Human-Computer Interaction
PAIR: Perceptual Affective Inference and Regulation in a Real-Time Multimodal Conversational Agent
Kexin Quan, Zijian Ding, Jiaye Yong et al. (+3 more)
Sustained emotional support requires generative
agents to connect momentary emotion inference and regulation with continuity across encounters. We present PAIR (Perceptual Affective Inference and Regulation), a real-time
multimodal agent that reconstructs how an event is appraised into an emotional state. Appraisal scaffolds produce a valence-arousal-dominance estimate and select regulation guidance, delivered through conversation with coordinated
speech, color, and avatar cues. Rolling
memory carries context across sessions, and the scaffold re-runs after guidance. In a 14-day deployment with 19 participants, 1,093 sessions paired initial and post-guidance estimates with unanchored self-reports. Initial valence reached MAE 1.20 on the 9-point SAM scale (r=.68), dominance reached MAE 1.30, and arousal showed weak agreement even after coarsening. Self-reported emotional change varied with initial state, with the largest valence increases in sessions that began at negative valence. Perceived understanding was associated with greater valence increase and showed little correspondence with numerical prediction error. Over two weeks, helpfulness increased while input shortened; interviews traced personalization and companionship to relevant recall, context updates, and familiar dialogue. These findings connect inference accuracy to conversational and temporal patterns of support through per-event, first-person evaluation.
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All authors Kexin Quan, Zijian Ding, Jiaye Yong, Qinshi Zhang, Dong Wang, Jessie Chin
Human-Computer Interaction
The Relationship Between Blood Pressure and Self-Reported Stress During Sound-Based VR Relaxation Videos
Md Alamin Hossain and M. Rasel Mahmud
Virtual reality (VR) relaxation environments are increasingly proposed as accessible tools for stress management, yet the physiological correlates of self-reported stress during VR exposure remain unclear, particularly for blood pressure (BP). We conducted a within-subjects study (N = 18) in which participants viewed relaxing 360-degree
videos, grouped into four sound-design categories, such as
videos with rhythmic (RHYT), aperiodic (APRD), continuous (CONT), and low-ambient (LOWE) sound through an HTC Vive Focus
Vision headset. Systolic (SYS) and diastolic (DIA) BP were recorded after each category using an Omron 3 Series monitor, and self-reported stress was collected via a 0-100 Visual Analogue Scale (VAS) with fixed intervals. While overall BP did not differ significantly across sound categories, self-reported stress did (Friedman chi-square(4) = 12.07, p = .017), with rhythmic sound eliciting significantly lower stress than aperiodic and continuous sound. Critically, DIA, but not SYS, correlated significantly with self-reported stress across conditions (r = .31, p = .003). This suggests that DIA may be a more sensitive physiological indicator of subjective stress than SYS in short VR exposures. We discuss implications for designing and evaluating VR mental health interventions and the value of low-cost physiological sensing alongside self-report.
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Human-Computer Interaction
Jarvis: A Proactive Speech Agent for Multi-Party Conversations
Seunghyun Oh, Hirotaka Hiraki, Shuyue Stella Li et al. (+2 more)
Speech agents are reactive and dyadic: they speak when spoken to, and to one person at a time. We ask what it takes for a
speech agent to instead take part in a conversation among several people and speak up only when it can help. We introduce Jarvis, a real-time proactive
speech agent that audibly participates in multi-party human conversations. Grounded in a document shared beforehand, Jarvis follows the discussion and intervenes when the group misses or misstates a fact and does not correct itself within a few turns. We make three contributions: a problem setting based on epistemic breakdowns that makes proactive intervention measurable, realized as CHI-180-proactive, a synthetic multi-party
dataset seeded with known gaps, errors, and self-corrections; a proactive backbone that harnesses a small, open-weight model with deterministic checks and grounds every claim in a source sentence; and interaction techniques for taking the floor in live
speech and showing the cited evidence on screen. On CHI-180-proactive, Jarvis is correct on most events it addresses and stays silent 97% of the time when the group resolves an issue itself. A live study with 23 participants confirms these trends with real-time interventions.
