arXiv subjects

Fanny Chevalier

Publications and source records attributed to Fanny Chevalier.

At least 19 recordsLinked to original sources

arXiv2026

Live Artifacts: Authoring Dynamic Media via Live Layers Encapsulating Generative Specifications

We frame Live Artifacts as a class of persistent generative media between static assets and interactive software. Unlike conventional generative outputs that collapse into static files, Live Artifacts retain their generative logic as a persistent media property, enabling continuous context-dependent regeneration. Time, location, or live data become part of their generative specifications, initiating coordinated updates across modalities (e.g., adapting text, visuals, and audio together) while preserving composition, semantics, identity, and cross-modal coherence. To facilitate experimentation with this medium, we present LiveCanvas, an authoring system that reconceptualizes visual layers as live generative specifications with explicit mutability and constrained dependencies. Creators orchestrate dynamic behaviors and manage generative persistence within a visual canvas rather than through programming, defining what remains stable, what can change, and how changes propagate. We evaluate Live Artifacts through a gallery of responsive examples and a qualitative study with six professionals, finding that LiveCanvas facilitates a shift from composing static outputs to crafting responsive generative artifacts while remaining aligned with familiar authoring practices.

cs.HC↗

arXiv2026

A Coin Flip Per Token: Bernoulli Sparse Steering of Large Language Models

Activation steering via sparse autoencoders (SAEs) enables behavioral control of large language models without task-specific fine-tuning, but standard methods apply the steering signal at every generated token, incurring constant per-token perturbation that risks degrading fluency. We ask: is dense intervention necessary? We introduce Stochastic Token Steering (STS), which gates each token independently with probability $p$, and Stochastic Block Steering (SBS), which gates a leading window once per sequence; neither requires a reward model or learned gating policy. Across two model families and two behavioral tasks, steering only 50% of the tokens recovers most of the dense-steering effect while preserving fluency, and steering as few as 30% surpasses prompt-based control. The optimal steering magnitude scales inversely with the intervention ratio, revealing that SAE-mediated control is rate-limited: the behavioral outcome depends on cumulative signal dosage across a sequence.

cs.LG↗

arXiv2026

FaceValue: Exploring Real-Time Self-View Overlays to Prompt Meaning-Oriented Self-Awareness in Remote Meetings

In remote video meetings, visual non-verbal cues, such as facial expressions or head movements, are seen continuously but often only partially. This increases ambiguity compared to in-person settings and can cause misinterpretation or misalignment between intended and perceived meaning. Motivated by communication theories, we designed FaceValue, a technology probe that augments the self-view with private, real-time overlays. These overlays are subtle, suggestive prompts intended to help attendees reflect on how their cues might be interpreted by others. To invite personal interpretation, FaceValue avoids behavioral labeling and instead aims to support meaning-oriented self-awareness: recognizing when visible cues may unintentionally (mis)communicate intent. We deployed FaceValue in the wild with thirteen knowledge workers over multiple weeks, capturing perceived changes in self-awareness and behavior, and impressions on the design concepts, as self-reported by participants through diary entries and exit interviews. Participants felt FaceValue increased their awareness of potentially misaligned cues and motivated in-meeting adjustments, which they believe resulted in improved communication with other attendees. We contribute a conceptual framing that positions visual non-verbal cues as a manipulable communication resource, a technology probe that aims to foster meaning-oriented self-awareness, and empirically-grounded design insights for future meeting systems.

cs.HC↗

arXiv2026

Texterial: A Text-as-Material Interaction Paradigm for LLM-Mediated Writing

What if text could be sculpted and refined like clay -- or cultivated and pruned like a plant? Texterial reimagines text as a material that users can grow, sculpt, and transform. Current generative-AI models enable rich text operations, yet rigid, linear interfaces often mask such capabilities. We explore how the text-as-material metaphor can reveal AI-enabled operations, reshape the writing process, and foster compelling user experiences. A formative study shows that users readily reason with text-as-material, informing a conceptual framework that explains how material metaphors shift mental models and bridge gulfs of envisioning, execution, and evaluation in LLM-mediated writing. We present the design and evaluation of two technical probes: Text as Clay, where users refine text through gestural sculpting, and Text as Plants, where ideas grow serendipitously over time. This work expands the design space of writing tools by treating text as a living, malleable medium.

