About The Teaching GOAT

ECON2110G · ECON2120G · ECON304 — Spring 2026

Author

Dr. Meghan Downes

Published

January 1, 2026

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 Quarto and deployed via GitHub Pages. Course dashboards are powered by JSON data files exported from a Neon Postgres database through a content pipeline.

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 → S3 → the-goat-hub
  • Dashboard data — JSON exports from Postgres stored in each course section’s data/ directory
  • The Goat Hub — this public Quarto site, rendered and deployed via GitHub Pages
  • Problem bank — AI-generated practice problems stored in Neon Postgres (ai_problems table)

Students should use this site alongside Canvas (for grades and submissions) and Excalidraw (used for daily in-class presentations).

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 — comprising a private source repository and the public-facing course website. It addresses authorship attribution, contribution roles, intellectual property compliance, and fair use under U.S. copyright law as applied to academic course materials at New Mexico State University.

Collaboration Summary

What We Built

The Teaching GOAT is an integrated system for managing, generating, and publishing economics course materials at NMSU. It consists of:

  • A private repository: R/Shiny applications, build scripts, data pipelines, and instructor-only materials
  • A public repository: A Quarto-rendered static website served via GitHub Pages containing syllabi, schedules, lectures, worksheets, and resources for students

The project was built across multiple collaborative sessions in February 2026, with all work conducted through conversational interaction between Dr. Downes and Perplexity Computer (powered by Claude, Anthropic).

How the Collaboration Worked

The collaboration followed an iterative pattern:

  1. Dr. Downes identified needs, set constraints, provided domain expertise, made all pedagogical decisions, and tested/validated every output
  2. Perplexity Computer generated code, wrote configuration files, debugged errors, drafted documentation, and proposed architectural solutions
  3. Dr. Downes reviewed, modified, accepted, or rejected all AI-generated output before integration
  4. All final decisions — pedagogical, architectural, and ethical — were made by Dr. Downes

No AI output was deployed without human review and approval.

Contribution Roles (CRediT Taxonomy)

The Contributor Roles Taxonomy (CRediT) provides a standardized framework for describing contributions to scholarly and creative outputs. The following table applies CRediT roles to this project.

CRediT Role Dr. Meghan Downes Perplexity Computer Notes
Conceptualization Lead Supporting Dr. Downes conceived the project, its goals, and its pedagogical philosophy. AI suggested implementation approaches.
Methodology Equal Equal Architecture decisions were collaborative — Dr. Downes defined requirements; AI proposed solutions (e.g., two-repo pattern, build pipeline).
Software Supporting Lead AI wrote the majority of R code, Shiny modules, Quarto config, bash scripts, and .qmd files. Dr. Downes wrote initial prototypes and modified AI output.
Validation Lead Supporting Dr. Downes tested all code, identified errors, and verified outputs. AI assisted with debugging.
Data Curation Lead None All course data (CSVs, schedules, SLOs) created and maintained by Dr. Downes.
Writing – Original Draft Equal Equal Lecture content was co-authored: Dr. Downes provided pedagogical framing, examples, and voice; AI structured the .qmd, wrote code chunks, and drafted explanatory text.
Writing – Review & Editing Lead Supporting Dr. Downes reviewed and revised all content. AI performed syntax cleanup.
Visualization Supporting Lead AI wrote ggplot2 code for all figures. Dr. Downes specified what to visualize and reviewed output.
Project Administration Lead None Dr. Downes managed the project timeline, priorities, and integration with her teaching schedule.
Resources Lead None Textbook materials, institutional knowledge, course structure, and NMSU-specific requirements provided by Dr. Downes.
Supervision Lead None Dr. Downes directed all work and made final decisions.

Estimated Contribution Percentages

Contribution percentages are inherently approximate and depend on what dimension is measured.

By Dimension

Dimension Dr. Downes Perplexity Computer Rationale
Pedagogical Content & Voice 85% 15% The pirate theme, example selection, scaffolding approach, and instructional design are Dr. Downes’s. AI assisted with formatting and structuring.
Code Generation 20% 80% AI wrote the bulk of R, Shiny, Quarto, and bash code. Dr. Downes wrote initial versions, provided specifications, and modified output.
Architecture & Design 40% 60% Collaborative — Dr. Downes defined the problem space and constraints; AI proposed and refined the technical architecture.
Debugging & Testing 60% 40% Dr. Downes identified most errors through testing. AI resolved syntax/config issues.
Documentation 30% 70% AI drafted architecture docs and inline documentation. Dr. Downes reviewed and corrected.
Deployment & DevOps 30% 70% AI designed the two-repo pattern and wrote deployment scripts. Dr. Downes executed all commands and resolved credential issues.
Domain Expertise 100% 0% All economics content, NMSU institutional knowledge, and teaching philosophy are solely Dr. Downes’s.

Overall Estimate

Contributor Overall % Basis
Dr. Meghan Downes 55-60% Conceptualization, domain expertise, all pedagogical decisions, testing, validation, final authority, project direction
Perplexity Computer 40-45% Code generation, technical architecture, debugging, documentation drafting, deployment scripting
ImportantCritical Distinction

Percentages measure effort and output volume, not authority or responsibility. Dr. Downes holds 100% of the intellectual responsibility for all published content. AI cannot be held accountable for errors, omissions, or pedagogical choices. Per academic publishing standards, the human author bears full responsibility for the integrity of the work.

