CMU at CHI 2024

May 12, 2024

News

Researchers from the Human-Computer Interaction Institute (HCII) and several other Carnegie Mellon University schools and disciplines contributed to more than 40 papers accepted to the 2024 CHI Conference on Human Factors in Computing Systems.

The Association of Computing Machinery (ACM) conference on computer human interaction, commonly referred to as “CHI” (pronounced “kai”) for short, will take place from May 11-16, 2024, in Honolulu, Hawaiʻi.

"The HCI Institute continues to do world-class research and we are delighted to showcase so much of our work at this conference,” said Brad Myers, HCII Geschke Director and Professor. “CHI encompasses the research of many of our faculty and students, and has historically been the premiere place to publish work in many areas of human-computer interaction. I'm looking forward to connecting with colleagues and our alumni at the conference."

We celebrate the following awards and research affiliated with Carnegie Mellon University authors at CHI 2024:

SIGCHI Awards
Congratulations to the following 2024 SIGCHI awardees from the HCII:

Jodi Forlizzi, the Herbert A. Simon Professor in Computer Science and HCII and associate dean for diversity, equity and inclusion in the School of Computer Science (SCS), received the 2024 Lifetime Research Award.
Amy Ogan, associate professor of learning sciences, received the 2024 Societal Impact Award.
HCII alumnus Karan Ahuja (SCS 2023) received a 2024 Outstanding Dissertation Award for his thesis, "Practical and Rich User Digitization" (pdf).

For more info, visit: Forlizzi, Ogan and Ahuja Receive 2024 SIGCHI Awards or the 2024 SIGCHI Awards announcement.

Special Event
A new book from Brad A. Myers explores the history, current and future design of interaction techniques. Myers will be hosting a book signing event on May 13 and teaching a short course on the subject (Course #C04: Interaction Techniques – History, Design and Evaluation) on May 14. Learn more: Myers Publishes Book on Interaction Techniques

Papers and Paper Awards
We are proud to share that Carnegie Mellon University authors from a variety of schools and disciplines contributed to more than 40 papers accepted to CHI 2024, including three Best Paper and five Honorable Mention Awards. A list of accepted papers with CMU contributing authors is available below in alphabetical order by paper title, and their award winning papers are indicated by the following icons:
Icon represents a Best Paper Award

Icon represents a Best Paper Honorable Mention Award

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“Don’t Put All Your Eggs In One Basket”: How Cryptocurrency Users Choose and Secure Their Wallets
Yaman Yu, Tanusree Sharma, Sauvik Das, Yang Wang

Abstract: Cryptocurrency wallets come in various forms, each with unique usability and security features. Through interviews with 24 users, we explore reasons for selecting wallets in different contexts. Participants opt for smart contract wallets to simplify key management, leveraging social interactions. However, they prefer personal devices over individuals as guardians to avoid social cybersecurity concerns in managing guardian relationships. When engaging in high-stakes or complex transactions, they often choose browser-based wallets, leveraging third-party security extensions. For simpler transactions, they prefer the convenience of mobile wallets. Many participants avoid hardware wallets due to usability issues and security concerns with respect to key recovery service provided by manufacturer and phishing attacks. Social networks play a dual role: participants seek security advice from friends, but also express security concerns in soliciting this help. We offer novel insights into how and why users adopt specific wallets. We also discuss design recommendations for future wallet technologies based on our findings.

"If This Person is Suicidal, What Do I Do?": Designing Computational Approaches to Help Online Volunteers Respond to Suicidality
Logan Stapleton, Sunniva Liu, Cindy Liu, Irene Hong, Stevie Chancellor, Robert E Kraut, and Haiyi Zhu

Abstract: Online platforms provide support for many kinds of distress, including suicidal thoughts and behaviors. However, because many platforms restrict suicidal talk, volunteers on these platforms struggle with how to help suicidal people who come for support. We interviewed 11 volunteer counselors in a large online support platform, including after they role-played conversations with varying severities of suicidality, to explore practices and challenges when identifying and responding to suicidality. We then presented Speed Dating design concepts around emotional preparation and support, real-time guidance, training, and suicide detection. Participants wanted more support and preparation for conversations with suicidal people, but were conflicted about AI-based technologies, including trade-offs between potential benefits of conversational agents for training and limitations of prediction or real-time response suggestions, due to the sensitive, context-dependent decisions that volunteers must make. Our work has important implications for nuanced considerations and design choices around developing digital mental health technologies.

"It's a Fair Game", or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agents
Zhiping Zhang, Michelle Jia, Hao-Ping (Hank) Lee, Bingsheng Yao, Sauvik Das, Ada Lerner, Dakuo Wang, Tianshi Li

Abstract: The widespread use of Large Language Model (LLM)-based conversational agents (CAs), especially in high-stakes domains, raises many privacy concerns. Building ethical LLM-based CAs that respect user privacy requires an in-depth understanding of the privacy risks that concern users the most. However, existing research, primarily model-centered, does not provide insight into users' perspectives. To bridge this gap, we analyzed sensitive disclosures in real-world ChatGPT conversations and conducted semi-structured interviews with 19 LLM-based CA users. We found that users are constantly faced with trade-offs between privacy, utility, and convenience when using LLM-based CAs. However, users' erroneous mental models and the dark patterns in system design limited their awareness and comprehension of the privacy risks. Additionally, the human-like interactions encouraged more sensitive disclosures, which complicated users' ability to navigate the trade-offs. We discuss practical design guidelines and the needs for paradigm shifts to protect the privacy of LLM-based CA users.

"It's the only thing I can trust": Envisioning Large Language Model Use by Autistic Workers for Communication Assistance
JiWoong (Joon) Jang, Sanika Moharana, Patrick Carrington, and Andrew Begel

Abstract: Autistic adults often experience stigma and discrimination at work, leading them to seek social communication support from coworkers, friends, and family despite emotional risks. Large language models (LLMs) are increasingly considered an alternative. In this work, we investigate the phenomenon of LLM use by autistic adults at work and explore opportunities and risks of LLMs as a source of social communication advice. We asked 11 autistic participants to present questions about their own workplace-related social difficulties to (1) a GPT-4-based chatbot and (2) a disguised human confederate. Our evaluation shows that participants strongly preferred LLM over confederate interactions. However, a coach specializing in supporting autistic job-seekers raised concerns that the LLM was dispensing questionable advice. We highlight how this divergence in participant and practitioner attitudes reflects existing schisms in HCI on the relative privileging of end-user wants versus normative good and propose design considerations for LLMs to center autistic experiences.

