Beyond Riding: Passenger Engagement with Driver Labor through Gamified InteractionsModern cities across the globe increasingly rely on ridehail services for on-demand transportation and mobility. But for drivers, such marketed affordances give rise to hidden burdens and vulnerabilities that evade the oversight of consumers and regulators. To effectively advance worker protections and motivate more socially responsible practices, consumers must understand the realistic labor, logistics and costs involved with ridehail driving. Through nine workshops with 19 drivers and 15 passengers, we explore the potential for gamified in-ride interactions to facilitate engagement with real (and lived) driver experiences, surfacing passenger knowledge gaps around latent ridehail conditions, prompting reflection and shifts in perception of their relative power and consumption behaviors, highlighting drivers' preferences for creating more immersive and contextualized service experiences, and identifying design opportunities for safe and appropriate passenger-driver interactions that motivate solidarity. In sum, we advance conceptual understandings of in-ride social and managerial relations, demonstrate potential for citizen-led advocacy in algorithmically-managed labor, and offer design guidelines for more human-centered workplace technologies.2026JHJane Hsieh et al.Carnegie Mellon UniversityRidesharing PlatformsGamification DesignBehavior Change & Reflection TechnologyCHI
Breaking Negative Cycles: A Reflection-to-Action System for Adaptive ChangeBreaking negative mental health cycles, including rumination and recurring regrets, requires reflection that translates awareness into behavioral change. Grounded in the Transtheoretical Model (TTM) and Gross’s Emotion Regulation (ER) Process Model, we examine how Technologies Supporting Self-Reflection (TSR) bridge reflection and action. In a 15-day in-the-wild study (N = 20), participants used a voice-based journaling system to capture regrets and wishes and engaged in WhatIf-Planning, a novel structured reflection module that integrates counterfactual thinking with if–then planning. Participants were randomized to either a free-form condition or Gross-guided condition, which maps the five processes of Gross’s ER model into explicit journaling prompts. We contribute (1) a unified reflection-to-action TSR system that operationalizes the Preparation stage of TTM to bridge Contemplation and Action, and (2) triangulated empirical evidence from an in-the-wild journaling study that operationalizes Gross’s Process Model, revealing effects on coping flexibility and emotion regulation in daily life. Results show significant pre–post improvements in coping flexibility across conditions, indicating adaptive self-regulation, with the Gross-guided group generating more counterfactual alternatives, articulating more concrete if–then action plans, and implementing more plans for self-driven change.2026MKMinsol Michelle Kim et al.Massachusetts Institute of TechnologyBehavior Change & Reflection TechnologyAffective Feedback & Emotion Regulation InterfacesMental Health Apps & Online Support CommunitiesCHI
Balancing Automation and Discretion: How Decision Stakes and Human-AI Collaboration Affect Citizen Perceptions in Public AdministrationThe growing use of AI in public administration improves efficiency, yet its use in discretionary decisions raises concerns about fairness and legitimacy. While prior research examined decision stakes and Human–AI decision-making configurations separately, their combined effect on citizens’ perceptions of fairness and adoption remains underexplored. We conducted a mixed-method Wizard-of-Oz study (n=43) using an Intelligent-Self-Service-Kiosk. Participants completed a low-stakes (ID renewal) and a high-stakes (social housing) task under one of three decision-making configurations: AI alone, AI with human supervision, and human with AI advice or recommendation. Quantitative analysis found no significant effects, highlighting the limits of standard metrics. However, qualitative interviews revealed that citizens valued human involvement, requiring meaningful over symbolic oversight. They emphasized interactive dialogue before decisions to capture their circumstances and after, to facilitate appeals. We contribute evidence of tensions between citizens’ desire for efficiency and need for human-control and fairness. We provide guidance for designing citizen-centered AI systems that align with democratic values.2026SASaja Aljuneidi et al.OFFIS - Institute for Information TechnologyAI-Assisted Decision-Making & AutomationAI Ethics, Fairness & AccountabilityPrivacy by Design & User ControlCHI
