# AI-assisted Script Management for Requirements Elicitation Interviews

arXiv: https://arxiv.org/abs/2608.01640 · HTML: https://mlsanity.com/paper/2608.01640/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 48 references that are in the map.
- First posted on arXiv 3 Aug 2026.

## Builds on (most cited first, 10 of 10)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Investigating ChatGPT's Potential to Assist in Requirements Elicitation Processes](https://mlsanity.com/paper/2307.07381/) (2023)
- [LLMREI: Automating Requirements Elicitation Interviews with LLMs](https://mlsanity.com/paper/2507.02564/) (2025)
- [The State-of-Practice in Requirements Elicitation: An Extended Interview Study at 12 Companies](https://mlsanity.com/paper/2102.11556/) (2021)
- [Requirements Elicitation Follow-Up Question Generation](https://mlsanity.com/paper/2507.02858/) (2025)
- [The Effects of Human Aspects on the Requirements Engineering Process: A Systematic Literature Review](https://mlsanity.com/paper/2109.08094/) (2021)
- [GPT-Powered Elicitation Interview Script Generator for Requirements Engineering Training](https://mlsanity.com/paper/2406.11439/) (2024)
- [Interview AI-ssistant: Designing for Real-Time Human-AI Collaboration in Interview Preparation and Execution](https://mlsanity.com/paper/2504.13847/) (2025)
- [Harnessing the Power of AI in Qualitative Research: Role Assignment, Engagement, and User Perceptions of AI-Generated Follow-Up Questions in Semi-Structured Interviews](https://mlsanity.com/paper/2509.12709/) (2025)
- [Generative Goal Modeling](https://mlsanity.com/paper/2509.01048/) (2025)

## Abstract (arXiv)

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.