Add pure correction and pedagogy logic with full test coverage

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>

tests/correction/test_answer_validator.gd

@@ -0,0 +1,77 @@
+extends GutTest
+## Tests de AnswerValidator (choix multiple et correction de phrase).
+
+var multiple_choice_exercise: Dictionary = {
+    "rule_id": "regle_test",
+    "answers": ["petit", "petits", "petite"],
+    "correct_answer": 1,
+    "hint": "indice",
+    "explanation": "explication",
+}
+
+var sentence_correction_exercise: Dictionary = {
+    "rule_id": "regle_test",
+    "hint": "indice",
+    "explanation": "explication",
+    "tokens":
+    [
+        {"text": "Les", "correct": true},
+        {
+            "text": "petit",
+            "correct": false,
+            "expected": "petits",
+            "rule_id": "regle_accord_adjectif",
+        },
+        {"text": "chiens", "correct": true},
+    ],
+}
+
+
+func test_multiple_choice_correct_answer() -> void:
+    var result := AnswerValidator.validate_multiple_choice(multiple_choice_exercise, 1)
+    assert_true(result.is_correct)
+    assert_eq(result.errors, [])
+
+
+func test_multiple_choice_wrong_answer_reports_found_and_expected() -> void:
+    var result := AnswerValidator.validate_multiple_choice(multiple_choice_exercise, 0)
+    assert_false(result.is_correct)
+    assert_eq(result.errors.size(), 1)
+    assert_eq(result.errors[0]["found"], "petit")
+    assert_eq(result.errors[0]["expected"], "petits")
+    assert_eq(result.errors[0]["rule_id"], "regle_test")
+
+
+func test_sentence_correction_fully_correct() -> void:
+    var result := AnswerValidator.validate_sentence_correction(
+        sentence_correction_exercise, [1], {1: "petits"}
+    )
+    assert_true(result.is_correct)
+
+
+func test_sentence_correction_missed_faulty_token() -> void:
+    var result := AnswerValidator.validate_sentence_correction(sentence_correction_exercise, [], {})
+    assert_false(result.is_correct)
+    assert_eq(result.errors.size(), 1)
+    assert_eq(result.errors[0]["rule_id"], "regle_accord_adjectif")
+    assert_eq(result.errors[0]["found"], "petit")
+    assert_eq(result.errors[0]["expected"], "petits")
+
+
+func test_sentence_correction_wrong_correction_text() -> void:
+    var result := AnswerValidator.validate_sentence_correction(
+        sentence_correction_exercise, [1], {1: "petite"}
+    )
+    assert_false(result.is_correct)
+    assert_eq(result.errors[0]["found"], "petite")
+    assert_eq(result.errors[0]["expected"], "petits")
+
+
+func test_sentence_correction_false_positive_on_correct_token() -> void:
+    var result := AnswerValidator.validate_sentence_correction(
+        sentence_correction_exercise, [1, 2], {1: "petits", 2: "chien"}
+    )
+    assert_false(result.is_correct)
+    assert_eq(result.errors.size(), 1)
+    assert_eq(result.errors[0]["error_type"], "unclassified")
+    assert_eq(result.errors[0]["position"], 2)

tests/correction/test_answer_validator.gd.uid

@@ -0,0 +1 @@
+uid://ctn0svsnq535v

tests/correction/test_dictation_analyzer.gd

@@ -0,0 +1,67 @@
+extends GutTest
+## Tests de DictationAnalyzer : alignement, classification lexicale/grammaticale
+## via annotation, majuscule, variantes acceptees.
+
+var dictation: Dictionary = {
+    "target_text": "Les petits chats dorment.",
+    "words":
+    [
+        {"text": "Les", "expects_capital": true},
+        {"text": "petits", "rule_id": "regle_accord_adjectif"},
+        {"text": "chats"},
+        {"text": "dorment."},
+    ],
+    "accepted_variants": [],
+}
+
+
+func test_exact_match_is_correct() -> void:
+    var result := DictationAnalyzer.analyze(dictation, "Les petits chats dorment.")
+    assert_true(result.is_correct)
+    assert_eq(result.errors, [])
+
+
+func test_annotated_word_error_is_classified_as_grammatical() -> void:
+    var result := DictationAnalyzer.analyze(dictation, "Les petit chats dorment.")
+    assert_false(result.is_correct)
+    assert_eq(result.errors.size(), 1)
+    assert_eq(result.errors[0]["error_type"], "grammatical")
+    assert_eq(result.errors[0]["rule_id"], "regle_accord_adjectif")
+    assert_eq(result.errors[0]["found"], "petit")
+    assert_eq(result.errors[0]["expected"], "petits")
+
+
+func test_unannotated_word_error_is_unclassified() -> void:
+    var result := DictationAnalyzer.analyze(dictation, "Les petits chat dorment.")
+    assert_false(result.is_correct)
+    assert_eq(result.errors.size(), 1)
+    assert_eq(result.errors[0]["error_type"], "unclassified")
+    assert_eq(result.errors[0]["rule_id"], "")
+
+
+func test_missing_word_does_not_cascade_into_further_errors() -> void:
+    # Le mot "petits" est omis : sans alignement, cela provoquerait deux
+    # erreurs en cascade sur "chats" et "dorment.".
+    var result := DictationAnalyzer.analyze(dictation, "Les chats dorment.")
+    assert_false(result.is_correct)
+    assert_eq(result.errors.size(), 1)
+    assert_eq(result.errors[0]["rule_id"], "regle_accord_adjectif")
+
+
+func test_missing_capital_is_reported_separately() -> void:
+    var result := DictationAnalyzer.analyze(dictation, "les petits chats dorment.")
+    assert_false(result.is_correct)
+    assert_eq(result.errors.size(), 1)
+    assert_eq(result.errors[0]["error_type"], "capitalization")
+
+
+func test_accepted_variant_is_treated_as_correct() -> void:
+    var dictation_with_variant: Dictionary = dictation.duplicate(true)
+    dictation_with_variant["accepted_variants"] = ["Les petits chats dorment"]
+    var result := DictationAnalyzer.analyze(dictation_with_variant, "Les petits chats dorment")
+    assert_true(result.is_correct)
+
+
+func test_normalizes_before_comparing() -> void:
+    var result := DictationAnalyzer.analyze(dictation, "Les petits chats dorment.")
+    assert_true(result.is_correct)

