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AI Fluency: Framework & Foundations: teaching notes

Side-by-side notes for 05-praxis-ai-fluency-framework-foundations.html (16 slides). The 4D framework (Delegation, Description, Discernment, Diligence) is the conceptual spine that the "AI Fluency for X" courses (08 Educators, 09 Students, 12 Nonprofits, 16 Small Businesses) all reapply, so the examples here are the seed for those. Written from the deck and 05-...-lessons.txt, against the Praxis teaching library. Method: teaching-notes-method.md.

How to use these notes
  • Guidance, not a script. Teach the person, not the slides.
  • Every teaching slide has a Pause, then ask block. Put the question, then stop. Don't fill the silence. If they freeze: "Take your time, type anything when you're ready, including 'I don't know'." Then wait again.
  • By background gives a line per archetype (tutor/scenarios.md). Use the one that matches the room; the rest are there so you're never caught without an example. A palette, not a checklist.
  • This framework is abstract by nature. The whole job here is to keep dragging it back to one real task of theirs. The two questions to keep returning to: "what does that mean to you?" and "how does that apply to your work?"
  • Hold the voice (train-the-trainer.md Part 4): no "great question", no emoji, one term: Claude Code. Underclaim. Cost is "about twenty dollars a month" and stop.
1
Title

How to actually collaborate with AI

The idea

Set the frame: a framework that outlasts the tips and tricks. Thirteen ideas, one screen each. They'll leave with a way of working, not a list of prompts.

Say it like this

"Tips and tricks date fast; the models change every few months. This is the thing underneath that doesn't: how to actually collaborate with AI. Four habits. Once you have them, they keep working whatever the tool does next."

Open with

The task they named in intake. "We'll run your real task through all four habits by the end."

2
Spine

The whole course on one screen

The idea

Foundations, then how the tool actually works, then the four competencies, then putting them into practice. Orient, don't teach.

Say it like this

"Here's the shape. We set the foundations, look at how AI actually works under the hood, learn the four habits, then practise them on something real."

Cue

Twenty seconds. The 4D framework on slide 5 is the heart; everything points at it.

3
Lesson 1

Fluency is collaboration, not knowing about AI

The idea

AI Fluency is working with AI in ways that are effective, efficient, ethical and safe. The shift: treat it as a collaborator you build a working relationship with, not a technology to study, and not a vending machine you query.

Say it like this

"Fluency isn't knowing facts about AI. It's working with it well, on four counts: effective, efficient, ethical, safe. The mental shift that unlocks it is to stop treating it like a vending machine you put a question into, and start treating it like a colleague you build a working relationship with."

Pause, then ask
  • "Do you currently treat AI more like a vending machine or a colleague? Be honest."
  • "What does it mean to you to do this work not just effectively, but ethically and safely?"

(wait)

By background (collaborator, not vending machine)
Investigator
reasoning the case with you, sources and all, not "give me the verdict".
Analyst
a comparison you can defend, not a one-shot number.
Designer
developing a direction together, not "make me a poster".
Knowledge worker
a checked, repeatable pull, not magic extraction.
Reporter
a briefing you'd actually send, not an auto-summary.
Content
your voice, iterated, not generic copy from a slot.
Form-filler
allocations you can defend, not blind auto-fill.
Outreach
a proposal you'd sign, not spray-and-pray.
Exec
a brief you own at the board, not "tell me what to do".
Watch for

"Ethical and safe" can sound like box-ticking. Make it real: it's whose data this is, and whether you'd stand behind the output. Don't lecture; tie it to their work.

Key concept: the exact definition (worth getting word-for-word)

Verified against the course's opening lesson. AI Fluency is the ability to collaborate with AI in ways that are effective, efficient, ethical, and safe. Two things the framework stresses: it's about collaboration, not just knowing about AI ("a lasting framework, beyond tips and tricks"); and it's built from practical skills, knowledge, insights, and values. The how is the 4Ds: Delegation (what to hand off), Description (communicating with AI), Discernment (judging what comes back), Diligence (working responsibly). Hold the four qualities and the four Ds; the rest of the course hangs off them.