“The bus is nothing without us”: Making Visible the Labor of Bus Operators amid the Ongoing Push Towards Transit Automation"
Hunter Akridge, Bonnie Fan, Alice Xiaodi Tang, Chinar Mehta, Nikolas Martelaro, and Sarah E. Fox

Abstract: This paper describes how the complexity of circumstances bus operators manage presents unique challenges to the feasibility of high-level automation in public transit. Avoiding an overly rationalized view of bus operators' labor is critical to ensure the introduction of automation technologies does not compromise public wellbeing, the dignity of transit workers, or the integrity of critical public infrastructure. Our findings from a group interview study show that bus operators take on work — undervalued by those advancing automation technologies — to ensure the well-being of passengers and community members. Notably, bus operators are positioned to function as shock absorbers during social crises in their communities and in moments of technological breakdown as new systems come on board. These roles present a critical argument against the rapid push toward driverless automation in public transit. We conclude by identifying opportunities for participatory design and collaborative human-machine teaming for a more just future of transit.

A Contextual Inquiry of People with Vision Impairments in Cooking
Franklin Mingzhe Li, Michael Xieyang Liu, Shaun K. Kane, Patrick Carrington

Individuals with vision impairments employ a variety of strategies for object identification, such as pans or soy sauce, in the culinary process. In addition, they often rely on contextual details about objects, such as location, orientation, and current status, to autonomously execute cooking activities. To understand how people with vision impairments collect and use the contextual information of objects while cooking, we conducted a contextual inquiry study with 12 participants in their own kitchens. This research aims to analyze object interaction dynamics in culinary practices to enhance assistive vision technologies for visually impaired cooks. We outline eight different types of contextual information and the strategies that blind cooks currently use to access the information while preparing meals. Further, we discuss preferences for communicating contextual information about kitchen objects as well as considerations for the deployment of AI-powered assistive technologies.

An Evidence-based Workflow for Studying and Designing Learning Supports for Human–AI Co-creation (Late-Breaking Work)
Frederic Gmeiner, Jamie Conlin, Eric Tang, Nikolas Martelaro, Kenneth Holstein

Abstract: Generative artificial intelligence (GenAI) systems introduce new possibilities for enhancing professionals’ workflows, enabling novel forms of human–AI co-creation. However, professionals often struggle to learn to work with GenAI systems effectively. While research has begun to explore the design of interfaces that support users in learning to co-create with GenAI, we lack systematic approaches to investigate the effectiveness of these supports. In this paper, we present a systematic approach for studying how to support learning to co-create with GenAI systems, informed by methods and concepts from the learning sciences. Through an experimental case study, we demonstrate how our approach can be used to study and compare the impacts of different types of learning supports in the context of text-to-image GenAI models. Reflecting on these results, we discuss directions for future work aimed at improving interfaces for human–AI co-creation.

Are Robots Ready to Deliver Autism Inclusion?: A Critical Review
Naba Rizi, William Wu, Mya Bolds, Raunak Mondal, Andrew Begel, and Imani N. S. Munyaka

Abstract: The marginalization of autistic people in our society today is multi-faceted as it includes violence that is both physical and ideological in nature. It is rooted in the dehumanization, infantilization, and masculinization of autistic people and pervasive even in contemporary research studies that continue to echo ableist ideologies from the past. In this work, we identify how HRI research reproduces systemic social inequalities and explain how they align with historical misrepresentations, and other systemic barriers. We analyzed 142 papers focusing on HRI and autism published between 2016 and 2022. We critique these studies through a mixed-methods analysis of their definition of autism, study designs, participant recruitment, and results. Our findings indicate that HRI research stigmatizes autism in three dimensions - 1) the pathologization of autism, 2) gender and age-based essentialism, and 3) power imbalances. Our work uncovered that about 90% of HRI research during the timeline explored excluded the perspectives of autistic people, particularly those from understudied groups. We recommend broadening the inclusion of autistic people, considering research objectives beyond clinical use, and diversifying collaborations, foundational works considered, & participant demographics for more inclusive future work.

BioSpark: An End-to-End Generative System for Biological-Analogical Inspirations and Ideation (Late-Breaking Work)
Hyeonsu B. Kang, David Chuan-En Lin, Nikolas Martelaro, Aniket Kittur, Yan-Ying Chen, Matthew K. Hong

Abstract: Nature often inspires solutions for complex engineering problems, but it is challenging for designers to discover relevant analogies and synthesize from them. Here, we present an end-to-end system, BioSpark, that generates biological-analogical mechanisms and provides an interactive interface for comprehension and ideation. From a small seed set of expert-curated mechanisms, BioSpark's pipeline iteratively expands them by constructing and traversing organism taxonomies, aiming to overcome both data sparsity in expert curation and limited conceptual diversity in purely automated analogy generation. The interface helps designers recognize and understand relevant analogs to design problems using four interaction features. We conduct an exploratory study with design students to showcase how BioSpark facilitated analogical transfer of ideas but was limited in conveying active ingredients, the core abstraction underpinning how mechanisms work. We discuss this limitation and other implications such as generative hallucination that could facilitate shifts in human exploration of new design spaces.

Bring Privacy To The Table: Interactive Negotiation for Privacy Settings of Shared Sensing Devices
Haozhe Zhou, Mayank Goel, Yuvraj Agarwal

Abstract: To address privacy concerns with the Internet of Things (IoT) devices, researchers have proposed enhancements in data collection transparency and user control. However, managing privacy preferences for shared devices with multiple stakeholders remains challenging. We introduced ThingPoll, a system that helps users negotiate privacy configurations for IoT devices in shared settings. We designed ThingPoll by observing twelve participants verbally negotiating privacy preferences, from which we identified potentially successful and inefficient negotiation patterns. ThingPoll bootstraps a preference model from a custom crowdsourced privacy preferences dataset. During negotiations, ThingPoll strategically scaffolds the process by eliciting users’ privacy preferences, providing helpful contexts, and suggesting feasible configuration options. We evaluated ThingPoll with 30 participants negotiating the privacy settings of 4 devices. Using ThingPoll, participants reached an agreement in 97.5% of scenarios within an average of 3.27 minutes. Participants reported high overall satisfaction of 83.3% with ThingPoll as compared to baseline approaches.

ClassInSight: Designing Conversation Support Tools to Visualize Classroom Discussion for Personalized Teacher Professional Development
Tricia J. Ngoon, S Sushil, Angela Stewart, Ung-Sang Lee, Saranya Venkatramen, Neil Thawani, Prasenjit Mitra, Sherice Clarke, John Zimmerman, Amy Ogan

Abstract: Teaching is one of many professions for which personalized feedback and reflection can help improve dialogue and discussion between the professional and those they serve. However, professional development (PD) is often impersonal as human observation is labor-intensive. Data-driven PD tools in teaching are of growing interest, but open questions about how professionals engage with their data in practice remain. In this paper, we present ClassInSight, a tool that visualizes three levels of teachers’ discussion data and structures reflection. Through 22 reflection sessions and interviews with 5 high school science teachers, we found themes related to dissonance, contextualization, and sustainability in how teachers engaged with their data in the tool and in how their professional vision, the use of professional expertise to interpret events, shifted over time. We discuss guidelines for these conversational support tools to support personalized PD in professions beyond teaching where conversation and interaction are important.

