The Teaching GOAT 🐐
The Teaching GOAT (Getting Organized for Awesome Teaching) is the course resource hub for Dr. Meghan Downes's economics courses at New Mexico State University. This site provides students with syllabi, schedules, lecture materials, worksheets, data resources, and links to all course tools.
About Dr. Downes
Dr. Meghan Downes is an economist and instructor in the Department of Economics at NMSU. She teaches Principles of Macroeconomics (ECON2110G), Principles of Microeconomics (ECON2120G), and Money & Banking (ECON304).
Her teaching philosophy centers on making economics accessible, memorable, and — when possible — fun. If you've encountered pirates, goats, or Gordon Ramsay in your econ class, you're in the right place.
How This Site Works
This site is built with Node.js and deployed on AWS Lightsail. Course dashboards are powered by live data from a Neon Postgres database.
The ecosystem consists of:
- The Teaching Goat Workbench — a private R/Shiny application on shinyapps.io for content management and course administration
- Content Studio pipeline — Content Studio → Neon Postgres → The Student Hub
- The Student Hub — this public site, serving live course data
- The Goat Hub — a companion Quarto site with static lecture materials, deployed via GitHub Pages
Students should use this site alongside Canvas (for grades and submissions) and Excalidraw (used for daily in-class presentations).
Textbook & Copyright
Course lectures reference and build upon:
Cowen, T., & Tabarrok, A. (2024). Modern Principles: Microeconomics (6th ed.). Worth Publishers.
All lecture materials on this site are original works by Dr. Downes that explain, illustrate, and extend textbook concepts using original examples, original R code, and original visualizations. No copyrighted textbook text, images, or problems are reproduced on this site. Students are required to purchase the textbook for each course.
Contact
Authorship, Attribution & AI Disclosure
Purpose
This section provides a transparent accounting of the collaboration between Dr. Meghan Downes (human author) and Perplexity Computer (AI collaborator) in the design, development, and deployment of The Teaching GOAT project.
Collaboration Summary
What We Built
The Teaching GOAT is an integrated system for managing, generating, and publishing economics course materials at NMSU. The project was built across multiple collaborative sessions, with all work conducted through conversational interaction between Dr. Downes and Perplexity Computer.
How the Collaboration Worked
- Dr. Downes identified needs, set constraints, provided domain expertise, made all pedagogical decisions, and tested/validated every output
- Perplexity Computer generated code, wrote configuration files, debugged errors, drafted documentation, and proposed architectural solutions
- Dr. Downes reviewed, modified, accepted, or rejected all AI-generated output before integration
No AI output was deployed without human review and approval.
Overall Contribution Estimate
| Contributor | Overall % | Basis |
|---|---|---|
| Dr. Meghan Downes | 55–60% | Conceptualization, domain expertise, all pedagogical decisions, testing, validation, final authority |
| Perplexity Computer | 40–45% | Code generation, technical architecture, debugging, documentation drafting, deployment scripting |
Percentages measure effort and output volume, not authority or responsibility. Dr. Downes holds 100% of the intellectual responsibility for all published content.
AI Disclosure Statement
AI Disclosure: The Teaching GOAT project infrastructure — including R/Shiny applications, Node.js applications, Quarto site configuration, deployment scripts, and lecture formatting — was developed with assistance from Perplexity Computer (Perplexity AI, Inc., 2026). All pedagogical content, course design, instructional decisions, and published materials were created, reviewed, and approved by Dr. Meghan Downes, who bears full responsibility for the accuracy and integrity of all course materials.
Proper Citation
Perplexity AI. (2026). Perplexity (Opus 4.6 version) [Large language model]. Perplexity AI, Inc. https://www.perplexity.ai