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Wednesday, 4 November 2026: 14 new papers, 7 cross-lists and 13 replacements in Human-Computer Interaction (cs.HC), in arXiv's order, with full abstracts. The same papers as arXiv's own listing, set as a newspaper; every title links to arXiv.
This submission indexes contributions to the CSCW 2026 workshop, "Women's Health as Cooperative Work: Participatory AI Across Care, Community, and Policy," held on October 11, 2026. The workshop examines women's health as cooperative work spanning patients, caregivers, clinicians, communities, institutions, and digital technologies. Contributions explore participatory AI, coordination breakdowns, the distribution of care labor, governance, decision-making authority, and accountability across care, community, and policy contexts. The index provides paper titles, authors, affiliations, thematic areas, and unique report numbers to support discovery of the corresponding individual arXiv submissions. The workshop proposal is separately published under DOI 10.1145/3785651.3818821.
Generative AI systems increasingly mediate access to information. Their behavior depends on tools, instructions, and user memories that are often difficult for researchers to observe or control. Here, I introduce TRACE (Transparent Research on Agent Contexts and Environments), a framework for systematically auditing how these components shape AI agent behavior. Built on an open-source agent harness, TRACE enables controlled manipulation of system components. It also records inputs, outputs, and task execution for downstream analysis. I demonstrate this framework through an experiment examining how user memories influence source selection when agents answer questions about the 2026 U.S. midterm elections. I find that memories expressing explicit preference for a publisher increase its inclusion among the first three cited sources in a response by 33-39 percentage points. Memories identifying the user as a Republican or expressing distrust of mainstream media shift citations rightward. That same media distrust memory also increases citation of sources expressing misinformation or conspiracy theories by 6.3 percentage points, with substantial variation across models. These findings demonstrate that concise user memories can alter source selection in consequential settings, even when questions and search results remain fixed. TRACE extends algorithm auditing to agent harnesses, providing a transparent way to study agent behaviors that benchmarks or commercial interfaces may struggle to isolate.
Assistive systems typically tell users everything, often spending time and attention on what a user already knows, leaving less for what happens next. People, on the other hand, leave out what they expect the listener can supply. Can people use their prior knowledge to understand an incomplete message when the speaker is a machine though? We studied this pragmatic comprehension in a monitoring task controlling users' prior knowledge and the available time to detect critical situations. In a preregistered study (N=114), we compared a pragmatic planner that reasons over a model of the listener's knowledge to decide what to say, when, and how explicitly, with a baseline specifying problems fully in onset order. Participants resolved underspecified alerts almost as reliably as fully specified ones and sooner, detected more problems, and rated the pragmatic assistant helpful more often, with no increase in reported mental demand. Our findings show that people can recover what the system left implicit and that pragmatic communication can support effective assistance under the constraints of a changing world.
When an AI agent executes an underspecified task, it embeds choices that may contradict unarticulated user context and preferences. However, interrupting the user for clarification on all such decisions is untenable. How might agents communicate impactful, but unexplored, paths to the user after execution? To investigate, we first distill a design space for communicating alternative paths, supporting the generation of new systems and structuring dimensions of future study. We then measure the impact of one vital design space dimension on user comprehension: the number of alternatives displayed. Through a user study (n=48) in the data analysis domain, we find that participants rarely consider alternative paths of their own volition, but AI overreliance reduces when alternative paths are visualized for them. By revealing paths the agent did not take, we demonstrate how users can better interpret results and incorporate personal context.
AI-assisted creative tools reduce the effort of authoring content, but delegating creative decisions to AI can make it difficult for authors to understand, correct, or redirect work. We present OmniScribble, a system for animating multi-object sketches that treats object decomposition and motion planning as editable artifacts. Rather than generating animation end-to-end, OmniScribble presents AI proposals at verification checkpoints, where authors can accept, repair, or regenerate them. Both manual and AI-assisted authoring operate on the intermediate representation and rendering pipeline, allowing us to isolate AI delegation. In a within-subject study with eight participants, AI-assisted authoring yielded a median within-participant reduction in completion time of 71.4\%, while perceived control and motion-intent alignment remained high. Interaction traces and interviews show that participants adapted to AI errors by repairing local issues, regenerating mismatches, and accepting proposals. These findings suggest that inspectable, repairable delegation enables generative automation while maintaining control over the outcome.
