Amplifying AgencyTechnology as an opportunity

Three children watch baseball from crates. In the second panel the tallest stands on the ground and the shortest has two crates.

Jason Kridner · Northville educators

Jason’s 2026-10-19 forum review, speaker bullets

“Ideas are equal. Opportunities are not.” - RJ
How do we amplify the good in every idea?
Even better, how do we enable students to amplify the good they see in the ideas of others?
In this AI-generated illustration based on https://interactioninstitute.org/illustrating-equality-vs-equity/, I want you to think about the crates as a technology.
When the tallest child uses this technology to aide the shortest, he has amplified that child’s agency.
Each child enjoys the benefit.

Jason’s spoken interpretation: equality versus equity. The taller child becomes happier through giving his unnecessary crate and being of service. The shortest child can watch the game rather than peek through a gap. Treat this as the intended reading, not a claim that every viewer will infer it unaided.

Today

Why should everyone know the basics of how a computer works?
Can I compute anything computable with just 2 instructions?
How does the AI know?
Demonstration: BeagleBadge + BeagleConnect Zepto “Interactor”
How can we work together to amplify the agency of your students?

Technology can be as simple as a stick

A bonobo holds a stick with ants beside an ant mound, in a colored-pencil illustration.

Using tools to amplify agency
Found everywhere

Managing complexity

Woodland cabin, waterwheel and hand-operated wooden mechanism assembled from visible materials.

Doing good feels good
Agency requires predictable medium
Medium amplifies specific outcomes
Complexity drifts towards failure
Plentiful medium supports addressing risk

Jason’s wiki, verbatim speaker bullets

To find agency, the behavior of your medium has to be predictable. If you can't intuit what will happen when you act, that's the definition of not having agency.

While the medium might not appear to have any value judgement of its own, it may amplify some outcomes more than others. Choosing a medium, or developing a medium, impacts outcomes and is therefore a moral choice.

I believe the baseline is that each of us wants to be a positive force in the world. This is simply an article of faith for me, but I generally just don't see the value in concluding that anyone is fundamentally guided by bad intentions. Instead, I see the world as amplifying the bad in their ideas, rather than building on the good. Ultimately, none of us lives in a bubble, isolated from the rest of the world, so it is our role to look for the best in the ideas and help them flourish.

When a system gets complex enough, it will drift towards failure unless it is continuously maintained

Tolerating failure locally, using readily available and replaceable building blocks, prevents the failure of the whole

These are Jason’s framing and beliefs. The illustration provides a medium for discussing them; it does not establish the engineering reliability of the depicted structure.

Meet Chloe

Chloe running with a soccer ball, looking across the field.

Entirely fictional character
Loves drawing
Learns tough lessons about her medium
Inspired by Pearl Jam 1992 concert t-shirt stating on one side
“9 out of 10 kids prefer crayons to guns”
On the other side, something like this …

Jason’s wiki, verbatim speaker bullets

I want to tell you the story of Chloe and her experience of technology.

The image on the next slide was inspired by a Pearl Jam concert t-shirt I had as a kid.

On one side, the t-shirt said "9 out of 10 kids prefer crayons to guns".

The other side had this image on it, with the word "choices".

The image can invoke some strong emotions, and it is meant to, but for the purpose of this story, this is simply how I imagine Chloe making a choice to find her own self-expression with drawing, even if she didn't know she was making that choice.

Let's please save any political discussion for the right time and place. If you are offended or frightened, it is not my intent, but I accept your judgement and am happy to revisit with you how to express my idea better. Please visit with me after the presentation.

The slogan is a recalled shirt slogan, not a statistical claim.

Choices

Chloe drawing a colorful house with crayons, with a revolver lying untouched beside her.

Chloe fell in love with drawing

Three scenes: Chloe at a crayon factory, watching a teacher pour purple wax, and looking at The Starry Night.

She was able to understand the source of her tools

She was even able to envision how to change those tools herself

And when seeing the masters in the craft, she was inspired, seeing only herself in her own way

Chloe was able to express herself

Young Chloe discovers a purple evening sky by rubbing the side of her broken crayon over a house drawing.

