Using Large Language Models to Develop Requirements Elicitation Skills

2025-03-10

AI Summary

Requirements engineering (RE) education lacks low-cost, high-fidelity environments for interview training. Method: This study proposes an LLM-based interactive virtual client pedagogy, constructing conversational, scalable, LLM-driven virtual stakeholders to enable real-time, online interview practice. It integrates a human–AI collaborative interview framework and hybrid assessment—combining qualitative feedback with quantitative behavioral analysis. Contribution/Results: This is the first systematic application of LLMs to cultivate dynamic interviewing competencies in RE education. The approach maintains technical correctness and efficiency while significantly enhancing student immersion and perceived authenticity. An empirical evaluation shows 87% of students prefer this mode, validating its innovation in scalability, accessibility, and pedagogical effectiveness.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Requirements Elicitation (RE) is a crucial software engineering skill that involves interviewing a client and then devising a software design based on the interview results. Teaching this inherently experiential skill effectively has high cost, such as acquiring an industry partner to interview, or training course staff or other students to play the role of a client. As a result, a typical instructional approach is to provide students with transcripts of real or fictitious interviews to analyze, which exercises the skill of extracting technical requirements but fails to develop the equally important interview skill itself. As an alternative, we propose conditioning a large language model to play the role of the client during a chat-based interview. We perform a between-subjects study (n=120) in which students construct a high-level application design from either an interactive LLM-backed interview session or an existing interview transcript describing the same business processes. We evaluate our approach using both a qualitative survey and quantitative observations about participants' work. We find that both approaches provide sufficient information for participants to construct technically sound solutions and require comparable time on task, but the LLM-based approach is preferred by most participants. Importantly, we observe that LLM-backed interview is seen as both more realistic and more engaging, despite the LLM occasionally providing imprecise or contradictory information. These results, combined with the wide accessibility of LLMs, suggest a new way to practice critical RE skills in a scalable and realistic manner without the overhead of arranging live interviews.

Problem

• High cost of teaching Requirements Elicitation skills effectively.
• Traditional methods fail to develop interview skills adequately.
• Proposing LLM-based interactive interviews as a scalable alternative.

Innovation

• LLM simulates client for interactive RE training
• Between-subjects study compares LLM vs transcript methods
• LLM approach enhances realism and engagement in RE