cs.HC · 2026-06-18 · No. 27

Human-Computer Interaction, 2026-06-18.

6 new papers in cs.HC. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

6 entries
  1. 01

    Correct Yourself, Keep My Trust: How Self-Correction and Social Connection Shape Credibility in Social Chatbots

    Biswadeep Sen, Yi-Chieh Lee

    cs.HC · cs.AI · cs.CY

    When social chatbots make mistakes, and they do, how they recover determines whether users trust them again. Social chatbots are increasingly integrated into everyday life, yet they remain prone to generating convincing but inaccurate information. The social connection they build with users makes such errors particularly consequential. We conducted a between-subjects experiment (N=120) comparing three error correction strategies: a webpage...

    arxiv.org/abs/2606.19286 · PDF

  2. 02

    A Taxonomy of Mental Health and Technology Needs for Alzheimer's and Dementia Caregivers

    Keran Wang, Drishti Goel, Jiayue Melissa Shi, Violeta J. Rodriguez, Daniel S. Brown, Dong Whi Yoo, Ravi Karkar, Koustuv Saha

    cs.HC · cs.AI · cs.CY

    Family members caring for individuals with Alzheimer's disease and related dementias (AD/ADRD) provide the foundation of long-term care worldwide. In 2023, more than 11 million U.S. family and friends contributed 18 billion hours of unpaid care, often at the cost of their own physical and mental health. These informal caregivers -- also referred as the "invisible second patients" -- experience elevated rates of mental health problems. Yet...

    arxiv.org/abs/2606.19247 · PDF

  3. 03

    A Clinician-Centered Pipeline for Annotation and Evaluation in Ultrasound AI Studies

    Fangyijie Wang, Jianjun Yu, Wentao Shi, Haixia Huang, Ran Shi, Guénolé Silvestre, Kathleen M. Curran

    cs.HC · cs.AI

    Clinician-centered evaluation is critical for validating medical AI systems, especially in ultrasound imaging where quantitative metrics do not always capture clinical usability. Existing medical image platforms primarily focus on dataset labeling. They lack integrated support for blinded model comparison and reproducible evaluation workflows. We present a clinician-centered pipeline for remote annotation and evaluation in ultrasound AI...

    arxiv.org/abs/2606.19174 · PDF

  4. 04

    Improving Human-Robot Teamwork in Urban Search and Rescue Through Episodic Memory of Prior Collaboration

    Taewoon Kim, Emma van Zoelen, Mark Neerincx

    cs.HC · cs.AI

    Effective human-robot teamwork requires robots to adapt to partners, situations, and task dynamics from the start of an interaction. In the MATRX Urban Search and Rescue (USAR) environment, people can externalize collaboration patterns (CPs) they discover during teamwork through a chat and reflection interface. We study whether a robot can use such prior team experience to become a better teammate in future interactions. To this end, we...

    arxiv.org/abs/2606.18836 · PDF

  5. 05

    SwitchBraidNet: Quantisation-Aware Lightweight Architecture for Hybrid Brain-Computer Interface

    Gourav Siddhad, Yogesh Kumar Meena

    cs.HC · cs.AI · cs.ET

    Hybrid brain-computer interfaces (BCIs) that integrate motor imagery (MI) and steady-state visual evoked potentials (SSVEP) provide high-dimensional neural decoding but typically exceed the computational limits of embedded hardware. To address this, we propose SwitchBraidNet, a compact EEG classification architecture designed for low-power deployment. The model employs a dual-path temporal braid to extract multiscale oscillatory features, an...

    arxiv.org/abs/2606.18816 · PDF

  6. 06

    Better Adherence, Richer Context: A Field Evaluation of LLM-Powered Conversational Voice Diaries for Sleep

    Amama Mahmood, Bokyung Kim, Honghao Zhao, Molly E. Atwood, Luis F. Buenaver, Michael T. Smith, Chien-Ming Huang

    cs.HC · cs.AI

    Sleep diaries are central to behavioral sleep medicine and cognitive behavioral therapy for insomnia, yet daily completion is difficult to sustain, and static forms often provide limited context for interpreting night-to-night sleep variation. We designed an LLM-powered conversational voice diary that delivers clinically grounded morning and evening sleep diary questions through proactive smart-speaker prompts, structured conversational...

    arxiv.org/abs/2606.18596 · PDF

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