AI in System Dynamics Education

The AI in SD Education white paper explores how artificial intelligence (AI), especially large language models, can be responsibly integrated into System Dynamics education to enhance learning while preserving methodological rigor. It outlines opportunities for AI to lower barriers to modeling, accelerate skill development, and provide personalized tutoring, while warning against risks such as over-reliance on automation, loss of critical thinking, and bias in AI outputs. The paper offers practical strategies for educators, including best practices for prompting, recommended learning objectives, and example classroom exercises across beginner to advanced levels. Its central message is that AI should be used to complement, not replace, human insight; helping students shift from rote tool use to deeper systems thinking, iterative model validation, and thoughtful policy analysis.

The purpose of the AI in SD Education Task Force is to guide System Dynamics educators in adapting teaching practices to an AI-enabled world. This work is not simply about using AI tools, but about ensuring that AI is integrated into SD education responsibly, effectively, and in a way that enhances systems thinking rather than undermines it.

As a group we want to…

• Support educators in adapting to AI in SD education: We want to help instructors at all levels (from pre-college to graduate programs) adjust their teaching methods to the rise of AI and large language models (LLMs) like ChatGPT.
• Promote responsible use of AI: We emphasize developing strategies to ensure AI is used to enhance learning rather than replace it. This includes teaching students to critically evaluate AI outputs, validate models, and maintain methodological rigor rather than blindly trusting AI-generated results.
• Lower barriers to SD learning while preserving depth: AI can help beginners quickly generate causal loop diagrams or stock-flow models, freeing students to focus on conceptual understanding, model validation, and policy experimentation instead of just technical modeling and diagramming skills.
• Encourage pedagogical innovation: We provide recommendations and example exercises that use AI as a mentor/tutor, inspiration source, model builder/fixer, and model analyst, supporting distinct learning objectives such as critical thinking, model interpretation, and iterative problem solving.
• Raise awareness of risks and ethics: We want to make educators aware of risks from over-reliance on automation, like loss of systems thinking, bias in AI outputs, and opacity of AI reasoning. Our goal is to teach students to be critical, reflective, and capable of validating AI-generated content.
• Prepare students for an AI-enabled professional world: We view AI as a tool that will be present in professional SD practice. We want students to be able to use AI productively, iteratively, and transparently.

In short, our purpose is to shape a future where AI strengthens, not weakens, system dynamics education by creating best practices, teaching students to stay in control of the modeling process, and fostering a critical, iterative, and transparent approach to AI-assisted modeling.

Join the AI in SD Education Task Force and help shape the future of system dynamics in an AI-enabled world! We’re bringing together educators, researchers, and practitioners to develop best practices, share classroom strategies, and ensure AI strengthens (not weakens) SD education. Your experience and insights can guide how we teach future modelers to think critically, model responsibly, and use AI as a powerful partner in learning.

Join the monthly meetings on AI in System Dynamics Education, led by the Task Force of the Pre-College Education SIG