Bridging Technology and Policy Design: A Robot Policy Design Toolkit to Support Collaborations in PolicymakingAdvancements in artificial intelligence are challenging current policy frameworks. Both the human-computer interaction (HCI) community and policymakers note that technologies are designed better when they take into account the impact on society, and that policies are more effective when they are grounded in technical knowledge. Design research can be a powerful lens to support policy design processes. Driven by the potential for design research in technology policy development, the Robot Policy Design Toolkit (RPDT) was designed to support forecasting of robot technology policy and facilitate policy design experiences through a speculative design approach, centering forecasting, compromise, and simplicity design principles. This paper introduces the toolkit's design, reveals insights from how technologists design policies around social robots, and provides reflections from technology policy experts on the value and potential for design research tools, such as the RPDT, in policymaking contexts.2026AOAnastasia Kouvaras Ostrowski et al.Purdue UniversitySocial Robot InteractionTechnology Ethics & Critical HCIParticipatory DesignCHI
Once Upon AI Time: Combining Narrative and Games for Early AI LiteracyArtificial intelligence (AI) is increasingly present in children’s lives, yet few tools support developmentally appropriate AI literacy for grades K-3. This work examines the role of narrative in early AI literacy by directly comparing two versions of interactive game-based digital storybooks for children ages 6-9. The "Book+" condition combined an overarching story and characters with mini-games and scaffolded AI interactions, designed to be enjoyable, provide narrative context, and to give hands-on AI experience. We compared this with a "Game" condition that included the same learning goals, mini-games, and AI interactions but replaced the narrative with primarily instructional text. Across 57 participants, both conditions elicited high engagement, but "Book+" participants showed significantly greater learning gains and higher perceived knowledge. Qualitative findings revealed that while both groups enjoyed the creative AI mini-games, "Book+" participants more frequently used AI vocabulary in responses, connected concepts to the learning context, and expressed stronger emotional connection.2026IPIsabella Pu et al.Massachusetts Institute of TechnologyChildren's AI Literacy & Data LiteracyProgramming Education & Computational ThinkingSpecial Education TechnologyCHI
Cultivating a Supportive Sphere: Designing Technology to Increase Social Support for Foster-Involved YouthApproximately 400,000 youth in the US are living in foster care due to experiences with abuse or neglect at home. For multiple reasons, these youth often don’t receive adequate social support from those around them. Despite technology’s potential, very little work has explored how these tools can provide more support to foster-involved youth. To begin to fill this gap, we worked with current and former foster-involved youth to develop the first digital tool that aims to increase social support for this population, creating a novel system in which users complete reflective check-ins in an online community setting. We then conducted a pilot study with 15 current and former foster-involved youth, comparing the effect of using the app for two weeks to two weeks of no intervention. We collected qualitative and quantitative data, which demonstrated that this type of interface can provide youth with types of social support that are often not provided by foster care services and other digital interventions. The paper details the motivation behind the app, the trauma-informed design process, and insights gained from this initial evaluation study. Finally, the paper concludes with recommendations for designing digital tools that effectively provide social support to foster-involved youth.2025IKIla Krishna Kumar et al.Supporting YouthCSCW
Text a Bit Longer or Drive Now? Resuming Driving after Texting in Conditionally Automated CarsIn this study, we focus on different strategies drivers use in terms of interleaving between driving and non-driving related tasks (NDRT) while taking back control from automated driving. We conducted two driving simulator experiments to examine how different cognitive demands of texting, priorities, and takeover time budgets affect drivers' takeover strategies. We also evaluated how different takeover strategies affect takeover performance. We found that the choice of takeover strategy was influenced by the priority and takeover time budget but not by the cognitive demand of the NDRT. The takeover strategy did not have any effect on takeover quality or NDRT engagement but influenced takeover timing.2024NCNabil Al Nahin Ch et al.Automated Driving Interface & Takeover DesignAutoUI
