Autistic adults often use different communication styles than neurotypical individuals (NTs). While prior research has documented how such gaps disadvantage autistic job seekers, no study has systematically examined when these differences arise in language use and why autistic adults encounter interpretive gaps. This work seeks to datafy and characterize these communication challenges. We built an annotation interface and recruited 20 autistic adults to analyze 10 job postings each that they had selected as cases where they felt “lost in translation.” Participants annotated text spans using six categories informed by speech and language literature: unclear, ambiguous, incomplete, inappropriate, negative, and other. Follow-up interviews showed that lexical difficulties were rarely barriers; rather, challenges stemmed from interpreting implicit social arrangements or unstated expectations. We release the anonymized annotation data as the first-of-its-kind dataset documenting autistic–NT communication style differences. We conclude with implications for designing supports that foster clearer autistic–NT communication.

Videos from fleets of ground robots can advance public safety by providing scalable situational awareness and reducing professionals’ burden. Yet little is known about how to design and integrate multi-robot videos into public safety workflows. Collaborating with six police agencies, we examined how such videos could be made practical. In Study 1, we present the first testbed for multi-robot ground video sensemaking. The testbed includes 38 events of interest relevant to public safety, a dataset of 20 robot patrol videos (10 day/night pairs) covering EoI types, and 6 design requirements aimed at improving current video sensemaking practices. In Study 2, we built MRVS, a tool that augments multi-robot patrol video streams with a prompt-engineered video understanding model. Participants reported reduced manual workload and greater confidence with LLM-based explanations, while noting concerns about false alarms and privacy. We conclude with implications for designing future multi-robot video sensemaking tools.

Computer Vision (CV) models can become outdated after deployment as real-world data evolves, requiring intensive attention from AI engineers to address degraded performance through tasks like data relabeling to update models with new human perceptions. Interactive human-in-the-loop systems have considerable potential to enhance model-steering practices. However, such workflows reveal two challenges: (1) scalability, where labor demands increase with data size, and (2) actionability, where human insights do not readily transform into model revisions. Based on our formative study (S1) on the current challenges faced by CV professionals, we developed De-Decay, an end-to-end Human-Data Alignment system offering scalable label-less assessment and actionable insight transformation . This enables engineers to investigate degradation and auto-retrain models with AI support, such as image clustering and regeneration. Our summative study (S2) showed that De-Decay helped engineers effectively identify and address CV degradation. We discuss how future research can enhance scalability and actionability in AI evaluation systems for aligning AI behaviors with human mental models.

Successful job search results from job seekers' well-shaped social communication. While well-known diferences in communication exist between people with autism and neurotypicals, little is known about how people with autism collaborate with their social surroundings to strive in the job market. To better understand the practices and challenges of collaborative job seeking for people with autism, we interviewed 20 participants including applicants with autism, their social surroundings, and career experts. Through the interviews, we identified social challenges that people with autism face during their job seeking; the social support they leverage to be successful; and the technological limitations that hinder their collaboration. We designed four probes that represent major collaborative features found from the interviews-executive planning, communication, stage-wise preparation, and neurodivergent community formation-and discussed their potential usefulness and impact through three focus groups. We provide implications regarding how our findings can enhance collaborative job seeking experiences for people with autism through new designs.

Data annotation interfaces predominantly leverage ground truth labels to guide annotators toward accurate responses. With the growing adoption of Artificial Intelligence (AI) in domain-specific professional tasks, it has become increasingly important to help beginning annotators identify how their early-stage knowledge can lead to inaccurate answers, which in turn, helps to ensure quality annotations at scale. To investigate this issue, we conducted a formative study involving eight individuals from the field of disaster management, each possessing varying levels of expertise. The goal was to understand the prevalent factors contributing to disagreements among annotators when classifying Twitter messages related to disasters and to analyze their respective responses. Our analysis identified two primary causes of disagreement between expert and beginner annotators: 1) a lack of contextual knowledge or uncertainty about the situation, and 2) the absence of visual or supplementary cues. Based on these findings, we designed a Context interface, which generates aids that help beginners identify potential mistakes and provide the hidden context of the presented tweet. The summative study compares Context design with two widely used designs in data annotation UI, Highlight and Reasoning-based interfaces. We found significant differences between these designs in terms of attitudinal and behavioral data. We conclude with implications for designing future interfaces aiming at closing the knowledge gap among annotators.

