Agents in Concert: A Case-Study of Bringing AI to the Stage in PracticeRecent years have seen a surge in musical performances accompanied by generative agents. Artificial voices, timbres synthesized by neural networks, and agents that mirror or respond to human performers are rapidly taking the stage. In parallel, practitioners in human-computer interaction (HCI) and music technology have called for practice-based research that identifies the most salient affordances of these developments by examining their use in the real-world contexts of music making. To advance practice-based research on human-AI music creation, we present a longitudinal account of two months of codesign with top local jazz musicians, spanning early explorations, the identification of emerging goals, and rehearsals. Our work culminates in a public concert for a live audience of 97, featuring three pieces co-improvised with AI agents. Drawing on systems including \vampnet, \somax, and the \jambot, each piece was tailored to the stylistic strengths of the performers and the unique strengths and limitations of each system. Through this extensive iterative process, we uncovered a wide range of design interventions, from augmenting GenAI systems with a guitar pedal to situate it in a loop-based creative practice, to enabling musicians to anticipate AI response by visually forecasting its predictions. Where musicians tended to rein in the wilder qualities of the generative systems, some audience members expected a human-AI performance to allow as much agency and spontaneity as possible. In post-concert reflection, musicians also expressed the desire to practice more which in turn could enable them to let the agency of the systems shine. They also encouraged future musicians to lean more into the uncertainty. Together, we see a unique practice emerging through this musician-AI live improv medium.2026SBStephen Brade et al.Massachusetts Institute of TechnologyMusic Composition & Sound Design ToolsGenerative AI (Text, Image, Music, Video)Creative Collaboration & Feedback SystemsIUI
Teaching Spell Checkers to Teach: Pedagogical Program Synthesis for Interactive LearningSpelling taught through memorization often fails many learners, particularly children with language-based learning disorders who struggle with the phonological skills necessary to spell words accurately. Educators such as speech-language pathologists (SLPs) address this instructional gap by using an inquiry-based approach to teach spelling that targets the phonology, morphology, meaning, and etymology of words. Yet, these strategies rarely appear in everyday writing tools, which simply detect and autocorrect errors. We introduce SPIRE(Spelling Inquiry Engine), a spell check system that brings this inquiry-based pedagogy into the act of composition. SPIRE implements Pedagogical Program Synthesis, a novel approach for operationalizing the inherently dynamic pedagogy of spelling instruction. SPIRE represents SLP instructional moves in a domain-specific language, synthesizes tailored programs in real-time from learner errors, and renders them as interactive interfaces for inquiry-based interventions. With SPIRE, spelling errors become opportunities to explore word meanings, word structures, morphological families, word origins, and grapheme-phoneme correspondences, supporting metalinguistic reasoning alongside correction. Evaluation with SLPs and learners shows alignment with professional practice and potential for integration into writing workflows.2026MSMomin Naushad Siddiqui et al.Georgia Institute of TechnologyPredictive Input & AutocorrectAI-Assisted Writing & Text GenerationMotor Impairment Assistive Input TechnologiesIUI
PopSignAI: Towards Using Sign Language Recognition Games to Improve American Sign Language Learning in Novice SignersTo help novice signers learn American Sign Language, we develop PopSignAI, a proof-of-concept smartphone-based bubble-shooter game that facilitates real-time interaction through isolated sign language recognition. In a 20-person user study, we demonstrate that encouraging novice signers to practice generating sign in PopSignAI is more efficient for teaching ASL skills than a version of PopSign focused on receptive signing ability. We use over 200,000 examples of 250 signs from 47 signers to train and test a user-independent LSTM recognizer that achieves 82.9\% accuracy on an independent test set. For the purposes of the game, the recognizer averages 99.6\% accuracy with a 7ms inference time using a 2.5MB model. Ablation studies suggest that as few as eight signers are need for training in order for adequate recognition accuracy for PopSignAI's gameplay. To encourage future sign language recognition games, we release the PopSignAI recognition pipeline and software. We identify hearing parents of deaf children as important potential users of sign games and conduct interviews with eight of these parents, investigating their motivation and challenges in learning sign.2026DMDavid Martin et al.Georgia Institute of TechnologyHand Gesture RecognitionSpecial Education TechnologyChild-Computer Interaction DesignIUI
