Human-Computer Interaction
A light-touch AI literacy intervention helps protect against AI political persuasion
Reed Orchinik and David Rand
Conversations with large
language models (LLMs) can substantially shift beliefs and attitudes, raising concerns about manipulation using AI persuasion. Here we test whether a light-touch AI literacy intervention - a brief warning that LLMs can be prompted to persuade and may present information selectively - helps protect users. Across two experiments (total N = 3,208 Americans) in which participants conversed with an LLM instructed to shift their views about different political topics, the presence of a warning reduced belief change by roughly one-half (-48.1%, 95% CI [-59.5%, -36.8%]) relative to the control. Importantly, the warning did not significantly reduce trust in generative AI more broadly. Light-touch literacy interventions can help protect users against AI political persuasion.
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Human-Computer Interaction
Context-Aware Emotionally Adaptive Voice Assistants: A Multimodal Framework for Empathetic Human-Agent Interaction
Tapon Kumer Ray and Rajkumar Yesuraj
Voice-assistant interruptions tend to be intrusive because existing systems fail to consider the affective state, cognitive load and situational context of the user when deciding when and how to interrupt.Voice-assistant interruptions tend to be intrusive, since existing systems do not consider the affective state, cognitive load or situational context of the user when determining when and how to interrupt. In this paper, EmpathicVA, a closed-loop framework integrating physiological sensing, vocal-affect analysis, contextual modeling and reinforcementlearning interruption policy, is introduced. A hierarchical fusion model involves integrating HRA, EDA, respiration, acousticprosodic features, linguistic
embeddings, and contextual cues and computing the probabilities of five affective states. A Double Deep Q-Network selects immediate response, brief or extended delay, empathetic response, or silent mode based on these probabilities, context and interaction history. The
multimodal model obtained an accuracy of 92.3% and an F1-score of 0.922 at the macro level on a held-out test set, outperforming the highest accuracy unimodal model by 6.0 percentage points. Comparing the six-week within-subject field study with 48 participants with a baseline and context-only assistants, there was a corresponding increase in satisfaction, trust, and appropriateness of timing, as well as a large reduction in interruption-related stress episodes. The results suggest that affect-aware timing and restraint are both important in voice interaction in addition to the response wording.
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Human-Computer Interaction
When a Story Feels Like Mine: How Personalized Narratives and Humor Shape Older Adults' Empathy toward LLM-Generated Peer Health Stories
Kexin Quan, Precious Olalere, Smit Desai et al. (+1 more)
Peer stories have been shown to boost self-efficacy in older adults' health behavior change. Despite their effectiveness, peer stories are difficult to deploy in health promotion at scale given the difficulty of matching the diverse health concerns and coping styles of heterogeneous older populations. Large
language models (LLMs) have been shown to generate authentic narratives, yet how personalization and narrative affective style, such as humor, jointly shape older adults' responses remains unknown. We developed a theory-driven system that generates first-person peer health narratives varying in personalization and humor through a three-stage LLM pipeline grounded in self-efficacy mechanisms. Thirty-one older adults were invited to participate in a within-subjects lab study. Results showed that personalization increased perceived relatability and relevance of peer stories, especially for older adults with lower humor preference. These findings position individual differences in affective styles as a second dimension in designing personalization for LLM-assisted health communication.
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All authors Kexin Quan, Precious Olalere, Smit Desai, Jessie Chin
Human-Computer Interaction · Cross-listed from cs.CL
How Humans and LLMs Read Gender into "Gender-Neutral" Physical Descriptions
Yingjia Wan, Lin Lin and Elisa Kreiss
When foundation models describe people, recent work in AI
fairness, accessibility, and ethics recommends avoiding inferred identity labels (e.g., "she", "his") in favor of seemingly "objective" physical descriptions (e.g., "short hair", "a defined jawline"). Yet whether such descriptive
language achieves gender-neutral communication remains an open empirical question. To study this, we introduce GAPA (Gender Associations of Physical Attributes), a
dataset of 316 common physical attributes drawn from diverse sources, paired with 14,706 gender-association ratings from 304 US-based annotators. Results show that physical descriptions carry structured and graded gender associations among readers, with more consistent and distinctive associations for women and men than for non-binary identities. Next, we evaluate 16 LLMs across model families, sizes, and post-training variants against human ratings. The models partially recover human associations but exhibit systematic
alignment biases, including compressed rating distributions, weaker
alignment for associations with men, and asymmetric abstention that disproportionately targets the non-binary category. Finally, we release the best-performing proxy model trained to predict humans' gender associations of descriptive
language and demonstrate its utility through a sociolinguistic analysis of character descriptions in LitBank. Together, our findings provide the first empirical evidence that seemingly "objective" physical descriptions can retain systematic gender associations in human interpretation, and uncover systematic patterns of model-human misalignment. This challenges the assumption that replacing explicit gender labels with physical descriptions necessarily yields gender-neutral communication, and highlights downstream challenges in using such descriptions to communicate subjective identity categories in human-AI interaction.