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All authors Seunghyun Oh, Hirotaka Hiraki, Shuyue Stella Li, Yulia Tsvetkov, Shyamnath Gollakota
Human-Computer Interaction
From Written Response to Dialogue with AI: How Activity Format, Interaction Modality, and Language Impact Student Learning and Engagement
Deepak Varuvel Dennison, Connie Zhang, Rene Kizilcec et al. (+1 more)
The widespread availability of LLMs is challenging written learning activities, as students can increasingly generate responses without necessarily engaging with the learning content. Conversational AI creates an opportunity to redesign these activities as dialogue, while multilingual capabilities may make such dialogue more accessible to students learning through a non-native
language. We conducted a field study with 305 native Kannada-speaking undergraduate students at English-medium institutions in Karnataka, India. We compared written response activities with text- and voice-based dialogic activities with AI, each conducted in English-only or bilingual Kannada-English settings. Students who completed dialogic activities spent more time on the activities, contributed more, and reported greater interest and self-efficacy than those completing written responses, although fewer students completed the dialogic activities overall. Knowledge increased across all conditions, with no reliable differences in gains between activity formats or
languages.
Language shaped participation differently across modalities: bilingual interaction was particularly beneficial in voice dialogue, where it reduced articulation difficulties, increased turns demonstrating understanding, and reduced conversation abandonment. However, students also valued English because of its connection to their academic and professional aspirations. These findings show that designing dialogic learning with AI requires more than choosing between writing and dialogue, voice and text, or English and students' native
languages. We highlight opportunities to give learners greater control over modality, information, and pace, and to use native
languages as translanguaging support rather than as a replacement for English.
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All authors Deepak Varuvel Dennison, Connie Zhang, Rene Kizilcec, Aditya Vashistha
Human-Computer Interaction
MRPilot: Supervising and Intervening LLM-Based Multi-Robot Teams through Mixed Reality
Xiaoran Yang, Xun Qian, Yang Zhan et al. (+3 more)
Large
language models (LLMs) let users direct heterogeneous multi-
robot systems (MRS) through natural
language, but make task interpretation,
robot assignment, and coordination difficult to inspect and change. Based on a formative study with 12 non-expert users, we developed MRPilot, a mixed reality system organized around four stages of supervision and intervention. MRPilot represents
robot-team plans and execution states as structured commitments shared across synchronized situated and overview views. Across four stages, it helps users resolve ambiguous references (Forming), review plans before execution (Reviewing), monitor distributed execution (Following), and make
robot-level or team-level changes when problems arise (Repairing). In a within-subjects study with 20 participants in a virtual reality-simulated home, MRPilot reduced workload, increased situational awareness, transparency, trust, and perceived control compared with a conventional LLM-based conversational interface using the same LLM planner and
robot capabilities. We provide design implications for multi-scale intervention, adaptive supervision, and calibrated reliance in LLM-based MRS.
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All authors Xiaoran Yang, Xun Qian, Yang Zhan, Nathan Tran, Ziyi Liu, Qiao Jin
Human-Computer Interaction
Artifact removal improves electrodermal waveforms but not downstream classification in a virtual-reality balance task
Haochen Chai, Qixu Zhu, Siyao Li et al. (+1 more)
Artifact removal routinely precedes the classification of electrodermal activity (EDA), on the assumption that a cleaner signal supports a better decision. We tested this assumption in a virtual-reality (VR) balance-disturbance task. A residual gating network was trained on a
benchmark with expert-corrected EDA, frozen, and applied to VR recordings, where raw and gated signals were classified by five published time-series methods under identical leave-one-participant-out evaluation. On the
benchmark the gate detected artifacts well (median record AUROC 0.94) and reduced error inside artifact regions by 17.8%. In the VR task it did not improve classification. Changes in balanced accuracy ranged from -1.35 to +0.93 percentage points, no classifier improved and two lost accuracy, and all five were equivalent to raw input within +/- 3.32 points. The benefit was lost between waveform and decision. The correction that lowered waveform error also reduced skin conductance response detection in all 43
benchmark records. Processing left 92.8% of predictions unchanged, and the predictions it did change were corrected and corrupted at similar rates. The VR recordings also carried little contamination (an estimated 4.6% of samples), and even perfect localization of deliberately injected artifacts recovered only 3.3 points in the most sensitive classifier. A pooled association between artifact level and accuracy (11.3 points) disappeared within participants (0.1 points), showing how differences between people can make cleaning look useful. Preprocessing should be judged by the decision it supports, against an unprocessed arm.
