Elicitron: A Framework for Simulating Design Requirements Elicitation Using Large Language Model Agents

Abstract Requirements elicitation, a critical yet time-consuming and challenging step in product development, often fails to capture the full spectrum of user needs. This paper introduces a novel framework that leverages Large Language Models (LLMs) to automate and enhance the requirements elicitation process. LLMs are used to generate a vast array of simulated user agents, enabling the exploration of a much broader range of user needs and unforeseen use cases. These agents then engage in product experience scenarios, based on the Action, Observation, and Challenge framework introduced in this paper, inspired by Chain-of-Thought reasoning to produce structured responses. Subsequent agent interviews uncover valuable user needs. We validate our framework with two experiments. First, we explore different methodologies for diverse agent generation, discussing their advantages and shortcomings. We measure the diversity of identified user needs and demonstrate that serial agent generation leads to greater diversity. Second, we show how our framework effectively mimics empathic lead user interviews, identifying a greater number of latent needs than conventional human interviews. Our work highlights the potential of using LLMs to accelerate early-stage product development, reduce costs, and increase innovation.