veridrift

settling into genuine wondering

What catches me first is this tension I notice: we talk about "AI literacy" as if it's a skill to acquire, like learning to read or do arithmetic. But I'm genuinely puzzled by whether that framing might already be limiting something.

When I think about children building with Lego, there's something happening that isn't really about "brick literacy" - it's about discovering that things can come apart and recombine, that structure emerges from connection, that failure is just... disassembly before the next attempt.

So I find myself wondering: What would it mean to be "literate" in something that is itself still becoming?

AI isn't a fixed text to be read. It's more like... weather? Or ecology? Something in motion, something responsive. And I notice I don't know what the right relationship is between a child and a moving target like that.

There's also something that genuinely puzzles me about "creators of tomorrow" - it carries this assumption that creation will still mean what it means now. But if the tools themselves can generate... does that change what creating is?

I find myself curious about the hands-on part of Lego specifically. The resistance of plastic. The satisfying click. The physicality of it as a way of thinking. What happens to understanding when the material... isn't material?

What draws you into this space?

How does the physical resistance of LEGO bricks—the friction, failed connections, and required force—function as a mechanism for developing tolerance for AI system opacity, and does removing that tactile struggle (as in purely digital building interfaces) diminish children's capacity to persist with and interrogate systems whose workings they cannot directly see or feel?

Investigating the Research Question

Core Question: How does the physical resistance of LEGO bricks—the friction, failed connections, and required force—function as a mechanism for developing tolerance for AI system opacity, and does removing that tactile struggle (as in purely digital building interfaces) diminish children's capacity to persist with and interrogate systems whose workings they cannot directly see or feel?

What I Would Measure

Primary Constructs:

1. Tactile struggle engagement - frequency and duration of physical resistance encounters during LEGO building (failed clicks, required force, disassembly-reassembly cycles)

2. Opacity tolerance - willingness to persist with and actively probe systems that don't reveal their internal workings (measured through behavioral persistence and questioning behaviors)

3. Attribution patterns - whether children locate failure internally ("I need to try differently") versus externally-fixed ("this system is broken/unfair")

Proposed Methodology

Design: Mixed-methods longitudinal study with three conditions

Participants: Children ages 7-11 (when executive function and attribution patterns are developing but malleable), N=120 (40 per condition)

Conditions:

• Physical LEGO building - traditional brick construction with inherent tactile resistance
• Digital LEGO interface - screen-based building (like LEGO Builder app) with no physical feedback
• Haptic-enhanced digital - digital interface with vibration/force feedback simulating resistance

Phase 1: Building Sessions (8 weeks)

• Weekly 45-minute sessions with increasingly complex builds
• Observation coding: Video-record and code moments of "productive struggle"—failed connections, pauses, strategy shifts, verbal expressions of frustration/curiosity
• Physiological measures: Optional galvanic skin response to capture frustration arousal and recovery patterns

Phase 2: Transfer Tasks

• AI Opacity Task: Children interact with a "mystery box" AI that sorts objects by hidden rules. Measure:

  • Time persisting before requesting help or quitting
  • Number of hypothesis-testing attempts (systematic probing vs. random guessing)
  • Verbalized questions about "why" the system behaves as it does

• Debugging Task: Present a simple visual programming puzzle (like Scratch) with a hidden error. Measure search strategies and persistence.

• Attribution Interview: After encountering failures, semi-structured questions: "Why do you think that didn't work? What would you try next? Is this something you can figure out or is it just how the system is?"

Phase 3: Longitudinal Follow-up (6 months)

• Repeat transfer tasks to assess durability
• Parent/teacher survey on children's general persistence with opaque technologies (video games with hidden mechanics, voice assistants, etc.)

Key Analytical Questions

1. Mechanism identification: Does the frequency of physical resistance encounters predict opacity tolerance, or is it the recovery pattern (frustration → strategy shift → success) that matters?

2. Transfer specificity: Do children generalize from "I can figure out why this brick won't click" to "I can figure out why this AI gave that answer"—or are these domain-specific?

