The course covers the scientific method with an emphasis on critical thinking, linking it to the AI-driven paradigm shift. It introduces key AI terms and explores how AI can aid scientific discovery and speed workflows in quantitative fields, examining practical use cases, biases, and potential pitfalls.

How can we make effective decisions, whether in research or in a broader context—decisions that are both rational and satisfying? In this workshop, we will systematically examine the decision-making process, identify personal bottlenecks, explore common biases, and test practical tools for effective decision-making that integrate analytical skills with intuition.

This experiential school develops evidence-informed teaching practice through hands-on classroom teaching, lab teaching, and project supervision. It includes practice with structured feedback on lesson planning, student engagement, and interactive teaching. It also builds mentoring skills and supports the drafting of a teaching statement that will be useful within and beyond academia.