Self-tracking and personal informatics offer important potential in chronic condition management, but such potential is often undermined by difficulty in aligning self-tracking tools to an individual's goals. Informed by prior proposals of goal-directed tracking, we designed and developed MigraineTracker, a prototype app that emphasizes explicit expression of goals for migraine-related self-tracking. We then examined migraine patient experiences in a deployment study for an average of 12+ months, including a total of 50 interview sessions with 10 patients working with 3 different clinicians. Patients were able to express multiple types of goals, evolve their goals over time, align tracking to their goals, personalize their tracking, reflect in the context of their goals, and gain insights that enabled understanding, communication, and action. We discuss how these results highlight the importance of accounting for distinct and concurrent goals in personal informatics together with implications for the design of future goal-directed personal informatics tools.

Many people prefer psychosocial interventions for mental health care or other concerns, but these interventions are often complex and unavailable in settings where people seek care. Intervention designers use technology to improve user experience or reach of interventions, and HCI researchers have made many contributions toward this goal. Both HCI and mental health researchers must navigate tensions between innovating on and adhering to the theories of change that guide intervention design. In this paper, we propose a framework that describes design briefs and evaluation approaches for HCI contributions at the scopes of capabilities, components, intervention systems, and intervention implementations. We show how theories of change (from mental health) can be translated into design briefs (in HCI), and that these translations can bridge and coordinate efforts across fields. It is our hope that this framework can support researchers in motivating, planning, conducting, and communicating work that advances psychosocial intervention design.

The emergence of generative artificial intelligence (GenAI) has ignited discussions surrounding its potential to enhance creative pursuits. However, distinctions between children's and adult's creative needs exist, which is important when considering the possibility of GenAI for children's creative usage. Building upon work in Human-Computer Interaction (HCI), fostering children's computational thinking skills, this study explores interactions between children (aged 7-13) and GenAI tools through methods of participatory design. We seek to answer two questions: (1) How do children in co-design workshops perceive GenAI tools and their usage for creative works? and (2) How do children navigate the creative process while using GenAI tools? How might these interactions support their confidence in their ability to create? Our findings contribute a model that describes the potential contexts underpinning child-GenAI creative interactions and explores implications of this model for theories of creativity, design, and use of GenAI as a constructionist tool for creative self-efficacy.

We explore the potential of generative AI text-to-image models to help designers efficiently craft unique, representative, and demographically diverse anthropographics that visualize data about people. Currently, creating data-driven iconic images to represent individuals in a dataset often requires considerable design effort. Generative text-to-image models can streamline the process of creating these images, but risk perpetuating designer biases in addition to stereotypes latent in the models. In response, we outline a conceptual workflow for crafting anthropographic assets for visualizations, highlighting possible sources of risk and bias as well as opportunities for reflection and refinement by a human designer. Using an implementation of this workflow with Stable Diffusion and Google Colab, we illustrate a variety of new anthropographic designs that showcase the visual expressiveness and scalability of these generative approaches. Based on our experiments, we also identify challenges and research opportunities for new AI-enabled anthropographic visualization tools.

As user-generated video dominates media landscapes, it poses an accessibility challenge. While disability advocacy groups globally have secured hard-won accessibility regulations for broadcast media, no such regulation of user-generated content exists. Yet, one major player in this shift, TikTok, has a culture of user-generated, creative captioning. We sought to understand how TikTok videos are captioned and the impact current practices have on those who need captions to access audio content. Therefore, we conducted a content analysis of 300 open-captioned TikToks and contextualized these findings by interviewing nine caption users. We found that the current state of TikTok captioning does facilitate access to the platform but that a user-generated, social video-specific standard for captioning could improve caption quality and expand access. We contribute an empirical account of the state of TikTok captioning and outline steps toward a standard for user-generated captioning.

Family informatics often uses shared data dashboards to promote awareness of each other's health-related behaviors. However, these interfaces often stop short of providing families with needed guidance around how to improve family functioning and health behaviors. We consider the needs of family co-regulation with ADHD children to understand how in-home displays can support family well-being. We conducted three co-design sessions with each of eight families with ADHD children who had used a smartwatch for self-tracking. Results indicate that situated displays could nudge families to jointly use their data for learning and skill-building. Accommodating individual needs and preferences when family members are alone is also important, particularly to support parents exploring their co-regulation role, and assisting children with data interpretation and guidance on self and co-regulation. We discuss opportunities for displays to nurture multiple intents of use, such as joint or independent use, while potentially connecting with external expertise.