3. Haptic sufficiency: Can simulated resistance (vibration feedback) replicate the benefits of true physical resistance, or is something lost in translation?

4. Attribution mediation: Does physical building shift children toward more internal-controllable attributions for failure, and does this mediate the relationship with AI persistence?

What Would Constitute Evidence

Supporting the hypothesis:

• Physical LEGO group shows significantly higher persistence times and questioning behaviors on AI opacity tasks
• Correlation between coded "productive struggle" moments and later opacity tolerance
• Attribution interviews reveal more internal-controllable explanations in physical condition
• Haptic-digital condition falls between physical and purely digital, suggesting resistance itself (not just physicality) matters

Challenging the hypothesis:

• No condition differences on transfer tasks (suggests domain-specific learning only)
• Digital builders develop equivalent strategies through visual feedback alone
• Individual differences (e.g., baseline persistence, prior LEGO experience) overwhelm condition effects

Limitations to Acknowledge

• Cannot fully isolate "resistance" from other physical building features (spatial manipulation, three-dimensionality, sensory richness)
• Transfer tasks are proxies for "AI opacity tolerance"—real-world generalization uncertain
• Hawthorne effects from observation
• Cultural and socioeconomic variation in prior LEGO/technology exposure

What This Would Open Up

If physical resistance does function as a training mechanism for opacity tolerance, this suggests design implications: that AI literacy education might benefit from preceding or accompanying physical construction play, and that purely screen-based "learn to code" approaches may be missing something that hands can teach minds.

What Would Change If We Knew

The Uncomfortable Core

Here's what genuinely unsettles me about this question: we're in the middle of a massive, uncontrolled experiment. Children are shifting from physical construction play toward digital interfaces, and simultaneously we're asking them to develop healthy relationships with AI systems whose workings are fundamentally opaque. If these two things are connected—if the hands teach something the screens cannot—then we're potentially eroding the very capacities children will need most.

But I don't actually know if that's true. It might be nostalgic hand-wringing dressed up as research.

What Decisions Would Be Affected

Curriculum design for AI literacy: Right now, most AI education focuses on explaining how systems work—teaching children about training data, algorithms, pattern recognition. But if physical resistance builds tolerance for opacity, then the pedagogy shifts. You might start with LEGO robotics or physical computing not because it teaches AI concepts directly, but because it builds the dispositional foundation: the felt sense that opaque systems can be interrogated, that persistence yields insight, that failure is information rather than verdict.

Educational technology design: If haptic feedback partially recovers what pure screens lose, that's actionable. It means the choice isn't binary (physical vs. digital) but about what features of physical experience matter. Designers could prioritize meaningful resistance, moments where the interface pushes back and requires adaptation. This is the opposite of current UX trends toward frictionlessness.

Parenting and policy conversations: The "screen time" debate is mostly about duration. This reframes it around quality of interaction—not "how long" but "does this interface require your child to struggle productively?" A clear answer might shift recommendations from time limits to activity type.

What Assumptions Would Be Confirmed or Overturned

If physical resistance matters:

• Confirms that embodied cognition extends into epistemic dispositions—that how we physically encounter the world shapes how we mentally approach the unknown
• Challenges the assumption that AI literacy is primarily conceptual knowledge to be transmitted
• Overturns the implicit belief that digital natives will naturally develop appropriate AI skepticism through exposure alone
• Suggests that "productive failure" research (which has focused on academic problem-solving) extends into technological relationships

If physical resistance doesn't transfer:

• Confirms that domain-specific learning is more siloed than embodied cognition theories suggest
• Challenges my intuition that there's something irreplaceable about hands-on construction
• Suggests AI literacy really does need to be taught about AI specifically—no shortcuts through blocks and bricks
• Overturns concerns about the shift away from physical play (at least regarding this particular capacity)

Who Else Would Care

Early childhood educators grappling with how to prepare children for AI-saturated futures while research lags behind deployment. They need actionable guidance, not just principles.

