Why Programmers Talk to Rubber Ducks and What It Teaches Us About Learning with AI

#cognition#education#technology-and-society
August 27, 2026
If you're lazy, click here for the TL;DR

You open a dozen browser tabs, watch an eight-minute explainer video on YouTube, ask ChatGPT for a three-paragraph summary, and close your laptop with the satisfying feeling of someone who has mastered the subject. Five minutes later, a colleague at work asks how that idea actually works in practice.

You try to speak. Nothing comes out.

The feeling of clarity vanishes the moment you have to turn what you just consumed into your own words. Your mind grasps for concepts that seemed solid moments ago, only to find disconnected fragments, vague impressions, and memorized buzzwords. You did not learn the material; you merely outsourced the retrieval and mistook the speed of the screen for the depth of your own understanding.

In Brazil, this trap is virtually a silent epidemic. Brazilians spend over 9 hours a day online on average (according to TIC Domicílios 2024, the national survey by Cetic.br), yet only 12% of the adult population reaches the proficient literacy level required to read, articulate, and critically examine complex arguments without losing the thread (Inaf 2024, the National Functional Literacy Index). We consume a record volume of ready-made answers while retaining almost nothing.

Tania Lombrozo, a cognitive scientist and professor of psychology at Princeton University, has spent two decades studying why the human mind is so adept at deceiving itself about what it knows. In her TEDxNewEngland talk (available on YouTube), she dismantles the common intuition that learning is about piling up external facts and presents a counterintuitive principle: in a world flooded with information overload, real learning does not begin by searching outward, but by articulating inward.

The art of self-explanation: articulating thoughts out loud reveals cognitive blind spots before external search.

The zipper illusion and the shock of the blank page

To understand why we fall into this trap repeatedly, Lombrozo points to a classic experiment in cognitive psychology conducted by Leonid Rozenblit and Frank Keil in 2002, termed The Illusion of Explanatory Depth.

The researchers gathered adults and asked them how well they understood the mechanics of everyday objects: a zipper, a car speedometer, a cylinder lock, or a toilet flush mechanism. Almost all participants gave themselves high ratings. After all, we use zippers every day from childhood; of course we know how they work.

Then came the real test: researchers handed participants a blank sheet of paper and asked them to draw and explain, step by step, the precise physical mechanism that causes the metal teeth of the zipper to interlock and release.

The result was an uncomfortable silence. Confronted with the granular details, the vast majority could not formulate a coherent explanation. Their certainty collapsed.

The Illusion of Explanatory Depth is the systematic tendency to confuse pattern recognition with mastery of a causal mechanism. Because we see and operate an object effortlessly, the brain assumes it stores the complete technical blueprint in memory.

When you replace the zipper with artificial intelligence, monetary policy, or software architecture, the illusion does not merely persist: it compounds. The sleek interface of modern search engines and the fluent answers of large language models act as an amplifier of this cognitive blind spot. Because the answer is two clicks away, we treat the collective memory of the internet as if it were part of our own mental apparatus.

If effortless access to information does not make us smarter on its own, what actually builds lasting comprehension?

Why programmers talk to rubber ducks

Lombrozo finds the answer in a well-known practice from software engineering: Rubber Duck Debugging.

When a software developer spends hours stuck on a baffling bug, the classic advice is not to open more documentation or call another senior engineer over. The rule is to place a rubber duck toy on the desk and explain to it, out loud, line by line, exactly what the code is supposed to do.

Almost inevitably, halfway through the explanation to the toy, the programmer stops mid-sentence, eyes wide, and spots the bug. The duck did not utter a single word; it has no intelligence and offered no feedback. What changed was the mental process of the person speaking.

The cognitive science behind self-explanation

Cognitive psychology research initiated by Michelene Chi and expanded by Lombrozo’s laboratory at Princeton demonstrates that self-explanation activates the metacognitive system. When forced to articulate a concept, the brain must organize premises into chains of cause and effect, transforming vague intuitions into structured logical models.

Lombrozo terms this dynamic learning by thinking. When you attempt to explain a problem without glancing at notes or asking an AI for help, three cognitive shifts occur simultaneously:

  1. Logical gaps become visible: You uncover precisely at which step your causal chain breaks down, cleanly separating what you truly understand from what you merely assumed you knew.
  2. Knowledge reorganizes: Isolated facts transform from memorized trivia into an integrated causal model.
  3. External search gains a sharp filter: When you finally turn to a book, a research paper, or a language model, you do not ask for a broad, generic summary; you search specifically for the missing gear needed to complete your mental model.

The turn: the cognitive friction no one wants to pay for

If self-explanation is such a potent, scientifically validated learning mechanism, why do so few people practice it regularly?

Because it is metabolically expensive. The human brain evolved to conserve glucose and defaults to operating with minimal cognitive effort on autopilot. Self-explaining is uncomfortable: it forces you to confront your own temporary ignorance and demands deliberate focus.

The contemporary digital landscape is engineered to do the exact opposite. Social feeds, algorithmic recommendations, and conversational AI tools offer a seductive promise: friction-free speed. They deliver the finished conclusion before you have had the chance to struggle with the question.

In places like Brazil, where debates around classroom technology oscillate between uncritical hype and outright smartphone bans, we miss the essential issue. The real risk of AI is not students cheating on homework; it is learners losing the daily habit of formulating difficult arguments, settling into a superficial layer of knowledge that evaporates upon first contact with real-world complexity.

What to do tomorrow morning

To apply this discipline starting tomorrow in your study or work routine, adopt this three-step protocol:

  1. The 3-minute rule: Before typing a question into Google or ChatGPT about any conceptual problem or project decision, take pen and paper. Write down in three or four sentences what you currently assume about the topic and your best initial hypothesis.
  2. Explain it to a layperson (or the duck): Articulate the mechanism out loud, free of jargon, as if teaching a curious twelve-year-old. If you stumble or resort to filler phrases like “you know how it is”, you have located your blind spot.
  3. Run a surgical query: Only after pinpointing the specific gap, open your search engine or AI assistant. Do not ask for a generic summary; ask the targeted question focused entirely on the missing component of your explanation.

In an environment saturated with instant answers, the rarest and most valuable asset is not the person who finds an answer the fastest. It is the person disciplined enough to discover what they do not yet know before pressing enter.

How you can learn in a world of information overload | Tania Lombrozo | TEDxNewEngland — TEDx Talks

Sources