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Computer Science Human-Computer Interaction

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Tuesday 4 August

Human-Computer Interaction

Designing and Evaluating Granular Consent for Data Sharing in Cardiac Disease Prevention

Pavithren V S Pakianathan, Rania Islambouli, Magenta Jade Shipsey et al. (+2 more)

Dynamic consent can promise end users with greater control, but little is known about how older adults with chronic conditions navigate the tradeoff between control and burden in granular consent mechanisms in health data life-cycles. Using a two-stage design process we evaluated this tradeoff. An expert workshop (n=5) informed the design requirements for granular dynamic consent prototype. We evaluated single step vs multi-step granularity in dynamic consent using prototypes with cardiac patients (n=7) using a mixed-methods study. Quantitative measures showed no significant differences between low- and high-granularity consent screens in usability, workload, perceived information control or willingness to share data. However, qualitative findings revealed a control-burden paradox and trust-dependent engagement with granularity. Participants sought greater transparency and control over AI-mediated data processing. We contribute implications for designing granular consent in health data life-cycles.
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All authors Pavithren V S Pakianathan, Rania Islambouli, Magenta Jade Shipsey, Laura Maaß, Jan Smeddinck

Human-Computer Interaction · Cross-listed from cs.AI

ChartAnno: Benchmarking Multimodal Large Language Models for Chart Annotation Generation

Zhenghan Chen, Zekai Shao, Lidan Tan et al. (+10 more)

Annotations are essential to communicative visualization, helping explain data, emphasize key findings, and guide attention. While multimodal large language models (MLLMs) offer new opportunities for automatic chart annotation authoring, their capabilities in this task remain underexplored. To address this gap, we introduce ChartAnno, a comprehensive benchmark for evaluating MLLMs on chart annotation generation. ChartAnno contains 1,200 real-world charts with paired annotated and unannotated executable code, along with 3,600 annotation instructions spanning three levels of specificity. We also develop a multidimensional evaluation framework combining rule-based and LLM-judged metrics to assess execution, structural compliance, semantic consistency, and design effectiveness. We evaluate 10 representative MLLMs under two primary chart input settings: (1) chart code alone and (2) both code and chart image. Results reveal that proprietary models lead overall, though open-source models narrow the gap. While higher instruction specificity improves annotation quality, inferring abstract communicative intent remains difficult across all models. Providing chart images yields marginal benefit when code is available. We also examine the effect of chart code through an image-only ablation and analyze the effects of multiple task complexity indicators and instruction-level transitions. Further analyses characterize common failure modes and validate the reliability of the LLM-based judge. Experiments with D3 and SVG demonstrate the generalizability of ChartAnno beyond its primary Python setting.
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All authors Zhenghan Chen, Zekai Shao, Lidan Tan, Xin Lin, Xingchen Zeng, Yi Shan, Ziyue Lin, Xiaoliang Fu, Xinyuan Liu, Yuetong Guo, Fen Wang, Bongshin Lee, Siming Chen

Human-Computer Interaction · Cross-listed from cs.SE

When AI Joins the Team! A Model of How AI Adoption Relates To Social Patterns in Software Engineering Teams

Giusy Annunziata, Rudrajit Choudhuri, Anita Sarma et al. (+2 more)

Context: The growing adoption of AI-assisted development tools is changing how software teams collaborate, share knowledge, and coordinate, yet its consequences for team social dynamics remain largely unexplored. Gap: It is unclear whether AI adoption is associated with an increase or reduction in community smells,socio-technical anti-patterns reflecting coordination and communication breakdowns,and through which mechanisms. Method: Grounded in Transactive Memory Systems (TMS) theory, we validate instruments for HumanAI and HumanHuman interaction along two TMS dimensions, Specialization and Coordination, and test five PLS-SEM models on survey data from 152 software professionals using AI tools. Community smell constructs were derived from the literature and validated through expert surveys and factor analysis. Results: AI adoption relates to community smells not in a single way, but through mechanisms depending on the work. In specialization work, AI is associated with higher knowledge-sharing peer interaction, which is in turn associated with fewer smells. In coordination work, AI is directly associated with higher communication quality, complementing rather than replacing human interaction. Contributions: We provide an empirically validated, TMS-grounded model showing that the AIcommunity-smell relationship is contingent on the type of collaboration, with a reusable instrument and evidence-based implications for research and practice.
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All authors Giusy Annunziata, Rudrajit Choudhuri, Anita Sarma, Gemma Catolino, Filomena Ferrucci

Human-Computer Interaction

Cross-cultural evaluation of taste-sound correspondences in AI-generated music

Matteo Spanio, Massimiliano Zampini, Luisa Torri et al. (+5 more)