Criticality: Scaffolding Decision-Making with Interactive Critical Thinking and Evidence-Based Reasoning TracesDecision-making requires examining underlying assumptions and concepts, considering diverse perspectives, and weighing potential consequences with clear, accurate reasoning. Recent large language models (LLMs) show promise for assisting decision-makers by combining reasoning capabilities with the ability to retrieve relevant information from large documents. However, our formative study with five professional decision-makers revealed key limitations of using LLM in workflow: time-consuming alignment of user goals, lack of evidence-based grounding, overwhelmingly long outputs, and unsurfaced assumptions undermined user trust in the LLM output and the validity of the final decision. We introduce Criticality, a system that operationalizes the Paul-Elder Critical Thinking framework to structure reasoning into interactive Elements of Thought (e.g., purpose, assumptions, perspectives, implications), and evaluates and guides reasoning using Intellectual Standards (e.g., clarity, fairness, logic). It also retrieves evidence for each claim, classifies it as supporting, neutral, or contradictory, and explains the claim-evidence link. A within-subjects study (n=13) comparing Criticality to ChatGPT 5 Pro, a state-of-the-art reasoning model in conversational interface, found that Criticality improved user interaction of steering and repairing through the decision-making process, producing better decision rationales compared to the baseline.2026MCMinsuk Chang et al.Georgia Institute of TechnologyHuman-LLM CollaborationExplainable AI (XAI)User Research Methods (Interviews, Surveys, Observation)IUI
The Promises and Perils of using LLMs for Effective Public ServicesGovernments are the primary providers of essential public services and are responsible for delivering them effectively. In high-stakes decision-making domains such as child welfare (CW), agencies must protect children without unnecessarily prolonging a family’s engagement with the system. With growing optimism around AI, governments are pushing for its integration but concerns regarding feasibility and harms remain. Through collaborations with a large Canadian CW agency, we examined how LocalLLM and BERTopic models can track CW case progress. We demonstrate how the tools can potentially assist workers in opportunistically addressing gaps in their work by signaling case progress/deviations. And yet, we also show how they fail to detect case trajectories that require discretionary judgments grounded in social work training, areas where practitioners would actually want support to pre-emptively address substantive case concerns. We also provide a roadmap of future participatory directions to co-design language tools for/with the public sector.2026EMErina Seh-Young Moon et al.University of TorontoHuman-LLM CollaborationAI-Assisted Decision-Making & AutomationParticipatory DesignCHI
Beyond Claiming Sovereign AI: Motivations, Challenges, and Contradictions in Developing and Deploying Local Foundation Models in South KoreaFoundation models are predominantly trained on English-language and Western-centric data, often marginalizing non-English contexts. While recent scholarship calls for more localized models, there remains limited empirical research on how such models are developed and deployed. This paper examines the sociotechnical dynamics of local model development and deployment in South Korea, where efforts to build “sovereign AI” reflect aspirations for greater autonomy over data, infrastructure, and cultural alignment. Drawing on semi-structured interviews with 15 Korean AI practitioners, we surface key motivations, such as linguistic and cultural specificity, regulatory compliance, and reduced dependence on foreign technologies, that are entangled with broader imaginaries of sovereignty. At the same time, these efforts face constraints including limited GPU access, scarcity of Korean-language data, and reliance on global infrastructures. We argue that AI sovereignty should be understood not as an abstract political principle but as situated practices shaped by opportunities and constraints of local sociotechnical and regulatory contexts.2026ICInha Cha et al.Georgia Institute of TechnologyHuman-LLM CollaborationAI Ethics, Fairness & AccountabilityLow-Resource Languages & Digital InclusionCHI