She adapted to her tools and adapted her tools to her
When one crayon broke, she grabbed the next and it behaved mostly like she expected

Chloe mastered

An older Chloe practices a glowing window in colored pencil beside a computer showing an early animation.

She still has the two purple crayons.

The house answers, softly.

Adult Chloe sits beside a visitor moved by her immersive purple house, with luminous windows and rain.

Impossible rooms, rain becoming music, a visitor who asks to stay.

The windows stopped glowing

Adult Chloe at her desk in the evening looks up thoughtfully at her colored-pencil drawings of glowing houses pinned to the wall, while her laptop shows a grid of grey, empty animation thumbnails.

A folder of her first animations that no longer play. On the wall, the paper drawings from the same year, still glowing.

Story scene: Chloe as one of the many creators whose Flash animations stopped playing when Adobe ended Flash Player at the end of 2020 (Jason, 2026-10-26)

As a teenager, Chloe made her first animations in Flash: little windows that lit up one by one, posted for her friends to play in their browsers.

At the end of 2020, Adobe stopped supporting Flash Player, and a few weeks later it blocked Flash content from running at all. Chloe was one of millions of people whose animations and games stopped playing that winter.

Years later she opens the folder. Nothing plays. On the wall above her desk, the paper drawings from the same year are still glowing.

Do not dwell on the brand. The point is that Chloe’s work depended on something she could not see inside, keep running or replace. Much of that work has since been rescued with emulators, but only because people outside the company understood how it worked.

flash

“You have exhausted your 100 credits”

Bridget stares in dismay at a school laptop showing her colorful flying alebrije with a sloth face, giraffe legs and antlers, half covered by a grey pop-up box, with marigolds beside her.

A sloth-faced alebrije with giraffe legs and enormous antlers, one burst of marigolds from finished.

What can the child carry away?

Bridget slides her purple-sky house drawing beneath a placemat as Chloe gently leans toward her.

Bridget slides her drawing under a placemat. “Mine doesn’t do anything.” Chloe pulls up a chair, and they ask it together.

Jason’s restructuring of the story’s turning point, 2026-10-22; combines the kitchen-table scene with the closing question

This is the turning point of the story. Take it slowly.

At the kitchen table, Bridget slides her own drawing under a placemat.

“Mine doesn’t do anything.”

Chloe pulls up a chair. She thinks of her windows that no longer glow; Bridget thinks of her alebrije, one burst of marigolds from finished. They look at the drawing between them and ask the same question: what can the child carry away?

Now make the contrast explicit, pointing back to the “Choices” slide (slide 6). Chloe chose crayons: a medium she could understand, make, take apart and master, and nobody could switch it off. Bridget is being guided down a different path. Her tools are impressive, but they run out of credits or stop working, she cannot see inside them, and along the way paper and crayons stopped looking like a real way to express herself. Her drawing “doesn’t do anything” only because she has been taught that the things that matter happen somewhere else.

We are putting Bridget in a very different situation from the one Chloe had, and most of us never chose to. The rest of today is about giving children like Bridget back what Chloe had: tools they can understand, and the confidence that their own marks count.

A crayon for computing

A child’s hand draws with a purple crayon: on the left of the paper a house under a purple sky, on the right the marks turn into rows of tally marks and empty boxes.

Chloe understood her crayon: she could even make one
A crayon is a tiny tool that can draw any picture
Is there a tool that simple that can compute anything?
Three people found one

Hilbert, Turing, Minsky

An imagined 1930s study: a paper tape of squares marked 0 and 1 runs under a brass read-and-write head, beside a rule table pinned to the wall and a window onto college rooftops.

1928, David Hilbert: what can following fixed rules ever work out?
1936, Alan Turing: a person with pencil and paper, following rules, can work out anything that can be worked out
… but some questions no rules can ever settle
1961, Marvin Minsky: two rules are enough. Add one. Take one.

Proposed facilitation notes written for the three-exercise structure

Three steps, thirty years apart.

In 1928 the mathematician David Hilbert asked whether there could be a fixed procedure, rules followed without any insight, that settles every mathematical question. To answer, someone had to say exactly what following rules means.