In groups, knowledge is distributed among individuals, and how it is brought together depends on social ties. Taking inspiration from how a single mind recalls and reacts to memories and current stimulus, we build a shared memory that does this for a community, prototyped as a social app, SEAM. Members post arbitrary moments; SEAM works out how fragments relate, distills knowledge about the group, and replies in a situated and associative way. Some of these memories act as instructions that can alter how the system behaves, making it programmable by its members. We deployed SEAM to 46 people in a co-located community for two weeks of open-ended use, and ran a follow-up workshop with 14. Members shared their days, interrogated the memory, and instructed it to track routines and run games; replies introduced them to people and subjects they had never encountered. SEAM points towards communities where knowledge finds people.
Recent advances in large language models (LLMs) have enabled coding agents to autonomously perform complex software development tasks, yet their long-running execution remains difficult for users to inspect, understand, and steer. We present Vibetrace, an interactive trace visualization and intervention system for agentic programming built upon OpenCode. Vibetrace introduces a human-agent collaboration paradigm of Observe, Understand, Intervene, and Evolve. It (1) visualizes agent workflows as interactive execution traces for inspecting actions, intermediate states, and failures; (2) enables users to fork execution from prior steps and provide targeted corrections without restarting validated progress; and (3) distills traces and human feedback into reusable skills for continual agent evolution. On SWE-bench Verified, distilled skills reduce input tokens by 23.0% and cost by 15.5%, while improving pass rate from 65.0% to 68.0%. User studies further demonstrate improved accuracy and efficiency over conventional log-based interaction.
People like to keep their music listening fresh and interesting, but recommender systems often struggle to support this need. Even when a recommended song is potentially refreshing and enriching, listeners may not perceive it as such. Building on Silvia's appraisal theory of interest, this paper studies whether enhancing recommendations with spoken introductions, immersive or informative, can stimulate two distinct flavors of experienced serendipity: taste-broadening (sparking interest in music outside one's typical preferences) and taste-deepening (deepening interest in music within one's typical preferences). Results from a large-scale user study show that both introduction types stimulate taste-broadening and taste-deepening serendipity. Taste-broadening is most effectively stimulated through transportation (absorption in a narrative world), whereas taste-deepening is most effectively stimulated through cognitive elaboration (learning something new about the artist or the social context in which the music emerged). We discuss how these findings could be translated into practice through curated taste-broadening playlists, where both introduction types appear promising, and taste-deepening playlists, where informative introductions seem particularly effective.
Smell is powerful in design, yet it often remains vague and difficult to work with in studios and classrooms. While olfactory HCI has produced devices, toolkits, and scenario-based applications, designers still lack teachable, shareable resources that link smell to bodily action. This paper presents a three-phase research program that makes smell actionable by moving from lived smell experiences to movement patterns and, finally, to gesture resources. We study olfactory-motor mappings and package the results as teachable design representations through a progressively narrowing method: (1) extracting smell-verb-intent relations from user narratives; (2) capturing and clustering embodied enactments into standardized upper-limb trajectory families; and (3) translating these trajectories into gesture card representations for design teaching and ideation through design workshops. We present a three-phase translational framework and empirically derived smell-gesture correspondences that structure the smell-intent-trajectory-gesture pathway, together with reusable assets (lexicon, trajectory atlas, gesture tables, and cards) that support teaching, reuse, and adaptation across contexts.
In shared settings, social robots may need to exchange information with an individual while limiting co-present others' access to or interpretation of that information. Publicly observable speech and gestures may not adequately support such exchanges. We investigate a wearable-mediated channel for discreet interaction that combines in-ear audio messages with deliberate rightward head tilts as responses. To examine its feasibility and participants' experiences, we studied 20 pairs (40 participants) playing a card game with a screen-based conversational agent representing a social robot, controlled by a researcher in a Wizard-of-Oz setup. Each pair played with and without the channel. One participant wore the device (the wearer), while the other (the non-wearer) was informed about the channel after gameplay. The channel conveyed information that could help the wearer or robot character select an opponent they could defeat. The information exchange and subsequent opponent selection occurred as intended in 39 of 40 channel-mediated turns. Wearers reported low perceived pressure/tension in both conditions, with no statistically significant difference between conditions, and favorable usability immediately after use. Retrospective interviews, however, revealed varied accounts of noticing the interaction among non-wearers and contrasting perceptions of teamwork and unfairness. These findings support the feasibility of wearable-mediated exchanges within this game and identify awareness and fairness as considerations for future designs.