Reflections Towards an Ecology of Internet Connectivity: Three Speculative Scenarios Involving Foot PedalsHCI's dominant assumptions of always-on and relatively ubiquitous internet connectivity often overlook other potential configurations of connectivity, which may embody alternative social values and politics, or promote alternative types of technology practices. Building on research exploring alternate configurations of connectivity, we develop and present three speculative scenarios in a North American context that configure internet connectivity differently than these assumptions. Each scenario features a "foot pedal" that mediates internet connectivity. Through the scenarios, we conceptualize connectivity as a multi-dimensional ecology. The scenarios explore how alternative configurations of connectivity implicate concerns related to dimensions of: social norms and rituals; maintenance, repair and governance; interests and decision-making beyond individual choice; and broader inequalities and systems of power. These suggest possible alternative ends and goals of internet connectivity. Finally, we offer reflections from our experience developing these scenarios for HCI scholars working with speculative practices.2026RWRichmond Y. Wong et al.Georgia Institute of TechnologyDesign FictionParticipatory DesignTechnology Ethics & Critical HCICHI
Sometimes You Need Facts, and Sometimes a Hug: Understanding Older Adults’ Preferences for Explanations in LLM-Based Conversational AI SystemsDesigning Conversational AI systems to support older adults requires these systems to explain their behavior in ways that align with older adults’ preferences and context. While prior work has emphasized the importance of AI explainability in building user trust, relatively little is known about older adults’ requirements and perceptions of AI-generated explanations. To address this gap, we conducted an exploratory Speed Dating study with 23 older adults to understand their responses to contextually grounded AI explanations. Our findings reveal the highly context-dependent nature of explanations, shaped by conversational cues such as the content, tone, and framing of explanation. We also found that explanations are often interpreted as interactive, multi-turn conversational exchanges with the AI, and can be helpful in calibrating urgency, guiding actionability, and providing insights into older adults’ daily lives for their family members. We conclude by discussing implications for designing context-sensitive and personalized explanations in Conversational AI systems.2026NMNiharika Mathur et al.Georgia Institute of TechnologyHuman-LLM CollaborationExplainable AI (XAI)Aging-Friendly Technology DesignCHI
AI as We Describe It: How Large Language Models and Their Applications in Health are Represented Across Channels of Public DiscourseRepresentation shapes public attitudes and behaviors. With the recent advances and rapid adoption of LLMs, the way these systems are introduced will negotiate societal expectations for their role in high-stakes domains like health. Yet it remains unclear whether current narratives present a balanced view. We analyzed five prominent discourse channels (news, research press, YouTube, TikTok, and Reddit) over a two-year period on lexical style, informational content, and symbolic representation. Discussions were generally positive and episodic, with positivity increasing over time. Risk communication was unthorough and often reduced to information quality incidents, while explanations of LLMs' generative nature were rare. Compared with professional outlets, TikTok and Reddit highlighted wellbeing applications and showed greater variations in tone and anthropomorphism but little attention to risks. We discuss implications for public discourse as a diagnostic tool in identifying literacy and governance gaps, and for communication and design strategies to support more informed LLM engagement.2026JZJiawei Zhou et al.Georgia Institute of TechnologyHuman-LLM CollaborationAI Ethics, Fairness & AccountabilityMental Health Apps & Online Support CommunitiesCHI
Situated Imaginaries: Designing AI Futures with Computer Science Teaching AssistantsTeaching assistants (TAs) play a critical role in computing and HCI education, yet little is known about how they perceive and use AI tools or imagine their future pedagogical uses. We report on a series of design workshops with 131 computing (CS) TAs across two U.S. universities. These workshops invited TAs to reflect on current AI use and envision future AI-enhanced tools and practices. Drawing on surveys and design artifacts, we (1) develop a cross-institutional typology of situated TA uses of AI, revealing opportunities and tensions; (2) show how TAs’ visions of AI are shaped by disciplinary norms, institutional structures, and their intermediary position as student-instructors; and (3) reveal ethical dilemmas. Our findings contribute to HCI by positioning TAs as AI-supported knowledge workers in the education domain; illustrating how design and speculation are shaped by people’s situated understandings of AI and their institutional contexts; and identifying a core tension in which TAs simultaneously preserve and erode the human dimensions of their work, with implications for future instructional tools and human–AI collaboration.2026GBGrace Barkhuff et al.Georgia Institute of TechnologyHuman-LLM CollaborationIntelligent Tutoring Systems & Learning AnalyticsParticipatory DesignCHI