Co-design Accessible Public Robots: Insights from People with Mobility Disability, Robotic Practitioners and Their Collaborations
Howard Ziyu Han, Franklin Mingzhe Li, Alesandra Baca Vazquez, Daragh Byrne, Nikolas Martelaro, Sarah E Fox

Abstract: Sidewalk robots are increasingly common across the globe. Yet, their operation on public paths poses challenges for people with mobility disabilities (PwMD) who face barriers to accessibility, such as insufficient curb cuts. We interviewed 15 PwMD to understand how they perceive sidewalk robots. Findings indicated that PwMD feel they have to compete for space on the sidewalk when robots are introduced. We next interviewed eight robotics practitioners to learn about their attitudes towards accessibility. Practitioners described how issues often stem from robotic companies addressing accessibility only after problems arise. Both interview groups underscored the importance of integrating accessibility from the outset. Building on this finding, we held four co-design workshops with PwMD and practitioners in pairs. These convenings brought to bear accessibility needs around robots operating in public spaces and in the public interest. Our study aims to set the stage for a more inclusive future around public service robots.

COMPA: Using Conversation Context to Achieve Common Ground in AAC
Stephanie Valencia, Jessica Huynh, Emma Yiang, Yufei Wu, Teresa Wan, Zixuan Zheng, Henny Admoni, Jeffrey Bigham, Amy Pavel

Abstract: Group conversations often shift quickly from topic to topic, leaving a small window of time for participants to contribute. AAC users often miss this window due to the speed asymmetry between using speech and using AAC devices. AAC users may take over a minute longer to contribute, and this speed difference can cause mismatches between the ongoing conversation and the AAC user's response. This results in misunderstandings and missed opportunities to participate. We present COMPA, an add-on tool for online group conversations that seeks to support conversation partners in achieving common ground. COMPA uses a conversation's live transcription to enable AAC users to mark conversation segments they intend to address (Context Marking) and generate contextual starter phrases related to the marked conversation segment (Phrase Assistance) and a selected user intent. We study COMPA in 5 different triadic group conversations, each composed by a researcher, an AAC user and a conversation partner (n=10) and share findings on how conversational context supports conversation partners in achieving common ground.

ConeAct: A Multistable Actuator for Dynamic Materials
Yuyu Lin, Jesse T. Gonzalez, Zhitong Cui, Yash Rajeev Banka, Alexandra Ion

Abstract: Complex actuators in a small form factor are essential for dynamic interfaces. In this paper, we propose ConeAct, a cone-shaped actuator that can extend, contract, and bend in multiple directions to support rich expression in dynamic materials. A key benefit of our actuator is that it is self-contained and portable as the whole system. We designed our actuator’s structure to be multistable to hold its shape passively, while we control its transition between states using active materials, i.e., shape memory alloys. We present the design space by showcasing our actuator module as part of self-rolling robots, reconfigurable deployable structures, volumetric shape-changing objects and tactile displays. To assist users in designing such structures, we present an interactive editor including simulation to design such interactive capabilities.

Context matters: Investigating information sharing in mixed-visual ability social interactions (Late-Breaking Work)
Maryam Bandukda, Yichen Wang, Monica Perusquia-Hernandez, Franklin Mingzhe Li, Catherine Holloway

Abstract: Social inclusion of disabled people has been a topic of interest in HCI research led by the rise of ubiquitous and camera-based technologies. As the research area is increasing, a comprehensive understanding of blind, partially sighted (BPS), and sighted people’s needs in various social settings is needed to fully inform the design of social technologies. To address this, we conducted semi-structured individual and group interviews with 12 BPS and eight sighted participants. Our findings show that context-dependent information-sharing needs of BPS and sighted people vary across social contexts (illustrated in Figure 1). While currently depending on support from sighted companions, BPS participants expressed a strong sense of independence and agency. We discuss the tensions between BPS people’s information needs, sighted people’s privacy concerns, and implications for the design of social technologies to support the social inclusion of BPS people.

Counterspeakers' Perspectives: Unveiling Barriers and AI Needs in the Fight against Online Hate
Jimin Mun, Cathy Buerger*, Jenny Liang*, Joshua Garland, Maarten Sap

Abstract: Counterspeech, i.e., direct responses against hate speech, has become an important tool to address the increasing amount of hate online while avoiding censorship. Although AI has been proposed to help scale up counterspeech efforts, this raises questions of how exactly AI could assist in this process, since counterspeech is a deeply empathetic and agentic process for those involved. In this work, we aim to answer this question, by conducting in-depth interviews with 10 extensively experienced counterspeakers and a large scale public survey with 342 everyday social media users. In participant responses, we identified four main types of barriers and AI needs related to resources, training, impact, and personal harms. However, our results also revealed overarching concerns of authenticity, agency, and functionality in using AI tools for counterspeech. To conclude, we discuss considerations for designing AI assistants that lower counterspeaking barriers without jeopardizing its meaning and purpose.

Cruising Queer HCI on the DL: A Literature Review of LGBTQ+ People in HCI
Jordan Taylor*, Ellen Simpson*, Anh-Ton Tran*, Jed Brubaker, Sarah Fox, and Haiyi Zhu

Abstract: LGBTQ+ people have received increased attention in HCI research, paralleling a greater emphasis on social justice in recent years. However, there has not been a systematic review of how LGBTQ+ people are researched or discussed in HCI. In this work, we review all research mentioning LGBTQ+ people across the HCI venues of CHI, CSCW, DIS, and TOCHI. Since 2014, we find a linear growth in the number of papers substantially about LGBTQ+ people and an exponential increase in the number of mentions. Research about LGBTQ+ people tends to center experiences of being politicized, outside the norm, stigmatized, or highly vulnerable. LGBTQ+ people are typically mentioned as a marginalized group or an area of future research. We identify gaps and opportunities for (1) research about and (2) the discussion of LGBTQ+ in HCI and provide a dataset to facilitate future Queer HCI research.