tests/correction/test_dictation_analyzer.gd.uid

@@ -0,0 +1 @@
+uid://ta26vku8mxa

tests/correction/test_text_normalizer.gd

@@ -0,0 +1,39 @@
+extends GutTest
+## Tests de TextNormalizer : espaces, apostrophes, caracteres francais decomposes.
+
+
+func test_normalizes_non_breaking_spaces() -> void:
+    var text := "Les petits chats"
+    assert_eq(TextNormalizer.normalize(text), "Les petits chats")
+
+
+func test_normalizes_typographic_apostrophe() -> void:
+    assert_eq(TextNormalizer.normalize("l’école"), "l'école")
+
+
+func test_collapses_multiple_spaces() -> void:
+    assert_eq(TextNormalizer.normalize("Les   petits  chats"), "Les petits chats")
+
+
+func test_recomposes_decomposed_accented_characters() -> void:
+    # "e" + accent aigu combinant (U+0301) doit devenir "é" precompose.
+    var decomposed := "école"
+    assert_eq(TextNormalizer.normalize(decomposed), "école")
+
+
+func test_recomposes_cedille() -> void:
+    var decomposed := "français"
+    assert_eq(TextNormalizer.normalize(decomposed), "français")
+
+
+func test_strips_leading_and_trailing_whitespace() -> void:
+    assert_eq(TextNormalizer.normalize("  bonjour  "), "bonjour")
+
+
+func test_split_words_splits_on_single_spaces() -> void:
+    var words := TextNormalizer.split_words("Les petits chats dorment.")
+    assert_eq(words, ["Les", "petits", "chats", "dorment."])
+
+
+func test_split_words_on_empty_text_returns_empty_array() -> void:
+    assert_eq(TextNormalizer.split_words("   "), [])

tests/correction/test_text_normalizer.gd.uid

@@ -0,0 +1 @@
+uid://dqrgabx44in1k

tests/correction/test_word_aligner.gd

@@ -0,0 +1,45 @@
+extends GutTest
+## Tests de WordAligner : l'alignement doit absorber les mots omis ou ajoutes
+## sans decaler les comparaisons suivantes.
+
+var target: Array[String] = ["Les", "petits", "chats", "dorment."]
+
+
+func test_identical_sequences_are_all_matches() -> void:
+    var ops := WordAligner.align(target, target.duplicate())
+    for op in ops:
+        assert_eq(op["op"], "match")
+    assert_eq(ops.size(), 4)
+
+
+func test_single_substitution_is_detected_at_the_right_position() -> void:
+    var submitted: Array[String] = ["Les", "petit", "chats", "dorment."]
+    var ops := WordAligner.align(target, submitted)
+    assert_eq(ops.size(), 4)
+    assert_eq(ops[1]["op"], "substitute")
+    assert_eq(ops[1]["target_index"], 1)
+    assert_eq(ops[0]["op"], "match")
+    assert_eq(ops[2]["op"], "match")
+    assert_eq(ops[3]["op"], "match")
+
+
+func test_missing_word_is_a_delete_and_does_not_shift_later_matches() -> void:
+    # L'enfant a oublie "petits" : sans alignement, "chats" se comparerait a
+    # "petits" et "dorment." a "chats", generant deux fausses erreurs en cascade.
+    var submitted: Array[String] = ["Les", "chats", "dorment."]
+    var ops := WordAligner.align(target, submitted)
+    assert_eq(ops.size(), 4)
+    assert_eq(ops[0]["op"], "match")
+    assert_eq(ops[1]["op"], "delete")
+    assert_eq(ops[1]["target_index"], 1)
+    assert_eq(ops[2]["op"], "match")
+    assert_eq(ops[2]["target_index"], 2)
+    assert_eq(ops[3]["op"], "match")
+    assert_eq(ops[3]["target_index"], 3)
+
+
+func test_extra_word_is_an_insert() -> void:
+    var submitted: Array[String] = ["Les", "petits", "petits", "chats", "dorment."]
+    var ops := WordAligner.align(target, submitted)
+    var insert_ops: Array = ops.filter(func(op: Dictionary) -> bool: return op["op"] == "insert")
+    assert_eq(insert_ops.size(), 1)

tests/correction/test_word_aligner.gd.uid

@@ -0,0 +1 @@
+uid://de278l35ygq6i