AI Fluency: Foundations

Accurate intuitions about what you're working with — no hype, no doom, just functional understanding.

• 1The AI landscape: AI ⊃ ML ⊃ Deep Learning ⊃ LLMs/Diffusion
• 2Discriminative vs. generative AI
• 3How LLMs work (intuition only — no math)
• 4How diffusion models work
• 5Why AI behaves the way it does: tokens, context, training cutoffs
• 6Tour of the AI ecosystem
• 7When to use which AI tool
• 8Three modes of interaction: Automation, Augmentation, Agency

Three frameworks side by side so students can pick the one that clicks.

• 1Why frameworks matter (and when they don't)
• 2The 4D Framework (Anthropic): Delegation, Description, Discernment, Diligence
• 3TCREI (Google): Task, Context, References, Evaluate, Iterate
• 4The Pattern Library approach (Vanderbilt)
• 5Comparing the three: when each shines
• 6Building your own hybrid workflow

When not to use AI is just as important as when to.

• 1Goal awareness: what does "done" look like?
• 2Task decomposition: AI vs. human vs. collaborative
• 3The feasibility × value matrix
• 4Tasks not jobs: AI's actual unit of impact
• 5Platform selection: model strengths and limitations
• 6Cost considerations: tokens, subscriptions, time, attention
• 7When NOT to use AI

Depth, clarity, and a memorable structure for the most-saturated topic in AI education.

• 1Anatomy of a good prompt
• 2Product description: describing the output you want
• 3Process description: dialogic, iterative prompting
• 4Performance description: defining future AI behavior
• 5Zero-shot prompting
• 6Few-shot prompting (examples)
• 7Chain-of-thought prompting
• 8Role and persona prompting
• 9Structured output (JSON, tables, schemas)
• 10Prompt chaining and decomposition
• 11Multimodal prompting (images, voice, files)
• 12Iteration: when to refine vs. start over

A full module on output evaluation — most courses bury this in 1–3 lessons.

• 1What you're really evaluating: quality vs. accuracy vs. usefulness
• 2Spotting hallucinations: cognitive heuristics
• 3The Fact-Check List Pattern
• 4Verifying sources and claims
• 5Spotting bias in AI outputs
• 6The Description-Discernment loop
• 7Process discernment: is this collaboration even working?
• 8When AI confidently lies: failure modes you'll see often

• 1Creation diligence: stakeholder impact, fairness, edge cases
• 2Bias as a societal concern
• 3Copyright, IP, and the training data debate
• 4Privacy: what you should never paste into AI
• 5AI security at the user level: prompt injection, data leakage, jailbreaks
• 6Disclosure norms: when and how to declare AI involvement
• 7Deployment diligence: verifying before shipping
• 8Job impact and the tasks-not-jobs framing
• 9Building your personal AI policy

Brief conceptual exposure to advanced topics — recognition, not deep dives.

• 1What are AI agents? (concept, not implementation)
• 2The four agent design patterns: reflection, tool use, planning, multi-agent
• 3RAG explained: giving AI a custom library to read from
• 4Memory and context management
• 5When prompting isn't enough: signs you need agents, RAG, or fine-tuning

Pick a real project. Use everything from M1–M7. Ship something. Document the process.

• 1Capstone Project