cs.HC · 2026-08-03 · No. 73

Human-Computer Interaction, 2026-08-03.

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

01 — The papers

4 entries
  1. 01

    The persuasive power of large language models does not depend on their perceived national origin

    Ningzhi Liu, Yannic Hinrichs, Jonas R. Kunst

    cs.HC · cs.AI

    Conversational AI developed by geopolitical rivals reaches citizens worldwide, raising concerns that it could sway public opinion or be rejected as foreign propaganda, with consequences for democratic discourse and information sovereignty. Yet, whether an AI's perceived national origin shapes its persuasive power is unknown. In a preregistered randomized experiment, 403 adults from a nationally representative United States sample held a...

    arxiv.org/abs/2607.29334 · PDF

  2. 02

    Design Concept: Scaffolding Geopolitical Reflection Among Tech Workers

    Sydney Reis

    cs.HC · cs.AI · cs.CY

    This paper presents a speculative Human-Computer Interaction design proposal for encouraging geopolitical reflexivity amongst tech workers at geopolitically relevant technology companies. Recent scholarship in International Relations and Science and Technology Studies increasingly recognizes technology firms and their workers as geopolitical actors whose decisions shape international dynamics. However, existing Responsible Innovation and...

    arxiv.org/abs/2607.28904 · PDF

  3. 03

    Agreement Is Not Quality: Blind Expert Verification of Human and LLM Qualitative Coding When Human Consensus Is Not Ground Truth

    Alex Liu, Lief Esbenshade, Michael Xiao, Victor Tian, Zachary Zhang, Kevin He, Min Sun

    cs.HC · cs.AI

    Evaluations of LLM-assisted qualitative coding almost universally measure model performance as agreement with human coders, a practice that presumes human coding is the standard to approximate. This study provides empirical evidence that the presumption fails in ways agreement metrics cannot detect. Five LLM systems and three trained human coders independently applied a 72-item hierarchical codebook to 2,560 educator messages from a K-12 AI...

    arxiv.org/abs/2607.28890 · PDF

  4. 04

    Human-LLM Collaborative Inductive Coding for Conceptualizing K-12 Educator AI Use

    Alex Liu, Min Sun, Lief Esbenshade, Michael Xiao, Victor Tian, Zachary Zhang, Kevin He

    cs.HC · cs.AI

    Qualitative researchers increasingly encounter interaction corpora whose scale exceeds what manual coding alone can address, and large language models (LLMs) are frequently proposed as analytic assistants. The open questions are not whether LLMs can participate in qualitative analysis but to what extent, in what phases, and under what safeguards. This article provides a detailed procedural account of a multi-phase human-LLM collaborative...

    arxiv.org/abs/2607.28889 · PDF

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