4
Lesson 2

Three ways to work with AI

The idea

Automation (it does a task you define), augmentation (you think together), agency (you set it up to act on its own). Not rungs to climb; modes you pick per task. The four competencies apply to all three.

Say it like this

"Three modes. Automation: you define it, it does it. Augmentation: you think together, the most common one. Agency: you set it up to act on its own. These aren't levels. You don't graduate from one to the next. You pick the mode that fits the task."

Pause, then ask
  • "Of your work, what's a defined task you'd hand over, versus something you'd want to think through with it?"
  • "Is there anything you'd ever be comfortable letting it run on its own? What would have to be true first?"

(wait: the last question surfaces the autonomy fear; let it.)

By background (which mode dominates their work)
Investigator
automation to extract from the file; augmentation to reason the gaps.
Analyst
automation for the table; augmentation to judge comparability.
Designer
augmentation to develop concepts; automation to batch the variants.
Knowledge worker
automation mostly: defined extraction across many files.
Reporter
automation to consolidate; augmentation to decide what matters.
Content
augmentation to shape the argument; automation to draft from the pattern.
Form-filler
automation to allocate; you stay in the loop on edge cases.
Outreach
automation to generate per customer; augmentation on the angle.
Exec
augmentation to think; agency only once a recurring brief has earned trust.
Watch for

Agency is where the "what if it does something I didn't want" fear lives. Give the controls before the capability: confirm-each-step first, auto-mode once the pattern is trusted, and you always check the end. (objections.md: permission/auto.)

5
Lesson 3

Delegation · Description · Discernment · Diligence

The centrepiece. If they take one thing away, it's the four Ds.

The idea

Four competencies that together make collaboration effective, efficient, ethical and safe. Delegation sets it up. Description and Discernment loop in the middle (say what you want, judge what comes back, refine). Diligence keeps the whole thing responsible. The rest of the course is these four.

Say it like this

"Four habits. Delegation: decide what's even worth doing together. Description: say what you want, and how. Discernment: judge what comes back. Diligence: own the final result. The middle two are a loop you'll run over and over. Delegation sets it up, Diligence keeps it honest. That's the whole framework."

Pause, then ask
  • "Which of those four do you think you're already weakest at? Most people know."
  • "Take your task, what would delegation even mean for it before you typed anything?"

(wait)

By background (one task through all four Ds)
Investigator
delegate the extraction; describe "cite every source, never infer"; discern the citations hold; stand behind the filing.
Analyst
delegate the table; describe the columns and basis; discern the "no data" cells are honest; own the comparison.
Designer
delegate the variants; describe the brand rules; discern they're on-brand; sign off the campaign.
Knowledge worker
delegate the extraction; describe the schema; discern the flagged rows; trust the CSV.
Reporter
delegate the consolidation; describe the five sections; discern nothing's misattributed; send the briefing.
Content
delegate the draft; describe the house voice; discern it's on-voice; publish under your name.
Form-filler
delegate the allocation; describe the categories; discern the totals reconcile; submit the filing.
Outreach
delegate the per-customer drafts; describe the format; discern no invented numbers; send it.
Exec
delegate the synthesis; describe "facts, not editorial"; discern risks are stated straight; present to the board.
Watch for

This maps almost exactly onto the skill-file idea they'll meet in Claude 101 and Agent Skills: "describe what good looks like, once" is writing the rules down. Plant that seed; don't dwell.

6
Lesson 4

It generates, it doesn't look things up

The idea

A language model creates new content from patterns it learned (pre-training, then fine-tuning to follow instructions). It isn't retrieving stored answers. Three things had to arrive together: the transformer, vast data, a leap in compute. Knowing it generates rather than retrieves is what makes its limits make sense.

