Large Language Models (LLMs) have the potential to fundamentally change the way people engage in computer programming. Agent-based modeling (ABM) has become ubiquitous in natural and social sciences and education, yet no prior studies have explored the potential of LLMs to assist it. We designed NetLogo Chat to support the learning and practice of NetLogo, a programming language for ABM. To understand how users perceive, use, and need LLM-based interfaces, we interviewed 30 participants from global academia, industry, and graduate schools. Experts reported more perceived benefits than novices and were more inclined to adopt LLMs in their workflow. We found significant differences between experts and novices in their perceptions, behaviors, and needs for human-AI collaboration. We surfaced a knowledge gap between experts and novices as a possible reason for the benefit gap. We identified guidance, personalization, and integration as major needs for LLM-based interfaces to support the programming of ABM.

翻译：大语言模型（LLM）有潜力从根本上改变人们参与计算机编程的方式。基于智能体建模（ABM）在自然科学、社会科学及教育领域已变得无处不在，然而尚无先前研究探索LLM辅助该建模的潜力。我们设计了NetLogo Chat以支持ABM编程语言NetLogo的学习与实践。为了解用户如何看待、使用及需求基于LLM的界面，我们访谈了来自全球学术界、产业界及研究生院的30名参与者。专家报告称其感知到的益处多于新手，且更倾向于将LLM纳入工作流程。我们发现专家与新手在人机协作的感知、行为及需求方面存在显著差异。我们揭示了专家与新手的知识差距可能是导致收益差异的原因。我们识别出指导、个性化及整合是基于LLM的界面支持ABM编程的主要需求。