About This Project
Why This Exists
AI tools are transforming how economists work — but most students learn to use them through trial and error, YouTube videos, or vibes. Meanwhile, most “AI literacy” resources are generic, platform-specific, or focused on policy rather than skill-building.
This project fills the gap: economics-specific AI literacy that teaches students how to think with AI, including the technical foundations that make them effective.
Who It’s For
Students: Advanced undergrad economics majors. The examples assume comfort with regression, economic intuition, and academic writing — but no prior coding or AI experience is required for the Foundations track.
Instructors: Anyone teaching economics who wants to incorporate AI literacy without building everything from scratch. Each module is designed to be dropped into an existing course.
Design Principles
- Modular: Every lesson is self-contained. Use one or use them all.
- Honest: We teach limitations and failure modes, not just capabilities.
- Economics-native: Examples come from real economics workflows — not generic prompt engineering.
- Platform-flexible: Concepts transfer. We note tool-specific details (like UVM’s enterprise Copilot) but don’t lock into any platform.
- Open: Licensed CC-BY 4.0. Adapt, remix, redistribute with attribution.
UVM Context
The University of Vermont provides enterprise-protected Microsoft Copilot to all students, faculty, and staff. This uses GPT-4 with enterprise data protections (your data is not used for model training). Some modules reference this tool, but all concepts apply to any LLM.
Contact
Emily Beam · Department of Economics · University of Vermont · emily.beam@uvm.edu
Acknowledgments
This project was developed with the assistance of AI tools (Claude, Copilot), which felt appropriate.