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Human-Computer Interaction · Cross-listed from cs.RO
Auto-HSI: Personalized human control of a robot swarm on demand by using LLMs for online automatic code generation
Alessandro Nazzari, Nathan Cerisara, Dorian Tonnis et al. (+5 more)
This paper presents Auto-HSI, a method for generating personalized human-swarm interaction (HSI) interfaces on demand. The objective is to enable untrained operators to use natural
language descriptions and gesture demonstrations to explain how they want the
robots to collectively behave in response to their gestures. Based on these inputs, the code should automatically be generated for personalized state machines that will control the
robots as desired, in response to the desired gesture inputs. In the developed Auto-HSI prototype, the generated code produces a personalized interface for centralized control using one- and two-handed gestures, enabling a user to teleoperate the
robots' motion, formation shape, and shape deformation. We test the gesture tracking and code generation components of Auto-HSI against performance
benchmarks. We then test the full Auto-HSI prototype in “live” operation experiments, in which real human operators centrally control 50 simulated
robots in a physics-based simulator, under nominal and noisy conditions. In these experiments,
robots are teleoperated to: score a goal, traverse a maze that requires shape deformation, and score two simultaneous goals by splitting into two groups. We also demonstrate a real human operator making live updates to their personalized Auto-HSI interface during operation (in simulation). Finally, we demonstrate live operation of real
robots.
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All authors Alessandro Nazzari, Nathan Cerisara, Dorian Tonnis, Raina Zakir, Lorenzo Labarile, Weixu Zhu, Marco Dorigo, Mary Katherine Heinrich
Human-Computer Interaction
From Momentary Emotion Inference to Sustained Emotion Support: Evaluating a Companion Agent in a Longitudinal Study
Kexin Quan, Zijian Ding, Jiaye Yong et al. (+3 more)
Sustained emotional support is a long-horizon interaction task closely tied to human well-being. Recent research demonstrates generative
agents' capacity for momentary emotional support, yet how these capabilities sustain support over time remains unclear. To examine this challenge, we deployed PAIR, a theory-based emotion-regulation companion, with 19 participants for 14 days. Across 1,093 sessions, we paired emotion estimates with self-reports before and after guidance and analyzed logs and interviews. Estimates corresponded more closely to self-reported valence and dominance than arousal. Guided conversations were followed by higher valence and state-dependent arousal changes. Participants felt understood through contextual exploration and emotional acknowledgment, acting on guidance suited to their needs and constraints. Perceived helpfulness of guided conversation significantly increased over time. Our findings link
memory updates and retained corrections to cross-session personalization, informing future emotional support tools that adapt to evolving needs, learn from prior outcomes, and preserve user control over
memory.
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All authors Kexin Quan, Zijian Ding, Jiaye Yong, Qinshi Zhang, Dong Wang, Jessie Chin
Human-Computer Interaction
Intelligent Interaction Techniques (IIxT) - Proposal
Brad A. Myers
Interaction techniques (IxTs) are the low-level, reusable components out of which user interfaces are designed, including menus, scroll bars, text input fields, and also copy-paste, text-entry, and selecting objects. The IxTs for graphical user interfaces (GUIs) were well established in the 1980s, with relatively minor additions and tweaks for smartphones in the 2000s. Most of today's AI user interfaces involve a chat window, which is an excellent interaction for some tasks, but is generally considered separate from the GUI IxTs. I argue for making the IxTs themselves more intelligent, so users can freely mix modalities, even within the same interaction. This will require research into new IxTs, and also into the infrastructure that will enable these intelligent IxTs (IIxTs) to be built. There are also significant security,
privacy and economic implications to this
vision.
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Human-Computer Interaction
When AI Says "I Am Unable to Answer": Understanding User Responses to AI Refusals
Mahjabin Nahar, Eun-Ju Lee, Yujin Heo et al. (+1 more)
While refusal-based safeguards to mitigate
hallucinations in large
language models (LLMs) are becoming increasingly common, they may conflict with users' preferences for definitive answers. However, we know little about how users respond to refusals across repeated interactions, when refusals become more or less acceptable, and for whom. In this work, we examine how refusal frequency, explanations, and need for cognitive closure (NFCC) shape responses to AI refusals. Participants (N=599) interacted with an AI system that never refused, refused infrequently, or refused frequently, with refusals either explained or unexplained. Participants were most satisfied with genuine responses, followed by
hallucinations and then refusals, despite recognizing
hallucinations as less accurate. Explanations increased satisfaction with infrequent, but not frequent, refusals. Higher-NFCC participants evaluated AI systems that refused more negatively. These findings reveal a tension between
hallucination avoidance and user satisfaction and highlight the importance of designing balanced refusal strategies.