Designing with Medical Mistrust: Perspectives from Black Older Adults in Publicly Subsidized HousingDespite increasing interest in culturally-sensitive health technologies, medical mistrust remains largely unexplored within human-centered computing. Considered a social determinant of health, medical mistrust is the belief that healthcare providers or institutions are acting against one's best interest. This is a rational, protective response based on historical context, structural inequities, and discrimination. To center race-based medical mistrust and the lived experiences of Black older adults with low income, we conducted interviews within publicly subsidized housing in the Southern United States. Our reflexive themes describe community perspectives on health care and medical mistrust, including accreditation and embodiment, skepticism of financial motivations, and the intentions behind health AI. We provide a reflective exercise for researchers to consider their positionality in relation to community engagements, and reframe our findings through Black Feminist Thought to propose design principles for health self-management technologies for communities with historically grounded medical mistrust.2026CBCynthia M Baseman et al.Georgia Institute of TechnologyMental Health Apps & Online Support CommunitiesCognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia)Elderly Care & Dementia SupportCHI
Designing Around Stigma: Human-Centered LLMs for Menstrual HealthMenstrual health education (MHE) in Pakistan is constrained by cultural taboos and inadequate formal curricula, leaving women with few trusted resources to lean on. In response to these challenges, we introduce a WhatsApp-based chatbot powered by a large language model (LLM) and Retrieval-Augmented Generation (RAG), co-designed with Pakistani college women. Workshops (N=30) revealed key design requirements—support for Roman Urdu, use of subsidized platforms, and an expert-curated knowledge base. We then deployed the chatbot with 13 participants for two weeks (403 messages + interviews). Women used it to challenge cultural taboos, legitimize health concerns often dismissed as “normal”, and build reproductive health knowledge through iterative questioning. Yet, interactions also exposed tensions: reliance on cultural explanatory models, questions of trust and validation, and gendered persona of the chatbot itself. We contribute empirical insights, a stigma-aware design framework for culturally sensitive conversational AI, and a methodological lens foregrounding expert validation in intimate health domains.2026ASAmna Shahnawaz et al.Lahore University of Management SciencesHuman-LLM CollaborationAI Ethics, Fairness & AccountabilityMental Health Apps & Online Support CommunitiesCHI
"It just requires so much more creativity": Barriers and Workarounds to Gathering Information for AI ContestationGathering information about AI systems is essential for contesting their use; it forms the basis of arguments about how AI is causing harm. Information thus plays a central role for advocates like lawyers, journalists, and auditors contesting harmful AI systems. However, there is little systematic understanding of how these actors, many of whom are newly encountering AI in their advocacy work, access and use information effectively in this process. Understanding this information work can offer valuable insights for supporting effective contestation of harmful AI systems. To better understand information work in AI contestation, we interviewed 18 advocates in the United States (US) who have contested the use of AI in high-stakes domains, such as public benefits and housing. We characterize advocates' strategies for accessing information that is useful for contestation, including a range of creative yet resource-intensive and risky workarounds that they use to overcome opacity. We discuss implications of our findings for the effectiveness of popular transparency policy strategies in the US and offer additional ways to support the social fabric that makes advocates' information work effective.2026SUSohini Upadhyay et al.Harvard UniversityExplainable AI (XAI)Privacy by Design & User ControlPrivacy Perception & Decision-MakingCHI