Shadowing allows artists to convey realistic volume and emotion of characters in comic colorization. While AI technologies have the potential to improve professionals’ shadowing experience, current practice is manual and time-consuming. To understand how we can improve their shadowing experience, we conducted interviews with 5 professionals. We found that professionals’ level of engagement can vary depending on semantics, such as characters’ faces or hair. We also found they spent time on shadow “landscaping”—deciding where to put big shadow regions to make a realistic volumetric presentation—while the final results can dramatically vary depending on their “staging” and “attention guiding” needs. We found they would accept AI suggestions for less engaging semantic parts or landscaping, while they would need to have the capability to adjust details. Based on our observations, we built ShadowMagic that (1) generates AI-driven shadows based on typically used light directions, (2) enables a user to selectively choose the results depending on the semantics, and (3) allows users to finish shadow areas by themselves for further perfection. Through a summative evaluation with 5 professionals, we found that they were significantly more satisfied with our AI-driven results than a baseline. We also found ShadowMagic’s “step by step” workflow helps participants more easily adopt AI-driven results. We conclude by providing implications.

The widespread consumer-grade 3D printers and learning resources online enable novices to self-train in remote settings. While troubleshooting plays an essential part of 3D printing, the process remains challenging for many remote novices even with the help of well-developed online sources, such as online troubleshooting archives and online community help. We conducted a formative study with 76 active 3D printing users to learn how remote novices leverage online resources in troubleshooting and their challenges. We found that remote novices cannot fully utilize online resources. For example, the online archives statically provide general information, making it hard to search and relate their unique cases with existing descriptions. Online communities can potentially ease their struggles by providing more targeted suggestions, but a helper who can provide custom help is rather scarce, making it hard to obtain timely assistance. We propose 3DPFIX, an interactive 3D troubleshooting system powered by the pipeline to facilitate Human-AI Collaboration, designed to improve novices' 3D printing experiences and thus help them easily accumulate their domain knowledge. We built 3DPFIX that supports automated diagnosis and solution-seeking. 3DPFIX was built upon shared dialogues about failure cases from Q&A discourses accumulated in online communities. We leverage social annotations (i.e., comments) to build an annotated failure image dataset for AI classifiers and extract a solution pool. Our summative study revealed that using 3DPFIX helped participants spend significantly less effort in diagnosing failures and finding a more accurate solution than relying on their common practice. We also found that 3DPFIX users learn about 3D printing domain-specific knowledge. We discuss the implications of leveraging community-driven data in developing future Human-AI Collaboration designs.

The local explanation provides heatmaps on images to explain how Convolutional Neural Networks (CNNs) derive their output. Due to its visual straightforwardness, the method has been one of the most popular explainable AI (XAI) methods for diagnosing CNNs. Through our formative study (S1), however, we captured ML engineers' ambivalent perspective about the local explanation as a valuable and indispensable envision in building CNNs versus the process that exhausts them due to the heuristic nature of detecting vulnerability. Moreover, steering the CNNs based on the vulnerability learned from the diagnosis seemed highly challenging. To mitigate the gap, we designed DeepFuse, the first interactive design that realizes the direct feedback loop between a user and CNNs in diagnosing and revising CNN's vulnerability using local explanations. DeepFuse helps CNN engineers to systemically search "unreasonable" local explanations and annotate the new boundaries for those identified as unreasonable in a labor-efficient manner. Next, it steers the model based on the given annotation such that the model doesn't introduce similar mistakes. We conducted a two-day study (S2) with 12 experienced CNN engineers. Using DeepFuse, participants made a more accurate and "reasonable" model than the current state-of-the-art. Also, participants found the way DeepFuse guides case-based reasoning can practically improve their current practice. We provide implications for design that explain how future HCI-driven design can move our practice forward to make XAI-driven insights more actionable.