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All authors Mahjabin Nahar, Eun-Ju Lee, Yujin Heo, Dongwon Lee
Human-Computer Interaction
Moral Missions: Sustaining Responsible Practice by Surfacing Practitioner Moral Decision-Making Procedures
Teanna Barrett, B. Biira, Jainaba Jawara et al. (+8 more)
A growing ecosystem of techniques, toolkits, and guidelines has been developed to help data scientists consider the social implications of data-driven technologies. However, prior literature highlights that even when this ecosystem of techniques is provided to professional data scientists, they still struggle to consistently adopt a responsible data science practice. We posit that the key to sustained responsible data science practice is to approach it as a moral mission: a conviction-driven technical practice that seeks to transform social conditions by any degree possible. In this paper, we present a semi-structured interview study with 15 responsible data scientists and AI practitioners to understand the moral decision-making procedures they use to articulate and actualize their moral missions. Through a phenomenological analysis of our participants' accounts, we find participants engage in embodied introspection, circumvent institutional expectations, and center relationality throughout their moral missions. We also present how our participants engage in similar processes to contend with generative AI (GenAI) in their responsible practice. We conclude by calling for subversive data science communities and identifying sociotechnical design implications to better support sustainable responsible data science practice.
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All authors Teanna Barrett, B. Biira, Jainaba Jawara, Andrew Shaw, Ziwei Dong, Chinasa T. Okolo, Seyi Olojo, Keerthana Kompella, Khadija Saho, Amy X. Zhang, Leilani Battle
Human-Computer Interaction
The CAST-framework: Measure and model social media use as a multi-level phenomenon through real-world applications
David Grüning, Jasper Doeninghaus, Zina Efchary et al. (+8 more)
Designing social media experiences that support well-being requires understanding when, how, and for whom use matters. Screen-time totals omit content and context, and connecting these with behavior and experience requires coordinating measurements across timescales. We introduce the CAST framework to connect measurement choices with person-specific models of exposure, behavior, physiology, and experience. Its dimensions specify where observations occur, how they are obtained, what they measure, and at what temporal resolution. Responses to interventions, such as whether to proceed after an app-opening pause, enter as behavioral measurements. We propose four synchronized measurement modules linking mobile and wearable data with self-reports and intervention responses. A synthetic demonstration with 120 simulated participants over 28 days illustrates how daily aggregation can obscure opposing effects of different activities under specified generating assumptions. The framework guides selection of measures and outcomes for evaluating social media interfaces and interventions.
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All authors David Grüning, Jasper Doeninghaus, Zina Efchary, Yui Kondo, Kevin Dunnell, Lennart Fischer, Isabella Zimmermann, Linnea Körte, Leo Mehlig, Frederik Riedel, Paul Schmiedmayer
Human-Computer Interaction
Pulla: A Parsons Problem Tool for Fine-Grained Behavioral Tracing and Instructor-Facing Problem-Solving Analysis
Daniel Prol, Juho Leinonen, Arto Hellas et al. (+2 more)
Existing Parsons problem tools primarily focus on correctness, indicating whether a student solved a problem, but providing limited visibility into the underlying problem-solving process. We address this gap by introducing Pulla, a Parsons problem tool that instruments programming assignments to capture fine-grained interaction data. These behavioral traces allow the system to surface recurring difficulty patterns, giving instructors actionable insights to inform targeted intervention decisions. This paper describes our experience in developing and deploying Pulla. We deployed the tool in two university courses: an upper-division software design course at the University of Houston (United States) and an introductory programming course at Aalto University (Finland). By analyzing the data collected, we identified common difficulty patterns, including misidentifying exception types, confusing return with the throw/raise mechanism, and incorrect control-flow ordering.
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All authors Daniel Prol, Juho Leinonen, Arto Hellas, Saleh Alkhamees, Amin Alipour
Human-Computer Interaction
How do people plan digitally: An in-the-wild investigation of task planning through a smartphone app
Srija Halder, Isabella Zimmermann, Linnea Körte et al. (+3 more)
Daily planning supports goal attainment and productivity, and people increasingly delegate it to digital tools. Plans are postponed, revised, and left unresolved rather than executed as intended. Understanding these changes requires following tasks from creation through later updates and recorded outcomes. We report an in-the-wild study of task planning using time-stamped logs of 24,265 tasks from 957 users of a widely used daily planner app, observed over six weeks alongside self-reported surveys. Following each task across its lifecycle, only 26.4% moved directly from creation to completion and 32.0% were abandoned; all-day and longer tasks were abandoned disproportionately, and 77.6% of timed tasks were marked complete later than intended. Latent profile analysis of 909 users indicated High Engagement (11.1%), Low Engagement (19.7%), and Passive (69.2%) profiles; active days on app distinguished the profiles and were associated with post-survey completion. These findings support evaluating planning tools across the task lifecycle.