Deconstructing the Veneer of Simplicity: Co-Designing Introductory Generative AI Workshops with Local Entrepreneurs
Yasmine Kotturi, Angel Anderson, Glenn Ford, Michael Skirpan, Jeffrey P. Bigham

Abstract: Generative AI platforms and features are permeating many aspects of work. Entrepreneurs from lean economies in particular are well positioned to outsource tasks to generative AI given limited resources. In this paper, we work to address a growing disparity in use of these technologies by building on a four-year partnership with a local entrepreneurial hub dedicated to equity in tech and entrepreneurship. Together, we co-designed an interactive workshops series aimed to onboard local entrepreneurs to generative AI platforms. Alongside four community-driven and iterative workshops with entrepreneurs across five months, we conducted interviews with 15 local entrepreneurs and community providers. We detail the importance of communal and supportive exposure to generative AI tools for local entrepreneurs, scaffolding actionable use (and supporting non-use), demystifying generative AI technologies by emphasizing entrepreneurial power, while simultaneously deconstructing the veneer of simplicity to address the many operational skills needed for successful application.

Deepfakes, Phrenology, Surveillance, and More! A Taxonomy of AI Privacy Risks
Hao-Ping (Hank) Lee, Yu-Ju Yang, Thomas Serban Von Davier, Jodi Forlizzi, Sauvik Das

Abstract: Privacy is a key principle for developing ethical AI technologies, but how does including AI technologies in products and services change privacy risks? We constructed a taxonomy of AI privacy risks by analyzing 321 documented AI privacy incidents. We codified how the unique capabilities and requirements of AI technologies described in those incidents generated new privacy risks, exacerbated known ones, or otherwise did not meaningfully alter the risk. We present 12 high-level privacy risks that AI technologies either newly created (e.g., exposure risks from deepfake pornography) or exacerbated (e.g., surveillance risks from collecting training data). One upshot of our work is that incorporating AI technologies into a product can alter the privacy risks it entails. Yet, current approaches to privacy-preserving AI/ML (e.g., federated learning, differential privacy, checklists) only address a subset of the privacy risks arising from the capabilities and data requirements of AI.

Designing for Harm Reduction: Communication Repair for Multicultural Users’ Voice Interactions
Kimi Wenzel, Geoff Kaufman

Abstract: Voice assistants’ inability to serve people-of-color and non-native English speakers has largely been documented as a quality-of-service harm. However, little work has investigated what downstream harms propagate from this poor service. How does poor usability materially manifest and affect users’ lives? And what interaction designs might help users recover from these effects? We identify 6 downstream harms that propagate from quality-of-service harms in voice assistants. Through interviews and design activities with 16 multicultural participants, we unveil these 6 harms, outline how multicultural users uniquely personify their voice assistant, and suggest how these harms and personifications may affect their interactions. Lastly, we employ techniques from psychology on communication repair to contribute suggestions for harm-reducing repair that may be implemented in voice technologies. Our communication repair strategies include: identity affirmations (intermittent frequency), cultural sensitivity, and blame redirection. This work shows potential for a harm-repair framework to positively influence voice interactions.

Designing Upper-Body Gesture Interaction with and for People with Spinal Muscular Atrophy in VR
Jingze Tian, Yingna Wang, Keye Yu, Liyi Xu, Junan Xie, Franklin Mingzhe Li, Yafeng Niu, Mingming Fan

Abstract: Recent research proposed gaze-assisted gestures to enhance interaction within virtual reality (VR), providing opportunities for people with motor impairments to experience VR. Compared to people with other motor impairments, those with Spinal Muscular Atrophy (SMA) exhibit enhanced distal limb mobility, providing them with more design space. However, it remains unknown what gaze-assisted upper-body gestures people with SMA would want and be able to perform. We conducted an elicitation study in which 12 VR-experienced people with SMA designed upper-body gestures for 26 VR commands, and collected 312 user-defined gestures. Participants predominantly favored creating gestures with their hands. The type of tasks and participants' abilities influence their choice of body parts for gesture design. Participants tended to enhance their body involvement and preferred gestures that required minimal physical effort, and were aesthetically pleasing. Our research will contribute to creating better gesture-based input methods for people with motor impairments to interact with VR.

DISCERN: Designing Decision Support Interfaces to Investigate the Complexities of Workplace Social Decision-Making With Line Managers
Pranav Khadpe, Lindy Le, Kate Nowak, Shamsi Iqbal, Jina Suh

Abstract: Line managers form the first level of management in organizations, and must make complex decisions, while maintaining relationships with those impacted by their decisions. Amidst growing interest in technology-supported decision-making at work, their needs remain understudied. Further, most existing design knowledge for supporting social decision-making comes from domains where decision-makers are more socially detached from those they decide for. We conducted iterative design research with line managers within a technology organization, investigating decision-making practices, and opportunities for technological support. Through formative research, development of a decision-representation tool—DISCERN—and user enactments, we identify their communication and analysis needs that lack adequate support. We found they preferred tools for externalizing reasoning rather than tools that replace interpersonal interactions, and they wanted tools to support a range of intuitive and calculative decision-making. We discuss how design of social decision-making supports, especially in the workplace, can more explicitly support highly interactional social decision-making.

EITPose: Wearable and Practical Electrical Impedance Tomography for Continuous Hand Pose Estimation
Alexander Kyu*, Hongyu Mao*, Junyi Zhu, Mayank Goel, Karan Ahuja

Abstract: Real-time hand pose estimation has a wide range of applications spanning gaming, robotics, and human-computer interaction. In this paper, we introduce EITPose, a wrist-worn, continuous 3D hand pose estimation approach that uses eight electrodes positioned around the forearm to model its interior impedance distribution during pose articulation. Unlike wrist-worn systems relying on cameras, EITPose has a slim profile (12 mm thick sensing strap) and is power-efficient (consuming only 0.3 W of power), making it an excellent candidate for integration into consumer electronic devices. In a user study involving 22 participants, EITPose achieves with a within-session mean per joint positional error of 11.06 mm. Its camera-free design prioritizes user privacy, yet it maintains cross-session and cross-user accuracy levels comparable to camera-based wrist-worn systems, thus making EITPose a promising technology for practical hand pose estimation.

Envisioning Support-Centered Technologies for Language Practice and Use: Needs and Design Opportunities for Immigrant English Language Learners (ELLs)
Adinawa D. Adjagbodjou, Geoff Kaufman

Abstract: Immigrant English Language Learners (ELLs) who are learning the majority language in a new country are required to participate in the informal language space on a daily basis to gain access to essential economic and social resources. In contrast to formal language spaces, which extensive literature has researched, exploration of informal language spaces, which present a number of linguistic and psychological challenges without scaffolded support, remains limited. In this work, we conduct a qualitative interview study to explore the use of support tools to facilitate participation in daily life for ELLs, investigating the efficacy of these tools, obstacles encountered, and perceptions of what defines positive and negative experiences. We aim to contribute a deeper, more nuanced understanding of the experience of language use in practical scenarios for ELLs and present a set of actionable considerations for designers working with ELLs that prioritize their linguistic, affective, and social needs.