Creating digital comics involves multiple stages, some creative and some menial. For example, coloring a comic requires a labor-intensive stage known as ‘flatting,’ or masking segments of continuous color, as well as creative shading, lighting, and stylization stages. The use of AI can automate the colorization process, but early efforts have revealed limitations—technical and UX—to full automation. Via a formative study of professionals, we identify flatting as a bottleneck and key target of opportunity for human-guided AI-driven automation. Based on this insight, we built FlatMagic, an interactive, AI-driven flat colorization support tool for Photoshop. Our user studies found that using FlatMagic significantly reduced professionals’ real and perceived effort versus their current practice. While participants effectively used FlatMagic, we also identified potential constraints in interactions with AI and partially automated workflows. We reflect on implications for comic-focused tools and the benefits and pitfalls of intermediate representations and partial automation in designing human-AI collaboration tools for professionals.

Despite the fast progress of explanation techniques in modern Deep Neural Networks (DNNs) where the main focus is handling "how to generate the explanations", advanced research questions that examine the quality of the explanation itself (e.g., "whether the explanations are accurate") and improve the explanation quality (e.g., "how to adjust the model to generate more accurate explanations when explanations are inaccurate") are still relatively under-explored. To guide the model toward better explanations, techniques in explanation supervision - which add supervision signals on the model explanation - have started to show promising effects on improving both the generalizability as and intrinsic interpretability of Deep Neural Networks. However, the research on supervising explanations, especially in vision-based applications represented through saliency maps, is in its early stage due to several inherent challenges: 1) inaccuracy of the human explanation annotation boundary, 2) incompleteness of the human explanation annotation region, and 3) inconsistency of the data distribution between human annotation and model explanation maps. To address the challenges, we propose a generic RES framework for guiding visual explanation by developing a novel objective that handles inaccurate boundary, incomplete region, and inconsistent distribution of human annotations, with a theoretical justification on model generalizability. Extensive experiments on two real-world image datasets demonstrate the effectiveness of the proposed framework on enhancing both the reasonability of the explanation and the performance of the backbone DNNs model.

While Deep Neural Networks (DNNs) are deriving the major innovations through their powerful automation, we are also witnessing the peril behind automation as a form of bias, such as automated racism, gender bias, and adversarial bias. As the societal impact of DNNs grows, finding an effective way to steer DNNs to align their behavior with the human mental model has become indispensable in realizing fair and accountable models. While establishing the way to adjust DNNs to "think like humans'' is in pressing need, there have been few approaches aiming to capture how "humans would think'' when DNNs introduce biased reasoning in seeing a new instance. We propose Interactive Attention Alignment (IAA), a framework that uses the methods for visualizing model attention, such as saliency maps, as an interactive medium that humans can leverage to unveil the cases of DNN's biased reasoning and directly adjust the attention. To realize more effective human-steerable DNNs than state-of-the-art, IAA introduces two novel devices. First, IAA uses Reasonability Matrix to systematically identify and adjust the cases of biased attention. Second, IAA applies GRADIA, a computational pipeline designed for effectively applying the adjusted attention to jointly maximize attention quality and prediction accuracy. We evaluated Reasonability Matrix in Study 1 and GRADIA in Study 2 in the gender classification problem. In Study 1, we found applying Reasonability Matrix in bias detection can significantly improve the perceived quality of model attention from human eyes than not applying Reasonability Matrix. In Study 2, we found using GRADIA significantly improves (1) the human-assessed perceived quality of model attention and (2) model performance in scenarios where the training samples are limited. Based on our observation in the two studies, we present implications for future design in the problem space of social computing and interactive data annotation toward achieving a human-centered steerable AI.