AI-Augmented Brainwriting: Investigating the use of LLMs in group ideationThe growing availability of generative AI technologies such as large language models (LLMs) has significant implications for creative work. This paper explores twofold aspects of integrating LLMs into the creative process – the divergence stage of idea generation, and the convergence stage of evaluation and selection of ideas. We devised a collaborative group-AI Brainwriting ideation framework, which incorporated an LLM as an enhancement into the group ideation process, and evaluated the idea generation process and the resulted solution space. To assess the potential of using LLMs in the idea evaluation process, we design an evaluation engine and compared it to idea ratings assigned by three expert and six novice evaluators. Our findings suggest that integrating LLM in Brainwriting could enhance both the ideation process and its outcome. We also provide evidence that LLMs can support idea evaluation. We conclude by discussing implications for HCI education and practice.2024OSOrit Shaer et al.Wellesley CollegeGenerative AI (Text, Image, Music, Video)Human-LLM CollaborationCHI
How Beginning Programmers and Code LLMs (Mis)read Each OtherGenerative AI models, specifically large language models (LLMs), have made strides towards the long-standing goal of text-to-code generation. This progress has invited numerous studies of user interaction. However, less is known about the struggles and strategies of non-experts, for whom each step of the text-to-code problem presents challenges: describing their intent in natural language, evaluating the correctness of generated code, and editing prompts when the generated code is incorrect. This paper presents a large-scale controlled study of how 120 beginning coders across three academic institutions approach writing and editing prompts. A novel experimental design allows us to target specific steps in the text-to-code process and reveals that beginners struggle with writing and editing prompts, even for problems at their skill level and when correctness is automatically determined. Our mixed-methods evaluation provides insight into student processes and perceptions with key implications for non-expert Code LLM use within and outside of education.2024SNSydney Nguyen et al.Wellesley CollegeHuman-LLM CollaborationProgramming Education & Computational ThinkingCHI
Creating Design Resources to Scaffold the Ideation of AI ConceptsAdvances in artificial intelligence have enabled unprecedented technical capabilities, yet making these advances useful in the real world remains challenging. We engaged in a Research through Design process to improve the ideation of AI products and services. We developed a design resource capturing AI capabilities based on 40 AI features commonly used across various domains. To probe its usefulness, we created a set of slides illustrating AI capabilities and asked designers to ideate AI-enabled user experiences. We also incorporated capabilities into our own design process to brainstorm concepts with domain experts and data scientists. Our research revealed that designers should focus on innovations where moderate AI performance creates value. We reflect on our process and discuss research implications for creating and assessing resources to systematically explore AI’s problem-solution space.2023NYNur Yildirim et al.Generative AI (Text, Image, Music, Video)Human-LLM CollaborationPrototyping & User TestingDIS
Climate Coach: A Dashboard to Help Open-Source Software Maintainers Manage Community DynamicsOpen-source software projects have become an integral part of our daily life, supporting virtually every software we use today. Since open-source software forms the digital infrastructure, maintaining them is of utmost importance. We present Climate Coach, a dashboard that helps open-source project maintainers monitor the health of their community in terms of team climate and inclusion. Through a literature review and an exploratory survey (N=18), we identified important signals that can reflect a project's health, and display them on a dashboard. We evaluated and refined our dashboard through two rounds of think-aloud studies (N=19). We then conducted a two-week longitudinal diary study (N=10) to test the usefulness of our dashboard. We found that displaying signals that are related to a project's inclusion help improve maintainers' management strategies.2023HQHuilian Sophie Qiu et al.Carnegie Mellon UniversityOpen-Source Collaboration & Code ReviewKnowledge Management & Team AwarenessCHI
WebUI: A Dataset for Enhancing Visual UI Understanding with Web SemanticsModeling user interfaces (UIs) from visual information allows systems to make inferences about the functionality and semantics needed to support use cases in accessibility, app automation, and testing. Current datasets for training machine learning models are limited in size due to the costly and time-consuming process of manually collecting and annotating UIs. We crawled the web to construct WebUI, a large dataset of 400,000 rendered web pages associated with automatically extracted metadata. We analyze the composition of WebUI and show that while automatically extracted data is noisy, most examples meet basic criteria for visual UI modeling. We applied several strategies for incorporating semantics found in web pages to increase the performance of visual UI understanding models in the mobile domain, where less labeled data is available: (i) element detection, (ii) screen classification and (iii) screen similarity.2023JWJason Wu et al.Carnegie Mellon UniversityUniversal & Inclusive DesignPrototyping & User TestingComputational Methods in HCICHI