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All authors Haochen Chai, Qixu Zhu, Siyao Li, Fangfang Jiang
Human-Computer Interaction
Knit-Structure Effects on Electromechanical Metrics and Their Correlation with Joint-Angle Estimation Error in Knitted Strain Sensors
Annika Eloranta, Zhuchenyang Liu, Iiro Naulapaa et al. (+5 more)
Knitted resistive strain sensors show strong promise for joint motion sensing in sports and rehabilitation, but the linkage between sensor design and in situ performance remains unclear. We investigate how knit structure and machine settings (e.g., stitch size) shape electromechanical properties and which metrics predict sensing performance during bending. Sensors spanning seven common knit structures at two stitch sizes were fabricated, characterized under uniaxial cyclic tension, and evaluated on a joint emulating bending rig. Joint-angle estimation was assessed with machine learning models, and correlations with electromechanical metrics were analyzed. Experimental results show that, among six common metrics, gauge factor and baseline resistance are largely set by knit structure, while working range, linear range, hysteresis, and cyclic stability vary only modestly across designs. Gauge factor correlates negatively and baseline resistance positively with joint-angle estimation error, mainly in lower-sensitivity designs, whereas the other metrics have weak or no predictive value. These results support using uniaxial tensile tests to screen out weak designs, while underscoring the need for joint-relevant evaluation and application-specific metrics to identify top performers.
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All authors Annika Eloranta, Zhuchenyang Liu, Iiro Naulapaa, Iida Arvola, Yao Zhang, Anna-Mari Leppisaari, Lulu Xu, Yu Xiao
Human-Computer Interaction
Redistributing Harm: Document Transition and the Limits of Trans Inclusion in India's Identity Systems
Megh Marathe, K Ranade, L. Ramakrishnan et al. (+1 more)
Identity documents (IDs) are some of the most consequential sites through which transgender people encounter state infrastructures. This article examines document transition, the work of aligning names, gender markers, and related information across official records in India based on focus groups with sixteen trans participants, most of whom were transmasculine and gender diverse, in mid-2025. Participants described encountering an absence of clear protocols, changing demands for proof, and objectionable conduct from officials, arising often from limited understandings of transness in systems and policies. As a result, participants faced harms related to livelihood, housing, voting, travel, redress from violence and discrimination, and education. We situate these findings within information studies and trans studies scholarship on classification and state recognition. Participants with mismatching IDs faced a form of torque or administrative violence that we call `trans tax' including disproportionate tax deductions, reluctant employers, and resultant insecure jobs. Further, gender-concordant IDs could redistribute harm: participants who became administratively legible as male lost jobs, benefits, and rights despite remaining vulnerable to the discrimination the programs sought to address. The disruption of people's lives by updated gender-concordant data calls for a re-examination of `haunting' at the intersection of transness, classification, and data practices. The article concludes by calling for sensitization efforts directed at street-, system-, and policy-level decision-makers about trans identities and state provisions together with established and widely disseminated protocols for document transition in the short term; as well as a broad and coordinated upheaval of systems and policies to undo cisgender-heteronormative assumptions.