Millions of children now use conversational agents (CAs), leading researchers and the public alike to ask how interactions with these devices might shape children’s communication with people. We conducted a single-session observational lab study with 22 five-to-ten-year-old children as a step toward understanding whether and how children might transfer a linguistic routine they learned from a CA to a conversation with another person. We found that 68% of children spontaneously used this routine in a conversation with their parent in the lab, and 55% continued to use it at home. When addressing parents, children infused the routine with warmth and playfulness that they did not use when addressing the CA, adapting it to suit their relationship with their parent. However, only 18% of children used it in conversation with an unfamiliar researcher, where they instead were more likely to follow conventional conversational norms. These findings suggest children are quick to learn linguistic routines from CAs but use social differentiation when they apply them. Children’s willingness to expand on and share the routine with their parent is consistent with the principles of the Joint Media Engagement (JME) framework and suggests CAs may be a productive medium for creating JME experiences.

In recent years, graph neural networks (GNNs) and the research on their explainability are experiencing rapid developments and achieving significant progress. Many methods are proposed to explain the predictions of GNNs, focusing on “how to generate explanations” However, research questions like “whether the GNN explanations are inaccurate”, “what if the explanations are inaccurate”, and “how to adjust the model to generate more accurate explanations” have not been well explored. To address the above questions, this paper proposes a GNN Explanation Supervision (GNES)1framework to adaptively learn how to explain GNNs more correctly. Specifically, our framework jointly optimizes both model prediction and model explanation by enforcing both whole graph regularization and weak supervision on model explanations. For the graph regularization, we propose a unified explanation formulation for both node-level and edge-level explanations by enforcing the consistency between them. The node- and edge-level explanation techniques we propose are also generic and rigorously demonstrated to cover several existing major explainers as special cases. Extensive experiments on five real-world datasets across two application domains demonstrate the effectiveness of the proposed model on improving the reasonability of the explanation while still keep or even improve the backbone GNNs model performance.1Code available at: https://github.com/YuyangGao/GNES.

Capturing human annotators' subjective responses in image annotation has become crucial as vision-based classifiers expand the range of application areas. While there has been significant progress in image annotation interface design in general, relatively little research has been conducted to understand how to elicit reliable and cost-efficient human annotation when the nature of the task includes a certain level of subjectivity. To bridge this gap, we aim to understand how different sampling methods in image batch labeling, a design that allows human annotators to label a batch of images simultaneously, can impact human annotation performances. In particular, we developed three different strategies in forming image batches: (1) uncertainty-based labeling (UL) that prioritizes images that a classifier predicts with the highest uncertainty, (2) certainty-based labeling (CL), a reverse strategy of UL, and (3) random, a baseline approach that randomly selects images. Although UL and CL solely select images to be labeled from a classifier's point of view, we hypothesized that human-side perception and labeling performance may also vary depending on the different sampling strategies. In our study, we observed that participants were able to recognize a different level of perceived cognitive load across three conditions (CL the easiest while UL the most difficult). We also observed a trade-off between annotation task effectiveness (CL and UL more reliable than random) and task efficiency (UL the most efficient while CL the least efficient). Based on the results, we discuss the implications of design and possible future research directions of image batch labeling.

Collaborative Sequencing (CoSeq) is the process by which a group collaboratively constructs a sequence. CoSeq is ubiquitous, occurring across diverse situations like trip planning, course scheduling, or book writing. Building a consensus on a sequence is desirable to groups. However, accomplishing this requires groups to dedicate significant effort to comprehensively discuss preferences and resolve conflicts. Furthermore, as numerous decisions must be assessed to construct a sequence, this challenge can be exacerbated in CoSeq. However, little research has aimed to effectively support consensus building in CoSeq. As a first step to systematically understand and support consensus building in CoSeq, we conducted a formative study to gain insights into how visual awareness may facilitate the holistic recognition of preferences and the resolution of conflicts within a group. From the study, we identified design requirements to support consensus building and designed a novel visual awareness technique for CoSeq. We instantiated this design in a collaborative travel itinerary planning system, Twine, and conducted a summative study to evaluate its effects. We found that visual awareness could decrease the effort of communicating preferences by 21%, and participants' comments suggest that it also encouraged group members to behave more cooperatively when building a consensus. We discuss future research directions to further explore the needs and challenges in this unique context and to advance the development of support for CoSeq tasks.