Sonic seasoning research has shown that listeners attribute systematic gustatory and emotional meaning to sound, and text-to-music generative artificial intelligence has recently been used to render gustatory prompts as musical stimuli. Whether the taste-sound correspondences acquired by such models hold beyond the cultural context in which they were validated remains untested. We extended a single-country study to a three-country online experiment conducted in Argentina, Italy, and Japan (N = 361). Participants first indicated their preference between base and fine-tuned MusicGen excerpts generated from four taste prompts (sweet, sour, bitter, salty), and then rated fine-tuned excerpts on twelve taste, emotion, and thermal descriptors. Preference for the fine-tuned model was confirmed in Argentina and Italy but not in Japan, and the salty prompt yielded the weakest correspondence in all three cohorts. Ratings differed substantially between countries, yet the main effect of country was no longer detectable once ratings had been standardized within participant, whereas the interactions characterizing the mapping of prompts onto descriptors remained essentially unchanged. Much of the apparent cross-cultural divergence is therefore attributable to differences in scale use; a structural component nevertheless persists. In addition an exploratory factor analysis indicated that the twelve descriptors were organized along different latent dimensions in each cohort. These results indicate that cross-cultural variation in AI-mediated sonic seasoning operates at two levels: the overall level at which taste is attributed to a given stimulus, and the relational structure of those attributions. Evaluations of generative music systems across populations should accordingly distinguish response-style bias from genuine perceptual reorganization.
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All authors Matteo Spanio, Massimiliano Zampini, Luisa Torri, Riccardo Migliavada, Bruno Mesz, Masaki Ohno, Yuji Wada, Antonio Rodà

Human-Computer Interaction

From Wearable Data to Personalized and Actionable Health Insights

Esther Brown, Karis Moon, Victoria Dean et al. (+1 more)

Commercial wearable devices continuously capture rich physiological data (e.g., heart rate, respiration), opening new possibilities for monitoring health conditions, notably around stress. Despite their promise, turning raw wearable physiological data streams into visualizations that surface stress-related insights in daily activities, and that ultimately foster reflection, awareness, and better stress management, remains a significant challenge. The data are noisy and context-dependent: the same spike in heart rate can come from sprinting, a tense presentation, or laughing with friends. To address these challenges, we propose a framework that combines user annotations with wearable data to support better stress management. We introduce a web framework offering interactive visualizations that layer daily activities, stress events, and interventions onto raw physiological streams, enabling users to reflect and identify trends. In a four-week pilot with seven university graduate and undergraduate student participants who logged 269 events, our tool revealed patterns between different types of interventions and stress: social interaction reduced average heart rate by 4.35 to 5.0 beats per minute, deliberate rest reduced average Garmin stress scores by 10.03 to 13.83 points, and mindfulness activities decreased average HRV by 6.61 to 13.22 milliseconds.
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All authors Esther Brown, Karis Moon, Victoria Dean, Finale Doshi-Velez

Human-Computer Interaction · Cross-listed from cs.CV

Frequency-Decorrelated Temporal Ensembles for EEG–fNIRS Imagined-Handwriting Decoding

Xiao Fan, Hongbin Guo, Yubo Han et al. (+1 more)

Imagined handwriting offers a temporally rich paradigm for non-invasive neural decoding, yet reliable recognition across unseen participants remains difficult because scalp EEG is noisy and internally generated stroke sequences vary across individuals. The Multimodal Brain-Computer Interface Grand Challenge provides synchronized EEG and fNIRS for four-class subject-independent handwriting-trajectory classification. We propose FRED, a task-adapted system that models imagined handwriting as a multi-second motor sequence and trains a compact multi-scale temporal network on three complementary EEG frequency views. With three seeds per view, cross-band members produce substantially less-correlated errors than same-band replicas, yielding a clean nine-member ensemble accuracy of 0.8076/0.7242/0.7492 on the public/private/overall test partitions without test-set adaptation or output constraints. The submitted pipeline further incorporates transductive pseudo-label training, three EEG-Conformer members, posterior aggregation, and a paradigm-aware decoder. Because every 12-trial randomization block contains three instances of each class, the final predictions are obtained by Hungarian assignment under the known block quota. On one fixed posterior pool, independent, session-constrained, and block-constrained decoding achieve 0.7600, 0.7758, and 0.7952 overall accuracy, respectively. The complete system reaches 0.8498/0.7718/0.7952, ranking fourth on the private split. A modality audit finds fNIRS-only decoding at chance (0.2511 overall), while adding fNIRS to EEG changes accuracy by only +0.0025. These results identify frequency-diverse temporal EEG modeling and protocol-matched structured inference as the principal sources of performance in this sparse-montage EEG–fNIRS setting. The source code is available at https://github.com/XiuFan719/EEG-fNIRS-fuse-method-for-MM-challenge.
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All authors Xiao Fan, Hongbin Guo, Yubo Han, Yi Zhang

Human-Computer Interaction

Efficient Optimal Mouse Sensor Position Estimation using Simulated Cursor Trajectories

Minhyeok Baek and Sunjun Kim

The optimal sensor position on a computer mouse can improve pointing performance, but existing calibration methods require time-consuming repetitions of pointing tasks. In this paper, we propose a novel calibration approach that dramatically reduces the time and effort required to determine a user's optimal mouse sensor position. Our method simulates cursor trajectories for different sensor positions using a dual-sensor mouse, eliminating the need for repetitive measurements with multiple sensor placements. By analyzing the straightness of the simulated paths, quantified by the mean absolute error (MAE) relative to an ideal straight-line path, we estimate the sensor position that would yield the most efficient pointing motion for the user. Our preliminary results indicate that the proposed simulation-based calibration method could reduce the calibration time from an hour to just five minutes, while providing a better identification of the optimal mouse sensor positions.
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Human-Computer Interaction