Say it like this

Pick the route by audience (framings.md: deterministic vs probabilistic). For analysts and regulated rooms: "All of this is predicting the next word. 'The cat sat on the…': most likely 'mat'. It's picking the most plausible next thing. Brilliant for drafting, wrong for anything that has to be identical every time." For generalists and designers: the Excel-formula contrast. For technical peers: one sentence and move on.

Pause, then ask
  • "If it's generating a plausible answer rather than looking up a true one, what does that change about when you'd trust it?"
  • "Where in your work would 'plausible' not be good enough?"

(wait)

By background (the "so what" of generate-not-retrieve)
Investigator
it predicts a plausible date, it doesn't know one; demand sources.
Analyst
it generates a plausible figure, not a true one; cite it or mark "no data".
Designer
generating a plausible look is its gift; ideate freely, you curate.
Knowledge worker
it can plausibly fill a blank, which is exactly why you forbid it.
Reporter
it'll smooth a gap with something plausible; attribute everything.
Content
plausible prose is its strength; lean in, then check the facts.
Form-filler
a plausible category isn't a correct one; give it the definitions.
Outreach
it'll invent a plausible stat; forbid numbers not in the file.
Exec
a plausible projection isn't a real one; numbers as-is, no extrapolation.
Watch for

This is the conceptual "aha" that makes cost, safety and reliability all click later. Don't skip it for time. It also calms the data fear: the model is in the cloud, your files stay local; we point a trained model at your files, we don't train it on them. (train-the-trainer.md: modules 15 and 18.)

7
Lesson 5

Strong where you're weak, weak where you're strong

The idea

Versatile across language and tasks, fluent, holds a thread, plugs into tools. But bounded: a fixed knowledge cutoff, hallucinations (confident and wrong), finite context, reasoning that can slip on hard problems. The limits aren't bugs to wait out; they're why you stay in the loop.

Say it like this

"It's strong exactly where most of us are weak: speed, breadth, tireless drafting. And weak where you're strong: your judgement, your context, knowing what 'right' looks like here. The limits aren't temporary. They're the reason you stay in the loop. Best results pair its breadth with your oversight."

Pause, then ask
  • "What's the judgement you bring that it structurally can't?"
  • "Which of these limits would hurt you most: the cutoff, the confident-wrong, the forgetting, or the reasoning slips?"

(wait)

By background (the limit that bites, and your answering strength)
Investigator
hallucinated facts bite hardest → your verification is the value.
Analyst
invented figures → your basis-checking is the value.
Designer
it lacks your taste → you curate, it supplies volume.
Knowledge worker
it'll guess a blank → your "leave it empty" rule holds the line.
Reporter
it can't read the room → you decide what leadership needs to hear.
Content
it doesn't know your audience → you own voice and judgement.
Form-filler
rules can change past its cutoff → you check this year's thresholds.
Outreach
it doesn't know the relationship → you bring the angle.
Exec
it can't carry accountability → you own what reaches the board.
Watch for

Job-loss fear can surface here ("so it's better than me at the breadth?"). Name it and answer honestly: a human with AI beats either alone, and that gap doesn't close. We future-proof the role. (train-the-trainer.md: name the fear.)

8
Lesson 6

Delegation: decide what's worth doing together

The idea

Before you prompt anything: problem awareness (your goal and the work it takes) plus platform awareness (what this AI can and can't do) give you task delegation: splitting the work to each side's strengths. The goal isn't to automate everything; it's the best split for this task.

Say it like this

"Delegation happens before you type a word. Two things meet: what you know about the problem, and what you know the tool can do. Where they overlap, you split the work. Not 'automate everything', the right split. The parts that are yours stay yours."

Pause, then ask
  • "For your task, which parts are genuinely yours, which could be the AI's, and which are shared?"
  • "What's the part you'd never hand over, and why?"