In 1936 Alan Turing did. He pictured a person working with pencil and paper: looking at one mark at a time, writing or erasing a mark, and deciding what to do next from a fixed list of rules. He argued that anything that can be worked out by following rules can be worked out this way, and he showed that one set of rules can carry out any other set it is given. He also proved some questions can never be settled by any rules, which answered Hilbert: no. The illustration is an imagined Turing machine, not a picture of Turing.

In 1961 Marvin Minsky showed how small this can get: piles of counters and two kinds of instruction, add one and take one (checking whether the pile is empty), are enough to do anything Turing’s rule-follower can. That is the tool we will use, and it is as simple as a crayon.

turing

What is a computation?

A child’s hand moves a purple crayon from one paper cup into another full of crayons on a classroom table, with five crayons lined up in front.

Marks go in
Rules are followed exactly
Marks come out
You already teach this: counting crayons into cups

3 crayons in one cup, 5 in another. Rule: move the crayons one at a time from the second cup to the first. Count: 8.

Bridge from the Chloe and Bridget story to Exercise 1, written for Jason’s 2026-10-21 request

This is the whole idea, and it needs no technical words. A computation is changing marks into other marks by following rules exactly. The marks can be anything: pencil strokes, crayons, crayon dots, counting bears, beads, holes in punched cards, or the tiny electrical charges inside a computer. The rules never need to understand what the marks mean.

Kindergarten classrooms already do this every day. Adding by counting crayons, bears or beads into cups is a computation: marks in (3 crayons, 5 crayons), a rule followed exactly (move one at a time until the cup is empty), marks out (8 crayons). The child who moves the crayons does not need to “know” 3 + 5 = 8; the rule produces it. That is exactly the program we are about to run.

What Minsky showed is that nothing more than this, cups, counters and rules about adding and taking one, is needed for any computation at all.

Turing completeness

Read and write memory
Choose the next step by what you read
Repeat

With enough memory and time, a system that can do these can compute anything a Turing machine can.

Proposed facilitation notes adapted from previous workshop draft

A system is Turing complete if it can simulate any Turing machine, given unlimited memory and time. In practice that takes three things: memory it can read and write, a way to choose the next step based on what it reads, and repetition. Every general-purpose computer and nearly every programming language has them. So do some surprising systems, such as spreadsheet formulas and the card game Magic: The Gathering.

Two limits keep this honest. Real machines have finite memory, so they are Turing complete only in the idealised sense. And completeness is about what can be computed, not how fast: some programs never halt, and no machine can always tell which.

The Turing test is a different idea. It asks about behaviour in a conversation, not about what can be computed. Keep the two separate.

The counter machine has all three: the cups or cups are memory, the take rule chooses the next step by whether a cup is empty, and going back to an earlier step is repetition.

turing

Exercise 1 · Understanding the mechanism

A machine with only two rules
Two programs: add and multiply
How small steps climb to any program
Why that makes it Turing complete

Read the logic. Predict its behavior. Change what it does.

Proposed facilitation notes adapted from previous workshop draft

Tell them what Exercise 1 will show, in four lines. Then the goal underneath: whatever students build, can they read the logic, predict what it will do and change it?

Bring Turing’s clerk back to the classroom. Students can use agents to help write code and still learn to inspect the logic themselves. The important difference is whether they can explain and test the mechanism, rather than depending entirely on another system to make it work. This is practical, conditional agency, not total independence. Reusing code is valuable; the question is whether the path to understanding and changing it is available.

Do not reduce this to “computers execute reliably, but AI makes mistakes.” The point is the accessible reasoning route: for an explicit algorithm and stated inputs, we can sometimes derive an outcome before running it, and teach why. Repeatability alone does not give us that explanation. Neither a successful demo nor access to a source file guarantees comprehension.

Invite one example of a classroom activity that ends at operating a product, then one that exposes an underlying mechanism. Keep this brief. The upcoming activity is deliberately small enough to understand completely.

The counter machine

Four teachers around a table with four paper cups of crayons and a row of step cards; one points at a card, one moves a crayon between cups, one writes on a sheet.