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All authors Srija Halder, Isabella Zimmermann, Linnea Körte, Leo Mehlig, Paul Schmiedmayer, David Grüning
Human-Computer Interaction
On Edge in the Dental Chair: Designing VR Support for Moments of Dental Anxiety
Zhu Guo, Junjie Zhao, Haofan He et al. (+13 more)
Dental anxiety can change as a procedure unfolds, yet dental virtual reality (VR) commonly provides continuous distraction or relaxation. We investigate how support can be coordinated with specific simulated dental events. Stakeholder interviews (N=36), participatory design with three returning dentists, and patient walkthroughs of a no-intervention prototype (N=12) informed five Anxiety Events and an intervention-module framework. Drawing on cognitive vulnerability and emotion regulation, we implemented a standardized event-contingent VR system with predefined event-module assignments and shared agency and safety controls. A randomized study (N=24) compared the intervention package with no-intervention VR. The adjusted intervention-minus-control difference averaged -12.83 VAS-A points across events (95% CI [-24.42, -1.70]). Physiological, behavioural, and qualitative measures contextualized participants' experiences. The findings inform timely, comprehensible support and reassuring social presence in simulated dental VR; they concern the complete package rather than individual modules or clinical effectiveness.
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All authors Zhu Guo, Junjie Zhao, Haofan He, Jiaming Zhang, Mingshi Deng, Mingjun Zhou, Dongyijie Primo Pan, Zikun Jin, Jianquan Li, Liangyi Chen, Zuolin Jin, Benyou Wang, Jie Li, Siying Hu, Shan Jiang, Junwen Wang
Human-Computer Interaction
Towards Scalable Measurement of Durable Skills
Amir Globerson, Amy Keeling, Anisha Choudhury et al. (+37 more)
Durable skills, such as collaboration, creativity and critical thinking, are instrumental to success in the modern workforce. Yet, measuring these skills remains a persistent challenge. Moreover, because what is not measured is often not taught, these skills are often overlooked in mainstream educational curricula. Designing effective assessments for these skills necessitates balancing two often-conflicting requirements: ecological validity and psychometric rigor. On the one hand, the assessment environment should emulate natural real-world human interaction between humans. On the other hand, it should be scalable, controllable and reproducible. Here we argue that LLMs can be used to better capture both of these aims. Concretely, we develop a framework where the subject converses with AI teammates in a way that resembles human-human interaction for authenticity, while also offering the psychometric control required for informative and robust assessment. Importantly, the AI participants not only act as teammates but also, in an "Executive LLM" setup, steer the conversation towards eliciting a high density of observable evidence for skill proficiency. We complement this with an AI evaluator that can be used to measure skill proficiency in such interactions. We evaluate our assessment protocol based on transcripts of interactions of human participants with our AI framework, for multiple durable skills. For the skill of creativity, we further demonstrate the efficacy of an autorater for evaluating complex tasks performed by real students. Our analysis shows that the use of the Executive LLM significantly increases elicited evidence and that LLM-automated scoring of conversations largely agrees with that of expert annotators. This research demonstrates the utility of orchestrated LLMs approaches for measuring complex social and cognitive constructs in a scalable and controllable manner.
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All authors Amir Globerson, Amy Keeling, Anisha Choudhury, Anna Iurchenko, Aviad Segal, Avinatan Hassidim, Ayça Çakmakli, Ben Gomes, Benn Witt, Cathy Cheunga, Cristine Legare, Diana Akrong, Eliad Carmi, Elisabeth Bauer, Gal Elidan, Hadas Gelbart, Hairong Mu, Katherine Chou, Lev Borovoi, Nir Kerem, Niv Efron, Noa Kerrem Gilo, Preeti Singh, Rajvi Kapadia, Rena Levitt, Roni Rabin, Ronit Levavi Morad, Rotem Yulzary, Shashank Agarwal, Sophie Allweis, Tracey Lee-Joe, Tzvika Stein, Yael Bar Moshe, Yael Haramaty, Yaniv Carmel, Yishay Mor, Yoav Bar Sinai, Yoav Bergner, Yossi Matias, Yuri Lev
Human-Computer Interaction · Cross-listed from cs.AI
Are LLMs Good Financial User Simulators? Multi-view Investor Logic Alignment (MILA)
Jiajie He, Jiangyuan Hong, Xintong Chen et al. (+2 more)
Large
language models (LLMs) are increasingly used as user simulators, yet it remains unclear whether their predictions faithfully reproduce the evolving decisions of individual users. We investigate this question in a controlled longitudinal paper-trading study with 80 participants, where user interactions, simulated transactions, virtual portfolio states, and point-in-time
market information are aligned under a rolling next-day prediction protocol. We evaluate behavioral fidelity hierarchically, from trade occurrence to action structure, asset selection, and downstream portfolio consequences. Across 1,239 aligned user-days, no evaluated LLM reliably outperforms a simple recent-activity persistence baseline for predicting whether a user trades. Fidelity further deteriorates at finer levels: models struggle to recover buy–sell structure and traded assets, and similar activity-level predictions can lead to substantially different portfolio trajectories. Controlled evidence ablations show that recent trading history strongly governs activity prediction, whereas asset selection is substantially more sensitive to the available evidence. An observational analysis further finds that intensified ticker-specific research predicts imminent trading, but diagnostic tests do not support a causal interpretation. These findings suggest that current LLMs capture useful short-term behavioral regularities without yet recovering a stable individual decision mechanism.
