AI辅助脚本管理让需求访谈更聚焦，提升问题质量与目标模型精度。
AI-assisted Script Management for Requirements Elicitation Interviews
- 结合业务目标生成脚本，实时跟踪话题覆盖并按需生成追问问题。
- AI辅助组提问覆盖率高（86%），每个话题追问3.43次，目标模型更精细。
- 受访者认为话题追踪最实用，适合需要高效访谈的开发团队使用。
需求获取访谈要求访谈者在实时回应利益相关方时平衡话题覆盖、主动倾听和灵活追问。尽管已有研究探索了脚本生成、追问问题生成等单一任务的AI支持，但对集成式支持如何影响访谈过程及产生的需求成果仍知之甚少。特别是脚本管理——帮助访谈者实时追踪话题覆盖并决定何时深入追问——仍未被充分研究。本文提出一种结合理论指导的脚本生成、实时话题跟踪与按需追问生成的AI辅助工作流。通过对照实验（无培训+AI辅助 vs. 培训+无AI）评估发现，基于最佳实践评分，AI生成脚本得分92.8（满分100），高于仅培训组的74.8。AI辅助访谈覆盖话题较少（9.6 vs. 14.5），但更充分执行脚本问题（86% vs. 69%），每话题追问次数更高（3.43 vs. 1.15），生成的目标模型更精细（底层目标占比0.653 vs. 0.598）。参与者普遍认可脚本管理价值，86%认为话题追踪最有用。结果表明，AI辅助工作流带来不同的访谈路径与需求产出，可作为未来研究的需求获取支架。
原文摘要 · Abstract (English)
Requirements elicitation interviews require interviewers to balance topic coverage, active listening, and adaptive probing while responding to stakeholders in real time. Although prior work has explored AI support for isolated interviewing tasks, such as script generation and follow-up question generation, little is known about how integrated support affects the interview and what requirements artifacts emerge. Furthermore, script management---which helps the interviewer track topic coverage in real time and decide when to probe further---remains underexplored. This paper presents an AI-assisted elicitation workflow that combines theory-guided script generation grounded in business goals with live support for topic coverage tracking and on-demand follow-up question generation. We evaluate the workflow in a between-subjects quasi-experimental study comparing a no-training, AI-assisted condition with a training, AI-unassisted condition. Based on a rubric derived from elicitation best practices, the AI-generated scripts score higher than training-only scripts (92.8 vs. 74.8 out of 100). AI-assisted interviews cover fewer topics (9.6 vs. 14.5), cover more scripted questions (86% vs. 69%), ask more follow-ups per topic (3.43 vs. 1.15), and produce more refined goal models (lowest-level goal fraction 0.653 vs. 0.598). Participants find script management useful, rating topic tracking as the most useful workflow feature (86% agreement). Collectively, these results show that the AI-assisted condition is associated with a different interview trajectory and different elicited requirements than a training-only condition, positioning AI-assisted workflows as elicitation scaffolds for future studies.
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