Reading motivation often declines as children transition to more complex literary texts, while technology-based interventions frequently conflate surface engagement with intrinsic motivation. We present StoryExplorers, a collaborative literacy activity that combines the turn-based, partially blind Cadavre Exquis (CE) technique with a Wizard-of-Oz GenAI peer and scaffolding cards to support narrative reimagining of canonical literature. In a study with 12 children aged 9-11 using The Odyssey, we examined how this combined design shaped creative divergence from the source text and children’s reported engagement. Findings suggest that the CE mechanic was the primary enabler of participation across reading profiles, while the AI peer plausibly contributed through source-anchored story initiation, introduction of unexpected narrative variation, and synthesised closure. Children produced collaborative adaptations, moral inversions, and narrative pastiche, and qualitative data also suggested a "comprehension inversion," where understanding the source became instrumentally useful for subverting it. The Game Experience Questionnaire (GEQ) results indicated high Positive Affect and Perceived Competence with low Tension, consistent with the activity’s low-judgment framing. The study contributes an early design account of how structural scaffolding and a bounded GenAI peer can be co-designed to position comprehension as a tool for creative subversion rather than its goal.

Understanding how users interact with systems and experience usability issues is vital in Human-Computer Interaction (HCI). Existing approaches often focus on isolated data types, such as interaction logs or subjective reports, providing only a partial view of the user experience. This work introduces AMUSED, a multimodal dataset that integrates user interaction logs, physiological signals, facial emotion features, and self-assessment reports. The dataset includes expert annotations of eleven types of usability smells, creating a rich resource for investigating relationships between usability problems, user behavior, and emotional states. We conducted experiments with 70 participants interacting with three social networks containing usability issues. We then evaluated various machine learning models to assess the feasibility of automatically detecting these issues. The dataset comprises 24 h of user recordings, with over 20,000 user events, such as clicks, scrolls, and input changes. Our analysis reveals that (i) usability smells frequently co-occur, (ii) negative emotions predominate when severe usability issues arise, and (iii) Gradient Boosting models achieve up to 92% accuracy in detecting usability smells, demonstrating the potential for computational methods in automated usability evaluation. Our findings emphasize the value of emotional metrics in HCI research and highlight promising uses of machine learning to automatically detect usability issues.

Usability evaluation is crucial for assessing systems and applications effectiveness and user-friendliness. Traditional methods, such as Cognitive Walkthrough and Usability Smells, often fail to capture emotional dimensions of user experiences. The emergence of facial emotion analysis offers a novel approach to addressing this limitation by providing a more comprehensive understanding of user interactions. This paper introduces “The Emotionality Tool”, an innovative solution that integrates facial emotion analysis into traditional usability evaluation methodologies. The tool features a user-friendly interface for conducting tests, capturing, and analyzing facial emotions, enabling expert evaluators to gain deeper insights into the emotional aspects of user interactions. Case studies demonstrate the tool's effectiveness in uncovering critical usability issues and its potential applications in refining interface designs. “The Emotionality Tool” represents a valuable resource for incorporating emotional metrics into usability evaluation, enriching methodologies and advancing the field of user experience research.

Understanding which factors affect information visualization transparency continues to be one of the most relevant challenges in current research, especially since trust models how users build on the knowledge and use it. This work extends the current body of research by studying the user’s subjective evaluation of the visualization transparency of hierarchical charts through the clarity, coverage, and look and feel dimensions. Additionally, we extend the user profile to better understand whether personality facets manifest a biasing effect on the trust-building process. Our results show that the data encodings do not affect how users perceive visualization transparency while controlling for personality factors. Regarding personality, the propensity to trust affects how they judge the clarity of a hierarchical chart. Our findings provide new insights into the research challenges of measuring trust and understanding the transparency of information visualization. Specifically, we explore how personality factors manifest in this trust-building relationship and user interaction within visualization systems.