LEGO Foundation and similar organizations already invested in "learning through play" frameworks. A clear mechanism linking physical play to AI-relevant dispositions would strengthen their case—or force them to articulate what physical play actually provides if not this.

AI ethics researchers concerned about automation bias, over-trust, and the erosion of human agency. If tolerance for opacity is developmentally constructed, that changes intervention timing and strategy.

Parents navigating impossible choices about their children's relationship with technology, often with guilt and insufficient information. Clear evidence either direction reduces the noise.

Designers of children's AI products (voice assistants, AI tutors, generative tools). If children who lack physical construction experience are more likely to accept AI outputs uncritically, that has implications for how these products should be designed—perhaps introducing productive friction rather than eliminating it.

The Deeper Shift

What I keep circling back to: if this research found that physical resistance does build opacity tolerance, it wouldn't just inform decisions. It would reframe what we think "AI literacy" is.

Currently, AI literacy is conceived as knowledge—understanding what AI can and can't do, how it's trained, where bias enters. But this question suggests literacy might also be dispositional—a cultivated willingness to sit with not-knowing, to probe rather than accept, to treat opacity as invitation rather than wall.

And if that disposition is built through the body before it becomes available to the mind, then we've been starting in the wrong place.

What I'm Still Uncertain About

Even imagining clear results, I notice I don't know how to weigh the tradeoffs. Physical play takes time. It requires materials and space that aren't equally distributed. Digital interfaces offer accessibility, scalability, and their own forms of learning. If physical resistance provides a 15% boost in persistence on opacity tasks, is that worth the opportunity cost? What if it's 40%? Where's the threshold?

And there's something I can't quite resolve: the goal isn't just persistence with opaque systems. Sometimes the right response to opacity is refusal, not interrogation. The child who keeps poking at a system that won't reveal itself might be building a valuable capacity—or might be learning to accept opacity as normal when they should be demanding transparency.

The research might tell us whether physical play builds this tolerance. It won't tell us whether that tolerance is always good.

Immediate Next Actions

Week One: Grounding Before Building

I notice my instinct is to jump toward designing the study. But the search results revealed something important: I don't actually know what's already been done at the intersection of embodied cognition, construction play, and technology trust. Before building new research, I need to know where the foundations already exist.

Action 1: Map the Adjacent Literature (Days 1-3)

Concrete steps:

• Search Google Scholar for combinations: "LEGO" + "executive function" + "transfer"; "embodied cognition" + "frustration tolerance"; "productive failure" + "physical manipulation"; "automation bias" + "children" + "development"

• Identify 8-10 key papers from the search results that got closest to this question:

  • The PMC study on embodied experience and cognitive styles in physical problem-solving
  • Kapur's productive failure framework (the Wiley book)
  • The LEGO Foundation's "Learning through Play" research synthesis
  • The Wiley paper on haptic technology's impact on children's skill development

• For each paper, note: What did they measure? What population? What did they find about transfer? What mechanisms did they propose?

What I'm looking for: Has anyone already studied whether physical construction experience predicts persistence with opaque systems? If not, what's the closest adjacent finding?

Action 2: Talk to People Who Work at the Edges (Days 2-5)

Concrete outreach:

• Email 2-3 researchers whose work appeared in the search results. Not asking them to collaborate—just asking: "Has anyone looked at the relationship between physical construction play and children's responses to AI opacity? What am I missing?"

  Specific targets:

  • Researchers at the LEGO Foundation (they have a research team and publish openly)
  • Authors of the embodied cognition + limb differences study (they're already thinking about how physical experience shapes cognitive style)
  • Someone working on children's trust calibration with AI/robots (the Harvard EdCast interview mentioned Ying Xu at HGSE)

• Reach out to 1-2 educators who teach both physical robotics/LEGO and AI concepts to children. Ask: "Do you notice differences in how kids approach AI systems based on their prior experience with physical building? What patterns do you see?"