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All authors Jiajie He, Jiangyuan Hong, Xintong Chen, Dongling Ni, Wenjin Liu
Human-Computer Interaction · Cross-listed from cs.AI
Data storytelling meets interpretable machine learning: Decoding AI decisions for non-experts without revealing sensitive data and model details
Lemen Chao, Zixuan Yang, Anran Fang et al. (+2 more)
AI-driven automated decision-making requires both predictive performance and interpretability. Recent advances in interpretable machine learning (IML) provide tools for explaining model predictions, but the technical complexity of these explanations may hinder accessibility to non-experts. To address this challenge, this study integrates data storytelling with IML to enhance the explainability of AI-generated decisions for a broader audience. Following the design science research (DSR) paradigm, this study proposes a formal definition of data storytelling in IML, introduces the DIST Pyramid to align data storytelling with IML, and presents the I-P-O Model to describe their interactions. It further develops an architecture to explain AI decisions through distinct "What-if" and "Why-not" event-generation processes. The architecture also employs data desensitization to protect sensitive input data. To validate the approach, a case study is conducted with the Boston Housing
dataset, using SHapley Additive exPlanations (SHAP) values and large
language models (LLMs) to generate data stories with And-But-Therefore (ABT) structures. An empirical evaluation shows that 76.4% and 74.3% of respondents rated the "What-if" and "Why-not" data stories as more comprehensible, with significantly higher accessibility scores than traditional SHAP visualizations. The paper concludes with the presentation of a narrative interpretation framework that integrates IML and data storytelling, thereby expanding the research scope as well as the practical applicability of AI decision-making.
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All authors Lemen Chao, Zixuan Yang, Anran Fang, Mingran Sun, Ming Lei
Human-Computer Interaction
More Than Just Access: Generative AI as Communication Intermediary for Blind and Low-Vision Users
Protik Dey, Mohd Saifuzzaman and Taslima Akter
Generative AI (GenAI) tools are increasingly woven into how blind and low-
vision (BLV) people communicate, not only with digital information, but with the physical world and with other people. Tools such as ChatGPT, Google Gemini, Be My AI, and Seeing AI translate visual and textual content into accessible form, and are beginning to substitute for interpersonal requests for help, such as asking a family member to read a label or describe a scene. Drawing on semi-structured interviews with 19 BLV participants, we examine GenAI as a communication intermediary and how it succeeds and fails as an alternative for reading, describing, and even asking another person for help. We also investigated what BLV users gain and risk when these tools take over that role. We conclude with design and policy implications for GenAI systems that communicate uncertainty honestly, protect information, and support BLV users' independence rather than substitute for it unsafely.
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Human-Computer Interaction
Can a Neural Encoding Model Replicate an fMRI Visualization Study?
Erfan Nasirzadeh Orang and Zack While
Most knowledge of graphical perception comes from behavioral studies. Understanding from a neural perspective is much more limited due in part to neuroimaging studies' expensiveness and difficulty to conduct. In this paper, we evaluate whether Meta's Tribe V2 neural encoding model can recover neural contrasts from a visualization fMRI study. Specifically, we evaluate Tribe V2 through a conceptual replication of the visualization-viewing component of a prior comparison of Bubble charts and three-dimensional Surface charts in color and grayscale. We generate TRIBE-predicted cortical responses for the original stimuli and compare the resulting contrasts with those reported in the human study. The model reproduced the direction of 11 of 14 reported cortical effects, with agreement concentrated in visual-processing regions. This agreement characterizes the model's
alignment with the prior human-generated fMRI results rather than independently confirming them. We discuss the limitations encountered when working with this model for in-silico replication and hope to encourage future work exploring this new avenue for neuroimaging studies in visualization. Supplemental materials are available at https://osf.io/8a96x/.