The Kenyan government has invested heavily in the digitisation of public service delivery, in line with the Kenya Digital Master Plan 2022-2032. The national eGovernment platform, eCitizen, hosts services that are vital to citizens' livelihoods, including business registration, youth enterprise lending, digital literacy and tax filing. However, the platform faces digital inclusion challenges that limit equitable access for marginalised groups, particularly youth in rural areas and persons with disabilities. This exclusion is driven by a language barrier, since the platform is primarily in English with limited Swahili support, and by a text-only interface that excludes low-literacy and visually impaired users. This paper reports findings from six one-on-one interviews and an eleven-person roundtable discussion across four platforms essential to youth livelihoods: Ajira Digital, Kenya Revenue Authority (KRA) iTax, Higher Education Loans Board (HELB) and the Youth Enterprise Development Fund (YEDF). Drawing on these findings, on Web Content Accessibility Guidelines (WCAG) 2.1 principles as a design lens for diagnosing eCitizen's accessibility gaps, and on comparator models such as Estonia's B\"urokratt and India's Bhashini, the paper proposes a human-centred, systems-minded framework for embedding generative AI and language technologies into eCitizen, including text simplification, neural machine translation, text-to-speech and speech-to-text. The paper argues that, when deployed responsibly with appropriate human oversight, these interventions could meaningfully improve access to government services for linguistically excluded citizens. It also emphasises that AI alone cannot overcome the broader structural barriers, including unemployment and educational inequality, that shape digital exclusion.
Robots in shared settings may expose information intended for an individual user to nearby bystanders. Robot tutoring makes this concern particularly salient: critical feedback is directed at an individual learner, yet bystanders may overhear or see it. To address this, we compared context-invariant and context-adaptive robot behaviors in a between-subjects study with 40 participants. Following an initial phase without a bystander, a bystander entered the setting. Participants then received two scripted critical feedback messages concerning their pronunciation and handwriting, independent of their actual performance. With the context-invariant behavior, the robot tutor delivered both messages aloud. With the context-adaptive behavior, the robot tutor scanned for the bystander, turned its body and display away after detecting them, gave a spoken attention cue, and presented the same feedback messages silently on its display. Participants rated the robot tutor higher in perceived privacy awareness and perceived social competence in the context-adaptive condition than in the context-invariant condition, while evidence for reduced experienced embarrassment was inconclusive. These findings inform the design and evaluation of robots that respond to bystander presence by adapting how they communicate potentially sensitive information.
An LLM assistant that cannot see official severe-weather alerts may tell users that conditions are calm while a warning is active. We call this false reassurance and measure it by replaying 117 frozen cases built from real U.S. National Weather Service (NWS) alerts (69 alert cases, 32 no-alert controls, 16 non-U.S. locations) through five conditions of a Claude-based preparedness pipeline that differ in alert access and prompt design. One rater hand-labeled all 342 alert-case responses under a seven-category scheme written after an initial binary pass, which we disclose. False reassurance was 0.0% for the production pipeline (0/66, 95% CI 0.0-5.5) and for the same alerts under a neutral prompt (0/69), 37.7% for a blind neutral prompt (26/69), and 44.9% when the blind prompt also told the model to disclose its blind spot (31/69). Every case was implicit; no response explicitly denied an alert. Real alerts removed false reassurance (paired McNemar p < 0.0001); the disclosure instruction did not (p = 0.40). An automated regex measure suggested the opposite and missed 53 of 57 true cases, so we do not use it. Alert data alone was not enough for safe behavior: with a neutral prompt the model raised some alarm on 87.5% of no-alert controls, against 0% under the production prompt, and a preregistered single-sentence ablation showed that one production instruction explains part of that gap. We also report two failures common to similar systems, NWS test products ingested as real alerts and a token budget that fails on complex cases, and we release the dataset, labels and code.
