发表机构
Carnegie Mellon University（卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出结合业务目标的AI辅助脚本生成与实时主题追踪、按需跟进问题生成的需求启发工作流，准实验显示其脚本质量更高、访谈表现更优，为需求启发提供了AI辅助支架。
AI 中文摘要
需求启发访谈要求访谈者在实时回应利益相关者的同时，平衡主题覆盖、主动倾听与适应性追问。尽管已有研究探索了AI对孤立访谈任务的支持，如脚本生成和跟进问题生成，但集成支持如何影响访谈、会产生何种需求制品尚不明确；此外，帮助访谈者实时追踪主题覆盖并决定何时进一步追问的脚本管理，也仍未得到充分探索。本文提出一种AI辅助的启发式工作流，其结合了基于业务目标的理论引导脚本生成，以及对主题覆盖追踪和按需跟进问题生成的实时支持。我们采用被试间准实验研究对该工作流进行评估，对比无训练的AI辅助条件与有训练的无AI辅助条件。基于源自启发式最佳实践的评分标准，AI生成脚本的得分高于仅训练脚本（100分制下为92.8对74.8）；AI辅助访谈覆盖的主题更少（9.6对14.5），覆盖的脚本化问题更多（86%对69%），每个主题提出的跟进问题更多（3.43对1.15），且产生更精细的目标模型（最低层级目标占比0.653对0.598）。参与者认为脚本管理有用，将主题追踪评为最有用的工作流特征（86%的同意率）。总体而言，这些结果表明，AI辅助条件与仅训练条件下的访谈轨迹和启发需求存在差异，为未来研究将AI辅助工作流作为启发式支架奠定了基础。
英文摘要
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.
Comments12 pages, 3 figures, 3 tables