Towards Wearable Opportunistic Crowdsensing for Open-Vocabulary Activity Data Collection Through User-Scheduled Trigger-Action Routines

Zeyu Wang, Yingke Ding, Mingze Gao et al. (+4 more)

Collecting richly labeled wearable activity data in everyday settings remains difficult because retrospective annotation is costly and often imprecise. Prior data collection apps rely on a labor-intensive self-reporting strategy and primarily treat participants as crowd labelers. We present Pebbl, a feasibility-stage system that incentivizes in-situ labeling through opportunistic crowdsensing. Pebbl lets users author trigger-action recipes on a smartphone and receive just-in-time reminders for beneficial actions when a trigger is detected. In the prototype, triggers are a limited set with four common audio cues, while actions are described in open-vocabulary natural language. Each confirmed execution yields a short sensor window with explicit start/end boundaries and a user-authored action label. We evaluate Pebbl through an expert workshop with wearable Human Activity Recognition (HAR) researchers (N = 6), a within-subject in-lab study (N = 21), and a pilot deployment (N = 8). Experts viewed the approach as lower burden and more ecologically valid than common labeling workflows. In the lab, Pebbl produced reliable execution logs under controlled conditions (recall = 97.30%, precision = 97.15%) and was preferred over comparison workflows on perceived burden and confidence. The pilot deployment shows that the interaction and sensing pipeline can function in free-living use, while surfacing practical constraints such as false triggers and context dependence. Overall, Pebbl represents a step toward a low-burden, distributable collection approach of user-contributed wearable activity data.
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All authors Zeyu Wang, Yingke Ding, Mingze Gao, Zhuolun Ren, Alex Mariakakis, Yuanchun Shi, Yuntao Wang

Human-Computer Interaction · Cross-listed from cs.CR

AirKey: Multimodal Acoustic-Assisted WiFi Sensing for Zero-Training Robust PIN Inference

BaiChuan Wu, Bin Liu, Xiang Zhang et al. (+6 more)

Contactless keystroke inference via WiFi sensing highlights severe privacy threats, yet its real-world feasibility is hindered by two fundamental physical and deployment bottlenecks: the strict requirement for network privileges to acquire stable sensing streams, and the inherent "waveform fusion" ambiguity of pure WiFi signals during rapid, muscle-memory typing. To overcome these limitations, we propose AirKey, a novel cross-modal sensing framework that achieves highly stealthy, zero-training PIN eavesdropping. First, to bypass network deployment barriers, AirKey exploits fundamental IEEE 802.11 mechanisms to predictably elicit Acknowledgment (ACK) responses from unmodified target devices. By passively harvesting Channel State Information (CSI) from these ACKs using a low-cost microcontroller, AirKey secures a continuous spatial sensing stream entirely without network association. Crucially, to resolve the WiFi waveform fusion bottleneck, AirKey introduces a cross-modal complementarity mechanism. By utilizing lightweight acoustic signals as precise temporal anchors, the system robustly guides the segmentation of overlapping CSI trajectories. This joint spatiotemporal fusion strictly intersects CSI-derived spatial similarities with acoustic-guided inter-keystroke timing. Extensive real-world evaluations demonstrate that AirKey achieves over 4x higher accuracy than state-of-the-art unimodal zero-training schemes, successfully recovering device-unlock PINs within 6 attempts. Ultimately, this work exposes a critical vulnerability in contemporary smart interfaces, underscoring the severe privacy implications of ubiquitous multimodal sensing.
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All authors BaiChuan Wu, Bin Liu, Xiang Zhang, Zhi Liu, Jie Zhang, Chao Liu, Huan Yan, Meng Li, Fusang Zhang

Human-Computer Interaction

Understanding Organizational Strategies Across Multimodal Artifacts in Immersive Computational Notebooks

Sungwon In, Minju Baeck, Yalong Yang et al. (+3 more)

Immersive Computational Notebooks (ICoN) extend traditional notebook environments into immersive spaces, enabling analysts to interact with multimodal artifacts, including code, narratives, data tables, and visualizations. By integrating multimodal artifacts into a single immersive workspace, ICoN enables analysts to transition between analytical tasks seamlessly. Meanwhile, understanding organizational strategies is critical for designing effective interactions to further support analysts. However, prior research on immersive computational notebooks has primarily examined organizational strategies centered on single-modality artifacts. Systematic investigations of how analysts spatially organize the complex relationships among multimodal artifacts in a single immersive workspace remain underexplored. To address this gap, we conducted a user study to examine organizational strategies for multimodal artifacts in immersive computational notebooks. Our findings show that participants predominantly adopted depth-based layouts, and their spatial organization was largely structured around cell-based artifacts.
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All authors Sungwon In, Minju Baeck, Yalong Yang, Sang Ho Yoon, Woontack Woo, Mallesham Dasari

Human-Computer Interaction · Cross-listed from cs.SE

Integration Barriers in Open-Source SSI Frameworks: An Exploratory Developer Experience Probe