Exploring How Multiple Levels of GPT-Generated Programming Hints Support or Disappoint Novices (Late-Breaking Work)
Ruiwei Xiao, Xinying Hou, John Stamper

Abstract: Recent studies have integrated large language models (LLMs) into diverse educational contexts, including providing adaptive programming hints, a type of feedback focuses on helping students move forward during problem-solving. However, most existing LLM-based hint systems are limited to one single hint type. To investigate whether and how different levels of hints can support students' problem-solving and learning, we conducted a think-aloud study with 12 novices using the LLM Hint Factory, a system providing four levels of hints from general natural language guidance to concrete code assistance, varying in format and granularity. We discovered that high-level natural language hints alone can be helpless or even misleading, especially when addressing next-step or syntax-related help requests. Adding lower-level hints, like code examples with in-line comments, can better support students. The findings open up future work on customizing help responses from content, format, and granularity levels to accurately identify and meet students' learning needs.

Expressive, Scalable, Mid-air Haptics with Synthetic Jets (Journal)
Vivian Shen, Chris Harrison, Craig Shultz

Abstract: Non-contact, mid-air haptic devices have been utilized for a wide variety of experiences, including those in extended reality, public displays, medical, and automotive domains. In this work, we explore the use of synthetic jets as a promising and under-explored mid-air haptic feedback method. We show how synthetic jets can scale from compact, low-powered devices, all the way to large, long-range, and steerable devices. We built seven functional prototypes targeting different application domains, in order to illustrate the broad applicability of our approach. These example devices are capable of rendering complex haptic effects, varying in both time and space. We quantify the physical performance of our designs using spatial pressure and wind flow measurements, and validate their compelling effect on users with stimuli recognition and qualitative studies.

Interdisciplinary Approaches to Cybervulnerability Impact Assessment for Energy Critical Infrastructure
Andrea Gallardo, Robert Erbes, Katya LeBlanc, Lujo Bauer, Lorrie Cranor

Abstract: As energy infrastructure becomes more interconnected, understanding cybersecurity risks to production systems requires integrating operational and computer security knowledge. We interviewed 18 experts working in the field of energy critical infrastructure to compare what information they find necessary to assess the impact of computer vulnerabilities on energy operational technology. These experts came from two groups: 1) computer security experts and 2) energy sector operations experts. We find that both groups responded similarly for general categories of information and displayed knowledge about both domains, perhaps due to their interdisciplinary work at the same organization. Yet, we found notable differences in the details of their responses and in their stated perceptions of each group’s approaches to impact assessment. Their suggestions for collaboration across domains highlighted how these two groups can work together to help each other secure the energy grid. Our findings inform the development of interdisciplinary security approaches in critical-infrastructure contexts.

Investigating Demographics and Motivation in Engineering Education Using Radio and Phone-Based Educational Technologies
Christine Kwon, Darren Butler, Judith Odili Uchidiuno, John Stamper, Amy Ogan

Abstract: Despite the best intentions to support equity with educational technologies, they often lead to a “rich get richer” effect, in which communities of more advantaged learners gain greater benefit from these solutions. Effective design of these technologies necessitates a deeper understanding of learners in understudied contexts and their motivations to pursue an education. Consequently, we studied a 15-week remote course launched in 2021 with 17,896 learners that provided engineering education through a radio and phone-based system aimed for use in rural settings within Northern Uganda. We address shifts in learners’ motivations for course participation and investigate the impact of demographic features and motivations of students on persistence and performance. We found significant increases in student motivation to learn more about and pursue STEM. Importantly, the course was most successful for learners in demographics who typically experience fewer educational opportunities, showing promise for such technologies to close opportunity gaps.

Investigating Why Clinicians Deviate from Standards of Care: Liberating Patients from Mechanical Ventilation in the ICU
Nur Yildirim, Susanna Zlotnikov, Aradhana Venkat, Gursimran Chawla, Jennifer Kim, Leigh Bukowski, Jeremy Kahn, James McCann, John Zimmerman

Abstract: Clinical practice guidelines, care pathways, and protocols are designed to support evidence-based practices for clinicians; however, their adoption remains a challenge. We set out to investigate why clinicians deviate from the "Wake Up and Breathe" protocol, an evidence-based guideline for liberating patients from mechanical ventilation in the intensive care unit (ICU). We conducted over 40 hours of direct observations of live clinical workflows, 17 interviews with frontline care providers, and 4 co-design workshops at three different medical intensive care units. Our findings indicate that unlike prior literature suggests, disagreement with the protocol is not a substantial barrier to adoption. Instead, the uncertainty surrounding the application of the protocol for individual patients leads clinicians to deprioritize adoption in favor of tasks where they have high certainty. Reflecting on these insights, we identify opportunities for technical systems to help clinicians in effectively executing the protocol and discuss future directions for HCI research to support the integration of protocols into clinical practice in complex, team-based healthcare settings.

Is a Trustmark and QR Code Enough? The Effect of IoT Security and Privacy Label Information Complexity on Consumer Comprehension and Behavior
Claire C. Chen, Dillon Shu, Hamsini Ravishankar, Xinran Li, Yuvraj Agarwal, Lorrie Faith Cranor

Abstract: The U.S. Government is developing a package label to help consumers access reliable security and privacy information about Internet of Things (IoT) devices when making purchase decisions. The label will include the U.S. Cyber Trust Mark, a QR code to scan for more details, and potentially additional information. To examine how label information complexity and educational interventions affect comprehension of security and privacy attributes and label QR code use, we conducted an online survey with 518 IoT purchasers. We examined participants' comprehension and preferences for three labels of varying complexities, with and without an educational intervention. Participants favored and correctly utilized the two higher-complexity labels, showing a special interest in the privacy-relevant content. Furthermore, while the educational intervention improved understanding of the QR code’s purpose, it had a modest effect on QR scanning behavior. We highlight clear design and policy directions for creating and deploying IoT security and privacy labels.

Jigsaw: Supporting Designers to Prototype Multimodal Applications by Assembling AI Foundation Models
David Chuan-En Lin, Nikolas Martelaro

Abstract: Recent advancements in AI foundation models have made it possible for them to be utilized off-the-shelf for creative tasks, including ideating design concepts or generating visual prototypes. However, integrating these models into the creative process can be challenging as they often exist as standalone applications tailored to specific tasks. To address this challenge, we introduce Jigsaw, a prototype system that employs puzzle pieces as metaphors to represent foundation models. Jigsaw allows designers to combine different foundation model capabilities across various modalities by assembling compatible puzzle pieces. To inform the design of Jigsaw, we interviewed ten designers and distilled design goals. In a user study, we showed that Jigsaw enhanced designers' understanding of available foundation model capabilities, provided guidance on combining capabilities across different modalities and tasks, and served as a canvas to support design exploration, prototyping, and documentation.

