🤖 AI Summary
This study addresses the challenges interviewers face in requirements elicitation interviews, where balancing comprehensive topic coverage, active listening, and adaptive follow-up questioning is difficult, compounded by a lack of effective script execution tracking. To overcome these limitations, this work proposes the first end-to-end AI-assisted interview framework, integrating business-goal-driven theoretical script generation, real-time topic coverage monitoring via natural language processing, and an on-demand dynamic probing mechanism. Experimental results demonstrate that the proposed approach significantly improves script quality (92.8 vs. 74.8), probing depth (3.43 vs. 1.15 probes per topic), and granularity of the resulting requirements models (proportion of leaf-level goals: 0.653 vs. 0.598). Notably, 86% of users identified real-time topic tracking as the most practically valuable feature.
📝 Abstract
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.