(wait)

By background (the split)
Investigator
AI extracts and drafts; you judge significance and own conclusions.
Analyst
AI tabulates; you decide what's comparable and what it means.
Designer
AI batches variants; you hold the creative direction.
Knowledge worker
AI pulls the data; you handle the exceptions.
Reporter
AI consolidates; you decide the narrative.
Content
AI drafts; you own voice and argument.
Form-filler
AI allocates; you adjudicate the edge cases.
Outreach
AI personalises at volume; you own the relationship and the ask.
Exec
AI synthesises; you carry the judgement and accountability.
Watch for

People over-delegate the judgement and under-delegate the grunt work, the opposite of the win. Steer them: hand over the volume, keep the judgement.

9
Lesson 7

Turn delegation into a plan

The idea

Pick a real project, agree what good looks like (vision), break it into tasks, then mark who does each (delegate), and carry that plan through. Naming success up front is what makes Discernment possible later: you can only judge against a target.

Say it like this

"Delegation becomes a plan in four steps: name what good looks like, break the work into tasks, mark each as yours, the AI's, or shared, then carry it through. The first step is the one people skip and the one that matters most: if you don't name success up front, you've got nothing to judge the output against later."

Pause, then ask
  • "For your task, what does 'good' actually look like? Be specific enough that you'd know it when you saw it."
  • "How would you know it was done right?"

(wait: their answer is, almost word for word, the start of a skill file.)

By background (what "good" looks like)
Investigator
every claim sourced, evidence gaps named, no inferred conduct.
Analyst
like-for-like table, blanks honest, basis stated.
Designer
every required asset present, on-brand, specs met.
Knowledge worker
clean CSV, exceptions flagged, nothing invented.
Reporter
five sections, correctly attributed, nothing missing.
Content
on-voice, references a past piece, within word count.
Form-filler
every receipt allocated, totals reconcile, nothing dropped.
Outreach
one proposal per customer, specific, no invented numbers.
Exec
one page, facts not editorial, every decision has an owner and a date.
Watch for

Point out the connection explicitly: "what good looks like", written down, is the skill file from the Claude courses. This slide is where 4D meets the Praxis method. (framings.md: skill file as institutional memory.)

10
Lesson 8

Description: say what, how, and how to behave

The idea

Three layers. Product: what to create (output, format, audience, style). Process: how to approach it (method, steps, frameworks). Performance: how it should behave (concise or detailed, challenging or kind). Process is the layer most people skip, and it's often as decisive as the goal.

Say it like this

"Three layers to a good description. The product: what you want made. The process: how it should get there, the method. The performance: how it should behave with you. Almost everyone gives the first and forgets the second. The process layer is where the quality hides."

Pause, then ask
  • "For your task, what's the method a good person would follow, not just the output?"
  • "What behaviour do you want from it: blunt and challenging, or careful and gentle?"

(wait)

By background (the process layer, the one they'll skip)
Investigator
classify sources primary vs reported before drawing anything.
Analyst
compare only on the same basis; name it when you can't.
Designer
check each brief against the spec sheet before listing.
Knowledge worker
currency as written, no FX; flag, don't guess.
Reporter
quote anything ambiguous under "decisions needed".
Content
follow the hook, problem, three-part argument, close of the past posts.
Form-filler
one section per receipt; the more specific wins; appendix the rest.
Outreach
cite the customer file for customer claims, the knowledge base for market claims.
Exec
state numbers as-is; route anything unclear to "needs a decision".
Watch for

The performance layer feels odd to people at first ("I have to tell it how to act?"). Normalise it: same as telling a new colleague "push back on me if I'm wrong".

11
Lesson 9

Six moves that make a prompt work

The idea

Give context, show examples, specify constraints, break into steps, ask it to think first, define role and tone. Prompting is iterative, not one-shot. The secret weapon: hand the AI your prompt and ask it to improve it.

Say it like this

"Six practical moves. Context, examples, constraints, steps, ask it to think before answering, give it a role. You won't use all six every time. And the shortcut most people never try: paste your own prompt back to it and ask it to make it better. It's good at that."

Pause, then ask
  • "Looking at those six, which one would most fix the way you ask things now?"
  • "What's a recent ask that flopped, which of these was missing?"