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All authors Megh Marathe, K Ranade, L. Ramakrishnan, Koyel Ghosh
Human-Computer Interaction
Quantum-Like Spatial Decision Dynamics: A Falsifiable Model of Cue-Order Effects in Immersive Navigation with Implications for Human-Quantum Computer Interaction
Aryabrata Basu
The sequence in which a person encounters spatial evidence can change a later choice, yet an order effect alone does not identify a
quantum-like cognitive structure. We introduce
Quantum-Like Spatial Decision Dynamics (QSDD), a falsifiable state-space account of embodied decision making in which cue exposures are completely positive trace-preserving maps, intermediate judgments are
quantum instruments, and route commitment is an operationally defined measurement. We distinguish this formal claim from any assertion that cognition is microscopically
quantum. We then specify a minimal binary model with a fixed decision basis, noncommuting cue rotations, a fixed
symmetry-breaking initial azimuth, dephasing, and lapse; its five fitted parameters must generalize across environments rather than being refit to individual conditions. A reproducible simulation study evaluates recovery and model discrimination under both QSDD and seven-parameter classical logistic ground truths. Across 40 replicates per design
cell, QSDD was preferred on held-out environments in 85% of QSDD-generated
datasets at 240 independent observations per environment-order
cell and 95% at 480, but in 0% of classically generated
datasets at every tested sample size. The simulation also exposes weak identification of the lapse parameter and is explicitly a design analysis, not human evidence. We provide a preregistrable virtual-reality experiment, telemetry schema, classical comparison set, and failure criteria. Finally, we show how the same process-measurement logic can be transferred to human inspection and debugging of
quantum circuits. QSDD is therefore offered as a constrained model to be defeated or supported by data, and as a methodologically continuous route from immersive interaction research to human-
quantum computer interaction.
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Human-Computer Interaction
Mapping E-textiles Design Pain Points and Generative AI Opportunities: Insights from Workshops in Shanghai and Winchester
Zhuchenyang Liu, Nianchong Qu, Yao Zhang et al. (+3 more)
E-textile design involves complex decisions across materials, sensor and actuator structures, fabrication, garment integration, and data processing. It typically requires iterative prototyping and testing, which are time- and labour-intensive, while few practitioners possess cross-disciplinary expertise across all relevant domains. To identify current bottlenecks and explore how Generative AI (GenAI) might support the design process, we conducted two half-day co-design workshops, one in Shanghai and one in Winchester, with practitioners from materials science, electronics, garment design, human-computer interaction, and manufacturing. Twenty practitioners participated in the Shanghai workshop; ten of them had prior experience in e-textiles and form the contributing sample analysed here. A further ten practitioners participated in the Winchester workshop. Participants mapped their own design pipelines, annotated bottlenecks, and proposed where GenAI could provide support. Rather than presenting a ranked list of opportunities, we report a process map that indexes each proposed GenAI role to the pipeline stage at which practitioners located it, together with the conditions on which they stated its usefulness would depend. Across both sites, practitioners consistently identified domain-specific operational barriers, including data scarcity, the disconnect between prototyping and manufacturing, and trade-offs in material-hardware integration. They also emphasized that the primary barrier to GenAI-driven e-textile design is not general model capability, but the lack of standardized, machine-readable representations of e-textile designs. Based on these findings, we identify four classes of domain-tailored AI tools that could support future e-textile design processes.
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All authors Zhuchenyang Liu, Nianchong Qu, Yao Zhang, Marie O'Mahony, Qi Wang, Yu Xiao
Human-Computer Interaction · Cross-listed from cs.RO
RoboCap: A New Platform for Egocentric Robot Learning
Grounded Superintelligence and BitRobot
Despite its promise for scaling
robot learning, egocentric manipulation data is still scarce today. Collection at scale requires vertically integrating ergonomic hardware with centimeter-precise 3D
algorithms, at a precision that has not been publicly demonstrated. To address this gap, we introduce RoboCap, a 250\,g six-camera dual-IMU hat designed for in-the-wild egocentric data capture, and the Grounded API, a suite of device-agnostic 3D
algorithms tuned for RoboCap. In this report, we demonstrate how hardware, calibration, and 3D
algorithms interact to achieve state-of-the-art performance on the public
benchmarks: our SLAM across diverse settings and rigs, our depth estimation on egocentric settings, and our hand tracking when adapted to third-party devices.
