发表机构
Nagoya University（名古屋大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出AI Soccer Analyst，一种阶段感知且可验证的混合主动性系统，通过可修订的流程支持足球数据分析，评估表明其能提升任务完成率与用户感知的可验证性。
AI 中文摘要
体育数据分析师通过将计算与特定领域的专业知识相结合，将领域问题转化为见解。大型语言模型简化了编程，但提示到报告的工作流程可能会模糊决策和证据。我们提出了AI Soccer Analyst，一个具有可修订阶段的混合主动性系统：数据理解、问题定义、结构化规划、执行、基于证据的报告以及交互与细化。一项由五位分析师参与的形成性研究首先为自动化、可验证性、人工控制和可访问性方面的设计目标提供了信息。随后，一项由16名参与者参与的任务型评估结合了系统日志、保留的工件、评分和开放式回答；48项任务中有33项达到了操作完成标准。探索性测试支持了参与者对已完成任务输出质量、任务达成度、可靠性和可验证性的积极感知（经Holm校正后）。交互记录显示，领域知识通过澄清、规划和细化得以显现。这些发现将阶段感知的人机协作定位为一种实用方法，用于生成可检查、可修订和可验证的分析，同时保留领域专家在关键决策中的参与。
英文摘要
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 formative study with five analysts first informed design goals for automation, verifiability, human control, and accessibility. Subsequently, a task-based evaluation with 16 participants combined system logs, retained artifacts, ratings, and open responses; 33 of 48 tasks met the operational completion criteria. Exploratory tests supported favorable participant perceptions of completed-task output quality, task achievement, reliability, and verifiability after Holm correction. Interaction records showed domain knowledge emerging through clarification, planning, and refinement. These findings position stage-aware human-AI collaboration as a practical approach for producing inspectable, revisable, and verifiable analyses while retaining domain-expert involvement in consequential decisions.