A US-UK Usability Evaluation of Consent Management Platform Cookie Consent Interface Design on Desktop and MobileWebsites implement cookie consent interfaces to obtain users’ permission to use non-essential cookies, as required by privacy regulations. We extend prior research evaluating the impact of interface design on cookie consent through an online behavioral experiment (𝑛 = 1359) in which we prompted mobile and desktop users from the UK and US to make cookie consent decisions using one of 14 interfaces implemented with the OneTrust consent management platform (CMP). We found significant effects on user behavior and sentiment for multiple explanatory variables, including more negative sentiment towards the consent process among UK participants and lower comprehension of interface information among mobile users. The design factor that had the largest effect on user behavior was the initial set of options displayed in the cookie banner. In addition to providing more evidence of the inadequacy of current cookie consent processes, our results have implications for website operators and CMPs2023EBElijah Robert Bouma-Sims et al.Carnegie Mellon UniversityPrivacy by Design & User ControlPrivacy Perception & Decision-MakingCHI
Identifying Cognitive and Creative Support Needs for Remote Scientific Collaboration using VR: Practices, Affordances, and Design ImplicationsRemote scientific collaborations have been pivotal in generating scientific discoveries and breakthroughs that accelerate research in many fields. Emerging VR applications for remote work, which utilize commercially available head-mounted displays (HMDs), offer the promise to enhance collaboration, through spatial and embodied experiences. However, there is little evidence on how professionals in general, and scientists in particular, could use existing commercial VR applications to support their cognitive and creative collaborative processes while exploring real-world data as part of day-to-day collaborative work. In this paper, we present findings from an empirical study with 14 coral reef scientists, examining how they chose to utilize available resources in existing virtual environments for their ongoing data-driven collaborative research. We shed light on scientists' data organization practices, identify affordances unique to VR for supporting cognition in a collaborative setting, and highlight design requirements for supporting cognitive and creative collaboration processes in future tools.2022MOMonsurat Olaosebikan et al.Social & Collaborative VRContext-Aware ComputingKnowledge Worker Tools & WorkflowsC&C
Innovating Novel Online Social Spaces with Diverse Middle School Girls: Ideation and Collaboration in a Synchronous Virtual Design WorkshopLeveraging social media as a domain of high relevance in the lives of most young adolescents, we led a synchronous virtual design workshop with 17 ethnically diverse, and geographically-dispersed middle school girls (aged 11-14) to co-create novel ICT experiences. Our participatory workshop centered on social media innovation, collaboration, and computational design. We present the culminating design ideas of novel online social spaces, focused on positive experiences for adolescent girls, produced in small-groups, and a thematic analysis of the idea generation and collaboration processes. We reflect on the strengths of utilizing social media as a domain for computing exploration with diverse adolescent girls, the role of facilitators in a synchronous virtual design workshop, and the technical infrastructure that can enable age-appropriate scaffolding for active participation and use of participatory design principles embedded within educational workshops with this population.2022CDCatherine Grevet Delcourt et al.Wellesley CollegeCollaborative Learning & Peer TeachingParticipatory DesignCHI
Perceptions of Trucking Automation: Insights from the r/Truckers CommunityRecent technological advancements in automation have sparked interest in how automation will affect truck drivers and the trucking industry. However, there is a gap in the literature addressing how truck drivers perceive automation and how they believe it will impact trucking. This study aims to understand truck drivers’ perspectives on automation in the trucking industry. Extending a preliminary study, we conducted a broader analysis of comments discussing automation in the r/Truckers subreddit from February 2017 to March 2021. In general, the community had negative sentiments towards automation in the trucking industry. Participants speculated when automation would become mainstream in trucking and discussed the feasibility of automation in the context of executing non-driving tasks and having accommodating infrastructure. Our findings indicate that truck drivers seek to participate in conversations about the future and to prepare themselves for when automation is more prominent in the trucking industry.2021LOLisa Orii et al.Impact of Automation on WorkAutoUI