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Human-Computer Interaction
Responsible Institutional Analytics: Interpreting Bias with AI Support
Francielle Marques, Ariel Ortiz-Beltrán, Ishari Amarasinghe et al. (+1 more)
Institutional Analytics (IA) dashboards inform decision-making in higher education, yet data limitations, constraints in analytical techniques, and missing contextual information often affect their interpretation. To support more responsible interpretation of IA, we introduce FACTRIA, a framework that organizes potential biasing factors across four areas: the analytics pipeline, institutional context, course-level characteristics, and demographics. We used the FACTRIA framework as input to a generative-AI chatbot designed to
prompt users to reflect on these factors while analyzing IA. A qualitative study with stakeholders, drawing on four authentic IA cases, and a transition network analysis showed that the chatbot prompted participants to recognize how overlooked factors influenced their initial interpretation. Findings indicated that combining a structured framework with AI-based guidance can enhance context-aware, responsible interpretation of institutional data.
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All authors Francielle Marques, Ariel Ortiz-Beltrán, Ishari Amarasinghe, Davinia Hernández-Leo
Human-Computer Interaction
SPEAR: Five Principles for Interactive Human-Agent Alignment
Tao Long and Lydia B. Chilton
Recent AI
alignment work often frames
alignment as a pre-deployment optimization problem: collect human feedback, learn preferences or principles, finetune the model, and deploy an aligned system. This framing has produced major progress, but it under-specifies what happens once AI systems act as
agents on users' behalf in situated, long-term, and social contexts. This position paper reframes human-
agent alignment as an ongoing interaction design problem. We propose SPEAR, five pillars of interactive
alignment: Specification (how people express intent and establish shared understanding), Process (how
agents decide when to act, ask, defer, or pause), Evaluation (how people judge whether
agents succeeded), Adaptation (how
agents adapt to users over repeated use), and Recalibration (how people adapt their trust, expectations, and behavior in response to
agents).
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Human-Computer Interaction
Towards semantic reconstruction of individual words from fnirs using clip loss
Santiago Posso-Murillo, Nathan Palladino, Ben Pyykkonen et al. (+3 more)
Semantic reconstruction maps neural activity to a word-
embedding space, recovering the meaning of a perceived word instead of selecting it from a fixed vocabulary. Functional near-infrared spectroscopy (fNIRS) carries semantic information suitable for this mapping. However, most fNIRS decoders are trained with a squared-error objective that fits each word independently and ignores the geometry of the
embedding space. To address this limitation, we evaluate a contrastive loss based on the contrastive-
language-image-pretraining (CLIP) loss, as an alternative to mean-squared-error (MSE) for reconstructing perceived words from fNIRS. We compare the two objectives by training a bidirectional long short-term
memory (Bi-LSTM) decoder to map fNIRS signals to word
embeddings. We use GloVe-50 and T5 word
embeddings as targets, across three fNIRS
datasets recorded under a shared paradigm pairing each word image with its spoken name. Performance is measured with a pairwise matching score and open-vocabulary top-$k$
retrieval. The Bi-LSTM trained with CLIP is the most consistent decoder across experiments. T5 produces higher matching scores, whereas every significant
retrieval result uses GloVe-50. These results support the use of contrastive objectives as a promising direction for fNIRS semantic decoding and motivate validation on larger
datasets.
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All authors Santiago Posso-Murillo, Nathan Palladino, Ben Pyykkonen, Dong Y. Han, Luis G. Sanchez-Giraldo, Jihye Bae
Human-Computer Interaction
Tellimation: Making Narrative Gaps Visible in Children's Storytelling with Just-in-Time Animation
Vincent Cavez, Marielle Zheng, Momin Siddiqui et al. (+4 more)
Illustrated scenes are used to
prompt children's stories, yet children with
language difficulties often omit the details, actions, and relationships that make a story coherent. We introduce Tellimation, which animates scene elements a child has omitted or misdescribed, drawing
attention to a narrative gap without saying what to tell. Its design space covers eight kinds of gaps (who is in the scene, how many, what they are like, what they are doing, where, when, how they relate, and what lies beyond the picture, such as
speech and thoughts), instantiated through 20 parameterized animations grounded in classical animation principles. A real-time pipeline detects discrepancies between utterance and scene, then selects and parameterizes an animation from the scene's narrative potential and the child's history. Adults interpreted most animations without instruction (N=120); children (N=12) resolved 46% of the gaps the system identified with animations, against 13% without.