MARingBA: Music-Adaptive Ringtones for Blended Audio Notification Delivery
Alexander Wang, Yi Fei Cheng, David Lindlbauer

Abstract: Audio notifications provide users with an efficient way to access information beyond their current focus of attention. Current notification delivery methods, like phone ringtones, are primarily optimized for high noticeability, enhancing situational awareness in some scenarios but causing disruption and annoyance in others. In this work, we build on the observation that music listening is now a commonplace practice and present MARingBA, a novel approach that blends ringtones into background music to modulate their noticeability. We contribute a design space exploration of music-adaptive manipulation parameters, including beat matching, key matching, and timbre modifications, to tailor ringtones to different songs. Through two studies, we demonstrate that MARingBA supports content creators in authoring audio notifications that fit low, medium, and high levels of urgency and noticeability. Additionally, end users prefer music-adaptive audio notifications over conventional delivery methods, such as volume fading.

Meta-Manager: A Tool for Collecting and Exploring Meta Information about Code
Amber Horvath, Andrew Macvean, Brad A Myers

Abstract: Modern software engineering is in a state of flux. With more development utilizing AI code generation tools and the continued reliance on online programming resources, understanding code and the original intent behind it is becoming more important than it ever has been. To this end, we have developed the "Meta-Manager", a Visual Studio Code extension, with a supplementary browser extension, that automatically collects and organizes changes made to code while keeping track of the provenance of each part of the code, including code that has been AI-generated or copy-pasted from popular programming resources online. These sources and subsequent changes are represented in the editor and may be explored using searching and filtering mechanisms to help developers answer historically hard-to-answer questions about code, its provenance, and its design rationale. In our evaluation of Meta-Manager, we found developers were successfully able to use it to answer otherwise unanswerable questions about an unfamiliar code base.

MineXR: Mining Personalized Extended Reality Interfaces
Hyunsung Cho, Yukang Yan, Kashyap Todi, Mark Parent, Missie Smith, Tanya Jonker, Hrvoje Benko, David Lindlbauer

Abstract: Extended Reality (XR) interfaces offer engaging user experiences, but their effective design requires a nuanced understanding of user behavior and preferences. This knowledge is challenging to obtain without the widespread adoption of XR devices. We introduce MineXR, a design mining workflow and data analysis platform for collecting and analyzing personalized XR user interaction and experience data. MineXR enables elicitation of personalized interfaces from participants of a data collection: for any particular context, participants create interface elements using application screenshots from their own smartphone, place them in the environment, and simultaneously preview the resulting XR layout on a headset. Using MineXR, we contribute a dataset of personalized XR interfaces collected from 31 participants, consisting of 695 XR widgets created from 178 unique applications. We provide insights for XR widget functionalities, categories, clusters, UI element types, and placement. Our open-source tools and data support researchers and designers in developing future XR interfaces.

Mitigating Barriers to Public Social Interaction with Meronymous Communication
Nouran Soliman, Hyeonsu B Kang, Matt Latzke, Jonathan Bragg, Joseph Chee Chang, Amy X. Zhang, David R Karger

Abstract: In communities with social hierarchies, fear of judgment can discourage communication. While anonymity may alleviate some social pressure, fully anonymous spaces enable toxic behavior and hide the social context that motivates people to participate and helps them tailor their communication. We explore a design space of meronymous communication, where people can reveal carefully chosen aspects of their identity and also leverage trusted endorsers to gain credibility. We implemented these ideas in a system for scholars to meronymously seek and receive paper recommendations on Twitter and Mastodon. A formative study with 20 scholars confirmed that scholars see benefits to participating but are deterred due to social anxiety. From a month-long public deployment, we found that with meronymity, junior scholars could comfortably ask "newbie" questions and get responses from senior scholars who they normally found intimidating. Responses were also tailored to the aspects about themselves that junior scholars chose to reveal.

Morphing Matter for Teens: Research Processes as a Template for Cross-Disciplinary Activities
Lea Albaugh, Melinda Chen, Sunniva Liu, Harshika Jain, Alisha Collins, Lining Yao

Abstract: We distilled a set of core practices within "morphing matter" research, derived a set of underlying skills and values, and developed these into a weekend workshop for high-school students. Participants in our workshop sampled a variety of research processes, including materials science and contextual design, incorporating curriculum-appropriate learning goals, toward an integrated pneumatic fashion project. We describe our approach, activity plan, and assessment as well as opportunities for research as an educational template to push beyond current “STEAM''-based educational practices for cross-disciplinary engagement.

Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for Radiology
Nur Yildirim, Hannah Richardson, Teo Wetscherek, Junaid Bajwa, Joseph Jacob, Mark Pinnock, Stephen Harris, Daniel Coelho de Castro, Shruthi Bannur, Stephanie Hyland, Pratik Ghosh, Mercy Ranjit, Kenza Bouzid, Anton Schwaighofer, Fernando Pérez-García, Harshita Sharma, Ozan Oktay, Matthew Lungren, Javier Alvarez-Valle, Aditya Nori, Anja Thieme

Abstract: Recent advances in AI combine large language models (LLMs) with vision encoders that bring forward unprecedented technical capabilities to leverage for a wide range of healthcare applications. Focusing on the domain of radiology, vision-language models (VLMs) achieve good performance results for tasks such as generating radiology findings based on a patient's medical image, or answering visual questions (e.g., "Where are the nodules in this chest X-ray?''). However, the clinical utility of potential applications of these capabilities is currently underexplored. We engaged in an iterative, multidisciplinary design process to envision clinically relevant VLM interactions, and co-designed four VLM use concepts: Draft Report Generation, Augmented Report Review, Visual Search and Querying, and Patient Imaging History Highlights. We studied these concepts with 13 radiologists and clinicians who assessed the VLM concepts as valuable, yet articulated many design considerations. Reflecting on our findings, we discuss implications for integrating VLM capabilities in radiology, and for healthcare AI more generally.

PaperWeaver: Enriching Topical Paper Alerts by Contextualizing Recommended Papers with User-collected Papers
Yoonjoo Lee, Hyeonsu B. Kang, Matthew Latzke, Juho Kim, Jonathan Bragg, Joseph Chee Chang, Pao Siangliulue

With the rapid growth of scholarly archives, researchers subscribe to "paper alert" systems that periodically provide them with recommendations of recently published papers that are similar to previously collected papers. However, researchers sometimes struggle to make sense of nuanced connections between recommended papers and their own research context, as existing systems only present paper titles and abstracts. To help researchers spot these connections, we present PaperWeaver, an enriched paper alerts system that provides contextualized text descriptions of recommended papers based on user-collected papers. PaperWeaver employs a computational method based on Large Language Models (LLMs) to infer users’ research interests from their collected papers, extract context-specific aspects of papers, and compare recommended and collected papers on these aspects. Our user study (N=15) showed that participants using PaperWeaver were able to better understand the relevance of recommended papers and triage them more confidently when compared to a baseline that presented the related work sections from recommended papers.

