Building and Learning With Models Using AI

This paper explores the integration of Artificial Intelligence (AI) via large language models (LLMs) into the practice of System Dynamics to lower barriers to entry and accelerate model development, understanding, and application. The core of this work consists of two new engines within the sd‐ai platform, an open‐source framework that links natural language interfaces to simulation‐ready models: the quantitative engine, which generates fully specified stock‐and‐flow models complete with equations, units, and documentation; and Seldon, an LLM‐based modeling companion that uses natural language dialogue to explain model behavior, feedback structures, and key insights. The paper details the sd‐json schema for encoding model structure, the prompting strategies that minimize hallucinations and enable iterative, human‐in‐the‐loop model development, and the use of Loops that Matter (LTM) analysis to ground Seldon's explanations in verifiable model behavior. Using a fermentation case study, we demonstrate how these tools can be used to collaboratively build, critique, and refine a model, as well as to identify structural limitations, and generate policy recommendations. The results show that LLM‐assisted modeling can replicate core phases of the System Dynamics methodology: problem definition, model formulation, behavior explanation, and policy design, while reducing the time traditionally required. We argue that these tools signal a new “third age” of System Dynamics modeling, in which human modelers focus on framing problems and interpreting results, while AI systems help the user to handle technical implementation and explanation.