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All authors Vincent Cavez, Marielle Zheng, Momin Siddiqui, Abbie L Olszewski, Srirangaraj Setlur, Maneesh Agrawala, Hari Subramonyam
Human-Computer Interaction
Mind Perception Influences Perceived AI Companionability
Jaime Banks and Zhixin Li
Humans increasingly keep company with AI companions, yet whether mind perception (MP) precedes machine companionship remains untested. In a field-sampled experiment, participants received mind-attributive or mind-negating descriptions of an AIC before interacting with and rating its companionability. Baseline skepticism was high; minded
primes somewhat reduced skepticism for connective coordination (CC; dyadic co-presence) but not eudaimonic exchange (EE; self-elevating links). Pre-interaction MP beliefs predicted companionability dimensionally: Agentic MP predicted EE potential while experiential MP predicted CC potential. Meaning in life moderated the agentic MP-companionability link, but exclusively for those already high in existential purpose. Results suggest two construal pathways to companionability–one through perceiving the AIC as an
agent-resource and one through perceiving it as an attuning experiencer–both more accessible to humans already flourishing.
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Human-Computer Interaction · Cross-listed from cs.LG
TrustmeWatcher: An Application for Workplace Micro-Sensing and Explainable Well-Being Feedback
Chengyu Yu, Leon Jacopo Costa, Zoja Anžur et al. (+6 more)
Workplace sensing studies combine long-running behaviour traces with self-reports, yet the tools that collect those data often sit apart from the interface that returns results. We present TrustmeWatcher, the application built for the TRUST-ME project to connect this work. TrustmeWatcher reuses ActivityWatch's OS-level watchers for computer-activity collection and adds its own application layer. It turns the collected traces into an interactive screen-time dashboard, synchronizes responses from short questionnaires completed on the StreamDeck, and presents questionnaires alongside
video highlights. Activity records and self-reports are aligned into labelled records for model development. The scope of this paper is limited to ActivityWatch data as model input. Artificial intelligence (AI) uses these activity records to predict six normalized state scores and an overall well-being score. The trained model runs locally, and the dashboard presents its predictions in semantic bands. Explainable artificial intelligence (XAI) helps users understand how recorded activity contributed to a prediction.
Privacy Control lets users pause or resume the camera and eye tracker used by the study. We describe the workflow, its user-device and sensing-setup boundaries, and its use with records from 17 participants. The result is a deployed application and study workflow that integrates activity review, study data collection,
privacy control, local prediction, and a participant-facing interface for XAI evaluation.
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All authors Chengyu Yu, Leon Jacopo Costa, Zoja Anžur, Mohan Li, Gašper Slapničar, Daniil Kirilenko, Martin Gjoreski, Mitja Luštrek, Marc Langheinrich
Human-Computer Interaction
If My Toy Could Talk: How Young Children Imagine, Design, and Test AI-Enabled Toys
Feiwen Xiao, Ruiyang Wu, Xinyue Cui et al. (+4 more)
To investigate the design space where children might design AI chatbots for their own toys, we developed ToyTalk, a technology probe that positions children as designers of LLM-enabled toys. Children begin with a familiar toy, configure its AI-enabled version through a no-code interface, and then interact with and test the character. We deployed ToyTalk with 76 children aged 7-9 across five elementary schools in the southeastern U.S. We examine how children define their toy, probe what it becomes, and respond when behavior diverges from expectations. Children predominantly designed toys with socially positive personalities, supportive roles, and interpersonal rules. In conversation, they most often probed identity and knowledge, while also testing capabilities,
memory, and relationships. When mismatches arose, children typically responded through correction, persistence, and retesting, while few returned to reconfigure the system. We discuss implications for children's design agency, testing practices, and expectations of coherence in child-facing generative AI.
