Project status: Japanese immersion learning with Anki and selective glossing: 9. Ranking and staged plan

9. Ranking and staged plan

Rank	Idea	Effort	Expected payoff	Evidence behind the mechanism	Depends on
1	Anki settings plus manual leech/Click-card pass	S	High, immediate	Manual and guides; varied-cue RCTs	nothing
2	(e) learner model v0	S	Enabling	FSRS/HLR lineage	Anki, ankimcp
3	(a) deck-hygiene agent and Jiten-based premine	S-M	High	Varied cues; retrieval beats generation	1, 2
4	(d) selective-gloss overlay	M	High	Glossing meta-analysis; hint-first RCT (other domain)	2
5	(c2) Japanese-page gloss mode	S after 4	Medium-high	Same as (d)	2, 4
6	(b) concept tutor revival	M-L	Medium	Hint-first tutoring RCTs (maths/physics)	2, corpus from 3
7	(c1) English-to-hybrid weave	L	Low-medium	Small code-switching studies; Storyfier caution	2

Rationale for the order:

Stage 1 first because it changes the daily experience immediately at no build cost. The settings and the Click-card/leech pass address two to-dos by hand, and the varied-cue result is the strongest evidence in the plan.
Learner model second, as a thin read-only service. It is small, and without it every later tool repeats the known-word plumbing that the premine script, Jiten and asbplayer each already rebuild.
Hygiene agent and Jiten-based premine third, because they finish the third to-do and produce the corpus of real sentences that the tutor and the overlay both need.
Overlay above tutor, because it sits inside the activity the learner already enjoys; the engagement evidence says tools that need a separate habit decay. It also yields the richest learner-model signal, and its Japanese-page mode covers the blog-reading goal almost for free.
Concept tutor sixth, worth reviving only once it can draw on vocabulary state and the learner's own corpus, which fixes what stalled it in 2025.
English-to-hybrid weave last: weakest evidence, hardest Japanese-specific engineering, and a paid baseline (Nuenki) that can be trialled in an afternoon.

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