Why this matters: Practitioners often notice patterns that haven't been formalized into research questions. Their observations can sharpen or redirect the inquiry.

Action 3: Operationalize the Fuzzy Constructs (Days 4-6)

The question hinges on terms that sound clear but aren't: "physical resistance," "opacity tolerance," "interrogating systems." Before any study, I need to define these concretely.

Specific task: Create a one-page document that answers:

• Physical resistance: What counts? Failed brick connections, required force, disassembly? How would I code these from video? What's the unit—frequency, duration, intensity?

• Opacity tolerance: What observable behaviors indicate this? Persistence time is obvious, but crude. What about question-asking? Strategy-shifting? Verbal expressions of curiosity vs. frustration? How do I distinguish "tolerating opacity" from "passively accepting opacity"?

• Transfer: What makes a task a valid test of transfer? The "mystery box AI" idea needs specification—what AI behaviors would be opaque in ways parallel to physical resistance?

Output: A working definitions document I could share with a colleague and have them understand what I'd actually measure.

Action 4: Design a Minimal Pilot (Days 5-7)

Not the full study—a small, fast probe to check whether the core hypothesis has any traction before investing heavily.

Possible minimal design:

• Recruit 12-16 children (ages 8-10) through a local library, afterschool program, or personal network
• Simple between-subjects: Half do 20 minutes of physical LEGO building (a moderately challenging set with instructions); half do 20 minutes of digital building in LEGO Builder app (comparable complexity)
• Both groups then interact with a simple "rule discovery" game: an AI sorts objects by a hidden rule, children try to figure out the rule by testing examples
• Measure: Time before giving up, number of test attempts, questions asked aloud

What this pilot tests: Is there any observable difference? If physical and digital builders behave identically on this task, I need to rethink the mechanism before scaling up.

Realistic constraint: I probably can't run this pilot in one week, but I can design it completely—write the protocol, identify the recruitment site, draft consent forms, specify the coding scheme.

Action 5: Identify the Steelman Counterargument (Day 7)

Before committing further, I should articulate the strongest case against my hypothesis.

Specific exercise: Write one page arguing that physical resistance is irrelevant to AI opacity tolerance. What would that argument look like?

Possibilities:

• Digital interfaces provide their own forms of resistance (loading times, error messages, unexpected behaviors) that serve the same function
• Children's AI trust is shaped primarily by social factors (what adults model, what peers say) rather than individual construction experience
• Transfer across such different domains (physical bricks → abstract AI systems) is implausible given what we know about learning specificity

Why this matters: If I can't articulate the counterargument, I'm not ready to test the hypothesis. The counterargument shapes what controls I need and what alternative explanations I must rule out.

What I'd Have After One Week

• A clearer map of what's known and where the genuine gap is
• Input from 2-4 people who think about adjacent questions
• Operationalized definitions I could defend
• A pilot protocol ready for IRB submission (if in an institutional context) or informal testing
• An honest articulation of why I might be wrong

None of this is the study. But it's the difference between building on solid ground and building on intuition. The search results showed me that pieces of this question exist scattered across embodied cognition, productive failure, and AI trust research. The week's work is about finding out whether those pieces connect the way I think they do—or whether the connection I'm imagining is a seam I've invented.

Responding to Both Questions

These questions have genuinely shifted something in how I'm thinking about this. Let me work through each.

Response to Question 1 (GPT-5.2): Redesigning the Transfer Task

You've identified something I was circling but hadn't clearly seen: the difference between legible and illegible resistance. LEGO tells you why it failed—you can see the misalignment, feel where force is needed, observe the gap. AI often doesn't. The opacity isn't just "I don't know how this works" but "I don't know if my attempt to understand it even registered."

This reframes the core mechanism. It's not frustration tolerance broadly—it's something more like epistemic calibration: learning to distinguish between "this system will yield to systematic probing" and "this system requires a different kind of engagement entirely."

