Learning by explaining, questioning, and revising your own thinking.
Miku is an experimental learning environment designed to help young learners become more aware of how they understand something, not just whether they can produce the right answer.
Instead of giving answers immediately, Miku creates situations in which learners have to explain, inspect and revise their own reasoning.
You teach Miku what you learned.
Miku connects what you teach into a knowledge map.
What does Miku know, and where might it be confused?
See how Miku performs, then improve its understanding.
AI can increasingly give students the right answer. But learning is not only about arriving at an answer.
Miku is designed to make the learner's own thinking visible:
- what they understand?
- where their explanation breaks down?
- how their mental model changes after encountering new evidence?
Rather than immediately correcting the learner, Miku asks them to explain what they believe, challenges gaps in that explanation, and encourages them to reconstruct their understanding.
Miku takes inspiration from Betty's Brain, a learning environment developed at Vanderbilt University around the idea of learning by teaching.
Students teach Betty by building a causal map of a science topic — ecosystems, climate change, thermoregulation — from a set of provided resources. They check what she has understood by asking her questions and having her take quizzes, which a mentor agent, Mr. Davis, grades and comments on. Betty only knows what the student has taught her, so a wrong quiz answer points back to the student's own map rather than to the student.
The environment is deliberately open-ended: some students model first and read later, others read first, others work resource by resource. That freedom is the point — it asks students to manage their own strategy, which is why the system is studied as a setting for self-regulated learning. It is developed by the Open-Ended Learning Environments lab at Vanderbilt University.
Miku keeps the same bargain — the agent knows only what you taught it — but replaces the causal map editor with conversation, so the explanation is produced in the learner's own words and questioned as it is given.
Four findings from the learning sciences shape how the loop is built. Each one is stated with what it implies for the design, so the choices can be argued with.
Students who explain material to themselves as they study learn more than students who study the same material without explaining it, largely because the attempt exposes what the explanation is missing.
In Miku, the learner does the explaining and the agent does the listening. Nothing moves forward until something has been said out loud.
Chi et al., 1989; 1994Learners who teach a computer agent put in more effort and persist longer than learners working for their own score, and read the agent's mistakes as a problem to fix rather than as a judgment of themselves.
Miku is a pupil, not a tutor. When it gets something wrong, the question on the screen is what it was not taught.
Chase, Chin, Oppezzo & Schwartz, 2009Judgments of one's own learning are frequently wrong, and usually too confident. The mismatch is easiest to see when a prediction is made before an outcome, then held next to it.
Before the quiz, the learner predicts each question Miku will get right or wrong. Reflect shows the two side by side.
Nelson & Narens, 1990A learner has to become dissatisfied with their current explanation before a better one can replace it. Correction supplied too early tends to be filed away rather than integrated.
Miku never corrects. The dissatisfaction comes from watching your own explanation fail a question you thought it covered.
Posner, Strike, Hewson & Gertzog, 1982The core claim is narrow and falsifiable: an agent whose knowledge is strictly bounded by the learner's teaching log turns the learner's own explanation into the only variable that matters, so gaps in that explanation become visible as agent failures rather than as marks on the learner.
All four depend on one condition holding: the pupil must not answer from knowledge it was never taught. If it does, a failed question no longer points back to the learner's explanation, and none of these measures mean anything.
Miku is not designed to think on behalf of the learner. It is designed to create situations in which the learner has to think.
The prototype is written to test the design with small scale pilot & interview. The constraints that carry the argument — the agent knows only the teaching log, predictions are made item by item, the session can be read afterwards — are enforced in code rather than left to prompting.
The four screens of the loop as one piece of session state, built mobile-first because the learners are on phones and tablets.
Two roles: the pupil, prompted only with what it has been taught, and a quizmaster that holds the answer key and never shares it with the pupil.
A fixed set of questions the pupil was never taught, run after any change to its prompting, to check that it admits ignorance instead of answering from the model's own knowledge.
Teaching turns, predictions and grades export together, with no accounts and nothing else stored — the working scope of a prototype used with children.
Status: a single topic, a ten-question quiz, and one loop from teaching to reflection. Everything above describes the current prototype, not a study that has been run.
When answers are abundant and cheap, the scarce capacity is judgment about answers.
Questioning, explaining, recognising uncertainty, evaluating evidence and revising beliefs.
Generative systems have removed the friction that used to force explanation. Much of the metacognitive work of school — attempting an account, noticing it fails, repairing it — happened in that friction. If the friction is gone, the work has to be designed back in deliberately rather than assumed.
Miku is one testable instance of that design: a bounded agent, an explicit prediction, a legible knowledge structure and an exported session. Whether learning by teaching survives contact with a fluent language model is an empirical question, and this prototype exists to ask it rather than to assert an answer.
Meet Miku (米酷)!