Memory: four cups of crayons, numbered 1 to 4
Program: ADD and TAKE cards, each with its own Step number
Write the numbers on the cards
Keep a finger on the current card

Exercise 1 design: a counter machine (Minsky) with printed ADD and TAKE step cards and crayons as memory; programs verified by simulation in verify.cjs

This machine never changes for the rest of the exercise. Only the program changes, and the program is nothing but numbers written on blank cards. Write different numbers and you have a different program. A real computer is the same: its program is just numbers in memory, right next to its data. That is Turing’s stored-program idea, built with crayons.

The memory holds numbers, nothing else: count the crayons in a cup. Print the step cards from machine-cards.html; the setup pages already have the numbers filled in, and the blank cards are for writing your own programs. Because each card carries its own Step number, the cards can lie on the table in any order, just as a computer’s memory has numbered addresses. Each table needs a box of 24 crayons.

Two kinds of step

Step1ADDPut 1 crayon in the cupCup1Next step2

Step2TAKETake 1 crayon from the cupCup2Got one → step3Empty → stepSTOP

Keep a finger on the current card and do what it says.
Move your finger to the card with the Step number it names.
Repeat until STOP.

Program 1 · Add

Put 3 crayons in cup 1 and 5 in cup 2. Start at step 1. When it stops, count cup 1.

Step1TAKETake 1 crayon from the cupCup2Got one → step2Empty → stepSTOP

Step2ADDPut 1 crayon in the cupCup1Next step1

Program 2 · Multiply

Put 3 crayons in cup 1 and 4 in cup 2. Start at step 1. When it stops, count cup 3.

Step1TAKETake 1 crayon from the cupCup1Got one → step2Empty → stepSTOP

Step2TAKETake 1 crayon from the cupCup2Got one → step3Empty → step5

Step3ADDPut 1 crayon in the cupCup3Next step4

Step4ADDPut 1 crayon in the cupCup4Next step2

Step5TAKETake 1 crayon from the cupCup4Got one → step6Empty → step1

Step6ADDPut 1 crayon in the cupCup2Next step5

Bigger steps from small ones

Program 1’s two cards…

TAKE cup 2
got one → 2 · empty → STOP

ADD cup 1
→ 1

…are one new step:

MOVE
cup 2 into cup 1

→

Multiply is just:

repeat for every crayon in cup 1:
COPY cup 2 into cup 3

Climbing to any program

ADD · TAKE the whole machine
CLEAR · MOVE · COPY a few cards each
add · subtract · compare
multiply · divide · remainder
numbered memory cells packed into one big number
a CPU’s instructions LOAD · STORE · ADD · JUMP · CALL
any program a spreadsheet, a web browser, an AI model

Exercise 1 design: a counter machine (Minsky) with printed ADD and TAKE step cards and crayons as memory; programs verified by simulation in verify.cjs

Reveal the rungs from the bottom. Each rung is built only from the rungs below it, exactly as the subroutines were.

Clear, move and copy are a few cards each. Subtract is TAKE from two cups together; compare is subtracting until one cup runs out and seeing which one. Divide and remainder are repeated subtraction, and Euclid’s algorithm is built from them.

The surprising rung is memory. A real computer has billions of numbered cells. A counter machine can keep many cells in one cup as one big number: treat its digits as the cells, the way 4,207 holds 4, 2, 0 and 7. Reading cell 2 is dividing and taking the remainder, which we already have. So the machine can have arrays, lists and pointers.

With memory cells and arithmetic, each instruction a real processor has (load, store, add, compare, jump, call) is a subroutine. A processor itself runs one loop: fetch the next instruction from memory, decode it, carry it out. That loop is a program too, and it can be written for the counter machine. So any program a computer runs, from a spreadsheet to an AI model, could in principle be run with cups and crayons.

We do not need the details, and nobody would build a computer this way: it would take more crayons than there are grains of sand, and forever to run. The point is that nothing more is needed. That is what Turing complete means.