Breno Cerqueira Reis Nakamura and Arlindo Flavio da Conceição

Self-Sovereign Identity (SSI) promises to decentralize digital identity, but widespread adoption remains hindered by integration complexity and tooling immaturity. This paper investigates the Developer Experience (DX) of open-source SSI tooling through an exploratory probe study. Nine developers with prior knowledge of decentralized identity concepts, representing early integrators building SSI applications, attempted core credential lifecycle tasks using Walt.id, Traction, and MetaMask. Our goal was to surface recurring integration barriers through qualitative thematic analysis of open-ended developer reports, complemented by task-level difficulty ratings. Our findings reveal a critical abstraction gap: while passive operations like credential receipt are relatively mature, active construction tasks, particularly schema customization, expose significant architectural friction. We identify that these barriers stem from inadequate API abstractions, brittle environment configurations, and documentation that fails to track the ecosystem's rapid evolution. These issues reflect structural design decisions in current frameworks. This study characterizes the structural integration barriers in the current SSI open-source ecosystem. To address the identified gaps, we propose three architectural shifts for developer tooling: web-based sandboxes, AI-assisted schema generators, and executable documentation strategies.
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Monday 3 August

Human-Computer Interaction

Large Language Models Exhibit Human-Like Bayesian Hypocrisy

Nykko Vitali and Mahzarin R. Banaji

Given recent achievements of large language models (LLMs), frontier models are expected to perform well on Bayesian reasoning tasks, at least as well as humans. Furthermore, there is no reason to expect that LLMs will condemn others who offer those very same Bayesian judgments, a fallibility observed in human decision-making (Cao, et al., 2019). In 5 experiments with 48 experimental conditions employing over 5,000 trials, GPT-4o and Claude 3.7 Sonnet were tested on two variations of a Bayesian reasoning task. We also assessed LLM evaluation of the competence and morality of a hypothetical person who had offered the same reasoning task as them. LLMs hovered near human performance on the Bayesian task, though their reasoning was more rule-based and rigid. Surprisingly, like humans but to a greater extent, LLMs also demonstrated the same hypocrisy in condemning others who, like them, had deployed Bayes' rule. In demonstrating Bayesian hypocrisy, LLMs highlight a humanlike error of a dissociation between self-performance and other-judgment, and caution against their use in domains where statistical fidelity and fairness norms collide.
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Human-Computer Interaction

Online Inference of Human Intention as a Latent Control State from Single-Trial EEG

Xiaowei Jiang, Daniel Leong, Yu-Cheng Chang et al. (+2 more)

Human intention can be modeled as a latent internal state that modulates how sensory information is evaluated and translated into action in human-machine systems. However, most existing brain-computer interfaces (BCIs) rely on control signals tightly coupled to externally imposed stimulation and do not explicitly infer whether perceived stimuli align with a user's internal goals. Here, we investigate whether intention can be inferred as a latent, goal-dependent state from single-trial electroencephalography (EEG). We introduce a stimulus-based paradigm in which intention is specified by an internally cued target category, while object identity varies independently across stimuli. To estimate intention under single-trial neural variability, we propose an interpretable fuzzy prototype-based network that maps each trial onto interpretable fuzzy prototypes encoding intention-specific dynamics. The model represents intention-related neural activity using a compact set of fuzzy prototypes with soft memberships, enabling robust decoding without reliance on engineered mediating stimuli. Experimental results demonstrate reliable within-subject single-trial intention decoding that outperforms representative deep learning baselines, achieving an accuracy of 93.22% +/- 3.21%. Online validation further confirms real-time feasibility, with an accuracy of 70.11% +/- 10.87%. Together, these findings advance intention-aware BCIs from stimulus-driven detection toward principled inference of goal-dependent internal states.
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All authors Xiaowei Jiang, Daniel Leong, Yu-Cheng Chang, Thomas Do, Chin-Teng Lin

Human-Computer Interaction · Cross-listed from cs.CL

Reconstructing Persistent Worlds from Narratives for Narrative-Grounded Interactive Experiences

Yi-Chun Chen

Designing narrative-grounded interactive experiences remains labor-intensive because interactive content must align with the underlying world implied by the narrative. Existing approaches formulate problems such as narrative planning, scene generation, and gameplay generation, each constructing computational representations tailored to specific downstream tasks rather than explicitly reconstructing and maintaining the persistent world that grounds them. We investigate reconstructing explicit persistent worlds from narrative descriptions as the central computational objective for narrative-grounded interactive realization. Rather than treating the world as an implicit by-product of downstream generation, our approach reconstructs and maintains persistent entities, locations, semantic relationships, and evolving world states while inferring only the contextual information required to support coherent interactive experiences. To investigate this perspective, we develop a reference prototype that reconstructs structured persistent world representations from narrative descriptions and subsequently instantiates playable tile-based environments. Through three representative case studies spanning a procedural scenario, an original fantasy narrative, and an adapted public-domain story, we demonstrate the feasibility of reconstructing persistent worlds and show how a shared world representation supports coherent gameplay while remaining grounded in the source narrative. By explicitly reconstructing persistent worlds prior to interactive realization, this work bridges computational narrative understanding and interactive content generation, providing a semantic foundation for AI-assisted game authoring, mixed-initiative design, educational simulations, and narrative-grounded interactive experiences.
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Human-Computer Interaction