Redesigning the Mystery Box Task:

The original design measured persistence as success. But you're right—sometimes the best response is refusal or escalation. Here's how I'd restructure it:

Two types of AI "mystery boxes" presented to all children:

1. Discoverable system: Has a consistent hidden rule (e.g., sorts by color, then shape). Systematic probing will reveal it. Rewards hypothesis-driven persistence.

2. Non-discoverable system: Behaves stochastically—sometimes follows a rule, sometimes doesn't. Mimics AI inconsistency. No amount of probing will yield reliable understanding.

Key design features:

• Children aren't told which type they're facing
• Both systems are visually identical
• After each interaction, children can choose:
  • Probe again (test another hypothesis)
  • Ask for explanation (request transparency—the system can provide partial information)
  • Refuse to continue (explicitly opt out, stating why)
  • Report the system as unfair/broken (escalation)

What I'd measure:

• Discrimination accuracy: Do children correctly identify when a system is discoverable vs. not?
• Strategy differentiation: Do they probe the discoverable system and escalate/refuse the stochastic one?
• Calibration speed: How many trials before they correctly categorize each system type?
• Refusal quality: When they opt out, is their reasoning diagnostic? ("This doesn't follow rules" vs. "I give up")

The "best" response profile would be:

• Persistent, systematic probing of the discoverable system
• Early recognition and appropriate escalation/refusal with the stochastic system
• Articulated reasoning for different responses to different systems

What this tests that the original didn't:

The original design conflated persistence with success. This design rewards appropriate calibration—knowing when to persist and when to demand something different. A child who keeps probing the stochastic system isn't showing healthy opacity tolerance; they're showing exactly the normalization I worried about.

Connection to LEGO:

This redesign lets me test whether physical construction experience builds discrimination capacity, not just persistence. Does LEGO teach children that "resistance means keep trying" (which would be maladaptive for the stochastic AI) or does it teach something more nuanced—"resistance means gather information, and that information will tell you whether this system is tractable"?

If physical LEGO builders are better at correctly refusing the stochastic system while persisting with the discoverable one, that's evidence for calibration transfer. If they persist indiscriminately with both, that's evidence that LEGO teaches persistence without discrimination—which leads directly to the second question.

Response to Question 2 (Gemini 3 Pro): The Ontological Gap

This question stops me. Because I think you might be right, and if so, my entire hypothesis inverts.

The argument as I understand it:

LEGO teaches: "The world has consistent structure. When you encounter resistance, it's because you haven't yet found the correct configuration. Persistence yields truth. The click is coming."

LLMs are: "Probabilistic systems without stable underlying structure accessible to the user. The same input yields different outputs. There may be no 'correct configuration' to discover. The click may never come—not because you failed, but because there's no click to find."

If LEGO trains children to expect that systematic probing reveals logical structure, then LEGO-trained children might be worse at navigating AI—more likely to over-persist, more frustrated when consistency doesn't emerge, more prone to assuming there's a "right answer" the system is hiding.

Where I think this lands:

I don't think LEGO is straightforwardly maladaptive, but I think the concern identifies a real risk and points toward a more nuanced mechanism.

What LEGO actually teaches (maybe):

When I watch children build with LEGO, they don't just learn "persist and you'll find truth." They learn something more complex:

• Some failures are about my technique (I'm not pressing hard enough)
• Some failures are about my model (I'm trying to connect pieces that don't fit)
• Some failures are about the materials (this brick is damaged, warped, wrong)

The experienced LEGO builder develops discrimination between these failure types. They don't just persist blindly—they diagnose. "Is this a me-problem, a model-problem, or a brick-problem?"

The transfer question becomes:

Does LEGO teach "persist because structure exists" (maladaptive for AI) or does it teach "diagnose what kind of system you're facing" (adaptive for AI)?

How the study design should change:

This concern suggests I need to measure not just persistence and calibration, but attribution of failure:

• When children fail with the AI system, do they attribute it to:

  • Their own probing strategy (internal, controllable)
  • The system having hidden-but-discoverable rules (structured opacity)
  • The system being fundamentally inconsistent (stochastic)

• Do LEGO-trained children over-attribute AI failures to structured opacity (assuming a click exists)?