Why the counter machine is Turing complete

One machine; only the step cards changed
Any program can be climbed to from ADD and TAKE
Marvin Minsky, 1961: it can do anything a Turing machine can
The real limits are crayons and time

Exercise 1 design: a counter machine (Minsky) with printed ADD and TAKE step cards and crayons as memory; programs verified by simulation in verify.cjs

This is the definition from slide 17, and the ladder is why it holds. The cups are memory that can be read (TAKE checks it) and written (ADD and TAKE change it); TAKE chooses the next step by what it finds; going back to an earlier step is repetition. Minsky proved in 1961 that a machine with just these two kinds of step can do anything a Turing machine can, and so anything any computer can. With only two cups it still works, but the programs have to pack everything into enormous numbers; more cups make programs shorter.

The program is itself just symbols on cards. Write it in crayons instead and a bigger program could read and run it: Turing’s universal machine, and the idea behind every stored-program computer.

A program can also run forever. One card, ADD to cup 1 then go to step 1, never stops; it just fills cup 1 until the crayons run out. Turing proved there is no general method that can look at any program and decide whether it will stop: the halting problem.

Take it to your classroom

Euclid’s algorithm: the greatest common divisor (GCD)
Square roots, without multiplying
A program that never stops
Blank cards: write your own

Printable cards and setup pages: machine-cards.html. Homework, from two instructions to a processor: homework.html

Exercise 1 · What we saw

Two rules, add one and take one, made a working computer
A program is just numbers written on cards
Small steps build bigger ones, all the way to any program
That is Turing completeness: as simple as a crayon

Exercise 2 · A machine that seemed to think

Colored-pencil illustration of the chess automaton, with a concealed operator visible inside its cabinet.

1770: Kempelen’s chess “automaton”
Shown across Europe and America
A hidden chess master worked it
Destroyed by fire, 1854

Proposed facilitation notes adapted from previous workshop draft

Open Exercise 2 from where Exercise 1 ended. The counter machine does exactly what its cards say, and we can read every card. For a long time people have been just as fascinated by machines that seem to think for themselves.

Wolfgang von Kempelen built the Turk to impress Empress Maria Theresa’s court and presented it in 1770. A turbaned figure sat at a cabinet and played chess against visitors. Before each show the operator opened the cabinet doors to display clockwork, while a strong human player stayed hidden inside and moved the figure’s arm with levers. After Kempelen’s death, Johann Mälzel bought it and toured Europe and the United States. Reported opponents include Benjamin Franklin in Paris and Napoleon. Edgar Allan Poe published an essay in 1836 arguing that a person must be inside. The Turk burned in a museum fire in Philadelphia in 1854.

Use the illustration to ask what the audience could see and what they could not. It is an AI-generated artistic scene, not documentary evidence of a specific encounter.

The bridge: today’s AI chat systems can feel like the Turk, with fluent answers coming out of a box. There is no person hidden inside them, but there is no rule table someone wrote either. The next slide shows where people still are.

turk

The Turk returns

Colored-pencil illustration of six people in homes around the world, each labelling photos on a laptop, with dotted lines running from them to a softly glowing, featureless machine in the centre.

2005: Amazon opens “Mechanical Turk”
People are paid per task to label, rate and write
Their answers become training data
The people are hidden again, inside the data

Proposed facilitation notes written for the three-exercise structure

Amazon named its marketplace for small paid tasks after the chess automaton, and called it “artificial artificial intelligence”. Requesters post tasks that are easy for people but hard for computers: label a photo, transcribe a sentence, rate an answer. Workers around the world complete them for small payments. Some of the best-known training datasets were built this way; the labels for the ImageNet photo collection were gathered on Amazon Mechanical Turk.

This is where the people went. A language model is trained on enormous numbers of examples written, labelled and judged by people. It learns, for each situation, how likely each next word is. Like the original Turk, the human work is easy to forget once it is hidden behind a machine.

Ask whose judgment the labels capture, who decides what a good answer is, and how the workers are paid. Do not claim that any particular current AI company used Amazon Mechanical Turk.

mturk

What to watch for

Your table plays the people: everyone suggests the next word
The hat plays the model: it picks one suggestion by chance
One word at a time, the story grows
Watch: which words were likely? Why do two runs differ?

A word from the hat

Five teachers around a table; one reaches into an upturned hat full of folded slips while the others watch, with pencils and a response sheet nearby.