Chat Debugging: An Exploratory Study of Human-AI Collaboration to Debug Analog Circuits

John Hu and Andrew Ash

This research paper describes an exploratory study on the effectiveness of Chat Debugging: troubleshooting malfunctioning analog circuits on breadboards and printed circuit boards (PCB) by undergraduates through conversations with public-domain large language models (LLMs). Through thematic analysis of students' voluntarily shared chat logs when debugging pre-determined buggy circuits under exam and time pressure, we discovered multimodal usage patterns by students and considerable domain knowledge and sensible debugging suggestions offered by off-the-shelf LLMs. Meanwhile, we also identified major gaps in LLM technologies and students' skills during human-AI collaborative debugging, such as LLMs' limitations in 2D/3D image-based reasoning, unjustified tone of confidence, and students' deficits in fundamental concepts and critical thinking.
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Human-Computer Interaction

Towards Designing for (Dis)Trust in Technologies for Aging

Muhid Hassan Risvy, Dimitri Theodoratos and Alisha Pradhan

Across cohorts of older adults and generations of technology, (dis)trust has continued to shape the acceptance of technologies for aging. Yet, we still know little about if or how to design for older adults' distrust in technology. Towards this, we reviewed empirical studies in which older adults discussed trust and distrust in relation to technology. Our analysis shows that (dis)trust is constituted in three key ways: emerges when material arrangements of digital systems become illegible, such as when older adults cannot determine where information is stored or trace its movement; is temporally constituted and fluid, building and shifting through prior experience and breakdowns, and at times distrust can operate as boundary work through which individuals limit technology's reach into their autonomy, relationships, and everyday lives. Our work yields design directions for supporting trust across different phases of technology use and by designing systems that make the movement and persistence of information more perceptible.
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Human-Computer Interaction · Cross-listed from cs.RO

Semantic Haptic Feedback Enhances Dexterous Robotic Teleoperation

Bingjian Huang, Sahar Aseeri, Jonas Schmidtler et al. (+9 more)

In robot teleoperation, haptic feedback can be used to help human operators accomplish dexterous manipulation tasks. However, existing haptic feedback methods try to replicate high-fidelity sensory haptics that are felt in real world interactions, which are constrained by the sensing and feedback hardware capability and may lead to higher workload. To addresses these limitations, this work introduces semantic haptics for teleoperation, which uses abstract haptic patterns to convey critical information about robot states. We categorize robot states into "Confirmations" and "Exceptions", implement a modular haptic rendering pipeline in robot simulation, and deliver semantic haptic feedback to operators through pneumatic and vibrotactile wristbands. This simplifies hardware requirements and enables one-to-many mappings between haptic patterns and robot states. Through three evaluation studies, we identify the most effective semantic haptic design for a common pick and place teleoperation task and compare semantic haptics to other teleoperation feedback approaches including sensory haptics and visual feedback. Results suggest that while semantic haptics performs similarly as other feedback in unimanual tasks, it achieves superior performance in bimanual tasks, with reduced task workload, increased situational awareness, and overall preference.
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All authors Bingjian Huang, Sahar Aseeri, Jonas Schmidtler, Joseph Zhang, Sonny Chan, Andrew Doxon, Jom Preechayasomboon, Evan Pezent, Alberto Rigo, Amir Memar, Nicholas Colonnese, Chase Tymms

Human-Computer Interaction

Sensus Pond: Exploring Water as Sensing Medium for More-than-Human Observation

Kuan-Ju Wu, Youyang Hu, Chiaochi Chou et al. (+1 more)

We introduce Sensus Pond, an interactive system that reconfigures water not merely as a static natural element but as an active sensing medium for registering more-than-human traces. In response to the limitations of anthropocentric approaches in interaction design, we propose a methodological framework of observation without translation, resisting the tendency to stabilize, decode, or humanize nonhuman presence. Drawing from critical theories of more-than-human design and ecological entanglement, Sensus Pond is a materially embedded and site-specific system that employs Swept Frequency Capacitive Sensing and an Artificial Neural Network to detect ephemeral interactions between the pond's surface and surrounding life forms. Rather than classifying or interpreting these events, the system visualizes temporal accumulations of overlapping traces, producing a layered archive of spatial and temporal entanglements. This approach emphasizes attunement over control, shifting the designer's role from interpreter to facilitator of open-ended, multispecies encounters. Sensus Pond invites reflection not only on what is sensed, but on how design itself can remain responsive to ambiguity, contingency, and the aesthetics of shared ecological life.
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All authors Kuan-Ju Wu, Youyang Hu, Chiaochi Chou, Yasuaki Kakehi

Human-Computer Interaction · Cross-listed from cs.SE

Decomposing the Doer Effect in Programming Practice: Code Writing Stands Out Among Active Practice

Arun Balajiee Lekshmi Narayanan, Gillian Gold, Jordan Barria-Pineda et al. (+3 more)