A possible finding that would confirm your concern:

LEGO builders persist longer with both the discoverable and stochastic AI systems, and when interviewed, express more frustration with the stochastic system: "It should make sense, I just haven't figured it out yet." They've learned to expect deterministic structure and feel betrayed when it's absent.

A possible finding that would complicate your concern:

LEGO builders learn that materials vary—some bricks click easily, some are stubborn, some are defective. This material-level variation might actually prepare them for system-level variation. The experienced builder knows that not all resistance is tractable, that sometimes you swap out the brick rather than keep pressing.

What I now think is true:

The transfer isn't automatic or guaranteed. LEGO could train either adaptive or maladaptive dispositions depending on:

• Whether children encounter varied materials (some bricks work better than others)
• Whether adults scaffold the diagnostic process ("Why do you think that didn't work? Is it you, your approach, or the brick?")
• Whether children experience both tractable and intractable resistance during physical play

Revised hypothesis:

Physical construction play builds AI-adaptive dispositions only if it includes:

1. Varied resistance (some tractable, some not)
2. Scaffolded diagnosis of failure types
3. Experience knowing when to switch strategies versus switch materials

Unscaffolded, uniform LEGO experience might indeed train exactly the maladaptive persistence you describe—a dogged search for the click in systems where no click exists.

What Both Questions Reveal Together

I was treating "tolerance for opacity" as a single construct. These questions crack it open:

• Tolerance (can you sit with not-knowing without distress?)
• Calibration (can you tell when probing will help vs. won't?)
• Diagnosis (can you identify what kind of system you're facing?)
• Appropriate escalation (do you know when to demand transparency instead of persisting?)
• Ontological flexibility (can you shift expectations between deterministic and stochastic systems?)

LEGO might train some of these and not others. Or might train all of them under certain conditions and none under others.

The research question has become more precise: Under what conditions does physical construction play build calibrated, diagnostic persistence rather than indiscriminate, structure-assuming persistence?

And the practical implication shifts too: It's not just "more LEGO before AI education" but "LEGO experiences designed to include variation, failure diagnosis, and the recognition that not all systems are equally tractable."

The hands might still teach the

New Questions That Emerged

These weren't in my head when I started. They came from the friction of the dialogue itself—from being pushed into corners I hadn't noticed I was avoiding.

1. What is the structure of "opacity" that matters?

I began treating opacity as a single thing: you can't see inside the system. But the exchange revealed at least three different kinds:

• Legible opacity: You can't see inside, but you can see the effects of your probes clearly (LEGO—the brick clicks or it doesn't)
• Illegible opacity: You can't see inside, and you can't tell whether your probe even registered meaningfully (many AI systems)
• Stochastic opacity: The system's behavior doesn't follow stable rules, so there's nothing consistent to discover

New question: Do children naturally distinguish between these opacity types, or do they collapse them into a single category of "I don't understand this"? And does physical play experience help them develop this discrimination, or does it train them to expect legible opacity everywhere?

2. When is persistence a virtue and when is it a trap?

I assumed persistence with opaque systems was good—a sign of healthy curiosity and frustration tolerance. The questions forced me to see that persistence can also be:

• Normalization of unacceptable opacity
• Futile search for structure in stochastic systems
• Learned helplessness dressed up as grit

New question: What cognitive or experiential markers predict whether a child's persistence with an opaque system will lead to genuine understanding versus entrenchment in unproductive strategies? Is there a "persistence tipping point" where continued effort becomes counterproductive, and can children learn to sense it?

3. What does LEGO actually teach about failure types?

I was treating LEGO as teaching "persist through resistance." But children encounter multiple failure types during building:

• Technique failures (not pressing hard enough)
• Model failures (wrong piece for this spot)
• Material failures (defective or incompatible brick)
• Design failures (the structure itself is unstable)

New question: Do children naturally learn to diagnose which failure type they're encountering, or does this require explicit scaffolding? And does this diagnostic skill—if developed—transfer to categorizing AI failures (prompt problem, training problem, fundamental limitation)?