As the use of machine learning (ML) models in product development and data-driven decision-making processes became pervasive in many domains, people's focus on building a well-performing model has increasingly shifted to understanding how their model works. While scholarly interest in model interpretability has grown rapidly in research communities like HCI, ML, and beyond, little is known about how practitioners perceive and aim to provide interpretability in the context of their existing workflows. This lack of understanding of interpretability as practiced may prevent interpretability research from addressing important needs, or lead to unrealistic solutions. To bridge this gap, we conducted 22 semi-structured interviews with industry practitioners to understand how they conceive of and design for interpretability while they plan, build, and use their models. Based on a qualitative analysis of our results, we differentiate interpretability roles, processes, goals and strategies as they exist within organizations making heavy use of ML models. The characterization of interpretability work that emerges from our analysis suggests that model interpretability frequently involves cooperation and mental model comparison between people in different roles, often aimed at building trust not only between people and models but also between people within the organization. We present implications for design that discuss gaps between the interpretability challenges that practitioners face in their practice and approaches proposed in the literature, highlighting possible research directions that can better address real-world needs.

Document labeling is a critical step in building various machine learning applications. However, the step can be time-consuming and arduous, requiring a significant amount of human efforts. To support an efficient document labeling environment, we present a system called Attentive Interactive Labeling Assistant (AILA). In its core, AILA uses Interactive Attention Module (IAM), a novel module that visually highlights words in a document that labelers may pay attention to when labeling a document. IAM utilizes attention-based Deep Neural Networks which not only support a prediction of which words to highlight but also enable labelers to indicate words that should be assigned a high attention weight while labeling to improve the future quality of word prediction.We evaluated the labeling efficiency and the accuracy by comparing the conditions with and without IAM in our study. The results showed that participants' labeling efficiency increased significantly under the condition with IAM than the condition without IAM, while the two conditions maintained roughly the same labeling accuracy.

When crowdsourcing the creation of machine learning datasets, statistical distributions that capture diverse answers can represent ambiguous data better than a single best answer. Unfortunately, collecting distributions is expensive because a large number of responses need to be collected to form a stable distribution. Despite this, the efficient collection of answer distributions-that is, ways to use less human effort to collect estimates of the eventual distribution that would be formed by a large group of responses-is an under-studied topic. In this paper, we demonstrate that this type of estimation is possible and characterize different elicitation approaches to guide the development of future systems. We investigate eight elicitation approaches along two dimensions: annotation granularity and estimation perspective. Annotation granularity is varied by annotating i) a single "best" label, ii) all relevant labels, iii) a ranking of all relevant labels, or iv) real-valued weights for all relevant labels. Estimation perspective is varied by prompting workers to either respond with their own answer or an estimate of the answer(s) that they expect other workers would provide. Our study collected ordinal annotations on the emotional valence of facial images from 1,960 crowd workers and found that, surprisingly, the most fine-grained elicitation methods were not the most accurate, despite workers spending more time to provide answers. Instead, the most efficient approach was to ask workers to choose all relevant classes that others would have selected. This resulted in a 21.4% reduction in the human time required to reach the same performance as the baseline (i.e., selecting a single answer with their own perspective). By analyzing cases in which finer-grained annotations degraded performance, we contribute to a better understanding of the trade-offs between answer elicitation approaches. Our work makes it more tractable to use answer distributions in large-scale tasks such as ML training, and aims to spark future work on techniques that can efficiently estimate answer distributions.