The "doer effect" suggests that actively doing practice activities is more strongly associated with learning outcomes than passively viewing content. In the doer effect literature, "doing" refers specifically to active practice. However, this categorization treats different forms of active practice as equivalent, leaving open whether some types of active practice are more effective than others. In this paper, we investigate whether the doer effect extends to computer science instruction and whether some forms of doing stand out compared to other forms. We analyze log data from 334 students across 11 semesters of introductory and intermediate Java who used an interactive practice system with five content types: Code Writing, Code Tracing, Code Completion, Code Visualizations, and Code Explanations. Consistent with prior doer effect work, we find that active practice activities were associated with 3.2 times better learning outcomes than passive activities. Interestingly, among the active practice, code writing was the most strongly associated with improved posttest performance, while no other activity type showed a comparable association. These results highlight the importance of challenging, feedback-supported practice activities, such as code writing problems.
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All authors Arun Balajiee Lekshmi Narayanan, Gillian Gold, Jordan Barria-Pineda, Quinn K Wolter, Peter Brusilovsky, Paulo Carvalho

Human-Computer Interaction

On Defining Chart Types Boundaries

Chang Han, Andrew Mcnutt and Katherine E. Isaacs

What makes a Gantt chart? This question proved unexpectedly difficult to answer when we set out to build a design space for Gantt charts. Existing definitions, each shaped by their respective research goals, made different scope choices that we could not directly reconcile. We reasoned about what should and should not count as a Gantt chart, developing concepts and tools along the way. We distinguish features that are essential to a chart type's identity from those that can vary, and use these distinctions to map how chart types relate through what they share and lack. Applying these ideas to Gantt charts, radar charts, and table cartograms, we produce key insights on what boundary work reveals: definitions diverge for functional reasons, drawing boundaries exposes hidden structure in descriptive vocabulary such as feature entanglements, and scope choices shape how far findings can generalize. We came to understand that there is not a definitive answer, but that working through the question produced a functional definition that guided the design space we originally set out to build. Additionally, we present vocabulary and tools for reasoning about chart type boundaries and surfacing these boundary decisions, alongside a documented Gantt chart design space. Our broader reflection is that scope choices in chart-type-centered research—which determine what design spaces include, what grammars generate, and what perceptual studies measure—are research decisions worth making visible.
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Human-Computer Interaction

WIP: Chat-Debugging: Large Language Model as a Hardware Debugging Assistant

Andrew Ash and John Hu

This work-in-progress research paper explores Chat-Debugging, a novel use case for large language models as an assistant for hardware debugging tasks to improve students' debugging skills. Hardware debugging can be a time-consuming and stressful skill to develop, leading to frustration and other negative emotions. While past work has explored streamlining and automating software-based circuit debugging where digital circuits are dominant, Chat-Debugging aids in physical hardware debugging where circuits may be analog, digital, or mixed-signal. Qualitative data were collected from LLM chat logs and interviews with a fourth-year electrical engineering undergraduate student. Major themes were extracted using a constant comparative analysis. Chat-Debugging incorporates accurate hardware information, properly handles natural language descriptions of circuits, and improves debugging confidence. A successful Chat-Debugging session includes investigating multiple potential root causes proposed by the LLM, the patience and determination to eliminate root causes, and a student who leads the debugging process by assertively correcting the LLM's misunderstandings. This human-computer interaction can improve electrical and computer engineering students' confidence during debugging and improve their debugging skills.
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Human-Computer Interaction

Embodied Empathy: A Multimodal AR and LLM-Powered System for Self-Attachment Psychotherapy with Self-Initiated Humour

Xinyan Ye, Gwyneth Phang, Anandha Gopalan et al. (+1 more)

The growing global demand for mental health support increasingly exceeds the supply of qualified practitioners, creating an urgent need for scalable digital interventions that can deliver meaningful emotional connection. In response, we present a novel multimodal application that operationalises the Self-Initiated Humour Protocol (SIHP) within a Self-Attachment Technique (SAT) framework. Our mobile application integrates customisable 3D childhood avatars, augmented reality, and an LLM-driven virtual therapist capable of automated emotion mirroring. An eight-day user study (N=16) indicates the system's feasibility and improvements in self-reported mood. Results show that personalised avatars and text-to-speech output strengthen emotional bonding and perceived empathy. Although emotion mirroring boosts engagement, its effectiveness depends heavily on classification accuracy and animation intensity. Moreover, findings indicate a shift in user expectations–from reactive chatbots to proactive conversational facilitators. We conclude with design implications for leveraging AI and AR to cultivate embodied empathy in digital mental health tools.
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All authors Xinyan Ye, Gwyneth Phang, Anandha Gopalan, Abbas Edalat

Human-Computer Interaction

ManyFold: A Design Exploration of Data Visualization on Foldable Mobile Devices

Julian Baader, Ricardo Langner, Can Liu et al. (+1 more)