Recent research focuses on understanding what triggers cognitive biases and how to alleviate them in the context of visualization use. Given its role in decision-making in other research fields, the Phantom Effect may hold exciting prospects among known biases. The Phantom Effect belongs to the category of decoy effects, where the decoy is an optimal yet unavailable alternative. We conducted a hybrid design experiment (N=76) where participants performed decision tasks based on information represented in different visualization idioms and phantom alternative’s unavailability presentation delays. We measured participants’ perceptual speed and visual working memory to study their impact on the expression of the Phantom Effect. Results show that visualization usually triggers the Phantom Effect, but two-sided bar charts mitigate this bias more effectively. We also found that waiting until the participant decides before presenting the decoy as unavailable helps alleviate the Phantom Effect. Although we did not find measurable effects, results also suggest that visual working memory and visualization literacy play a role in bias susceptibility. Our findings extend prior research in visualization-based decoy effects. They are the first steps to understanding the role of individual differences in the susceptibility to cognitive bias in visualization contexts. • The Phantom Effect manifests in decision-making supported by visualization. • Two-sided bar charts with an unknown presentation delay mitigate the Phantom Effect. • Better visualization literacy may increase priming by the Phantom Effect.

Visual analytics combines automated analysis techniques with interactive visualizations for effective understanding, reasoning, and decision-making on complex data. However, accurately classifying sentiments and stances in sentiment analysis remains challenging due to ambiguity and individual differences. This survey examines 35 papers published between 2016 and 2022, identifying unaddressed sources of friction that contribute to a gap between individual sentiment, processed data, and visual representation. We explore the impact of visualizations on data perception, analyze existing techniques, and investigate the many facets of uncertainty in sentiment and stance visualizations. We also discuss the evaluation methods used and present opportunities for future research. Our work addresses a gap in previous surveys by focusing on uncertainty and the visualization of sentiment and stance, providing valuable insights for researchers in graphical models, computational methods, and information visualization.

The impact of individual differences, such as personality traits, on user performance and human behaviors has been studied in several fields of human–computer interaction, with a growing interest in information visualization. Although most visualizations are still designed with a universal approach, great strides have been made toward the development of user-adaptive visualizations catered to the user’s personality. We analyze how personality affects user interaction with high-dimensional visualizations. Specifically, we explore the impact of neuroticism (including its facets) and locus of control in user performance and confidence. Results suggest that neurotic individuals are faster with the scatterplot matrix, while individuals with an internal locus are more confident when using the parallel coordinates plot. In addition, results indicate that the facets of neuroticism have significant – and distinct from neuroticism – impact on user performance and confidence, pointing toward the need to analyze traits, such as neuroticism, at a lower level.

Visualization is a powerful way to convey data, showing potential for joining and interrelating data items. However, when dealing with large amounts of data, visually merging different classes of information poses several challenges. Color, however, due to its effectiveness for labeling and categorizing information, may be a solution to this shortcoming. Merging items with different colors may suggest mixing their original colors. This approach generates an immediately perceivable way to represent merged items. It also keeps context through the association of the mixed color to its original colors. We studied to which extent color blending provides users with the means to understand the provenience of data items by conducting two user studies using CIE-LCh, CMYK and HSV blending to ascertain (i) to which extent people are able to, given a particular color, understand its provenience, and (ii) the color model in which to perform color blending so that users find it intuitive. Results showed colors which are more suitable for blending so that users understand their provenience and indicated that the CIE-LCh model is more effective for representing color blending.

Nowadays, electronic devices are part of our daily routines, resulting in information generation at virtually any time and context. Due to different styles of interaction, data produced by human activities is not only in considerable quantities, but it is also extremely rich, which makes it difficult to manage and analyze. Visualization has the potential to overcome this limitation: not only is it an excellent means to display large quantities of information, but it also alleviates cognitive load associated with data interpretation. We created an interactive multi-level layered visualization, in which time may be represented sequentially through layers. Data entities are displayed as circles with size proportional to a particular data feature we need to highlight, allowing immediate comparison between entities. By selecting an entity, we may see, through visual connectors, all the interrelated entities over the different time layers. User tests have shown that our visualization makes important information immediately perceivable, in a way that is easy to navigate and analyze.

Providing the educational community with tools to analyze educational processes may result in a more effective education. Applying Data Mining techniques to educational data results in information on educational settings which, however, comprehend an extensive set of symbolic patterns that are usually difficult to understand. Visualization, due to its potential to display large quantities of data, may overcome this limitation. We used the results of educational data mining techniques that had been applied to analyze the interdependence among courses in a university program and studied visualization mechanisms to enable the analysis of such patterns. We created a multi-level visualization, in which each level depicts a semester with corresponding courses. We have studied visual connectors to display a high number of interrelations between courses. User tests have shown the effectiveness of a connector which combines visual merging techniques with Bezier curves to represent course interrelation.