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Human-Computer Interaction
Beyond AI Literacy: A Structured Review and Exploratory Meta-Analysis of Measures for Competent Generative-AI Use
Daniele Veri'
Researchers assessing competent generative-AI use at work must choose among self-reports, objective tests, and measures of oversight and reliance. We conducted a structured, seeded review of 24 focal empirical publications, starting from the 2024 COSMIN-based review and adding a targeted update through 17 August 2026. We grouped the measures into four domains: knowledge and use, epistemic oversight, reliance calibration, and operational control of tool-using
agents. In an exploratory meta-analysis, we pooled three direct subjective-objective correlations from one research program (REML r = .055; Hartung-Knapp 95% CI [-.047, .156]; combined reported N = 2,765). We could not resolve a discrepancy between the largest study's reported correlation and p-value, leaving its weight uncertain. Adding a synthetic mean of 12 cross-factor correlations from a fourth study gave r = .079 (95% CI [-.025, .181]). This sensitivity analysis concerns a broader comparison. From this small evidence base, we cannot establish a population correlation, validate workplace cutoffs, or justify substituting self-ratings for performance scores. We identified tests of foundation knowledge (AICOS-S and GLAT) and measures of verification, reliance, trust, and dependency. We found no validated individual-level instrument in the focal corpus that tests the full combination of
agent scope, permissions, recovery, state isolation, independent review, and evidence-based closure; some cover subsets. We propose a four-layer workplace battery with non-compensatory decision rules, but have not tested its thresholds or whether it improves on other assessment approaches.
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Human-Computer Interaction · Cross-listed from cs.IR
Beyond Retrieval: Scaffolding Children's Online Learning
Diletta Micol Tobia, Hrishita Chakrabarti, Maria Soledad Pera et al. (+1 more)
Children increasingly turn to online information access systems that are primarily designed for the mainstream population, e.g., adults, but possess a limited understanding of how these systems work, contributing to their unstructured and ineffective search practices. This lack of knowledge can hinder their curiosity and the development of critical search skills. Grounded on the existing literature of both child-oriented Information
Retrieval and Human Computer Interaction, our work positions children as active participants in the search process, framing it as a scaffolded learning experience rather than a simple
retrieval task.
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All authors Diletta Micol Tobia, Hrishita Chakrabarti, Maria Soledad Pera, Monica Landoni
Human-Computer Interaction
Who Chooses the Artwork? Curatorial Agency and Distributed Intent in Botto
Haoting Alexa Yu and Bea Wohl
Botto is often described as a decentralized autonomous artist, but its authorship cannot be located in image generation alone. This paper examines Botto as an agentic curatorial system in which generation, ranking, voting, feedback, and minting form a recursive loop. Drawing on Botto's documentation and prior accounts of the project, we argue that agency in Botto is distributed but asymmetrical: community participants influence artistic direction through voting and governance, while Botto's internal models structure which images become visible, selectable, and recognizable as works. The case shows that, for AI art systems, explainability should include not only model behavior but also the curatorial procedures through which outputs acquire artistic status.
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Human-Computer Interaction
When AI Companions Disappear: Relational Continuity and Collective Contestation during China's National AI Regulatory Transition
Yunhao Yuan, Kejia Zhang, Yuqi Niu et al. (+3 more)
AI model updates and service withdrawals can disrupt relationships with AI companions, but research has largely examined individual platform events. Less is known about users' responses when multiple providers implement shared national regulations. We examine users' reactions and collective contestation surrounding China's 2026 regulation of anthropomorphic AI interaction services. We collected RedNote discussions from April 10 to August 10, 2026, used a validated
language model for relevance screening, and conducted qualitative content and thematic analyses of 89 posts, 1,425 comments, and 2,005 replies. Users retained, migrated, and reconstructed companions, finding that preserving conversation records did not necessarily restore shared
memories or familiar interactions. They compared regulations, platform explanations, and implementations to assign responsibility. Solidarity emerged through mutual aid and appeals to respect other communities' attachments, while disputes over targets and tactics exposed contested terms of collective action. Infrastructural dependence connected relational continuity and collective contestation during this national regulatory transition.
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All authors Yunhao Yuan, Kejia Zhang, Yuqi Niu, Yajing Wang, Renwen Zhang, Talayeh Aledavood
Human-Computer Interaction
Spook the Machine: Gamified Exploration of Human Imagination of Machine Fear
Levin Brinkmann, Hiromu Yakura, Sonia Nicoletti et al. (+9 more)
What happens when AI machines express fear? Do humans engage differently depending on how they express it? And what does it take to design for affective human-AI interaction? We present Spook the Machine, a gamified platform where participants generate images to frighten AI
agents endowed with personality-driven phobias. Machines respond with emotional reactions ranging from calm analysis to begging for mercy, and a gallery of successful scares becomes visible to subsequent users. In a public deployment during Halloween 2024, 832 participants created 15,719 artifacts across 89 machines in a $2\times2$ design varying the machine's emotional expressiveness (neutral vs. high-emotion) and reward structure (rewarding scariness alone vs. scariness plus novelty). Emotionally expressive machines deepened engagement at moments of failure: users deliberated longer even when the machine did not express fear, and learned faster from the gallery, yet their creative output remained unchanged across all measures. Rewarding novelty sustained collective creative diversity over time; without it, users increasingly repeated what had previously worked. Each machine developed its own trajectory through accumulated social learning, with the gallery shaping what participants created next. These findings show that emotional expression and reward design are complementary levers for steering collective human-AI interaction: emotional expression shapes how deeply users engage, while reward structure shapes how they explore.