With this work, we explore the unique potential of data visualization on novel foldable mobile devices (foldables). Even though foldables are already commercially available, there is limited knowledge of how to leverage their distinct characteristics for visualization. This gap will only grow as their form factors become increasingly diverse. To address this, we use a two-step approach. First, we present a device-centered design space, structured around physical and usage properties of foldable devices. Second, we introduce a conceptual framework that investigates visualization on foldables from four complementary perspectives: More Displays - distributing multiple views to leverage additional display space; More Shapes - mapping visualizations to spatial fold states; More Interactions - coupling visualization tasks and folding interactions; and More States - enabling responsive visualization through folding. We complement the design space and conceptual framework with a low-fidelity ideation workshop and the subsequent prototyping of interactive artifacts. We reflect on the results and lessons learned from our exploratory, design-driven process and discuss opportunities and challenges, including the gap between our proposed concepts and commercially available foldable devices. By providing conceptual foundations and illustrating the potential of foldables, we hope to inspire and inform the development of future visualization applications for this evolving class of devices.
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All authors Julian Baader, Ricardo Langner, Can Liu, Raimund Dachselt

Human-Computer Interaction

From Information to Delegation: Mapping Human-AI Financial Decision Making

Iman Munire Bilal, Yingcan Carol Wang, Ajan Raj et al. (+5 more)

As AI increasingly participates in human decision making, understanding how decision-making authority is distributed between humans and AI has become a fundamental behavioural question. We introduce a behavioural measurement framework combining intent and delegated decision authority to quantify what consumers seek from AI and how much decision-making authority they assign to it. Applied to 1.5 million real-world ChatGPT and Gemini interactions from 6,304 users in the United States and India, we find that financial services are already a substantial AI use case. Consumers overwhelmingly use AI to retrieve information and shape financial judgement, while delegation of financial execution remains rare. By shifting attention from conversation topics to delegated decision authority, this work establishes a behavioural baseline for measuring the transition to increasingly agentic AI.
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All authors Iman Munire Bilal, Yingcan Carol Wang, Ajan Raj, Filippo Giovagnini, Pranav Tewari, Yuwei Zhang, Mei-Chen Zoe Liou, Qamar Zaman

Human-Computer Interaction · Cross-listed from cs.LG

A 2-Block Architecture for Real-Time EEG Gait Decoding: A Pilot Study

Shantanu Sarkar, Saurabh Prasad and Jose L. Contreras-Vidal

Closed-loop lower-limb exoskeleton control via Electroencephalography (EEG) remains limited by motion artifacts, low signal-to-noise ratio, and binary gait formulations that fail to capture full cortical gait complexity. We propose a 2-block Brain-Computer Interface (BCI) architecture: a trainable session-specific Feature Extraction Block with real-time artifact suppression and multi-domain feature extraction, coupled with a Decoder Block built on a novel Polynomial Time-Varying Layer (PolyTVL)+LSTM for four-state gait classification (Stand, Initiate, Execute, Terminate). Ablation confirmed v01 (PolyTVL+LSTM) outperformed all variants (validation MCC: 0.435, gap: 0.187), with consistent EEG feature discriminability across ROIs and sub-bands (p<0.05). Closed-loop deployment with v01 achieved 55.3% (Rex-assisted) and 52.7% (volitional) gait initiation success, with a mean end-to-end processing time of 70.5~ms (+/-41.5), validating real-time feasibility in this pilot study.
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Human-Computer Interaction

HaptoFlow: High-Fidelity Real-Time Vibrotactile Generation via Flow Matching for Virtual Reality

Michikuni Eguchi, Yuichi Hiroi and Takefumi Hiraki

Haptic feedback is widely employed to enhance immersion in Virtual Reality (VR) environments. However, designing haptic stimuli that cover diverse interaction conditions remains a significant scalability challenge. Data-driven haptic generation has emerged as a promising approach, yet existing models face an inherent trade-off between waveform expressiveness and inference responsiveness, which becomes increasingly critical as training data grow in scale and diversity. To address this challenge, we propose HaptoFlow, a vibrotactile generative model based on Flow Matching, designed for interactive real-time haptic rendering in VR. Flow Matching learns a continuous vector field that transforms a base distribution into the target data distribution, enabling efficient representation of complex haptic data distributions and thereby facilitating both high-quality generation and computational efficiency. We train HaptoFlow conditioned on material labels and interaction parameters (stroking velocity and applied force), and integrate it into a VR system. Technical evaluation demonstrates that HaptoFlow outperforms all baseline methods in both waveform reproduction accuracy and inference latency. Furthermore, user studies confirm that the system latency falls well within the perceptual threshold of visual-haptic delay, and statistically significant improvements in perceived haptic quality are observed for a subset of materials. These findings establish a practical foundation for scalable, data-driven haptic content creation in VR, and provide latency benchmarks that inform the design of future real-time haptic rendering systems. Project page: https://tamago117.github.io/HaptoFlow/.
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Human-Computer Interaction

3D Gaussian Splatting and Mesh-Based Digital Twins: An Exploratory Study for Virtual Reality Tourism

Maximilian Warsinke, Francesco Vona, Abm Tariqul Islam et al. (+3 more)