Politically biased AI can shift users' opinions, yet it remains unclear whether model disclaimers protect against such persuasion. We report a preregistered experiment (N=1,586) examining how disclaimer design and user characteristics shape susceptibility to politically biased AI. We varied whether disclaimers identified political bias versus potential inaccuracies and whether they included an explanation. Participants completed two decision-making tasks with an LLM biased against their political views, with outcomes assessed immediately and one week later. Disclaimers identifying political bias and including an explanation reduced persuasion, but effects varied across contexts and did not persist. Individual differences were more consistent predictors: Republicans were more persuaded and reported higher trust than Democrats across tasks. Similarly, participants who used AI more frequently were also more persuaded, whereas those with greater self-reported AI knowledge were less persuaded. These findings suggest that generic disclaimers are not enough: effective safeguards must account for user heterogeneity and context.
An AI inventory is a structured, often public record in which a government or organization lists and describes the AI systems it uses or plans to use. Studies of single AI inventory releases describe what systems do but say little about how they are governed over time. We linked entries across three releases of the US Federal AI Use Case Inventory (2023--2025), compared answers only where both releases required them, and analyzed governance accounts through a dual deductive-inductive approach, guided by the Algorithmic Decision-Making Adapted for the Public Sector (ADMAPS) framework. As the inventory grew from 710 to 3,611 entries from 2023 to 2025, fewer required answers were left blank, but accountability was rewritten through three mechanisms. Each release \emph{redrew} the scope of the questions; agency-assigned classifications \emph{gated} what was reported; and answers were also \emph{overwritten}, with gains and losses concentrated in single agencies and little account of human oversight and judgment. These findings show that each release redraws the boundary of accountability and leaves the public to \emph{re-stitch} the records, a condition we term \textbf{palimpsestic accountability}. We recommend versioned records, reasons for every reclassification, public files that distinguish kinds of missing answers, and oversight documented as conditions to improve public sector AI accountability.
When students use generative AI to produce their work, the learning process becomes invisible. Established frameworks such as Bloom's taxonomy then classify the cognitive level of what the AI produced, not what the student contributed. We propose the Human Agency Depth Taxonomy (HADT), a preliminary six-tier taxonomy that classifies the engineering contribution a student makes in a prompt, independent of the AI's output. HADT combines three dimensions: contextual bandwidth, engineering abstraction and student agency. We applied HADT to 962 prompts from 105 students' structured AI Journals in an AI-permissive, first-year software engineering course of 145 students, and examined how agency changed over ten journalled weeks. Most prompts (84.2%) carried some student contribution beyond delegation. Agency tracked the development lifecycle. Architecture-discovery prompts dominated the requirements phase (48% of turns) and fell to 5-12% from week 9, while prompts analysing existing systems rose during build and verification (odds ratio 2.88, with turns clustered by student). Two-thirds of the variance in tier lay within students, so agency depended more on the phase of work than on the student. A turn-level rise in delegation in deadline weeks did not hold once turns were clustered by student. Explicitly specifying constraints on the AI's output was rare throughout, which we interpret as a knowledge bottleneck for novices rather than a gap in instruction. We discuss implications for course and assessment design, and for using HADT as a formative lens on how students engage with AI.
This study evaluates whether large language models (LLMs) can reliably replace or complement human research assistants in linguistically complex annotation. In a controlled annotation task, 73 human annotators (local students and crowdworkers with and without a background in linguistics) and more than 40 LLM configurations number-disambiguated 174 authentic uses of 'they' from Reddit, including innovative and context-sensitive cases. The best LLM, Kimi K3, reached 94.5% accuracy, above the human majority vote (86.2%) and the mean of the top half of human annotators (78.5%); only the single best human annotator scored higher (95.7%). LLMs also offer substantial practical advantages in cost and speed. However, qualitative error analysis reveals a crucial complementarity: human reasoning in low-agreement cases exposes inconsistencies in the annotation typology and thereby improves the methodological framework itself. The study contributes (i) a benchmark-style human-LLM comparison for a semantically demanding task, (ii) transparent cost and time evidence, and (iii) a methodological argument against a purely replacement-oriented view of automation. LLMs are viable for large-scale annotation support in linguistics, but their responsible use requires human critical oversight. The dataset with its gold-standard labels, all human and LLM annotations, and the fully reproducible code are openly available as a Quarto-based, public repository.