Although Collaborative Information-Seeking (CIS) is becoming prevalent as people engage in shared decision-making, interface components adopted in the most commonly used information seeking tools (e.g., search, filter, select, and sort) are designed for individual use. To deepen our understanding of (1) how such single-user designs affect people's consensus building processes in CIS and (2) how to devise an alternative design to improve current practices, we conducted two 4-week diary studies and observed how groups seek out places together. Our studies focus on social event coordination as a case where CIS is necessary and important. In Study 1, we examined the major challenges people encounter when performing CIS using their preferred tools. These challenges include difficulties in capturing mutual preferences, high communication cost, and disparity of work depending on a group member's perceived role as an organizer or invitee. We discovered that improving a group's shared understanding of the target information they seek (e.g., places, products) could potentially address the challenges. In Study 2, we designed, deployed, and evaluated ComeTogether, a novel system that supports a group's social event coordination. ComeTogether adopts Collaborative Dynamic Queries (C-DQ), an interface designed to allow a group to share their preferences regarding potential destinations. Study 2 results indicate that using C-DQ increased users' awareness of other group members' preferences in performing CIS, making their coordination more transparent, more inviting, and fairer than what their current practice allows. Meanwhile, ComeTogether improved communication efficiency of groups while presenting opportunities to learn about others and to discover new places. We provide implications for design that explain considerations for adopting C-DQ and identify future research directions.

In this study, we present Coco's Videos, a video-viewing platform for preschoolers designed to support them in learning to self-manage their media consumption. We report results from a three-week experimental deployment in 24 homes in which preschoolers used three different versions of the platform: one that is neutral to the limits they set, one that enforces the limits they set, and one that attempts to erode the limits they set by automatically playing additional content after the planned content is finished ("post-play"). We found that post-play significantly reduced children's autonomy and likelihood of self-regulation, extended video-viewing time, and led to increases in parent intervention. We found that the lock-out mechanism did not reduce video-viewing time or the likelihood of parent intervention. Together, our results suggest that avoiding platforms that work to undermine the user's intentions is more likely to help children self-regulate their media use than rigid parental controls.

Communication is critical in small group decision-making processes during which each member must be able to express preferences to reach consensus. Finding consensus can be difficult when each member in a group has a perspective that potentially conflicts with those of others. To support groups attempting to harmonize diverse preferences, we propose Collaborative Dynamic Queries (C-DQ), a UI component that enables a group to filter queries over decision criteria while being aware of others' preferences. To understand how C-DQ affects a group's behavior and perception in the decision-making process, we conducted 2 studies with groups who were prompted to make decisions together on mobile devices in a dispersed and synchronous situation. In Study 1, we found showing group preferences with C-DQ helped groups to communicate more efficiently and effectively. In Study 2, we found filtering candidates based on each member's own filter range further improved a groups' communication efficiency and effectiveness.

Distance Cartograms (DC) distort geographical features so that the measured distance between a single location and any other location on a map indicates absolute travel time. Although studies show that users can efficiently assess travel time with DC, distortion applied in DC may confuse users, and its usefulness "in the wild" is unknown. To understand how real world users perceive DC's benefits and drawbacks, we devise techniques that improve DC's presentation (preserving topological relationships among map features while aiming at retaining shapes) and scalability (presenting accurate live travel time). We developed a DC-enabled system with these techniques, and deployed it to 20 participants for 4 weeks. During this period, participants spent, on average, more than 50% of their time with DC as opposed to a standard map. Participants felt DC to be intuitive and useful for assessing travel time. They indicated intent in adopting DC in their real-life scenarios.

A distance cartogram (DC) is a technique that alters distances between a user-specified origin and the other locations in a map with respect to travel time. With DC, users can weigh the relative travel time costs between the origin and potential destinations at a glance because travel times are projected in a linearly interpolated time space from the origin. Such glance-ability is known to be useful for travelers who are mindful of travel time when finding their travel destinations. When constructing DC, however, uneven urban traffic conditions introduce excessive distortion and challenge user intuition. In addition, there has been little research focusing on DC's user interaction design. To tackle these challenges and realize the potential of DC as an interactive decision-making support tool, we derive a set of useful interactions through two formative studies and devise two novel techniques called Geo-contextual Anchoring Projection and Scalable Road-network Construction. We develop an interactive map system using these techniques and evaluate this system by comparing it against an equidistant map (EM), a widely used conventional layout that preserves the geographical reality. Based on the analysis of user behavior and qualitative feedback, we identify several benefits of using DC itself and of the interaction techniques we derived. We also analyze the specific reasons behind these identified benefits.