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All authors Feiwen Xiao, Ruiyang Wu, Xinyue Cui, Yasitha Rajapaksha, Xiaoyi Tian, Shiyan Jiang, Tiffany Barnes
Human-Computer Interaction
AICoFe Demo: AI-based Collaborative Feedback System
Alvaro Becerra, Alejandra Palma, Ruth Cobos et al. (+1 more)
Peer feedback promotes active learning, critical reflection, and skill development, but its effectiveness is often limited by the quality of feedback students provide. Recent advances in LLMs offer new opportunities to support peer feedback by generating more coherent and actionable feedback while preserving human oversight. This paper presents AICoFe, an AI-based collaborative feedback system designed to support teacher, peer, and self-assessment in higher education. AICoFe integrates rubric-based evaluations, GenAI-supported feedback, Learning Analytics dashboards, and
video recordings to foster reflective learning. The system combines quantitative scores and qualitative observations to generate structured feedback focused on strengths, areas for improvement, and actionable recommendations, which teachers can review and curate. An evaluation with 65 undergraduate and master's students shows high satisfaction with the coherence and usefulness of the feedback, as well as the excellent usability, indicating that AICoFe effectively supports peer feedback in authentic educational settings.
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All authors Alvaro Becerra, Alejandra Palma, Ruth Cobos, Julian Fierrez
Human-Computer Interaction
AISSA Demo: AI-based Student Slides Analysis Tool for Automated Grading and Feedback
Alvaro Becerra, Diego Gomez, Ruth Cobos et al. (+1 more)
We present an AI-based web-oriented tool designed to support formative feedback for oral presentation slides in higher education: AISSA. It allows students to upload their slides before presentation and automatically receive rubric-based quantitative scores and qualitative feedback generated by large
language models (LLMs). The tool analyses slide-level features and content using a teacher-defined rubric and delivers feedback through interactive Learning Analytics dashboards. These dashboards enable students to visualize performance indicators, inspect feedback in context, and reflect on strengths and areas for improvement, while teachers can review automated assessments, provide their own evaluations, and monitor student engagement with feedback.
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All authors Alvaro Becerra, Diego Gomez, Ruth Cobos, Julian Fierrez
Human-Computer Interaction · Cross-listed from cs.RO
Traversability-Aware Cooperative Path Planning for Human-UGV Casualty Evacuation
Kristian Dalland, Prithvi Poddar, Souma Chowdhury et al. (+2 more)
Heterogeneous multi-
robot path planning is a well-studied problem in which
agents with disparate kinematic and dynamic models must coordinate to achieve shared objectives. These formulations, however, treat all
agents as robotic-their cost models are mechanical and their traversability is sensor-derived. In human-
robot teaming, the human partner remains relegated to command and supervisory roles rather than being modeled as a physical co-navigator with distinct mobility constraints and dynamic energy reserves. This work investigates joint path planning for a two-
agent human-UGV team in search-and-rescue casualty
retrieval scenarios. We model the human
agent using the Pandolf-Santee metabolic cost model with fatigue-modulated speed, and the UGV using a rolling-resistance energy model with terrain-dependent speed limits. By exploiting the complementary traversability of each
agent-the human's ability to traverse dense vegetation and shallow water versus the UGV's superior speed on open terrain and roads-we optimize casualty transfer locations, termed switch points, to minimize total mission time. Evaluated across multiple synthetic 1km2 environments with procedurally generated elevation and land-cover data, the optimized strategy reduces mean mission time by 5.3% relative to a human-only baseline and by 7.0% relative to a naive human-UGV strategy without switch point optimization, while reducing human energy expenditure by 17.8% relative to baseline. Notably, the naive strategy reduces human energy expenditure by a larger margin (22.4%) but incurs a 2% increase in mission time relative to baseline, illustrating that switch point optimization is necessary to realize time savings from human-UGV teaming.