Digital Twins (DTs) are increasingly used for immersive experiences in virtual tourism. Virtual Reality (VR) enables remote visits to replicated locations for promotional purposes or access to fragile and rural cultural heritage sites. However, developing high-fidelity DTs of tourist destinations is costly, due to the manual creation of 3D environments. Novel 3D rendering techniques, such as 3D Gaussian splatting (3DGS), pose a promising approach to creating immersive experiences. This study investigates the user experience (UX) of a 3D-mesh-based scene and a 3DGS-based scene within a VR tourism application. In a laboratory study, 20 participants engaged with both versions and rated UX, cybersickness, presence and affect through standardized questionnaires. A custom questionnaire was created to measure the perception of the DTs. The collected data suggests that both versions were enjoyed and induced positive affect, with the Mesh version receiving good UX ratings. While the 3DGS version scored higher in terms of experienced realism, it showed clear weaknesses in pragmatic quality. Further, the results suggest that the feeling of presence could be enhanced and cybersickness reduced in both versions. Overall, the study contributes to the understanding of UX in VR tourism applications by implementing mesh-based and 3DGS-based DTs and raising important questions about the perception of realism.
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All authors Maximilian Warsinke, Francesco Vona, Abm Tariqul Islam, Tanja Kojić, Jan-Niklas Voigt-Antons, Sebastian Möller

Human-Computer Interaction

Emotional Expression in Persuasion by Quadruped Virtual Agents: Toward Cross-Species Design Patterns

Kaoru Sumi and Souki Osawa

Persuasive technologies increasingly use virtual agents to influence attitudes and behavior, but research has focused mainly on humanoid agents. The persuasive design of non-humanoid, quadruped agents remains underexplored, and it is unclear whether emotional expression works consistently across animal species or whether species-specific motion is necessary. We developed virtual dog, cat, and horse agents and compared three behavioral conditions: species-specific behavior, shared behavior across species, and a bark-only baseline. Participants completed everyday tasks involving trash disposal, feeding, and refraining from smartphone use. We evaluated intention understanding, behavioral intention, actual behavior, psychological reactance, discomfort, familiarity, and agent acceptance. In several task contexts, the bark-only baseline produced lower intention-understanding and behavioral scores than the expressive conditions. Emotional expression and attention-guiding cues therefore appear to improve interpretation of agent intention and support behavior change. However, no consistent significant differences emerged between species-specific and shared behavior, suggesting that faithful reproduction of animal-specific motion is not the main determinant of persuasive effectiveness. Psychological reactance and discomfort remained low, while familiarity with an animal species was associated with actual behavior in some conditions. These findings indicate that persuasion by quadruped virtual agents depends more on functional cues, including emotional expression, attention guidance, and intention readability, than on accurate species-specific behavior. The results support cross-species generalizability and provide a basis for reusable design patterns in persuasive technology and human-AI interaction.
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Human-Computer Interaction · Cross-listed from eess.SP

UART for Wearables (U4We): DC Power and Carrierless Signal Transfer over Conductive Textiles

Akihito Noda

This brief presents a circuit-level conductive-textile interconnection technique for batteryless distributed wearable modules. Two conductive textile layers separated by an insulating fabric layer are used as a garment-wide shared bus that simultaneously conveys DC power and pulse-based data signals without point-to-point wiring. To minimize the circuit overhead of each module, universal asynchronous receiver/transmitter (UART) pulses are directly coupled onto the textile through AC-coupling capacitors without carrier modulation. The textile is modeled as a lumped resistance–capacitance network. The textile waveform is then modeled as the transient response of a second-order circuit. From this model, design conditions for comparator-based waveform recovery and high-bitrate transmission are analytically derived. The resulting design framework determines whether a given combination of data rate, textile capacitance, and resistance is feasible, and also provides the corresponding design range of the decoupling inductors. The required textile parameters are characterized, and robustness to parameter drift is analyzed. These results establish a basic methodology for textile-based simultaneous power and data transfer.
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Human-Computer Interaction

Reassessing the Feasibility of PPG-Based Non-Invasive Blood Glucose Level Estimation

Supraja Ramesh, Markus Neufeld, Michael Küttner et al. (+2 more)

Non-invasive blood glucose level (BGL) estimation from photoplethysmography (PPG) holds great promise for wearable health monitoring, but results across studies are hard to compare due to inconsistent datasets, data leakage, and non-standardized evaluation metrics. We present the first reproducible, extensible evaluation pipeline and use it to reassess five representative PPG-based BGL methods on published datasets under three increasingly strict data-split protocols: random window-level, participant-aware, and leave-some-participants-out (LSPO). Models appeared competitive under random splitting but collapsed under participant-aware and LSPO evaluation, with nearly all yielding near-zero or negative R$^2$ values comparable to a mean-prediction baseline. Critically, across every model and split, over 90% of predictions fell within clinically acceptable zones (Clarke Error Grid A+B), including the baseline. This reveals a fundamental disconnect: clinical zone metrics systematically conceal model failure in this domain. Our findings demonstrate that random train-test splits substantially overestimate the generalization of PPG-based BGL models due to sample-level data leakage, and that robust ML evaluation must precede clinical validation to meaningfully assess real-world utility.
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All authors Supraja Ramesh, Markus Neufeld, Michael Küttner, Tobias Röddiger, Michael Beigl