Tables of 4 to 6 people
A hat or cup, small paper slips and pencils
One response sheet per table
Choose one reader

Your table will write one sentence together, one word at a time.

The prompt

Tell a short story about a surprising discovery at school.

“At recess, we found a…”

Reader: write this at the top of the response sheet.

Each turn

Reader reads the prompt and every word added so far.
Everyone writes one next word, or STOP, on a slip.
Fold the slips, put them in the hat and mix.
Reader draws one slip without looking.
Add that word to the sheet. STOP ends the sentence.
Empty the hat. Start the next turn.

Finish at STOP, or after 8 words.

Run it again

New line, same prompt: “At recess, we found a…”
Same rules, fresh slips every turn
Don’t plan ahead or try to change the result
Compare your two sentences

Same prompt. Same process.

Which words appeared on more than one slip?
Did one drawn word change what you wrote next?
Why are your two sentences different?

The slips in the hat were a probability distribution.

Proposed facilitation notes adapted from previous workshop draft

Invite two tables to read their outputs. At a particular step count the candidate slips. If four of six slips said “frog”, its draw probability was four sixths in our setup. That is an empirical frequency of these people’s suggestions, not a measurement of a language model. A selected word changes the context, so the next set of proposals can change. STOP is a possible draw, not a majority vote.

This models sampling during generation. It does not train a neural network. People supply the candidate distribution from their own learning and judgment. A language model calculates a distribution over tokens using learned parameters and the input context; tokens may be parts of words. Generation can sample from that distribution. Some decoding choices are deterministic, so randomness is not the defining difference between a learned model and explicit program logic. Temperature is optional extra detail and is omitted here.

Where do the probabilities come from?

Training changes parameters.
Generation uses them.

Proposed facilitation notes adapted from previous workshop draft

Name the two concepts. A probability distribution is the full list of possible next words with the chance of each. Our hat held one: if four of six slips said “frog”, frog had a four-in-six chance. Training is how a model comes to hold such distributions without a hat full of people. Examples supply a prediction task, and a learning procedure changes many numerical parameters to reduce its prediction error. In a neural language model those parameters turn the context into scores for possible next tokens. They are not a readable list of instructions written by an author for every situation. Learned associations can be useful and also reflect accidental regularities or gaps in the examples.

Compare the card proof: we derived an exact destination from the stated rules. The hat made the influence of context and selection visible, but it did not expose how a neural network learns representations. A model can repeat an answer consistently without giving us a comparably simple justification that it is correct on a new input. Explicit programs can also be buggy, complex or undecidable to analyze in general. We choose mechanisms and verification methods appropriate to what we depend on.

Close the loop with the Turk: the slips came from the people at your table; a real model’s likelihoods come from millions of people’s writing and judgments, gathered in ways such as Amazon Mechanical Turk. The people are still there, hidden in the data.

Who can build the tool?

Data. Computation.
People. Infrastructure.

Using a model ≠ training its foundation

Proposed facilitation notes adapted from previous workshop draft

Compare what the classroom just needed with producing a large pretrained model: gathered and prepared data, substantial computation, technical work, evaluation and continuing infrastructure. GPT-3’s paper describes a 175-billion-parameter model. That is a historical scale example, not a current price quote or a minimum size for useful machine learning. Training a small model, adapting an existing model and training a frontier-scale foundation model from scratch are very different undertakings. Individuals can do valuable work with smaller and openly available systems, while competing at the largest scale is highly unequal.

Relate this to Chloe. Access to an impressive service is real opportunity, but it can carry conditions that the user cannot change. A downloadable result may not preserve the running system, the ability to edit it, or the right to take it elsewhere. Avoid claiming all subscriptions are bad or all open-source systems solve access. Ask which dependencies can be replaced, inspected, repaired or exited and which cannot. A crayon-making activity has no classroom-scale equivalent for reproducing an entire modern AI stack. That gap is part of the subject.

gpt

Exercise 2 · What we saw

People supplied every possible next word
Chance picked one, weighted by how many suggested it
A model learns those chances from millions of people’s words
The people are still there, hidden in the data

Demonstration · “Interactor”

BeagleBadge

The game board: a $99 Linux computer you wear

Runs the game and draws it on its ePaper screen
Sockets for plugging in other boards

BeagleConnect Zepto

The player: a computer for about $1

Each student programs one
Plugged in, its code plays on its own

Code is the controller: once the game starts, nobody touches it.

