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. Yet students often struggle to distinguish how different forms of prediction work and when they should, or should not, be used.

Economist Jeff Prince introduces a powerful framework for teaching prediction through two distinct lenses: active prediction and passive prediction. This simple but meaningful distinction helps students better understand causality, experiments, forecasting, AI tools, and modern data analytics methods.

The framework strengthens data literacy and critical thinking while helping students apply analytics tools more appropriately across economics, strategy, and related disciplines.

Key areas include:

• The difference between active and passive prediction and why it matters
• How experiments and econometric models differ from AI and data driven prediction models
• Strategies for strengthening student data literacy and critical thinking
• Practical examples for economics courses
• How the framework helps students engage more thoughtfully with emerging AI technologies

For instructors teaching principles, econometrics, analytics, or applied economics, this approach provides an accessible way to help students navigate an increasingly predictive world.