Many successful digital interfaces employ visual metaphors to convey features or data properties to users, but the characteristics that make a visual metaphor effective are not well understood. We used a theoretical conception of metaphor from cognitive linguistics to design an interactive system for viewing the citation network of the corpora of literature in the JSTOR database, a highly connected compound graph of 2 million papers linked by 8 million citations. We created 4 variants of this system, manipulating 2 distinct properties of metaphor. We conducted a between‐subjects experimental study with 80 participants to compare understanding and engagement when working with each version. We found that building on known image schemas improved response time on look‐up tasks, while contextual detail predicted increases in persistence and the number of inferences drawn from the data. Schema‐congruency combined with contextual detail produced the highest gains in comprehension. These findings provide concrete mechanisms by which designers presenting large data sets through metaphorical interfaces may improve their effectiveness and appeal with users.

Though many people report an interest in self-limiting certain aspects of their phone use, challenges adhering to self-defined limits are common. We conducted a design exercise and online survey to map the design space of interventions for smartphone non-use and distilled these into a small taxonomy of intervention categories. Using these findings, we implemented "MyTime," an intervention to support people in achieving goals related to smartphone non-use. We conducted a deployment study with 23 participants over two weeks and found that participants reduced their time with the apps they feel are a poor use of time by 21% while their use of the apps they feel are a good use of time remained unchanged. We found that a small taxonomy describes users' diverse set of desired behavior changes relating to smartphone non-use, and that these desired changes predict: 1) the hypothetical features they are interested in trying, 2) the extent to which they engage with these features in practice, and 3) their changes in behavior in response to the intervention. We link users' desired behaviors to the categories of our design taxonomy, providing a foundation for a theoretical model of designing for smartphone non-use.

Though toddlers and preschoolers are regular touchscreen users, relatively little is known about how they learn to perform unfamiliar gestures. In this paper we assess the responses of 34 children, aged 2 to 5, to the most common in-app prompting techniques for eliciting specific gestures. By reviewing 100 touchscreen apps for preschoolers, we determined the types of prompts that children are likely to encounter. We then evaluated their relative effectiveness in teaching children to perform simple gestures. We found that children under 3 were only able to interpret instructions when they came from an adult model, but that children made rapid gains between age 3 and 3-and-a-half, at which point they were able to follow in-app audio instructions and on-screen demonstrations. The common technique of using visual state changes to prompt gestures was ineffective across this age range. Given that prior work in this space has primarily focused on children's fine motor control, our findings point to a need for increased attention to the design of prompts that accommodate children's cognitive development as well.

Despite the growing interests in video games as consumer products as well as objects of research, current methods for accessing video games are limited. We present Vizmo as a new way of browsing video games based on their visual style and mood. In order to test the usability and usefulness of Vizmo, we asked 19 video game experts to evaluate their interaction with the tool. The results show that experts perceived Vizmo as a novel and aesthetically pleasing game discovery tool which would be most useful for game research on historical and aesthetic aspects. We discuss five key points for improving the design of Vizmo as well as our future plan for the next iteration of this prototype game browser.

Most geographic maps visually represent physical distance; however, travel time can in some cases be more important than distance because it directly indicates availability. The technique of creating maps from temporal data is known as isochronal cartography, and is a form of distortion for clarification. In an isochronal map, congestion expands areas, while ideal travel conditions make the map shrink in comparison to the actual distance scale of a traditional map. Although there have been many applications of this technique, detailed user studies of its efficacy remain scarce, and there are conflicting views on its practical value. To attempt to settle this issue, we utilized a user-centered design process to determine which features of isochronal cartography might be most usable in practice. We developed an interactive cartographic visualization system, Traffigram, that features a novel combination of efficient isochronal map algorithms and an interface designed to give map users a quick and seamless experience while preserving geospatial integrity and aesthetics. We validated our design choices with multiple usability studies. We present our results and discuss implications for design.