How Will Drivers Take Back Control in Automated Vehicles? A Driving Simulator Test of an Interleaving FrameworkWe explore the transfer of control from an automated vehicle to the driver. Based on data from N=19 participants who participated in a driving simulator experiment, we find evidence that the transfer of control often does not take place in one step. In other words, when the automated system requests the transfer of control back to the driver, the driver often does not simply stop the non-driving task. Rather, the transfer unfolds as a process of interleaving the non-driving and driving tasks. We also find that the process is moderated by the length of time available for the transfer of control: interleaving is more likely when more time is available. Our interface designs for automated vehicles must take these results into account so as to allow drivers to safely take back control from automation.2021DNDivyabharathi Nagaraju et al.Automated Driving Interface & Takeover DesignAutoUI
Viral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science OnlineControversial understandings of the coronavirus pandemic have turned data visualizations into a battleground. Defying public health officials, coronavirus skeptics on US social media spent much of 2020 creating data visualizations showing that the government’s pandemic response was excessive and that the crisis was over. This paper investigates how pandemic visualizations circulated on social media, and shows that people who mistrust the scientific establishment often deploy the same rhetorics of data-driven decision-making used by experts, but to advocate for radical policy changes. Using a quantitative analysis of how visualizations spread on Twitter and an ethnographic approach to analyzing conversations about COVID data on Facebook, we document an epistemological gap that leads pro- and anti-mask groups to draw drastically different inferences from similar data. Ultimately, we argue that the deployment of COVID data visualizations reflect a deeper sociopolitical rift regarding the place of science in public life.2021CLCrystal Lee et al.Massachusetts Institute of TechnologyInteractive Data VisualizationVisualization Perception & CognitionContent Moderation & Platform GovernanceCHI
Prototyping for Social Wellbeing with Early Social Media UsersMany 10-14 year olds are at the early stages of using social media, habits they develop on popular platforms can have lasting effects on their socio-emotional wellbeing. We led a remote innovation workshop with 23 middle schoolers on digital wellbeing, identity exploration, and computational concepts related to social computing. This workshop was a unique opportunity to reflect on emergent habits, discuss them with peers, and imagine oneself as an ICT innovator. Resulting themes related to participants’ social wellbeing online included a) sense of belonging to communities of interest, friends, and family, b) self-care and social support strategies involving managing risks, control, and empathy, and c) experimentation while building self-confidence and bravely exploring audience reactions. Participants iteratively designed and tested a sandbox social network website, resulting in Social Sketch. Reflecting on our study, we describe the process for conceptualizing Social Sketch, and challenges in social media innovation with teenagers.2021LCLinda Charmaraman et al.Wellesley Centers for WomenSocial Platform Design & User BehaviorParticipatory DesignCHI
Hidden Interaction Techniques: Concealed Information Acquisition and Texting on Smartphones and WearablesThere are many situations where using personal devices is not socially acceptable, or where nearby people present a privacy risk. For these situations, we explore the concept of hidden interaction techniques through two prototype applications. HiddenHaptics allows users to receive information through vibrotactile cues on a smartphone, and HideWrite allows users to write text messages by drawing on a dimmed smartwatch screen. We conducted three user studies to investigate whether, and how, these techniques can be used without being exposed. Our primary findings are (1) users can effectively hide their interactions while attending to a social situation, (2) users seek to interact when another person is speaking, and they also tend to hide the interaction using their body or furniture, and (3) users can sufficiently focus on the social situation despite their interaction, whereas non-users feel that observing the user hinders their ability to focus on the social activity.2021VMVille Mäkelä et al.LMU MunichFoot & Wrist InteractionDeaf & Hard-of-Hearing Support (Captions, Sign Language, Vibration)Smart Home Privacy & SecurityCHI