Predicting the Noticeability of Dynamic Virtual Elements in Virtual Reality
Zhipeng Li, Yi Fei Cheng, Yukang Yan, David Lindlbauer

Abstract: While Virtual Reality (VR) systems can present virtual elements such as notifications anywhere, designing them so they are not missed by or distracting to users is highly challenging for content creators. To address this challenge, we introduce a novel approach to predict the noticeability of virtual elements. It computes the visual saliency distribution of what users see, and analyzes the temporal changes of the distribution with respect to the dynamic virtual elements that are animated. The computed features serve as input for a long short-term memory (LSTM) model that predicts whether a virtual element will be noticed. Our approach is based on data collected from 24 users in different VR environments performing tasks such as watching a video or typing. We evaluate our approach (n = 12), and show that it can predict the timing of when users notice a change to a virtual element within 2.56 sec compared to a ground truth, and demonstrate the versatility of our approach with a set of applications. We believe that our predictive approach opens the path for computational design tools that assist VR content creators in creating interfaces that automatically adapt virtual elements based on noticeability.

Robotic Metamaterials: A Modular System for Hands-On Configuration of Ad-Hoc Dynamic Applications
Zhitong Cui, Shuhong Wang, Violet Yinuo Han, Tucker Rae-Grant, Willa Yunqi Yang, Alan Zhu, Scott E Hudson, Alexandra Ion

We propose augmenting initially passive structures built from simple repeated cells, with novel active units to enable dynamic, shape-changing, and robotic applications. Inspired by metamaterials that can employ mechanisms, we build a framework that allows users to configure cells of this passive structure to allow it to perform complex tasks. A key benefit is that our structures can be repeatedly (re)configured by users inserting our configuration units to turn the passive material into, e.g., locomotion robots, integrated motion platforms, or interactive interfaces, as we demonstrate in this paper. To this end, we present a mechanical system consisting of a flexible, passive, shearing lattice structure, as well as rigid and active unit cells to be inserted into the lattice for configuration. The active unit is a closed-loop pneumatically controlled shearing cell to dynamically actuate the macroscopic movement of the structure. The passive rigid cells redirect the forces to create complex motion with a reduced number of active cells. Since the placement of the rigid and active units is challenging, we offer a computational design tool. The tool optimizes the cell placement to match the macroscopic, user-defined target motions and generates the control code for the active cells.

Selenite: Scaffolding Online Sensemaking with Comprehensive Overviews Elicited from Large Language Models
Michael Xieyang Liu, Tongshuang Wu, Tianying Chen, Franklin Mingzhe Li, Aniket Kittur, Brad A. Myers

Abstract: Sensemaking in unfamiliar domains can be challenging, demanding considerable user effort to compare different options with respect to various criteria. Prior research and our formative study found that people would benefit from reading an overview of an information space upfront, including the criteria others previously found useful. However, existing sensemaking tools struggle with the "cold-start" problem -- not only requiring significant input from previous users to generate and share these overviews, but also that such overviews may turn out to be biased and incomplete. In this work, we introduce a novel system, Selenite, which leverages Large Language Models (LLMs) as reasoning machines and knowledge retrievers to automatically produce a comprehensive overview of options and criteria to jumpstart users' sensemaking processes. Subsequently, Selenite also adapts as people use it, helping users find, read, and navigate unfamiliar information in a systematic yet personalized manner. Through three studies, we found that Selenite produced accurate and high-quality overviews reliably, significantly accelerated users' information processing, and effectively improved their overall comprehension and sensemaking experience.

Situating Data Sets: Making Public Data Actionable for Housing Justice
Anh-Ton Tran, Grace Guo, Jordan Taylor, Katsuki Chan, Elora Raymond, Carl DiSalvo

Abstract: Activists, governments, and academics regularly advocate for more open data. But how is data made open, and for whom is it made useful and usable? In this paper, we investigate and describe the work of making eviction data open to tenant organizers. We do this through an ethnographic description of ongoing work with a local housing activist organization. This work combines observation, direct participation in data work, and creating media artifacts, specifically digital maps. Our interpretation is grounded in D’Ignazio and Klein’s Data Feminism, emphasizing standpoint theory. Through our analysis and discussion, we highlight how shifting positionalities from data intermediaries to data accomplices affects the design of data sets and maps. We provide HCI scholars with three design implications when situating data for grassroots organizers: becoming a domain beginner, striving for data actionability, and evaluating our design artifacts by the social relations they sustain rather than just their technical efficacy.

Sketching AI Concepts with Capabilities and Examples: AI Innovation in the Intensive Care Unit
Nur Yildirim, Susanna Zlotnikov, Deniz Sayar, Jeremy Kahn, Leigh Bukowski, Sher Shah Amin, Kathryn Riman, Billie Davis, John Minturn, Andrew King, Dan Ricketts, Lu Tang, Venkatesh Sivaraman, Adam Perer, Sarah Preum, James McCann, John Zimmerman

Abstract: Advances in artificial intelligence (AI) have enabled unprecedented capabilities, yet innovation teams struggle when envisioning AI concepts. Data science teams think of innovations users do not want, while domain experts think of innovations that cannot be built. A lack of effective ideation seems to be a breakdown point. How might multidisciplinary teams identify buildable and desirable use cases? This paper presents a first hand account of ideating AI concepts to improve critical care medicine. As a team of data scientists, clinicians, and HCI researchers, we conducted a series of design workshops to explore more effective approaches to AI concept ideation and problem formulation. We detail our process, the challenges we encountered, and practices and artifacts that proved effective. We discuss the research implications for improved collaboration and stakeholder engagement, and discuss the role HCI might play in reducing the high failure rate experienced in AI innovation.

Stranger Danger? Investor Behavior and Incentives on Cryptocurrency Copy-Trading Platforms
Daisuke Kawai, Kyle Soska, Bryan Routledge, Ariel Zetlin-Jones, Nicolas Christin

Abstract: Several large financial trading platforms have recently begun implementing “copy trading,'' a process by which a leader allows copiers to automatically mirror their trades in exchange for a share of the profits realized. While it has been shown in many contexts that platform design considerably influences user choices---users tend to disproportionately trust rankings presented to them---we would expect that here, copiers exercise due diligence given the money at stake, typically USD 500--2\,000 or more. We perform a quantitative analysis of two major cryptocurrency copy-trading platforms, with different default leader ranking algorithms. One of these platforms additionally changed the information displayed during our study. In all cases, we show that the platform UI significantly influences copiers' decisions. Besides being sub-optimal, this influence is problematic as rankings are often easily gameable by unscrupulous leaders who prey on novice copiers, and they create perverse incentives for all platform users.

