AI Skills & Skill Threads

Learning Objectives

• Understand the five persistent skill threads woven across all 10 weeks

• Know how AI literacy progresses through three developmental stages

• Apply reproducibility milestones to your own lab workflow

Instead of front-loading geophysics content and back-loading professional skills, the course distributes five threads across all 10 weeks. Each week advances every thread, not just the geophysics content.

The five threads

Thread	What it builds
🌊 Geophysics content	Domain knowledge: waves → seismics → gravity → magnetics → earth interior → inversion
🐍 Python / computing	Scientific Python stack, ObsPy, scipy, matplotlib, ObsPy, PyTorch
📋 Reproducibility	Git, conda environments, data provenance, pipeline automation
✍️ Technical writing	Figure standards, IMRaD structure, citation, methods/results/interpretation
🤖 AI literacy	Tutor → writing coach → fact-checker → rubric-defined agent

Thread map — all 10 weeks

Thread	Wk 1	Wk 2	Wk 3	Wk 4	Wk 5	Wk 6	Wk 7	Wk 8	Wk 9	Wk 10
🌊 Geophysics	Waves	Snell	Refl.	Signal	Grav.	Mag.	Earth int.	—	Inv.	ML
🐍 Python	env	fig	NMO	FFT	grav	mag	TauP	—	inv	CNN
📋 Repro	Git	commit	env	doc	cite	pipeline	DOI	—	test	audit
✍️ Writing	—	fig	IMRaD	para	cite	data	methods	rubric	results	full
🤖 AI literacy	tutor	quiz	debug	paper	coach	check	fact	agent	rubric	eval

Week 8 is the AI agent design week — the geophysics content thread pauses while students learn to write rubric-driven AI agents.

Reproducibility arc — 4 milestones

Week 1 — Environment setup

Milestone 1

Set up conda environment + Git repo. Every notebook goes in the repo from day one. Standard end-of-session workflow: one meaningful commit.

Week 3 — Data provenance

Milestone 2

Add environment.yml to repo. Verify your NMO notebook runs in a clean environment. Add data/README.md with data provenance for all external datasets.

Week 6 — Pipeline automation

Milestone 3

Add a run_all.sh script that runs all notebooks in order via jupyter nbconvert. Test it on a clean environment. Add DOIs or persistent URLs for all datasets.

Week 10 — Reproducibility audit

Milestone 4

git clone into a new directory, run run_all.sh, verify all figures regenerate. Target: under 10 minutes. Fix what breaks. Tag the final commit v1.0.

AI literacy arc — 3 stages

Stage 1 · Weeks 1–4 · AI as tutor

Use an AI assistant to derive equations interactively, get quizzed on concepts, debug code, and have papers explained. The goal is building the habit of asking well-specified questions.

Example prompts:

• “Derive the acoustic wave equation from Newton’s second law, one step at a time. Stop after each step and check that I follow.”

• “Quiz me on wave impedance and reflection/transmission coefficients.”

• “I’m a junior undergrad in geophysics at UW. I have solid math through PDEs and Python basics. Explain [concept].”

Stage 2 · Weeks 5–7 · AI as writing coach + fact-checker

Submit your paragraph to an AI assistant with an explicit rubric you wrote:

“Does this methods paragraph have (a) what was done, (b) why, (c) what software with version, (d) what parameters, (e) enough detail to reproduce?”

Then fact-check the AI’s critique against your notebook and textbook. Document one wrong AI claim per session. This builds the critical evaluation reflex that Stage 3 depends on.

Stage 3 · Weeks 8–10 · AI as designable agent

Write your own system prompt for a “Geophysics Report Reviewer” agent. Define what a good geophysics results section looks like. Run your draft through it. Evaluate whether the agent caught real problems. Revise the rubric if it missed things.

The system prompt lives in your Git repo as agent_instructions/report_reviewer_v1.md — a genuine 2026 portfolio artifact.

See AI Literacy Guide for the full prompting guide and the Week 8 agent design deliverable.