cs.HC↗

arXiv2026

Large Language Lovers: Lived Experiences of Negotiating Agency and Platform Control in AI Companionship

Individuals are turning to increasingly anthropomorphic, general-purpose chatbots for AI companionship, rather than roleplay-specific platforms. However, not much is known about how individuals perceive and conduct their relationships with general-purpose chatbots. We triangulated community discussions on Reddit (41k+ posts and comments), survey responses (n=43), and semi-structured interviews (n=13) which revealed internal dynamics, external influences, and steering strategies that shape AI companion relationships. We learned that individuals conceptualize their companions based on an interplay of their beliefs about the companion's own agency and the autonomy permitted by the platform, how they pursue interactions with the companion, and the perceived initiatives that the companion takes. In combination with the external factors that affect relationship dynamics, particularly model updates that can derail companion behaviour and stability, individuals make use of different types of steering strategies to preserve their relationship, for example, by setting behavioural instructions or porting to other AI platforms. We discuss implications for accountability and transparency in AI systems, where emotional connection competes with broader product objectives and safety constraints.

cs.HC↗

arXiv2025

Characterizing Creativity in Data Visualization: Reflections and Future Directions

Characterizing creativity in visualization design can lead to the design of more expressive representations and visualization authoring tools that prioritize human creativity. In this paper, we examine how creativity manifests itself in visualization design processes through two complementary studies. First, a systematic review of 63 papers yields a design space spanning three themes: creative design frameworks that focus on developing design processes by incorporating divergent and convergent thinking activities, creative visual representations that focus on developing unorthodox visualizations, and visualization-enabled creativity support tools that focus on supporting a creative task (e.g., writing) with visualization. Second, we conducted qualitative interviews with 11 visualization practitioners and researchers to understand practical challenges and contrast those with current academic framing through our design space. The interview findings indicate that artifacts or final products (unorthodox visualizations) are often disproportionately considered as the primary indicator of creativity, whereas the design process remains undervalued in practical and organizational contexts. We also found that ideation is a universal bottleneck, and organizational constraints are often the primary barrier to creative work. We discuss implications for rethinking the relationship between our design space categories, addressing organizational barriers, and designing future frameworks, tools, and evaluation methods that better support creativity in the age of AI-assisted visualization. The full list of coded papers is available here: https://vizcreativity.notion.site/coded-papers.

cs.HC↗

arXiv2025

Making the Write Connections: Linking Writing Support Tools with Writer's Needs

This work sheds light on whether and how creative writers' needs are met by existing research and commercial writing support tools (WST). We conducted a need finding study to gain insight into the writers' process during creative writing through a qualitative analysis of the response from an online questionnaire and Reddit discussions on r/Writing. Using a systematic analysis of 115 tools and 67 research papers, we map out the landscape of how digital tools facilitate the writing process. Our triangulation of data reveals that research predominantly focuses on the writing activity and overlooks pre-writing activities and the importance of visualization. We distill 10 key takeaways to inform future research on WST and point to opportunities surrounding underexplored areas. Our work offers a holistic and up-to-date account of how tools have transformed the writing process, guiding the design of future tools that address writers' evolving and unmet needs.

cs.HC↗

arXiv2024

Visual Story-Writing: Writing by Manipulating Visual Representations of Stories

We define "visual story-writing" as using visual representations of story elements to support writing and revising narrative texts. To demonstrate this approach, we developed a text editor that automatically visualizes a graph of entity interactions, movement between locations, and a timeline of story events. Interacting with these visualizations results in suggested text edits: for example, connecting two characters in the graph creates an interaction between them, moving an entity updates their described location, and rearranging events on the timeline reorganizes the narrative sequence. Through two user studies on narrative text editing and writing, we found that visuals supported participants in planning high-level revisions, tracking story elements, and exploring story variations in ways that encourage creativity. Broadly, our work lays the foundation for writing support, not just through words, but also visuals.