LL.me: Supporting Identity Work through Human-AI AlignmentProfessional self-representation involves constructing identities that reflect personal values while aligning with the norms of professional communities. Many people turn to generative AI for help, but misalignments between LLM outputs and self-understanding hinder authenticity and accuracy of the content. To explore how LLMs can support co-creation aligned, authentic self-representational content, we designed LL.me, a web-based probe based on bi-directional alignment that utilizes users’ resumes and guides them through iterative cycles of refining AI-generated self-representations. Our user study with 14 participants showed users engaged in identity work with the tool, re-framing content to emphasize their personal values, imparting tacit knowledge from their communities of practice, and leveraging system explainability features as a proxy for how the representation would be perceived by others. We demonstrate how LLM-based tools can facilitate a co-constructive process of identity formation, helping individuals actively shape their professional self-representations in collaboration with the AI.2026KHKaely Hall et al.Georgia Institute of TechnologyHuman-LLM CollaborationExplainable AI (XAI)AI-Assisted Creative WritingCHI
Localized Imaginaries, Global Assets: Sociotechnical Imaginaries and the Assetization of Data Centers in SingaporeAs data infrastructures expand globally, the environmental and spatial imaginaries guiding data center development have become standardized, privileging temperate climates, abundant land, and low-cost energy. Singapore presents a paradox: a tropical, land-scarce city-state that nonetheless ranks among the densest data center hubs in the world. This paper examines how state and industry actors co-produce and contest localized sociotechnical imaginaries to legitimize this growth and, in the process, reconfigure what a “data center” is. Using a critical discourse analysis of government policy briefs, industry press releases, and national media, we show how global standards are adapted to Singapore’s resource constraints and how discursive practices position data centers as strategic, investable assets within its urban digital economy. By situating sociotechnical imaginaries in a postcolonial context and linking them to assetization theory, this study advances HCI understandings of data center infrastructure as financialized assets, offering insight into emerging trajectories of global digital infrastructure.2026TKTanmaie Kailash et al.Georgia Institute of TechnologySustainable HCIEcological Design & Green ComputingSmart Cities & Urban SensingCHI
Does a Picture Paint a Thousand Words? Using Visual and Textual Channels to Understand Attitudes and BeliefsIn Human-Computer Interaction, eliciting user attitudes and beliefs is crucial for understanding user interactions with technology. Existing elicitation methods range from expressive open-ended text to structured formats like Likert scales. Expressive methods yield rich insights but are difficult to systematically analyze. On the other hand, structured methods guide users to efficiently map attitudes and beliefs to clear visual scales, yet may oversimplify complex attitudes and beliefs. Recent work has explored alternative methods including visual elicitation techniques; however, the understanding of how users mentally represent attitudes and beliefs remains limited, making it challenging to validate the effectiveness of these techniques. Through a qualitative study of US-based participants (N=41), we captured how people mentally represent their attitudes and beliefs through free-form drawings and complementary textual descriptions. Our findings reveal how the strategies participants employed to represent attitudes and beliefs can inform the design of future visual elicitation techniques that balance both expressiveness and analyzability.2026SLShiyao Li et al.Emory UniversityUser Research Methods (Interviews, Surveys, Observation)Prototyping & User TestingInteractive Data VisualizationCHI
Whose Time Counts? Temporal Arrangements in Sociotechnical InfrastructuresThis paper examines how infrastructures organize time in ways that unevenly distribute burden, access, and opportunity across communities. We draw on two ethnographic cases: eviction case filings in Atlanta, part of the state’s legal and housing governance infrastructure, and a sexual healthcare intervention in Chicago, situated within the city’s public health services. We advance HCI’s engagement with temporality by demonstrating how infrastructures sediment layers of political, social, and technical decisions over time. We conceptualize infrastructures as stratified formations where earlier allocations of power become materially and procedurally embedded, configuring present-day experiences of public systems. We define \emph{temporal arrangements} as the patterned ways infrastructures shape and allocate time, producing unequal demands on who waits, who moves, and who must continually adjust. We describe two temporal arrangements—\emph{compression} and \emph{gaps}—to show how systems structure and constrain access to care, support, and basic services. By linking inherited infrastructural logics to everyday temporal burdens, we offer HCI a framework for examining how inequities persist through time.2026CWCatherine Wieczorek et al.Georgia Institute of TechnologyTechnology Ethics & Critical HCIPrivacy by Design & User ControlHCI in Public Health Crises (e.g., COVID-19)CHI
