  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.