Despite the evidence of harm that technology can inflict, commensurate policymaking to hold tech platforms accountable still lags. This is pertinent to app-based gig workers, where unregulated algorithms continue to dictate their work, often with little human recourse. While past HCI literature has investigated workers’ experiences under algorithmic management and how to design interventions, rarely are the perspectives of stakeholders who inform or craft policy sought. To bridge this, we propose using data probes---interactive visualizations of workers’ data that show the impact of technology practices on people---exploring them in 12 semi-structured interviews with policy informers, (driver-)organizers, litigators, and a lawmaker in the rideshare space. We show how data probes act as boundary objects to assist stakeholder interactions, demystify technology for policymakers, and support worker collective action. We discuss the potential for data probes as training tools for policymakers, and considerations around data access and worker risks when using data probes.

Blind individuals commonly share photos in everyday life. Despite substantial interest from the blind community in being able to independently obfuscate private information in photos, existing tools are designed without their inputs. In this study, we prototyped a preliminary screen reader-accessible obfuscation interface to probe for feedback and design insights. We implemented a version of the prototype through off-the-shelf AI models (e.g., SAM, BLIP2, ChatGPT) and a Wizard-of-Oz version that provides human-authored guidance. Through a user study with 12 blind participants who obfuscated diverse private photos using the prototype, we uncovered how they understood and approached visual private content manipulation, how they reacted to frictions such as inaccuracy with existing AI models and cognitive load, and how they envisioned such tools to be better designed to support their needs (e.g., guidelines for describing visual obfuscation effects, co-creative interaction design that respects blind users’ agency).

With their generative capabilities, large language models (LLMs) have transformed the role of technological writing assistants from simple editors to writing collaborators. Such a transition emphasizes the need for understanding user perception and experience, such as balancing user intent and the involvement of LLMs across various writing domains in designing writing assistants. In this study, we delve into the less explored domain of personal writing, focusing on the use of LLMs in introspective activities. Specifically, we designed DiaryMate, a system that assists users in journal writing with LLM. Through a 10-day field study (N=24), we observed that participants used the diverse sentences generated by the LLM to reflect on their past experiences from multiple perspectives. However, we also observed that they are over-relying on the LLM, often prioritizing its emotional expressions over their own. Drawing from these findings, we discuss design considerations when leveraging LLMs in a personal writing practice.

Toolkits are an important means of sharing expertise and influencing practice. However, the work of making and sustaining toolkits is not well understood. We address this gap by conducting 20 semi-structured interviews with toolkit designers, focusing on toolkits intended to help practitioners such as librarians, teachers, and community workers. We analyze these interviews to surface key aspects of participants’ design journeys: (1) how their projects began; (2) how they conceptualized use; (3) how they collaborated with users; (4) and what happened once their toolkit was released. We illustrate these aspects through three narratives, and discuss our findings to provide considerations for designers and scholars. We highlight how designers co-construct communities alongside their toolkits, helping us form a more nuanced understanding of the social aspects underpinning toolkit projects. Collectively, these contributions can help us identify challenges and opportunities in this design space, laying the groundwork to increase toolkits' social impact.

Communicating design implications is common within the HCI community when publishing academic papers, yet these papers are rarely read and used by designers. One solution is to use design cards as a form of translational resource that communicates valuable insights from papers in a more digestible and accessible format to assist in design processes. However, creating design cards can be time-consuming, and authors may lack the resources/know-how to produce cards. Through an iterative design process, we built a system that helps create design cards from academic papers using an LLM and text-to-image model. Our evaluation with designers (N=21) and authors of selected papers (N=12) revealed that designers perceived the design implications from our design cards as more inspiring and generative, compared to reading original paper texts, and the authors viewed our system as an effective way of communicating their design implications. We also propose future enhancements for AI-generated design cards.

Data analysis is challenging as analysts must navigate nuanced decisions that may yield divergent conclusions. AI assistants have the potential to support analysts in planning their analyses, enabling more robust decision making. Though AI-based assistants that target code execution (e.g., Github Copilot) have received significant attention, limited research addresses assistance for both analysis execution and planning. In this work, we characterize helpful planning suggestions and their impacts on analysts’ workflows. We first review the analysis planning literature and crowd-sourced analysis studies to categorize suggestion content. We then conduct a Wizard-of-Oz study (n=13) to observe analysts’ preferences and reactions to planning assistance in a realistic scenario. Our findings highlight subtleties in contextual factors that impact suggestion helpfulness, emphasizing design implications for supporting different abstractions of assistance, forms of initiative, increased engagement, and alignment of goals between analysts and assistants.

