific results.
Although menstruation is a recurring physiological phenomenon, even in the 21st century, it is still taboo, fueling stigmas and inequalities. The cooperative board game Ciclo do Poder aims to facilitate the dialog about menstruation and promote attitudes that support menstrual dignity. This article presents findings from observations of game sessions conducted in non-formal educational spaces to assess the contributions of the game to dissemination information about the menstrual cycle. In addition, it examines player performance, the importance of mixed groups, cards that raised doubts, and the duration of the game. The methodology consisted of mediated game sessions and observation sheets completed by the research team. As a result, the observations helped to verify the values incorporated into the game, the importance of mediation, the benefits of mixed groups, the necessary design and wording adjustments to the cards, and the game's balance.
Digital transformation in public administration can improve efficiency, transparency, and accessibility. This study evaluates the selection of software systems for judicial process management in a public institution by comparing the Electronic Process System (SPE) and the Electronic Information System (SEI). The study combines feature analysis and process simulation to compare performance, functionality, and fit to the institutional context. The results indicate that SEI, a government software platform shared free of charge with public institutions, better supports workflow automation, data security, document management, and system integration. Compared with SPE, SEI showed greater potential to improve legal case management and support institutional modernization. The paper contributes a comparative framework for evaluating software systems in public-sector settings and supports more evidence-based technology selection in public administration.
The adaptation of board games to digital platforms, while preserving elements familiar to players, requires developing strategies that maintain the consistency of these characteristics across both versions. However, the scarcity of technical references makes this process complex, especially for game developers without specific knowledge on the subject. This article presents the creation, validation, and refinement of a guide to support this process, structured around the common core elements of games — defined as mechanics, rules, narrative, aesthetics, and technology. The methodology comprised a bibliographic review to establish an operational definition of the concept of core, the development of a guide structured around the core, its application in the adaptation of a real game as an example model, and, finally, its validation through semi-structured interviews with experienced professionals. The specialists’ observations resulted in 24 positive points and 13 points for improvement, organized into 15 questions distributed across four criteria: applicability and practical usefulness; clarity and structure; currency and relevance; and guidance for use. Approximately 34.2% of the observations were favorable to the guide, indicating that it constitutes a promising tool to assist in the identification and maintenance of the core during the adaptation of analog games, while the remaining 34.2% reinforce the final stage of the methodology through adjustments and additions aimed at making the guide more robust and concise.
Museums are increasingly adopting interactive, adaptive, and data-driven technologies, yet their conceptual integration into exhibition design often remains underexamined. Visitor behavior follows recurrent patterns shaped by personal, physical, and social factors, calling for approaches that balance technological potential with curatorial control. This paper presents a scenario-based approach to interactive museum systems, distinguishing interactivity, adaptivity, and personalization with personalization as the core design principle. The approach uses behavioral typologies to define scenario branching, eliminating the need for real‑time individual user profiling. A mixed conceptual and design-oriented methodology synthesizes museum studies, interaction design, and learning models to derive visitor typologies. These typologies approximate diverse behaviors into finite pattern types. Interactivity is conceptualized as trigger points embedded in the exhibition narrative, activating predefined scenario branches. The framework is implemented in Écho d’Azur, an interactive installation combining audiovisual media with machine-learning-based emotion recognition. Results show that scenario-based personalization enables controlled, multidimensional narrative structures, shifting from linear storytelling. Attention management is supported through spatial layout, audiovisual components, and interaction points based on behavioral patterns. Real-time adaptation is contrasted with design-time personalization, reducing technical complexity and ethical concerns while maintaining curatorial coherence. Thus, scenario-based personalization offers a viable framework for interactive museum design, supporting diverse visitor behaviors without continuous real-time adaptation.
This paper presents an enhanced approach for detecting and analyzing anomalies in public expenditure time series. The method combines statistical techniques and machine learning models to identify unusual spending behaviors and to rank expenditure items according to both the frequency of anomalies and the monetary impact involved. The statistically generated anomalies produce suspicion alerts of potential fraud, with the objective of prioritizing cases and directing human audit efforts more effectively. In this extended version, we introduce a finer-grained analysis that examines spending patterns at a more detailed level within the governmental budget structure, allowing the detection of irregularities that may not be visible under more aggregated views. The approach is validated on a real-world dataset comprising more than one million city expenditure records from the state of Minas Gerais, and the results demonstrate its ability to reveal irregularities that may remain hidden under higher levels of aggregation.