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All authors Levin Brinkmann, Hiromu Yakura, Sonia Nicoletti, Mar Canet Sola, Thomas F. Eisenmann, Ali Dasmeh, Omar Sherif, Bramantyo Ibrahim Supriyatno, Prateek Gupta, Ignacio Serna, Rodrigo Bermudez Schettino, Iyad Rahwan
Human-Computer Interaction
Time Machine Experiments: Using Historically-Bounded AI for Inquiry into the Human Mind
Hiromu Yakura, Robin Schimmelpfennig, Ezequiel Lopez-Lopez et al. (+5 more)
Can interacting with someone from 1930, with no knowledge of what happened after, influence a person's perception of the past? People reason about the present against a picture of the past without observing it. The past is reconstructed from
memory and testimony, but this reconstruction has been filtered through everything that happened since. Historically-bounded large
language models (LLMs) make that past available for interaction. As a proof-of-concept for the impact of interacting with historical minds, we ran a preregistered randomized experiment ($N=240$), where participants interacted with an LLM trained on pre-1930 text. The interaction reduced the illusion of moral decline, the tendency to view the past as more moral than the present, compared to the contemporary-model control. This Time Machine Experiment paradigm informs new forms of interactive experiments, where temporal knowledge boundaries become experimental variables, and expands the realm of science fiction science, which turns thought experiments into actual experiments.
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All authors Hiromu Yakura, Robin Schimmelpfennig, Ezequiel Lopez-Lopez, Alejandro H. Artiles, Levin Brinkmann, Jean-François Bonnefon, Azim Shariff, Iyad Rahwan
Human-Computer Interaction
Profiling Handwriting-Process Deviations in Developmental Dysgraphia: An Open, Normatively-Referenced Instrument
Jihyun Mun and Mounîm A. El-Yacoubi
Online handwriting captured on digitizing tablets yields hundreds of kinematic, temporal, spatial, and dynamic features. These features are rarely organized into interpretable, reusable constructs, and, although standardized tests norm the handwritten product, no openly available instrument places a child's handwriting process relative to a verified-typical reference. We introduce and validate an open measurement instrument for profiling handwriting-process deviations in children with developmental dysgraphia from sentence-level online handwriting. The instrument comprises (i)~a literature-only vocabulary, fixed before any cohort analysis, organizing 136 online-handwriting features into 12 handwriting-process domains, and (ii)~an age- and sex-adjusted normative reference framework, fit on verified-typical children only, that turns those features into a 12-axis deviation profile with per-child bootstrap uncertainty. We establish its measurement properties on the DiaGraMo cohort (N=257 Czech children aged 8–12; 110 verified-typical, 147 with dysgraphia): the vocabulary is structurally coherent, and the reference is calibrated, parsimonious, and leakage-free. On the same cohort, five of twelve domains separate the groups at BH~$q<.05$ (Cliff's~$\delta$ +0.19 to +0.51), on spatial, temporal, and pen-orientation processes, and 96.5% of participant~$\times$~domain scores have a bootstrap CI narrower than one z-unit. No classifier is trained: the instrument reports uncertainty-aware deviation scores rather than a diagnostic label, and its outlier rate is not a diagnostic rate. The vocabulary, the analysis code, and an open reference implementation that scores new participants are all openly released, giving handwriting and dysgraphia researchers a reusable, validated instrument for situating individual children against a normative reference.
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Human-Computer Interaction
Mapping justice as a plural and contested concept in human–computer interaction
Henrik Rydenfelt and Matti Nelimarkka
Human-computer interaction increasingly studies and promotes justice. Often, this work does not treat justice through a pluralistic lens Justice, however, is not one thing: political philosophy offers several well-established, mutually incompatible conceptions of it. To navigate these meanings, we draw on a map of justice, a philosophical framework identifying six distinct conceptions of justice situated throughout (Western) philosophical thought. We apply this framework to four cases that illustrate different approaches to social justice: the COMPAS system, food democracy, gentrification, and robotics. Our analysis shows that each case already reflects a position on the map, but more importantly, one might adapt the case to other conceptions as well, leading to very different systems and analyses. We discuss how HCI might engage more carefully with such contested concepts, and warn that without pluralistic understanding, academic discourse risks a spiral of silence.