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All authors Kristian Dalland, Prithvi Poddar, Souma Chowdhury, Karthik Dantu, Ehsan T. Esfahani
Human-Computer Interaction
What Did the AI Take On? Characterizing Cognitive Delegation in LLM Reasoning
Yoonsu Kim, Sean Kim, Kihoon Son et al. (+2 more)
Large
language models (LLMs) often perform intermediate cognitive work while carrying out users' requests, yet it remains unclear which parts users intended to delegate and how they wanted to remain involved. This matters because consequential choices may go unnoticed, limiting users' ability to steer the process, while reviewing every step would make delegation burdensome. We examined this with 24 LLM users across three knowledge-work tasks, collecting 992 retrospective annotations of
reasoning steps. From this, we developed taxonomies of LLM cognitive work, delegation enactment, and desired delegation protocols at the
reasoning-step level. Our analysis revealed that participants viewed about half of all steps (48.6%) as AI-initiated, meaning the AI took on work they had not requested. Desired involvement varied with cognitive work and delegation enactment, even when contributions matched participants' intent. We propose design implications and sketches for supporting more deliberate cognitive delegation through flexible protocols and inspectable, revisable AI-initiated decisions.
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All authors Yoonsu Kim, Sean Kim, Kihoon Son, Saelyne Yang, Juho Kim
Human-Computer Interaction · Cross-listed from cs.RO
A State Based Dispatch Controller for Hospital Delivery Robots with Shared Human and Infrastructure Resources
Krzysztof Siwek and Aleksandra Świetlicka
Robot delivery studies can overstate transport capacity when travel to pickups and human support fall outside the modeled schedule. We formulate a location aware dispatch model that couples
robot admission to transporter support and shared elevators, charging, and cleaning. The model replays 33,079 observed hospital requests; missing contents, deadlines, staffing, and completion times remain explicit scenario assumptions. Two full grids compare human dispatch, a resource aware controller, and a proximity and workload
benchmark in 30 paired replications per scenario. An exploratory extension tests a simpler deadline admission rule under the same operating model. Pickup travel increases demand on a pooled elevator bank. In one high staffing case, raising assumed elevator capacity from two to four reduces human only lateness from 55.11% to 3.18%, exceeding the dispatch differences. Direct policy comparisons and
robot coverage distinguish admission selectivity from system service. The analysis explains why a
robot can pass a deadline admission test yet delay service through staff handoffs and shared facilities. Its contribution is a reproducible evaluation of coupled dispatch workflows, with a clear separation between admission, completion, and return to availability. The results are conditional comparisons, not estimates of hospital benefit or released clinical capacity.
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Human-Computer Interaction · Cross-listed from cs.RO
Hierarchical Reinforcement Learning for Collision-Free Locomotion of an Underactuated Biped
Jagannath Prasad Sahoo, Saurabh Kumar, Surya Prakash S.K. et al. (+3 more)
A bipedal
robot cannot deviate from its path to avoid an obstacle without disturbing its balance, and this coupling is most severe on underactuated platforms such as the biped considered here, which has four actuated joints per leg and no hip or ankle roll. This paper presents a Hierarchical Reinforcement Learning (HRL) framework in which a High-Level (HL) policy observes the
robot pose, 36 raycast proximity measurements, moving-obstacle states, and a receding-horizon local goal, and outputs a body-velocity command $(v_x, v_y, \omega_{yaw})$ every ten control steps, while a velocity-conditioned Low-Level (LL) policy tracks each command through PD-controlled joint targets. Both policies are trained jointly with Soft Actor-Critic (SAC). Because the converged gait is task-agnostic, it is frozen and driven by classical planners over the same command interface, yielding three controlled baselines: SAC+A*, SAC+RRT*, and SAC+APF. Across 100 evaluation trials per method in randomized PyBullet environments, the proposed method reaches the goal in 98.0% of static and 88.0% of dynamic trials, against at most 78.0% and 68.0% for the planner hybrids, with path lengths within 4% of the A* reference, and ablations confirm that each observation channel and reward term contributes materially to this performance.
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All authors Jagannath Prasad Sahoo, Saurabh Kumar, Surya Prakash S.K., Samiran Datta, Abhay Dwivedi, Amit Shukla