Use Cases
During target-language conversation between an AI tutor and a learner, the agent calls `record-learner` on each learner message to log lemma usage (automatic lemmatization, excluding quoted words and misspellings), then calls `preview-reply` on draft replies to surface unknown words and calibrate response difficulty. Throughout the session, `mark-known` is used to update the known set, enabling personalized difficulty adaptation.
After accumulating sufficient lemma exposure data over multiple conversation turns, the agent calls `due-for-practice` to retrieve unknown lemmas ranked by predicted recall probability (based on a Leitner half-life regression variant). Using `--max-p-recall` and `--limit` flags, the agent filters the most due words and generates targeted practice exercises or questions to reinforce weak vocabulary before forgetting occurs.
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Mechanism:
record-learner is called on learner messages only (writes); preview-reply on draft replies (read-only). Never call record-learner twice on the same messagerecord-learner supports --exclude-lemmas (quoted words, noise) and --mark-misuse (spelling errors, wrong words) for data quality(learner_id, language); known_lemmas table PK is (learner_id, language, lemma)due-for-practice uses a Leitner special-case of half-life regression, calculating half-life as 2^(produced_count - failed_attempt) (days), sorting by p_recall ascendingmark-known returns "already_known", repeated seed-known returns added: 0, no state changeOutput:
record-learner / preview-reply return unknown lemmas list + usage details (produced_count, first/last used timestamps)lookup-lemma returns status + full stats for a single lemmadue-for-practice returns lemmas ranked by predicted recall probabilitySkill Relationships
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