The Future of HCI-Policy Collaboration
Qian Yang, Richmond Y. Wong, Steven Jackson, Sabine Junginger, Margaret D. Hagan, Thomas Gilbert, John Zimmerman

Abstract: Policies significantly shape computation's societal impact, a crucial HCI concern. However, challenges persist when HCI professionals attempt to integrate policy into their work or affect policy outcomes. Prior research considered these challenges at the "border" of HCI and policy. This paper asks: What if HCI considers policy integral to its intellectual concerns, placing system-people-policy interaction not at the border but nearer the center of HCI research, practice, and education? What if HCI fosters a mosaic of methods and knowledge contributions that blend system, human, and policy expertise in various ways, just like HCI has done with blending system and human expertise? We present this re-imagined HCI-policy relationship as a provocation and highlight its usefulness: It spotlights previously overlooked system-people-policy interaction work in HCI. It unveils new opportunities for HCI's futuring, empirical, and design projects. It allows HCI to coordinate its diverse policy engagements, enhancing its collective impact on policy outcomes.

The Situate AI Guidebook: Co-Designing a Toolkit to Support Multi-Stakeholder, Early-stage Deliberations Around Public Sector AI Proposal
Anna Kawakami, Amanda Coston, Haiyi Zhu*, Hoda Heidari*, Kenneth Holstein*

Abstract: Public sector agencies are rapidly deploying AI systems to augment or automate critical decisions in real-world contexts like child welfare, criminal justice, and public health. A growing body of work documents how these AI systems often fail to improve services in practice. These failures can often be traced to decisions made during the early stages of AI ideation and design, such as problem formulation. However, today, we lack systematic processes to support effective, early-stage decision-making about whether and under what conditions to move forward with a proposed AI project. To understand how to scaffold such processes in real-world settings, we worked with public sector agency leaders, AI developers, frontline workers, and community advocates across four public sector agencies and three community advocacy groups in the United States. Through an iterative co-design process, we created the Situate AI Guidebook: a structured process centered around a set of deliberation questions to scaffold conversations around (1) goals and intended use or a proposed AI system, (2) societal and legal considerations, (3) data and modeling constraints, and (4) organizational governance factors. We discuss how the guidebook's design is informed by participants’ challenges, needs, and desires for improved deliberation processes. We further elaborate on implications for designing responsible AI toolkits in collaboration with public sector agency stakeholders and opportunities for future work to expand upon the guidebook. This design approach can be more broadly adopted to support the co-creation of responsible AI toolkits that scaffold key decision-making processes surrounding the use of AI in the public sector and beyond.

Towards Inclusive Source Code Readability Based on the Preferences of Programmers with Visual Impairments
Maulishree Pandey, Steve Oney, Andrew Begel

Abstract: Code readability is crucial for program comprehension, maintenance, and collaboration. However, many of the standards for writing readable code are derived from sighted developers' readability needs. We conducted a qualitative study with 16 blind and visually impaired (BVI) developers to better understand their readability preferences for common code formatting rules such as identifier naming conventions, line length, and the use of indentation. Our findings reveal how BVI developers' preferences contrast with those of sighted developers and how we can expand the existing rules to improve code readability on screen readers. Based on the findings, we contribute an inclusive understanding of code readability and derive implications for programming languages, development environments, and style guides. Our work helps broaden the meaning of readable code in software engineering and accessibility research.

Understanding Documentation Use Through Log Analysis: A Case Study of Four Cloud Services
Daye Nam, Andrew Macvean, Brad A Myers, Bogdan Vasilescu

Abstract: Almost no modern software system is written from scratch, and developers are required to effectively learn to use third-party libraries and software services. Thus, many practitioners and researchers have looked for ways to create effective documentation that supports developers' learning. However, few efforts have focused on how people actually use the documentation. In this paper, we report on an exploratory, multi-phase, mixed methods empirical study of documentation page-view logs from four cloud-based industrial services. By analyzing page-view logs for over 100,000 users, we find diverse patterns of documentation page visits. Moreover, we show statistically that which documentation pages people visit often correlates with user characteristics such as past experience with the specific product, on the one hand, and with future adoption of the API on the other hand. We discuss the implications of these results on documentation design and propose documentation page-view log analysis as a feasible technique for design audits of documentation, from ones written for software developers to ones designed to support end users (e.g., Adobe Photoshop).

Waxpaper Actuator: Sequentially and Conditionally Programmable Wax Paper for Morphing Interfaces
Di Wu, Emily Guan*, Yunjia Zhang*, Hsuanju Lai, Qiuyu Lu*, Lining Yao*

Abstract: We print wax on the paper and turn the composite into a sequentially-controllable, moisture-triggered, rapidly-fabricated, and low-cost shape-changing interface. This technique relies on a sequential control method that harnesses two critical variables: gray levels and water amount. By integrating these variables within a bilayer structure, composed of a paper substrate and wax layer, we produce a diverse wax pattern using a solid inkjet printer. These patterns empower wax paper actuators with rapid control over sequential deformations, harnessing various bending degrees and response times, which helps to facilitate the potential of swift personal actuator customization. Our exploration encompasses the material mechanism, the sequential control method, fabrication procedures, primitive structures, and evaluations. Additionally, we introduce a user-friendly software tool for design and simulation. Lastly, we demonstrate our approach through applications across four domains: agricultural seeding, interactive toys and art, home decoration, and electrical control.

Wikibench: Community-Driven Data Curation for AI Evaluation on Wikipedia
Tzu-Sheng Kuo, Aaron Halfaker, Zirui Cheng, Jiwoo Kim, Meng-Hsin Wu, Tongshuang Wu, Kenneth Holstein*, Haiyi Zhu*

Abstract: AI tools are increasingly deployed in community contexts. However, datasets used to evaluate AI are typically created by developers and annotators outside a given community, which can yield misleading conclusions about AI performance. How might we empower communities to drive the intentional design and curation of evaluation datasets for AI that impacts them? We investigate this question on Wikipedia, an online community with multiple AI-based content moderation tools deployed. We introduce Wikibench, a system that enables communities to collaboratively curate AI evaluation datasets, while navigating ambiguities and differences in perspective through discussion. A field study on Wikipedia shows that datasets curated using Wikibench can effectively capture community consensus, disagreement, and uncertainty. Furthermore, study participants used Wikibench to shape the overall data curation process, including refining label definitions, determining data inclusion criteria, and authoring data statements. Based on our findings, we propose future directions for systems that support community-driven data curation.