A modeling dialog with AI – what do we learn about what we have to learn?

Abstract

Generative AI (GenAI) is a relatively new phenomenon with already an enormous impact. Many tasks, from searching for information to computer coding have fundamentally changed. For instance, creating computer code is a task that a chatbot like ChatGPT can do for many programming problems. This might move the focus of programming away from the code itself to the more conceptual aspects of the task. In this paper we will explore the consequences for science education, in particular for scientific modeling.

In science education teaching about and with models has a central place, as science is essentially a modeling endeavor (Louca & Zacharia, 2012). Especially creating and exploring computational models with the help of computer simulations is an often-used educational approach (Bravo et al., 2009; van Joolingen et al., 2005). Students create and explore models by entering equations (Teodoro, 2004) or graph-based representations (Löhner et al., 2003).

GenAI is changing all this. Models can be generated and simulated with the help of AI, moving the focus from the construction of models to specifying it in terms that the AI can “understand”. Phrasing proper questions requires for the AI requires understanding of the modeling process at a higher level of abstraction.

In this presentation I will discuss some examples of dialogues held with ChaptGPT on various physics models. Using these dialogues, we can discuss the changing view of what and how students need to learn about scientific models in the light of the developments in AI.