Generative AI tools, like ChatGPT, haven’t necessarily created new challenges in data science education. Rather, the availability and rapidly growing capabilities of these tools have exposed weaknesses in learning design and assessment that educators have been ignoring for a while.

In this talk, I will draw on my experience in teaching psychology students about computational reproducibility and the value of R-based data workflows. I will argue for the need to rethink what learning to learn looks like and talk about how we might reframe assessment as the process of gathering evidence that learning has occurred.

There is no avoiding the fact that students now need to learn how to learn alongside Generative AI. Data science educators can help them by designing learning experiences that build self-regulated learning skills and normalise what real learning feels like.