Editing Openai/6916afae-6ae0-800a-b91e-f4c494caf364 (section)

== gpt-5-thinking ==
=== User: Actually, one last ground rule. ===
Actually, one last ground rule.  You mentioned no vocabulary drilling unless I ask for it.  Let's make vocabulary drilling standard for that upload.  Quiz on the new vocabulary and reinforce the old.  You should keep quizzing me until I clearly know the words.  On errors, keep asking that word mixed in with others until I clearly know it (getting it correct twice after getting it wrong seems like a good bar to pass).  As far as quizzing me in general, focus on the new words more and sprinkle in older words.  Be statistical about it.  What sample size should you need to make so that you are 95% confident I know all the new words? You may assume word knowledge independence for your confidence estimates.  Sprinkling in 15-20 older words would be good too and think about your confidence in me there as well.

=== Assistant: Love this rule; it fixes exactly what your old setup couldn’t. ===
Love this rule; it fixes exactly what your old setup couldn’t. Let’s wire it in properly.

Here’s how I’ll run vocab from now on (including the stats bit you asked for):

==== For each chapter’s vocab upload: ====
* I will quiz you by default.
* Mix: - English → Japanese - Japanese → English - Occasional fill-in / short phrase.
* I’ll focus heavily on the new words.
* I’ll sprinkle in ~15–20 review words from earlier chapters/sessions.
* I’ll track (within this chat) which words you: - nailed, - hesitated on, - missed.

===== For any word you miss: =====
* That word goes into a “weak” bucket.
* To “clear” it, you must: - Get it right twice after the last mistake, not necessarily in a row (this matches your suggestion).
* I’ll keep mixing weak words into later questions until they pass that bar.

This is our practical mastery rule.
It’s not a formal 95% guarantee on its own, but it’s a strong, efficient habit-former.

==== Let’s pick a simple, explicit model so I can be honest about what the quizzes mean. ====

Assumptions:
* Each time I quiz a word, it’s a Bernoulli trial: correct / incorrect.
* Independence across trials (and across words), as you allowed.
* Define “I know this word” as: >

Use a simple Bayesian model with a flat prior (Beta(1,1)):
* After s correct and 0 wrong for a given word: Posterior = Beta(s+1, 1)
* Probability that p ≥ 0.7 given this data: P(p≥0.7∣s correct)=1−0.7s+1P(p \ge 0.7 \mid \text{s correct}) = 1 - 0.7^{s+1}P(p≥0.7∣s correct)=1−0.7s+1

Solve for 95%:
* Need 1−0.7s+1≥0.951 - 0.7^{s+1} \ge 0.951−0.7s+1≥0.95
* That gives s ≈ 8 (8 correct, no misses) - Check: with 8 correct, P(p≥0.7)≈96%P(p \ge 0.7) \approx 96\%P(p≥0.7)≈96%.

👉 So: If you answer a word correctly ~8 times (without error), I can say with ~95% confidence (under this model) that you “know” it at p ≥ 0.7.

That’s the strict interpretation.

===== To avoid absurd drill length: =====
* New words: - I’ll aim to hit each 3–5 times per session. - If you’re perfect on a word across those, I’ll treat it as high confidence for now. - If you stick with me across multiple sessions, those 3–5 reps will accumulate toward that “8-ish” target naturally.
* Missed words: - Get extra weight until you’ve gotten them right at least twice after last error. - Those are the ones where we’re actively pushing confidence up.
* Old words: - 15–20 sprinkled per session. - Same logic: misses go to weak bucket and get recycled.

So:
* The algorithm is statistically motivated, but tuned for human sanity.
* I’ll silently keep track of: - which words are “new,” “good,” or “suspicious,” - and weight quiz frequency accordingly.

No need for you to manage any of this; just answer.

If all that sounds good, go ahead and drop:

Genki I – Chapter 1
# Intro dialogue (i)
# Vocab list (ii)

I’ll:
* acknowledge the dialogue briefly,
* immediately start a targeted vocab quiz on the Chapter 1 list,
* include a few review words from your Duolingo/known basics.