TY - GEN
T1 - Learning Agent-based Modeling with LLM Companions
T2 - 2024 CHI Conference on Human Factors in Computing Sytems, CHI 2024
AU - Chen, John
AU - Lu, Xi
AU - Du, David
AU - Rejtig, Michael
AU - Bagley, Ruth
AU - Horn, Michael Stephen
AU - Wilensky, Uri J.
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s)
PY - 2024/5/11
Y1 - 2024/5/11
N2 - 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.
AB - 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.
KW - Agent-based Modeling
KW - ChatGPT
KW - Learning with LLMs
KW - LLM Companion
KW - NetLogo Chat
KW - Programming Assistant
UR - http://www.scopus.com/inward/record.url?scp=85194900868&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85194900868&partnerID=8YFLogxK
U2 - 10.1145/3613904.3642377
DO - 10.1145/3613904.3642377
M3 - Conference contribution
AN - SCOPUS:85194900868
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2024 - Proceedings of the 2024 CHI Conference on Human Factors in Computing Sytems
PB - Association for Computing Machinery
Y2 - 11 May 2024 through 16 May 2024
ER -