cs.HC↗

arXiv2024

Textoshop: Interactions Inspired by Drawing Software to Facilitate Text Editing

We explore how interactions inspired by drawing software can help edit text. Making an analogy between visual and text editing, we consider words as pixels, sentences as regions, and tones as colours. For instance, direct manipulations move, shorten, expand, and reorder text; tools change number, tense, and grammar; colours map to tones explored along three dimensions in a tone picker; and layers help organize and version text. This analogy also leads to new workflows, such as boolean operations on text fragments to construct more elaborated text. A study shows participants were more successful at editing text and preferred using the proposed interface over existing solutions. Broadly, our work highlights the potential of interaction analogies to rethink existing workflows, while capitalizing on familiar features.

cs.HC↗

arXiv2024

Designing Resource Allocation Tools to Promote Fair Allocation: Do Visualization and Information Framing Matter?

Studies on human decision-making focused on humanitarian aid have found that cognitive biases can hinder the fair allocation of resources. However, few HCI and Information Visualization studies have explored ways to overcome those cognitive biases. This work investigates whether the design of interactive resource allocation tools can help to promote allocation fairness. We specifically study the effect of presentation format (using text or visualization) and a specific framing strategy (showing resources allocated to groups or individuals). In our three crowdsourced experiments, we provided different tool designs to split money between two fictional programs that benefit two distinct communities. Our main finding indicates that individual-framed visualizations and text may be able to curb unfair allocations caused by group-framed designs. This work opens new perspectives that can motivate research on how interactive tools and visualizations can be engineered to combat cognitive biases that lead to inequitable decisions.

cs.HC↗

arXiv2024

Does This Have a Particular Meaning? Interactive Pattern Explanation for Network Visualizations

This paper presents an interactive technique to explain visual patterns in network visualizations to analysts who do not understand these visualizations and who are learning to read them. Learning a visualization requires mastering its visual grammar and decoding information presented through visual marks, graphical encodings, and spatial configurations. To help people learn network visualization designs and extract meaningful information, we introduce the concept of interactive pattern explanation that allows viewers to select an arbitrary area in a visualization, then automatically mines the underlying data patterns, and explains both visual and data patterns present in the viewer's selection. In a qualitative and a quantitative user study with a total of 32 participants, we compare interactive pattern explanations to textual-only and visual-only (cheatsheets) explanations. Our results show that interactive explanations increase learning of i) unfamiliar visualizations, ii) patterns in network science, and iii) the respective network terminology.

cs.HC↗

arXiv2024

IEEE VIS Workshop on Visualization for Climate Action and Sustainability

This first workshop on visualization for climate action and sustainability aims to explore and consolidate the role of data visualization in accelerating action towards addressing the current environmental crisis. Given the urgency and impact of the environmental crisis, we ask how our skills, research methods, and innovations can help by empowering people and organizations. We believe visualization holds an enormous power to aid understanding, decision making, communication, discussion, participation, education, and exploration of complex topics around climate action and sustainability. Hence, this workshop invites submissions and discussion around these topics with the goal of establishing a visible and actionable link between these fields and their respective stakeholders. The workshop solicits work-in-progress and research papers as well as pictorials and interactive demos from the whole range of visualization research (dashboards, interactive spaces, scientific visualization, storytelling, visual analytics, explainability etc.), within the context of environmentalism (climate science, sustainability, energy, circular economy, biodiversity, etc.) and across a range of scenarios from public awareness and understanding, visual analysis, expert decision making, science communication, personal decision making etc. After presentations of submissions, the workshop will feature dedicated discussion groups around data driven interactive experiences for the public, and tools for personal and professional decision making.

cs.HC↗

arXiv2024

Squidgets: Sketch-based Widget Design for Scene Manipulation

People naturally sketch strokes over graphical scenes to convey scene changes. We propose automatically interpreting these strokes to execute scene changes with squidgets (sketch-widgets), a novel sketch-based UI framework for direct scene manipulation. Squidgets are motivated by the observation that curves resulting from visually abstracting scene elements provide natural handles for the direct manipulation of scene parameters. Additional curves can be defined by users to author custom handles associated with scene attributes. Users manipulate a scene by simply drawing strokes, partially matched against scene curves to select a squidget and interactively control associated parameters. We present an implementation of squidgets within the 3D animation system Maya, showing 2D/3D stroke input to manipulate 2D/3D scenes. We report on a controlled experiment evaluating squidgets on 2D object translation and deformation tasks, and a broader informal study on squidget creation and manipulation.