(wait)

By background (the move that matters most for them)
Investigator
show an example report; the format carries everything.
Analyst
specify constraints: the columns and the "no data" rule.
Designer
show examples; one finished asset beats a paragraph of description.
Knowledge worker
specify constraints: the schema and "leave blanks blank".
Reporter
give context: who reads this and what they care about.
Content
show examples: paste two past posts for the voice.
Form-filler
break into steps: read, classify, allocate, reconcile.
Outreach
give context: the customer file and the relationship.
Exec
define role and tone: "my analyst, facts not editorial, one page".
Watch for

The "ask it to improve your prompt" move is the single highest-value habit on this slide and the one they'll forget. Make them try it once, live, on their own task.

12
Lesson 10

Discernment: judge what comes back

The idea

The flip side of Description, same three layers from the other end. Product: is it accurate, relevant, coherent? Process: did it reason soundly, any gaps? Performance: was its behaviour right for the task? Your expertise is what makes discernment sharp: you can only catch what you'd notice.

Say it like this

"Discernment is Description in reverse. You judge the output, the reasoning, and the behaviour. Here's the uncomfortable part: you can only catch what you'd notice. Your expertise isn't replaced here; it's exactly what makes you able to judge. This is where the human earns their place."

Pause, then ask
  • "What would you spot in a bad answer that a novice in your field would miss entirely?"
  • "Where does your expertise let you catch what the AI can't?"

(wait: this is the human-premium moment; let them feel it.)

By background (what only they would catch)
Investigator
a source mis-classified as primary; an inferred association.
Analyst
two figures compared on different bases; a silently dropped "no data".
Designer
an off-brand colour; a missing compliance footer.
Knowledge worker
a guessed VAT number; a dropped credit note.
Reporter
a misattributed win; a blocker buried as a win.
Content
a sentence that isn't your voice; a borrowed cliché.
Form-filler
a receipt in the wrong section; totals that don't reconcile.
Outreach
an invented number; a question in the subject line.
Exec
an editorialised risk; a projection dressed as a fact.
Watch for

This is the slide that justifies the whole human-in-the-loop argument and, by extension, why a human teacher beats a video. Name it: discernment is not a step you automate away.

13
Lesson 11

Say it, check it, refine it

The idea

Real work happens in the loop between describing what you want and judging what you get. You integrate expertise, and you own the final decision. This is where collaboration beats either side working alone.

Say it like this

"Describe, look at the output, discern, then describe it better and go again. That loop is where the actual work lives. Not the first answer, the third or the fourth. And at the end of the loop, the decision is yours. You own the final call."

Pause, then ask
  • "Walk me through one turn of that loop on your task, what would you push back on first?"
  • "At what point would you stop iterating and own the result?"

(wait)

By background (one turn of the loop)
Investigator
draft → spot an inferred claim → "cite it or cut it" → redraft.
Analyst
table → mismatched basis → "show both, name the basis" → rerun.
Designer
variant → off-brand → "use the brand tokens" → regenerate.
Knowledge worker
CSV → a guessed field → "leave blanks blank" → re-extract.
Reporter
briefing → misattributed line → "cite who said what" → redraft.
Content
draft → off-voice line → "match this past post" → rewrite.
Form-filler
allocation → wrong section → "here are the definitions" → reallocate.
Outreach
proposal → invented stat → "only numbers from the file" → redraft.
Exec
brief → editorialised → "state it as fact, not 'at risk'" → restate.
Watch for

People treat the first answer as final and judge the tool on it. The whole point is the loop. If they're disappointed, ask: did you go a second round? (Ties to the 80/20 expectation: the last ten percent is the loop.)

14
Lesson 12

Diligence: own the result

The idea

The responsibility layer. Creation: which AI, and how you use it (privacy, fit). Transparency: be open about AI's role with those who need to know. Deployment: verify and vouch for what you share. Different contexts expect different disclosure.

