Same directives as the CE1 chapter: 4 rules with a substitution test
each, 8 multiple-choice + 4 sentence corrections + 3 dictées per rule
(draft), 3 missions per rule, Piper audio for the 12 dictées.
21 missions don't fit on one map, so the world map now shows the
missions of the profile's school level (derived from the rule's level,
no data-format change), with a button letting a CE2 profile revisit the
CE1 map. Mission-position guard test now checks each level separately.
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
Manual test: "La clairière" and "La cabane" overlapped on screen.
Their fractional positions (0.8,0.65) and (0.92,0.78) were too close
given the marker size at the project's default window size — exactly
the risk flagged but unverified in the previous commit.
Repositioned all 9 missions (not just the new ones) onto a regular
3x3 grid with 0.35 spacing, well clear of marker dimensions at the
default resolution. Added a headless regression test
(tests/data/test_mission_positions.gd) that fails if any two
missions are closer than a 0.3 fractional gap on both axes — a
data-level guard, not a substitute for visually checking actual
on-screen layout.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Manual testing confirmed the "La clairière de l'écho" dictée mission
was unplayable: no audio file existed yet (decisions.md point 8), so
there was nothing to listen to before transcribing the sentence.
Adds tools/generate_dictation_audio.py (offline eSpeak NG synthesis +
ffmpeg encoding to Ogg Vorbis, exposed as `make
generate-dictation-audio`) and uses it to generate
ce1_accord_adjectif_dictee_01.ogg. A new headless test
(tests/data/test_dictation_audio.gd) guards against future dictées
being added without regenerating their audio.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Wires every autoload up to a real UI: boot -> profile select/create
-> world map (three mission markers, positioned from data, not
hardcoded pixels) -> mission intro (narrative from data/missions/,
a new data type with its own schema) -> rule panel -> exercise host
-> one of the three exercise type scenes -> feedback panel -> reward
screen -> back to the map.
exercise_host is the only piece with real orchestration logic: picks
exercises via ExerciseEngine (now fully implemented, validating at
load like make validate-data does), validates answers through the
pure AnswerValidator/DictationAnalyzer, escalates aid over 3 attempts
via AttemptFlow, feeds DifficultyManager so adaptive difficulty is
actually wired in rather than dead code, and records every attempt
through ProgressManager. Every exercise-type scene (multiple_choice,
sentence_correction, dictation) only emits its answer by signal, per
the architecture invariant.
All child-facing text now comes from data/localization/fr.json via
UIText, not hardcoded in scripts or scenes. Getting that right
required extending SchemaValidator to support "additionalProperties"
as a schema (dynamic keys), which also closed a real gap: save.skills
was previously unvalidated against SkillState's actual shape.
16 new headless tests, including two integration tests that instance
exercise_host.tscn directly and drive it by emitting child-scene
signals, stopping short of the final scene transition (which would
tear down the test runner's own tree). Everything that needs a
screen -- layout at different sizes, touch, virtual keyboard, actual
audio playback (no .ogg files exist yet) -- is listed in the new
docs/testing/manual-checklist.md instead of claimed as verified.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
scripts/correction/: AnswerValidator (multiple choice, sentence
correction via tap-the-faulty-word + retype) and DictationAnalyzer
build the CorrectionResult structure from cahier des charges §6.
Dictation analysis aligns words with WordAligner (Wagner-Fischer edit
distance) before any character comparison, so an omitted or added
word doesn't cascade into false errors on every following word.
TextNormalizer handles non-breaking spaces, typographic apostrophes,
and decomposed-vs-composed French accented characters via an
explicit table (GDScript has no built-in NFC/NFD normalization).
Error classification follows the data annotations only (rule_id
present -> grammatical, absent -> unclassified), never a runtime
linguistic guess.
scripts/pedagogy/: DifficultyManager (per-skill difficulty from
recent results), AttemptFlow (3-attempt graduated aid: hint, then
explanation, then reveal, matching exercise-lifecycle.md's second
gap), and ReviewScheduler (the five mastery states from §9, with
session-counted spaced review intervals [1, 3, 6, 12] rather than
wall-clock dates, since a family device's clock isn't trustworthy).
All of it is pure: no scene tree, no clock or RNG read directly,
every effectful input (session number, attempt outcome) is a
parameter. 65 headless tests across both layers, including the
word-omission-doesn't-cascade regression and the full
decouverte -> acquise -> a_reviser -> acquise path.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Introduces a generic JSON-Schema-subset validator in GDScript
(scripts/data/schema_validator.gd) used both by `make validate-data`
and, later, by ExerciseEngine at load time, plus semantic_checks.gd
for cross-field constraints a schema can't express (answer index
bounds, at least one faulty token in sentence correction, word count
matching between a dictation's target_text and its annotations).
Schemas cover rule, exercise (dispatched per type: multiple_choice,
sentence_correction), dictation, curriculum, and save (save.schema.json
is not yet wired into validate-data; saves live in user://, handled
at the Profiles/Saves step).
First draft content for the CE1 "accord_adjectif" rule (one rule, two
multiple-choice exercises, one sentence correction, one dictation,
one curriculum entry) exercises the full pipeline end to end and
seeds docs/review/content-review.md, regenerated by the new
tools/generate_content_review.py (`make content-review`).
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Denis Defolie