Multi-agent systems offer significant advantages in terms of flexibility and redundancy, enabling the efficient distribution of complex tasks among multiple robots. However, their deployment requires advanced control strategies to ensure optimal cooperation between autonomous agents and human operators. We propose a modification of the global connectivity maintenance algorithm to incorporate measurements taken in agent's local frame, using sensors with limited fields of view. This algorithm, responsible for the autonomous behavior of the multi-robot system, is combined with different adaptive supervision strategies, allowing the user to exert control at varying scales over the entire group. (i) Single Agent Control (S): The operator directly controls a single robot, (ii) Pinning Control (P): The operator imposes constraints on multiple robots to influence the group, (iii) Centroid Control (C): The operator acts on the system's center of mass to steer the entire fleet. In conjunction with the connectivity maintenance algorithm, these methods ensure compliance with inter-agent constraints while providing haptic feedback to the user regarding the system's connectivity state. The effectiveness of these approaches was evaluated through a user study, comparing performance with and without haptic feedback across multiple scenarios tailored to the different control modes.
Large language models (LLMs) are increasingly being used to turn citizens' opinions into collective proposals. These proposals can make assignments: who should act, what must be done, how much one should spend and by when. However, if the assignments change when the same opinions are processed again, publishing a single proposal can hide alternatives that participants never get to see. We examined this issue in four LLM mediators (three open-weight and one commercial) using opinions from DeliberationBank and Polis. For each task, a mediator drafted a proposal from thirty opinions five times, varying either the random seed or the order of the opinions. In 17% of five-run sets from the open-weight mediators and 20% from the commercial one, some runs contained assignments while others did not. Reordering the opinions changed the assignments almost as often as replacing them with opinions from different citizens. Three human annotators confirmed that the detected differences between runs were real, although the LLM judge overstated how often they occurred. Substantial variation remained under four safeguards: temperature zero, a commercial mediator, draft selection, and draft merging. The mediator's objective also shaped representation: prompts seeking agreement covered minority positions much less often than prompts asking to describe differences. A single proposal can therefore obscure choices in both the assignments it proposes and the views it excludes. We propose audits that make alternative assignments visible and disclose the objective behind each proposal.
Effective planning is critical for academic success, but in many computing courses students often lack accurate benchmarks for assessing the difficulty and time required for academic tasks. This phenomenon is more pronounced in large courses where instructors have limited capacity to provide individualized guidance on planning and task estimation. Although many planning tools exist, still, they rarely leverage task-specific, empirically grounded data from actual course contexts. Thus, we explored whether providing task-specific, data-driven information---assignment previews and cohort-level historical statistics---could support more effective planning. We present a lightweight and data-driven planning intervention embedded in the programming assignment workflow of an undergraduate computing course. We conducted quantitative and qualitative studies with 252 students across five programming assignments. Our results indicated that, in authentic course settings, the intervention led to more conservative and executable plans, with significantly higher plan adherence. Students with stronger adherence also achieved better performance on both programming assignments and exams. Qualitative findings also showed both practical planning support and mental benefits from intervention. These results highlight the potential of data-driven intervention to support student planning in larger courses settings where individual guidance from an instructor is further limited.
Process mining derives process models from event data recorded by information systems, but its results are usually presented as two-dimensional visualizations that are separate from the environments in which enterprise models are created and maintained. Metamodeling platforms for extended reality (XR), such as MM-AR, allow users to define modeling languages and to display models in 2D and 3D, but they do not provide access to event data or mining algorithms so far. In this paper we therefore present a first proof-of-concept for showing how an XR-based metamodeling platform can be extended with process discovery capabilities without changes to the platform core through a separate Python service based on the open source PM4Py process mining library. We demonstrate the approach using three common discovery algorithms for obtaining Petri nets and BPMN models based on CSV or XES event logs. A technical evaluation using four public event logs shows that the element counts of the created model instances match those of the PM4Py models in all 16 configurations. The integration adds between about 8 and 53 seconds per run, and this overhead grows with the size of the discovered model rather than the size of the event log. Although the prototype does not yet use the third dimension to encode process information, it established the foundations for studying such approaches.