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Human-Computer Interaction
Personalizing Personal Health Interfaces: Co-Design with Generative AI
Karthik S. Bhat, Vidhi Shah, Vedika Agnihotri et al. (+2 more)
Personal health interfaces present wellbeing data through standardized dashboards that rarely fit how people interpret or act on it. Personalizing them to what people would like to see for themselves often requires design and technical expertise, a barrier that generative AI may potentially lower. Therefore, we ask what designs emerge and how it enables and constrains the design process. We conducted a co-design study where 14 participants redesigned Google and Apple Health interfaces using Figma Make. Participants reimagined interfaces that supported personal context, future planning, and interactive experiences, yet conversational AI designs converged around chat-window conventions. AI helped materialize loosely articulated ideas, but model defaults and generation latency shaped iteration. The process more readily operationalized interpretability and accountability than
privacy, trust, and emotional safety. Generative co-design let participants create interfaces directly, blurring the boundary between intentions and model defaults. We discuss implications for preserving agency and flexible user-directed interfaces.
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All authors Karthik S. Bhat, Vidhi Shah, Vedika Agnihotri, Dong Whi Yoo, Koustuv Saha
Human-Computer Interaction
Exploring Avatar-Based Representations of Desktop Analytical Workflows for Asymmetric Collaborative Visual Analytics
Tiansu Chen, Yalong Yang and Wai Tong
Collaborative visual analytics increasingly occurs across asymmetric desktop-VR settings, with desktop analysis offering precision and efficiency and VR providing spatial and embodied affordances. However, maintaining workspace awareness remains challenging because desktop collaborators are often represented in VR only through indirect cues such as perspective sharing, shared visualization state, or lightweight cursor traces, which do not convey their ongoing analytical activity in a VR-native manner. To address this gap, we present Desk2Avatar, which explores avatar-based representations of desktop analytical workflows in VR. We introduce two representation strategies, Distanced and Embodied, inspired by remote pointing and direct manipulation. We conducted a within-subject study with 18 participants comparing these strategies against a depth-adaptive cursor baseline. Our findings show that avatar-based representations improved collaborator awareness and attentional guidance over cursor cues, while excessive embodiment introduced occlusion, distraction, and additional workload. Finally, we discuss design implications for future desktop-to-VR representations, focusing on balancing collaborator presence, attentional guidance, and workspace readability.
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Human-Computer Interaction · Cross-listed from cs.GR
Opacity Is Not Just Opacity
Chunran Zhang and Tianrui Li
Web graphics travel with content across pages and themes, where changing backgrounds can require recoloring and maintenance. Opacity already makes a fixed object's appearance depend on its background, yet is usually understood only as how much the object obscures it. In fact, opacity controls the scaling of the object-background color difference; transparency is only one effect of this relationship. Zero places the output at the background and one at the source color, but difference scaling need not stop at either position. We retain the compositing expression and extend the coefficient domain from $[0,1]$ to the real numbers: negative values reverse the difference, whereas values above one expand it in the same direction. We focus on same-direction expansion for reusing Web graphics across backgrounds. Each object carries a fixed source color and coefficient, while the actual background determines the enhancement direction. Background-adaptive contrast enhancement thus becomes part of the object's compositing properties, reducing the design and maintenance of separate color variants. The implementation reuses the original equation without increasing the per-pixel arithmetic operation count within the same pipeline. Enumerating all 8-bit sRGB source colors on 16 predefined light and dark canvases, a fixed $\alpha=1.1$ increases the contrast ratio in 99.8145% of combinations. Without changing source colors, 4.8346% of all combinations newly reach the $3:1$ contrast threshold. Output validation and timing across three browsers demonstrate implementation in the same WebGL pipeline, with no sustained additional runtime observed.
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Human-Computer Interaction
The Dynamic Organization of Sustained Human-AI Cognition: From Construct-Level Change to Relational Structure
Zijian Ru
As generative artificial intelligence becomes a routine participant in writing, learning, information
retrieval, analysis, decision making, and problem solving, human-AI cognition research must address not only whether AI changes psychological constructs, use intensity, or task performance, but also how human cognitive activity is organized beneath similar aggregate indicators. This article proposes a dynamic cognitive organization framework that shifts analysis from construct-level change to relational organization anchored in the person's current task-cognitive state under sustained AI participation. The framework distinguishes five relational dimensions: execution locus, cognitive governance, representational reorganization, process organization, and reachable cognitive space; it also proposes a path-specific recursive principle whereby interaction outcomes, costs, and experiences may selectively reweight future probabilities of different organizational pathways. Five sets of testable propositions follow: the same overall AI-use intensity can correspond to different cognitive organizations; similar immediate outcomes can arise from different organizations with different predictive value for proximal subsequent outcomes; longitudinal organizational change need not track overall AI-use intensity; expansion of reachable cognitive space and displacement of pre-existing or emerging human-originated pathways may coexist within one episode; and recurrent cognitive organizations may redistribute cognitive practice opportunities, with accumulated differences potentially corresponding to different developmental trajectories in strategies, habits, and abilities. The contribution is an analytic level and five-dimensional relational structure for describing, comparing, measuring, and testing process differences that aggregate indicators or construct-level analyses do not uniquely determine.
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