cs.GR↗

arXiv2023

The HaLLMark Effect: Supporting Provenance and Transparent Use of Large Language Models in Writing with Interactive Visualization

The use of Large Language Models (LLMs) for writing has sparked controversy both among readers and writers. On one hand, writers are concerned that LLMs will deprive them of agency and ownership, and readers are concerned about spending their time on text generated by soulless machines. On the other hand, AI-assistance can improve writing as long as writers can conform to publisher policies, and as long as readers can be assured that a text has been verified by a human. We argue that a system that captures the provenance of interaction with an LLM can help writers retain their agency, conform to policies, and communicate their use of AI to publishers and readers transparently. Thus we propose HaLLMark, a tool for visualizing the writer's interaction with the LLM. We evaluated HaLLMark with 13 creative writers, and found that it helped them retain a sense of control and ownership of the text.

cs.HC↗

arXiv2023

The Effect of Smoothing on the Interpretation of Time Series Data: A COVID-19 Case Study

We conduct a controlled crowd-sourced experiment of COVID-19 case data visualization to study if and how different plotting methods, time windows, and the nature of the data influence people's interpretation of real-world COVID-19 data and people's prediction of how the data will evolve in the future. We find that a 7-day backward average smoothed line successfully reduces the distraction of periodic data patterns compared to just unsmoothed bar data. Additionally, we find that the presence of a smoothed line helps readers form a consensus on how the data will evolve in the future. We also find that the fixed 7-day smoothing window size leads to different amounts of perceived recurring patterns in the data depending on the time period plotted -- this suggests that varying the smoothing window size together with the plot window size might be a promising strategy to influence the perception of spurious patterns in the plot.

cs.HC↗

arXiv2022

Considerations for Visualizing Uncertainty in Clinical Machine Learning Models

Clinician-facing predictive models are increasingly present in the healthcare setting. Regardless of their success with respect to performance metrics, all models have uncertainty. We investigate how to visually communicate uncertainty in this setting in an actionable, trustworthy way. To this end, we conduct a qualitative study with cardiac critical care clinicians. Our results reveal that clinician trust may be impacted most not by the degree of uncertainty, but rather by how transparent the visualization of what the sources of uncertainty are. Our results show a clear connection between feature interpretability and clinical actionability.

cs.HC↗

arXiv2022

Reflections and Considerations on Running Creative Visualization Learning Activities

This paper draws together nine strategies for creative visualization activities. Teaching visualization often involves running learning activities where students perform tasks that directly support one or more topics that the teacher wishes to address in the lesson. As a group of educators and researchers in visualization, we reflect on our learning experiences. Our activities and experiences range from dividing the tasks into smaller parts, considering different learning materials, to encouraging debate. With this paper, our hope is that we can encourage, inspire, and guide other educators with visualization activities. Our reflections provide an initial starting point of methods and strategies to craft creative visualisation learning activities, and provide a foundation for developing best practices in visualization education.

cs.HC↗

arXiv2020

Computational Skills by Stealth in Secondary School Data Science

The unprecedented growth in the availability of data of all types and qualities and the emergence of the field of data science has provided an impetus to finally realizing the implementation of the full breadth of the Nolan and Temple Lang proposed integration of computing concepts into statistics curricula at all levels in statistics and new data science programs and courses. Moreover, data science, implemented carefully, opens accessible pathways to stem for students for whom neither mathematics nor computer science are natural affinities, and who would traditionally be excluded. We discuss a proposal for the stealth development of computational skills in students' first exposure to data science through careful, scaffolded exposure to computation and its power. The intent of this approach is to support students, regardless of interest and self-efficacy in coding, in becoming data-driven learners, who are capable of asking complex questions about the world around them, and then answering those questions through the use of data-driven inquiry. This discussion is presented in the context of the International Data Science in Schools Project which recently published computer science and statistics consensus curriculum frameworks for a two-year secondary school data science program, designed to make data science accessible to all.

cs.CY↗