cs.HC · 2026-09-11 · No. 112

Human-Computer Interaction, 2026-09-11.

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

    Understanding Operator Attitudes Toward AI-Supported Decision Making in Maritime Operations

    Doreen Jirak, Armeen Saroukanoff, Dirk van Rooy

    cs.HC · cs.AI

    Maritime Autonomous Surface Ships (MASS) and AI- supported decision assistants are expected to transform maritime operations, but their safe integration depends on how maritime professionals perceive and trust such systems. This paper presents a survey study on maritime stakeholders' attitudes toward an AI-supported assistant in collision-avoidance scenarios. Participants evaluated technology anxiety, trust in automation, and explanation...

    arxiv.org/abs/2609.11805 · PDF

  2. 02

    AI Soccer Analyst: Stage-Aware and Verifiable Human-AI Collaboration for Soccer Data Analysis

    Calvin Yeung, Keisuke Fujii

    cs.HC · cs.AI

    Sports data analysts translate domain questions into insights by combining computation with sport-specific domain expertise. Large language models ease programming, but prompt-to-report workflows may obscure decisions and evidence. We present AI Soccer Analyst, a mixed-initiative system with revisable stages: Data Understanding, Problem Definition, Structured Planning, Execution, Evidence-Grounded Reporting, and Interaction and Refinement. A...

    arxiv.org/abs/2609.11224 · PDF

  3. 03

    How AI Coders Discuss, Disagree, and Reach Consensus: Challenges and Opportunities for LLM-Based Qualitative Coding

    Jeongyeon Kim, John Mitchell

    cs.HC · cs.AI

    The utility of AI in multi-coder qualitative coding has been widely discussed, yet little empirical evidence exists to delineate the contexts in which it performs reliably. We address this gap by quantifying the effectiveness of multi-agent LLM coding across varied qualitative datasets, revealing key contextual and structural factors that mediate coding outcomes. We developed a literature-informed baseline pipeline that enables AI agents to...

    arxiv.org/abs/2609.11109 · PDF

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