Understanding Prediction: A Critical Data Literacy Skill in the Age of AI with Jeff Prince

From randomized experiments to machine learning and large language models, prediction is everywhere in today’s data-driven world. But students often struggle to distinguish how different forms of prediction work, and when they should (or shouldn’t) be used.

In this webinar, economist Jeff Prince introduces a powerful framework for teaching prediction through two distinct lenses: active prediction and passive prediction. This simple but transformative distinction can help students better understand causality, experiments, forecasting, AI tools, and modern data analytics methods.

This session will explore how instructors can use this distinction to build stronger data literacy skills, sharpen critical thinking, and help students apply analytics tools more appropriately in economics, strategy, and beyond.

Attendees will explore:

• The difference between active and passive prediction (and why it matters)
• How experiments and econometric models differ from AI- and data-driven prediction models
• Ways to strengthen student data literacy and critical thinking
• Practical examples to bring directly into economics courses
• How this framework can help students engage more thoughtfully with emerging AI technologies

Whether you teach principles, econometrics, analytics, or applied economics, this session will offer a timely and accessible way to help students navigate an increasingly predictive world.