# GPT-Powered Elicitation Interview Script Generator for Requirements Engineering Training

arXiv: https://arxiv.org/abs/2406.11439 · HTML: https://mlsanity.com/paper/2406.11439/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 45 references that are in the map.
- First posted on arXiv 17 Jun 2024.

## Builds on (most cited first, 27 of 27)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping](https://mlsanity.com/paper/2002.06305/) (2020)
- [Evaluation of Text Generation: A Survey](https://mlsanity.com/paper/2006.14799/) (2020)
- [Re3: Generating Longer Stories With Recursive Reprompting and Revision](https://mlsanity.com/paper/2210.06774/) (2022)
- [Large Language Models for Software Engineering: Survey and Open Problems](https://mlsanity.com/paper/2310.03533/) (2023)
- [Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs](https://mlsanity.com/paper/2312.05934/) (2023)
- [Meta-learning via Language Model In-context Tuning](https://mlsanity.com/paper/2110.07814/) (2021)
- [Automatic Generation of Programming Exercises and Code Explanations using Large Language Models](https://mlsanity.com/paper/2206.11861/) (2022)
- [PlotMachines: Outline-Conditioned Generation with Dynamic Plot State Tracking](https://mlsanity.com/paper/2004.14967/) (2020)
- [Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Book](https://mlsanity.com/paper/2211.02265/) (2022)
- [A Survey of Knowledge Enhanced Pre-trained Language Models](https://mlsanity.com/paper/2211.05994/) (2022)
- [ChatGPT and Software Testing Education: Promises & Perils](https://mlsanity.com/paper/2302.03287/) (2023)
- [Advancing Requirements Engineering through Generative AI: Assessing the Role of LLMs](https://mlsanity.com/paper/2310.13976/) (2023)
- [GRUEN for Evaluating Linguistic Quality of Generated Text](https://mlsanity.com/paper/2010.02498/) (2020)
- [Large Language Models (GPT) Struggle to Answer Multiple-Choice Questions about Code](https://mlsanity.com/paper/2303.08033/) (2023)
- [Investigating ChatGPT's Potential to Assist in Requirements Elicitation Processes](https://mlsanity.com/paper/2307.07381/) (2023)
- [Scalable Educational Question Generation with Pre-trained Language Models](https://mlsanity.com/paper/2305.07871/) (2023)
- [Trustworthy and Synergistic Artificial Intelligence for Software Engineering: Vision and Roadmaps](https://mlsanity.com/paper/2309.04142/) (2023)
- [LongStory: Coherent, Complete and Length Controlled Long story Generation](https://mlsanity.com/paper/2311.15208/) (2023)
- [Improving Requirements Completeness: Automated Assistance through Large Language Models](https://mlsanity.com/paper/2308.03784/) (2023)
- [How Do Requirements Evolve During Elicitation? An Empirical Study Combining Interviews and App Store Analysis](https://mlsanity.com/paper/2208.00825/) (2022)
- [Generative AI in Undergraduate Information Technology Education -- Insights from nine courses](https://mlsanity.com/paper/2311.10199/) (2023)
- [Exploring the ChatGPT Approach for Bidirectional Traceability Problem between Design Models and Code](https://mlsanity.com/paper/2309.14992/) (2023)

## Built on by (most cited first, 3 of 3)

- [Requirements Elicitation Follow-Up Question Generation](https://mlsanity.com/paper/2507.02858/) (2025)
- [Knowledge-Guided Multi-Agent Framework for Automated Requirements Development: A Vision](https://mlsanity.com/paper/2506.22656/) (2025)
- [AI-assisted Script Management for Requirements Elicitation Interviews](https://mlsanity.com/paper/2608.01640/) (2026)

## Abstract (arXiv)

Elicitation interviews are the most common requirements elicitation technique, and proficiency in conducting these interviews is crucial for requirements elicitation. Traditional training methods, typically limited to textbook learning, may not sufficiently address the practical complexities of interviewing techniques. Practical training with various interview scenarios is important for understanding how to apply theoretical knowledge in real-world contexts. However, there is a shortage of educational interview material, as creating interview scripts requires both technical expertise and creativity. To address this issue, we develop a specialized GPT agent for auto-generating interview scripts. The GPT agent is equipped with a dedicated knowledge base tailored to the guidelines and best practices of requirements elicitation interview procedures. We employ a prompt chaining approach to mitigate the output length constraint of GPT to be able to generate thorough and detailed interview scripts. This involves dividing the interview into sections and crafting distinct prompts for each, allowing for the generation of complete content for each section. The generated scripts are assessed through standard natural language generation evaluation metrics and an expert judgment study, confirming their applicability in requirements engineering training.