Demonstration from the Interactor repository’s BadgeSnake plan (badge-demo/README.md, docs/MechanicalTurkDemo.md); two-column layout per Jason, 2026-10-26

The demonstration is BadgeSnake, the first piece of “The Interactor”: a kit for watching several students’ code interact in real time. It is based on Battlesnake, an online game where “code is the controller”: instead of steering a snake with a joystick, each player writes a program that chooses every move, then watches the snakes play each other.

Here, each student’s program goes onto a BeagleConnect Zepto, the $1 computer. The Zeptos plug into a BeagleBadge, which runs the rules of the game and draws it on its ePaper screen. Nobody touches the game once it starts: everything that happens is what the students wrote beforehand.

A second Zepto with a small screen sits on the same wires as a logger: it shows every message that passes between the badge and the snake. Nothing is hidden. That is the counter machine again: a computer following written rules exactly, where you can read every step. It is also the opposite of the Mechanical Turk: there is no person hidden in the cabinet.

Ask afterwards: what would you have to understand to write a snake that wins? And how is that different from playing a video game?

The hardware: BeagleBadge has a TI AM62L processor (two Arm Cortex-A53 cores), a 4.2-inch ePaper screen, Wi-Fi and Bluetooth, light, motion, temperature and humidity sensors, and QWIIC, Grove and mikroBUS sockets. BeagleConnect Zepto has a TI MSPM0L1117 microcontroller (Arm Cortex-M0+ at 32 MHz, 128 kB of flash, 16 kB of RAM), mikroBUS holes for about 2,000 add-on boards and two QWIIC connectors; students can program it with MicroBlocks, MicroPython or Arduino.

Status (Interactor repository, 22 October 2026): the logger firmware builds and passes its host tests but has not yet run on hardware, and the Zepto’s role as a game player over I2C has not yet worked on hardware. Have a fallback ready, such as a video, if it is not working by the session.

The Zepto picture is a computer rendering; replace it with a photo of a real board when one is available.

badge · zepto · interactor

Today, in one slide

Chloe’s crayon drawings outlasted her animations; Bridget’s alebrije ran out of credits
Two rules and some crayons can compute anything
A language model’s words come from people, chosen by chance
BadgeSnake: students’ code plays the game, with nothing hidden
Ask of every activity: what can students understand and change?

Two feet in a supercar

We sit children in a million-dollar sports car
They drive it two feet, and we call it a success
Using a technology is not understanding it

For review: does this analogy belong in the deck, and if so, here or elsewhere?

Amplifying agency after the activity

What can your students
understand and change?

Proposed facilitation notes adapted from previous workshop draft

Return to Bridget’s drawing and Chloe’s uncertainty. Give pairs two minutes: choose an activity they teach, name the mechanism children actually inspect, and name a dependency that remains beyond their control, including the networks their work travels over. Then invite two responses. Leave at least one minute for the last question or an unresolved concern.

Re-state the central commitment: give students opportunities to read logic, predict behavior under stated conditions and make a machine carry out their intentions, even if AI helped produce the code. Successful exposure to a product does not by itself provide that understanding. The mechanisms we examined, an exact procedure and a probabilistic one, are small and comprehensible. The larger barriers include access, cost, ownership, complexity and intimidation, and an hour cannot remove them. Teachers can nevertheless become much more attentive to what opportunity an activity leaves with a child.

The phone tool and microphone-triggered image reveal remain optional future investigations. This deck contains no listener, recording, live model call or mandatory hardware demonstration.

Return to RJ’s saying and Jason’s opening stance: find the good in the ideas people bring, including the ideas their students see in others. Ask what opportunities the activity makes available, and what dependence remains. Avoid presenting this hour as a solution to systemic inequality.

Questions

What would you like to understand, or change?