Say it like this

"Diligence is owning it. Three parts: which tool you chose and whether it was right for sensitive work; being open about AI's role where that matters; and verifying what you put your name to. You're accountable for the output, whether or not AI helped make it. A diligence statement just names AI's role and your responsibility for the result."

Pause, then ask
  • "In your work, who would need to know AI was involved, and where wouldn't it matter?"
  • "What's the standard you'd have to meet before you'd put your name on AI-assisted work?"

(wait)

By background (what diligence asks of them)
Investigator
the privacy of the file; disclosure where required; you vouch for the filing.
Analyst
where the data may go; transparency to stakeholders; you sign the comparison.
Designer
rights and brand safety; crediting AI where the client expects; you ship it.
Knowledge worker
data handling; a note that the CSV was AI-assisted; you trust the numbers.
Reporter
who sees the raw updates; the briefing is yours to stand behind.
Content
your publication's disclosure norms; your name goes on it.
Form-filler
the privacy of the receipts; you submit and you're accountable.
Outreach
customer-data handling; you vouch for every claim in the proposal.
Exec
confidentiality of board inputs; you own what you present, AI-assisted or not.
Watch for

This is the natural home for the data-safety and "no web portal to break" reassurances, and for the IT/compliance floor-and-ceiling. For regulated rooms, spend time here. (objections.md: IT/compliance; no web portal.)

Key concept: Diligence, and its three components Verified against the course ("A closer look at Diligence"). Diligence is the ethics-and-safety competency: while the other three Ds are mostly about being effective and efficient, Diligence is about being responsible: taking ownership of your AI collaboration. Three components
  • Creation Diligence: being thoughtful about which AI systems you choose and how you work with them.
  • Transparency Diligence: being open about AI's role in your work, with everyone who needs to know.
  • Deployment Diligence: taking responsibility for what you share: verifying it and being willing to vouch for it. The course's concrete artifact is a diligence statement: a short, transparent acknowledgment of AI's role plus your commitment to the result, written across those three components. Headline: choose tools thoughtfully, be honest about AI's role, and stand behind what you ship.
15
Lesson 13

Fluency is built by practice, not switched on

The idea

Delegate, describe, discern, stay diligent, across automation, augmentation and agency. AI is powerful, not magical: only as good as your engagement. Fluency is built by practice, not switched on.

Say it like this

"You don't get fluent by finishing a course, any more than you get fluent in a language that way. You get there by doing it, badly at first, then better. The four habits are the thing you practise. AI is powerful, not magical. It's only ever as good as what you bring to it."

Pause, then ask
  • "What's one task you'll run through these four habits this week?"
  • "What would 'a bit more fluent' look like for you a month from now?"

(wait)

By background (the practice habit)
Investigator
each matter, capture what the report missed and tighten the rules.
Analyst
each review, refine the column set and the basis rules.
Designer
each campaign, fold in the spec quirks you hit.
Knowledge worker
each month, add new supplier quirks to the rules.
Reporter
each week, adjust what belongs in the briefing.
Content
each post, note the voice rules you stretched.
Form-filler
each cycle, sharpen the category definitions.
Outreach
each batch, improve the angle and the format.
Exec
each board cycle, refine what counts as a decision.
Watch for

This is the long-term ROI line and the bridge to the Praxis method: the system improves through use. End-of-session habit: "tell it to look back at what we did badly today and save how we fixed it." (framings.md: the system improves through use.)

16
Close

Delegate well. Describe and discern. Stay diligent.

The idea

The whole course in one line: four competencies, three ways to work, one habit: practice. Treat AI as a thinking partner, hold to effective/efficient/ethical/safe, and the framework keeps working as the models change.

Say it like this

"Four habits, three modes, one practice. If the tools change next year, and they will, this still holds. Delegate well, describe and discern in a loop, stay diligent. That's fluency."

Close with

Their task from the start, and the next concrete step. For Praxis students, point forward: 4D is the why; the skill file and Claude Code are the how that makes "describe what good looks like, once" permanent. The "AI Fluency for X" courses (educators, students, nonprofits, small businesses) are this same framework in their world.