DraftMarks: Enhancing Transparency in Human-AI Co-Writing Through Interactive Skeuomorphic Process TracesAs generative AI becomes part of everyday writing, questions of transparency and productive human effort are increasingly important. Educators, reviewers, and readers want to understand how AI shaped the process. Where was human effort focused? What role did AI play in the creation of the work? How did the interaction unfold? Existing approaches often reduce these dynamics to summary metrics or simplified provenance. We introduce DraftMarks, an augmented reading tool that supports readers in interpreting how text was constructed with AI through familiar physical metaphors. DraftMarks employs skeuomorphic encodings such as eraser crumbs to convey the intensity of revision, and masking tape or smudges to mark AI-generated content, simulating the process within the final written artifact. By using data from writer-AI interactions, DraftMarks’ algorithm computes various collaboration metrics and writing traces. Through a formative study, we identified computational logic for different readership, and evaluated DraftMarks through a Prolific study for its effectiveness in assessing AI co-authored writing.2026MSMomin Naushad Siddiqui et al.Georgia Institute of TechnologyHuman-LLM CollaborationAI-Assisted Writing & Text GenerationExplainable AI (XAI)CHI
Everyday Design with Surrounds: Rehearsing Alternatives Amid Urban Sociotechnical ChangesThis paper rethinks everyday design by shifting from materially available, physical ``surroundings'' as design resources to the unsettled, open-ended, and politically entangled ``surrounds'' as sites of practice. Drawing from ethnographies in New York City and Detroit, we describe how urban communities rehearse alternative ways of living and relating by navigating the interstices of layered infrastructures of governance and development. Rather than locating agency in oppositional acts of resistance or formal interventions, we show how everyday design can unfold through improvisational collectives and situated practices that have yet to be captured or defined by any single layer of dominant infrastructure, but emerge in-between them. We propose surrounds as a generative analytic for understanding the subtle, often fleeting experiments through which people enact alternative relations to governance and order. Positioning everyday design as navigational, rather than apolitical or oppositional, offers HCI new ways to understand the positionalities of design, intervention, and alternatives amid shifting urban sociotechnical conditions.2026ALAlex Jiahong Lu et al.Rutgers UniversityCommunity Engagement & Civic TechnologyTechnology Ethics & Critical HCIDeveloping Countries & HCI for Development (HCI4D)CHI
Whose Data Builds the City? Critical Data Practices for Socio-Environmentally Just UrbanizationData-driven systems, such as satellite imagery, often dictate urban planning; however, they frequently neglect local, situated, and embodied knowledges. This paper examines the epistemic, political, and socio-ecological frictions that surface when city data (e.g., aerial imagery and administrative records) is brought into dialogue with community data (e.g., lived experiences and shared epistemologies) to guide more equitable urban planning. We employ Research through Design, complemented by ethnographic inquiry and auto-ethnographic reflection, to create a speculative probe that helps foreground the frictions embedded in urban data infrastructures in Bangalore, India. Our analysis reveals the limitations of dominant top-down urban data systems, which routinely obscure socio-ecological dependencies and selectively define what constitutes legitimate urban knowledge. We employ environmental justice as an analytical lens to analyze our findings, highlighting how urban data infrastructures can reproduce or contest inequalities and identify opportunities to foreground care, accountability, and equity, particularly in postcolonial contexts, toward cultivating socially just and climate-resilient urban futures.2026VSVishal Sharma et al.University of Notre DameSmart Cities & Urban SensingUrban SustainabilityAlgorithmic Fairness & BiasCHI