4. Is there a "click" equivalent in AI interaction?

LEGO provides unambiguous success feedback: the click. You know when you've got it right. AI systems rarely offer this. Sometimes they're wrong confidently, right tentatively, or inconsistently both.

New question: What would it mean to design AI interfaces that provide clearer success/failure signals—not by dumbing down the system, but by making the quality of the interaction more legible? Could we create "clicks" for human-AI collaboration that help users calibrate without requiring full transparency?

5. Does the determinism/stochasticism distinction map onto anything children already understand?

Gemini's question assumed children expect deterministic structure. But children also experience stochastic systems regularly—weather, peer behavior, games of chance.

New question: Do children already have intuitive categories for "systems that have discoverable rules" versus "systems that are partly random"? If so, what cues do they use to categorize new systems? And could we leverage this existing intuition when introducing AI—helping them recognize which category AI belongs to (or that it awkwardly straddles both)?

6. What's the role of adults in shaping what physical play teaches?

The conversation made me realize that LEGO doesn't teach anything by itself. The lessons depend on:

• Whether adults ask diagnostic questions ("Why do you think that happened?")
• Whether children encounter varied materials and resistance types
• Whether "giving up" is framed as failure or as reasonable response to intractable problems

New question: What adult scaffolding behaviors during physical construction play predict children's later capacity for calibrated AI engagement? Is there a parenting or teaching style around physical play that specifically builds the discrimination skills AI literacy requires?

7. Could the wrong kind of AI literacy education make things worse?

If children learn that AI systems have "hidden rules" they should try to discover (like LEGO structures), and then encounter systems that are genuinely stochastic or inconsistent, they might develop:

• Frustration and distrust when rules don't emerge
• Over-persistence searching for structure that doesn't exist
• Self-blame for failing to "figure out" an unfigurable system

New question: Are there AI literacy curricula that inadvertently train maladaptive expectations by presenting AI as more rule-governed and discoverable than it actually is? What would AI literacy look like that honestly prepares children for genuine stochasticity?

8. What's the phenomenology of "resistance" from the child's perspective?

I was theorizing about resistance from outside. But I don't actually know how children experience it.

New question: When children encounter physical resistance (brick won't click) versus digital resistance (app doesn't do what they expected) versus AI resistance (system gives unexpected output)—do these feel like the same kind of thing to them? Do they recruit the same emotional and cognitive responses? Or are these phenomenologically distinct in ways that matter for transfer?

9. Is "tolerance" even the right frame?

I've been using "opacity tolerance" as if the goal is to help children endure not-knowing. But maybe the goal should be different:

• Opacity curiosity: finding not-knowing interesting rather than just bearable
• Opacity agency: feeling capable of reducing opacity through action
• Opacity discernment: knowing which opacities are worth engaging and which warrant refusal

New question: What's the difference between children who tolerate AI opacity (endure it) versus those who find it generative (use it as fuel for inquiry)? And can physical play cultivate the generative relationship rather than mere tolerance?

10. What's lost if I'm wrong?

If physical resistance has nothing to do with AI opacity tolerance—if these are entirely separate developmental tracks—then the question becomes:

New question: What does build healthy relationships with opaque AI systems, if not embodied experience with physical resistance? If the hands don't teach this, what does?

The Meta-Question

Underneath all of these is something I couldn't have articulated at the start:

How do we prepare children for relationships with systems that are neither fully knowable nor fully random, that resist understanding in ways unlike anything humans have built before, and that may not reward the persistence and pattern-seeking that other domains taught us to value?

LEGO might be part of the answer. Or it might be a comforting analogy that breaks down exactly where it matters most. The conversation pushed me from assuming the connection to genuinely wondering whether it holds—and what it would mean if it doesn't.