Proper Citation of AI Contributions

APA 7th Edition Format

Following APA guidelines for citing generative AI tools:

Perplexity AI. (2026). Perplexity (Opus 4.6 version) [Large language model]. Perplexity AI, Inc. https://www.perplexity.ai

In-Text Citation

The project architecture and build pipeline were developed in collaboration with Perplexity Computer (Perplexity AI, 2026), a large language model assistant. All AI-generated code and documentation were reviewed, tested, and approved by the human author prior to deployment.

AI Disclosure Statement

AI Disclosure: The Teaching GOAT project infrastructure — including R/Shiny 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. AI tools were used as collaborative development aids, not as autonomous content creators.

Intellectual Property & Fair Use Compliance

Textbook Materials — Fair Use Analysis (17 U.S.C. Section 107)

Factor Analysis Favors
1. Purpose and Character Use is for nonprofit educational instruction at a public university (NMSU). Materials are transformative — textbook concepts are reframed through original examples, original R code, and original visualizations. Fair Use
2. Nature of the Work The copyrighted work is a published educational textbook — factual/instructional in nature rather than creative/artistic. Fair Use
3. Amount Used We reference specific figures, page numbers, and concepts but do not reproduce textbook text, images, or problems verbatim. Lecture materials present original explanations of the same economic concepts. Fair Use
4. Market Effect Materials supplement the required textbook rather than substitute for it. Students are required to purchase the textbook. The course website does not reduce the market for the original work. Fair Use
TipNMSU Guidance

NMSU’s copyright policy states: “Using materials in a presentation, workshop, or training session favors fair use (as long as the materials are not published or otherwise distributed).” Published course websites are a gray area, but our transformative use and the requirement that students still purchase the textbook strongly favors fair use.

What We Do and Do Not Include

Content Type Included on Public Site? Compliance
Original lecture explanations of concepts Yes Original work by Dr. Downes
Original R code and visualizations Yes Original work
References to textbook figures/pages Yes Citation, not reproduction
Verbatim textbook text or images No Not reproduced
Publisher test banks or answer keys No Private repo, gitignored
Publisher PowerPoint slides No Private repo, gitignored
Publisher instructor resource manuals No Private repo, gitignored

YouTube Embedded Videos

The lecture materials embed YouTube videos using Quarto’s video shortcode. This uses YouTube’s official embed API, which is explicitly permitted by YouTube’s Terms of Service. The videos remain hosted on YouTube; we do not download, modify, or redistribute them.

AI-Generated Code

All R code, Quarto configuration, and bash scripts generated by Perplexity Computer during this collaboration are original functional works — they are not reproductions of existing copyrighted code. The AI generated code based on Dr. Downes’s specifications, using standard open-source libraries (ggplot2, dplyr, Shiny, Quarto). There are no licensing conflicts.

Per Perplexity AI’s terms of service, output generated through the platform may be used by the user for any lawful purpose, including commercial and educational use.

Compliance Summary

Area Status Notes
AI authorship attribution Compliant AI is not listed as author; contributions disclosed transparently
APA citation of AI tool Compliant Proper reference format provided
Textbook fair use Compliant Transformative use; no verbatim reproduction; textbook required for course
Publisher materials Compliant Gitignored; never published; reference use only
Student data privacy (FERPA) Compliant No student data in any repository
YouTube embeds Compliant Uses official embed API per ToS
Open-source license compliance Compliant MIT license on project; dependencies are open-source
Two-repo security architecture Compliant Private source; public output only

A Note on This Collaboration

This project represents an emerging model of human-AI collaboration in academic work. The human author provided irreplaceable contributions: domain expertise, pedagogical vision, institutional context, quality judgment, and ethical responsibility. The AI provided scalable contributions: rapid code generation, technical architecture, syntax debugging, and documentation drafting.

Neither contributor could have produced this project alone in the time available. The collaboration was most productive when each party operated in their area of comparative advantage — a concept, fittingly, that Dr. Downes teaches to her students.

The key ethical principle throughout: the human author retains full responsibility for every piece of published content. AI is a tool in the workshop, not a co-author on the storefront.

References

NISO. (2022). Contributor Roles Taxonomy (CRediT). https://credit.niso.org/

New Mexico State University. (n.d.). Copyright and fair use. NMSU Publications. https://pubs.nmsu.edu/guidelines/copyright_and_fair_use.pdf

New Mexico State University, Dona Ana Community College. (n.d.). Fair use. NMSU Library Guides. https://dacc.nmsu.libguides.com/c.php?g=1360673&p=10048386

Perplexity AI. (2026). Perplexity (Opus 4.6 version) [Large language model]. Perplexity AI, Inc. https://www.perplexity.ai

U.S. Copyright Office. (2023). More information on fair use. https://www.copyright.gov/fair-use/

Contact

  • Email: cmdownes@nmsu.edu
  • Office: DOM224
  • Office Hours: By Appointment: Monday-Thursday 1:30 pm - 2:30 pm
  • Office Phone: 575.646.3295

Built with goat energy at New Mexico State University.

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