cs.HC · 2026-05-30 · No. 12

Human-Computer Interaction, 2026-05-30.

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

01 — The papers

3 entries
  1. 01

    LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback

    Jiwon Kim, Maya Ajit, Sherry Gong, Soorya Ram Shimgekar, Dong Whi Yoo, Eshwar Chandrasekharan, Koustuv Saha

    cs.HC · cs.AI · cs.CL · cs.CY · cs.SI

    Large language models (LLMs) show promise in generating supportive responses for mental health queries, but improving their usefulness, empathy, and safety often requires substantial compute, expert input, and labeled data. At the same time, deploying proprietary, cloud-based models for mental health-related interactions raises important privacy and data-governance concerns, given the sensitivities. To address this challenge, we introduce...

    arxiv.org/abs/2605.30273 · PDF

  2. 02

    A Domain-Informed Multi-Objective Framework for EEG Channel Selection in Motor Imagery BCIs

    Dekka Muni Kumar, Dhruba Jyoti Kalita, Yogesh Kumar Meena

    cs.HC · cs.ET · cs.LG

    Motor imagery (MI) classification using electroencephalography (EEG) signals is essential for advancing brain-computer interfaces (BCIs). Traditional EEG channel selection methods often face limitations, such as dependency on single-objective criteria and susceptibility to local optima. To address these challenges, this work proposes a multi-objective optimisation framework that employs non-dominated sorting genetic algorithm,...

    arxiv.org/abs/2605.29943 · PDF

  3. 03

    Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs

    Mahjabin Nahar, Nafis Irtiza Tripto, Aiping Xiong, Ting-Hao `Kenneth' Huang, Dongwon Lee

    cs.HC · cs.AI

    As AI-generated and AI-assisted content floods online spaces, source labels attached to such content can distort human reasoning judgments, with downstream consequences for moderation, evaluation, and decision-making. Whether LLMs share this vulnerability, or offer more source-agnostic evaluation, remains an open question with direct implications for human-AI collaboration. We examine this issue using logical fallacies as a controlled setting...

    arxiv.org/abs/2605.29928 · PDF

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