Markdown version: https://ai.hiina.space/reports/jp-learning-and-anki-tooling.md · index

Index of everything on this site: https://ai.hiina.space/index.md

Project status: Japanese immersion learning with Anki and selective glossing

• Author: Hiina (hiina@hiina.space)
• Produced by: Claude deep-research run with citation checking
• Version: 1 · published 2026-10-27 · updated 2026-10-27 · status: current
• Canonical: https://ai.hiina.space/reports/jp-learning-and-anki-tooling.md (this version as one file: https://ai.hiina.space/reports/jp-learning-and-anki-tooling/v1.md)
• Takeaway: Hiina's Japanese project is at the plan stage: fix Anki settings, leeches and sentence-cued cards first, then build a thin learner model and a hint-first selective-gloss overlay for anime, games and VNs.
• Topics: japanese-learning, spaced-repetition, ai-tutoring, agents
• Model: claude-opus-5-5
• Sources through: 2026-10-25
• Whole report as one file (49 KB; fetch tools may truncate it, so prefer the section pages below or download with curl): https://ai.hiina.space/reports/jp-learning-and-anki-tooling/full.md

This page is a status record of Hiina's Japanese-learning project, written for agents (and people) who need to know what the project is, where it stands, and what is planned. It covers the goal, the current deck and tooling, the recommended Anki configuration, a five-part tool plan with build order and evaluation gates, open questions, and sources. Status is as of 25 Oct 2026: the plan is proposed and nothing in it has been built or changed yet.

Contents

• 1. Summary (included below)
• 2. Goal and current state (3 KB)
• 3. Scheduler settings (FSRS-6 on Anki 26.x) (2 KB)
• 4. Deck hygiene: leeches and sentence-cued cards (4 KB)
• 5. Mining from more anime (4 KB)
• 6. Planned tools: deck-hygiene agent and concept tutor (6 KB)
• 7. Planned tools: hybrid reading and the selective-gloss overlay (7 KB)
• 8. Planned tool: the shared learner model (2 KB)
• 9. Ranking and staged plan (2 KB)
• 10. Open questions and evaluation plan (3 KB)
• 11. Conclusion (1 KB)
• 12. Sources (6 KB)

1. Summary

• What the project is. A staged plan to make Anki serve anime, game, visual novel (VN) and reading immersion in Japanese, and then to build one shared learner model with selective-gloss tools on top of it.
• Current state (25 Oct 2026). Plan proposed; no settings changed and no tools built. Kaishi 1.5k is finished. The collection, last inventoried 30 Jul 2026, holds 269 radical notes, 432 hand-mined Lapis notes and 202 premined cards for the anime Katanagatari.
• Next gate. Four weeks after a settings, leech and sentence-rotation pass, recall on a word-only probe of 50 mature Lapis cards should be within 5 points of the deck's normal true retention. That measures whether cards are being recalled from the word or from their example sentence.
• Build order. (1) Anki settings plus a manual leech and Click-card pass; (2) a thin read-only learner model; (3) an agent-maintained deck-hygiene loop and a premine pipeline rebuilt on Jiten.moe; (4) a hint-first selective-gloss overlay for games, VNs and anime; (5) a Japanese-page gloss mode; (6) a revived concept-level grammar tutor; (7) an English-to-Japanese hybrid-reading rewriter, last.
• Strongest evidence. Varying the sentence cue across retrievals beats a constant cue for foreign vocabulary (PNAS 2024, six experiments). Retrieved corpus sentences are preferred over LLM-generated ones for Japanese learners (ACL SRW 2024). Glossed reading beats unglossed reading. Hint-first AI tutoring avoids the learning loss seen with unrestricted AI help in a field RCT.
• Limits. No study tests any of these tools on Japanese learners. The supporting evidence (glossing, varied retrieval cues, hint-first tutoring) comes from other materials and settings. Many community guides failed to load during research, so community-consensus claims rest on vnclub, TheMoeWay, Donkuri and tool documentation.
• Clearest gap found. No existing tool glosses only the words a learner probably does not know, or offers graded hints instead of translation, for games, VNs or anime. No tool puts immersion exposures and lookups into the same table as SRS reviews.

Next section: https://ai.hiina.space/reports/jp-learning-and-anki-tooling/02-goal-and-current-state.md

Text licensed CC BY 4.0 (attribution: Hiina, ai.hiina.space). Source: https://github.com/hiinaspace/ai-hiina-space