Despite decades of effort, pedestrian infrastructure in cities continues to be unsafe or inaccessible to people with disabilities. In this paper, we examine the potential of community-driven digital civics to assess sidewalk accessibility through a deployment study of an open-source crowdsourcing tool called Project Sidewalk. We explore Project Sidewalk's potential as a platform for civic learning and service. Specifically, we assess its effectiveness as a tool for community members to learn about human mobility, urban planning, and accessibility advocacy. Our findings demonstrate that community-driven digital civics can support accessibility advocacy and education, raise community awareness, and drive pro-social behavioral change. We also outline key considerations for deploying digital civic tools in future community-led accessibility initiatives.

Slipped and tucked stitches introduce small areas of deformation that compound and result in emergent textures on knitted fabrics. When used together with color changes and ladders, these can also produce dramatic colorwork and openwork effects. However, designing slip and tuck colorwork patterns is challenging due to the complex interactions between operations, yarns, and deformations. We present KnitScape, a browser-based tool for design and simulation of stitch patterns for knitting. KnitScape provides a design interface to specify 1) operation repeats, 2) color changes, and 3) needle positions. These inputs are used to build a graph of yarn topology and run a yarn-level spring simulation. This enables visualization of the deformation that arises from slip and tuck operations. Through its design tool and simulation, KnitScape enables rapid exploration of a complex colorwork design space. We demonstrate KnitScape with a series of example swatches.

Social media feeds are deeply personal spaces that reflect individual values and preferences. However, top-down, platform-wide content algorithms can reduce users' sense of agency and fail to account for nuanced experiences and values. Drawing on the paradigm of interactive machine teaching (IMT), an interaction framework for non-expert algorithmic adaptation, we map out a design space for \textit{teachable social media feed experiences} to empower agential, personalized feed curation. To do so, we conducted a think-aloud study (N=24) featuring four social media platforms---Instagram, Mastodon, TikTok, and Twitter---to understand key signals users leveraged to determine the value of a post in their feed. We synthesized users' signals into taxonomies that, when combined with user interviews, inform five design principles that extend IMT into the social media setting. We finally embodied our principles into three feed designs that we present as sensitizing concepts for teachable feed experiences moving forward.

Understanding how professionals use digital fabrication in production workflows is critical for future research in digital fabrication technologies. We interviewed thirteen professionals who use digital fabrication for the low-volume manufacturing of commercial products. From these interviews, we describe the workflows used for nine products created with a variety of materials and manufacturing methods. We show how digital fabrication professionals use software development to support physical production, how they rely on multiple partial representations in development, how they develop manufacturing processes, and how machine control is its own design space. We build from these findings to argue that future digital fabrication systems should support the exploration of material and machine behavior alongside geometry, that simulation is insufficient for understanding the design space, and that material constraints and resource management are meaningful design dimensions to support. By observing how professionals learn, we suggest ways digital fabrication systems can scaffold the mastery of new fabrication techniques.

Experimental digital fabrication workflows are increasingly common in human-computer interaction research, but are difficult to reproduce. We present Tandem, a software library that lets a fabricator implement an end-to-end fabrication workflow as a computational notebook program that others can run to physically reproduce the workflow. Tandem notebook programs read and write to CAD and CAM software, project augmented reality interfaces onto machines for manual interventions, and directly control fabrication machines. Fabricators can also denote potential mismatches between the physical and the digital as explicit assertions in code. Using two-sided CNC milling as an example, we demonstrate how to implement a complex workflow as a single program that can be re-run by others while supporting quality control and improving reproducibility.

This paper presents a reflection on the role of ontological inquiry in HCI research and practice. Specifically, we introduce philosopher Gilbert Simondon's proposal of technical mentality, an onto-epistemology based on direct knowledge of technical objects and systems. This paper makes the following contributions: an analysis of Simondon's ontological critique and its connection to technical mentality; a reflection on the ethical and practical implications of Simondon's proposal for systems research; an example of technical mentality in practice; and a discussion of how technical mentality might be extended into a design program for HCI through four principles: extension, integration, legibility, and expression.