feat(study): Phase 4-A 운영 가시화 — 통계 대시보드 AI 풀이 카드

Phase 4-A 가 wrong/unsure 풀이를 background batch 로 캐시하는데, 사용자/운영자
입장에서 (1) 지금까지 얼마나 캐시 채워졌는지, (2) 환각 차단/파싱 실패/자료 없음
같은 worker 결과 분포를 볼 수 없었음. 통계 대시보드에 카드 추가.

Backend (study_question_progress.py /stats):
- StatsAiExplanation 신규 응답 섹션
  · status_distribution — 토픽 전체 study_questions.ai_explanation_status 분포
    (none/ready/failed/skipped/stale/pending 6 키 default 0)
  · target_total / target_ready — wrong/unsure progress 의 ready 비율
    (캐시 hit 가능성 추정 핵심 지표)
  · recent_jobs — 최근 7일 study_question_jobs 의 (status, error_code) 분포
    ('completed', 'failed:guard_fail', 'failed:parse_fail', 'skipped:evidence_missing'
    같은 합성 키)

Frontend (/study/topics/[id]/stats):
- 신규 Card "AI 풀이 캐시" — Sparkles 아이콘
  · 큰 숫자 + 진행률 바: ready / wrong+unsure
  · 토픽 전체 status 분포 inline (한국어 라벨)
  · 최근 7일 worker 결과 grid (환각 차단 / 파싱 실패 / 자료 없음 skip 등 분리)
- statusLabel / jobLabel 헬퍼 — 운영자 친화 한국어

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

app/api/study_question_progress.py

@@ -353,6 +353,17 @@ class StatsSubjectBreakdown(BaseModel):
     chronic: int
 
 
+class StatsAiExplanation(BaseModel):
+    """Phase 4-A 운영 관찰 — AI 풀이 캐시 진척 + 최근 7일 worker 결과."""
+    # study_questions.ai_explanation_status 분포 (이 토픽 전체)
+    status_distribution: dict  # 'none' / 'ready' / 'failed' / 'skipped' / 'stale' / 'pending'
+    # wrong/unsure 중 ready 박힌 비율 (캐시 hit 가능성 추정)
+    target_total: int        # progress.last_outcome IN (wrong, unsure) 의 qid 수
+    target_ready: int        # 그 중 ai_explanation_status='ready' 인 수
+    # 최근 7일 study_question_jobs 의 (status, error_code) 분포
+    recent_jobs: dict  # {'completed': N, 'failed:guard_fail': N, 'failed:parse_fail': N, 'skipped:evidence_missing': N, 'pending': N, ...}
+
+
 class StatsResponse(BaseModel):
     questions: StatsQuestions
     pattern_distribution: dict  # state(or "unattempted") → count
@@ -361,6 +372,7 @@ class StatsResponse(BaseModel):
     session_trend: list[StatsSessionTrendItem]  # 최근 done 세션 newest→oldest
     daily_attempts_30d: list[StatsDailyAttempt]
     subject_breakdown: list[StatsSubjectBreakdown]
+    ai_explanation: StatsAiExplanation
 
 
 @router.get("/{topic_id}/stats", response_model=StatsResponse)
@@ -615,6 +627,88 @@ async def topic_stats(
         for r in subj_rows
     ]
 
+    # 8. Phase 4-A: AI 풀이 캐시 진척 + 최근 7일 worker 결과
+    # 8a. study_questions.ai_explanation_status 분포 (토픽 전체)
+    ai_status_rows = (
+        await session.execute(
+            select(
+                func.coalesce(StudyQuestion.ai_explanation_status, "none").label("st"),
+                func.count().label("cnt"),
+            )
+            .where(
+                StudyQuestion.user_id == user.id,
+                StudyQuestion.study_topic_id == topic_id,
+                StudyQuestion.deleted_at.is_(None),
+            )
+            .group_by("st")
+        )
+    ).all()
+    ai_status_distribution = {r.st: int(r.cnt) for r in ai_status_rows}
+    for k in ("none", "ready", "failed", "skipped", "stale", "pending"):
+        ai_status_distribution.setdefault(k, 0)
+
+    # 8b. wrong/unsure 의 ready 비율 (캐시 hit 가능성)
+    target_total_row = await session.execute(
+        select(func.count())
+        .select_from(StudyQuestionProgress)
+        .where(
+            StudyQuestionProgress.user_id == user.id,
+            StudyQuestionProgress.study_topic_id == topic_id,
+            StudyQuestionProgress.last_outcome.in_(("wrong", "unsure")),
+        )
+    )
+    target_total = int(target_total_row.scalar() or 0)
+
+    target_ready_row = await session.execute(
+        select(func.count())
+        .select_from(StudyQuestionProgress)
+        .join(
+            StudyQuestion,
+            and_(
+                StudyQuestion.id == StudyQuestionProgress.study_question_id,
+                StudyQuestion.deleted_at.is_(None),
+            ),
+        )
+        .where(
+            StudyQuestionProgress.user_id == user.id,
+            StudyQuestionProgress.study_topic_id == topic_id,
+            StudyQuestionProgress.last_outcome.in_(("wrong", "unsure")),
+            StudyQuestion.ai_explanation_status == "ready",
+        )
+    )
+    target_ready = int(target_ready_row.scalar() or 0)
+
+    # 8c. 최근 7일 study_question_jobs 분포 — terminal status × error_code
+    from models.study_question_job import StudyQuestionJob
+
+    recent_cutoff = now - timedelta(days=7)
+    job_rows = (
+        await session.execute(
+            select(
+                StudyQuestionJob.status.label("st"),
+                func.coalesce(StudyQuestionJob.error_code, "").label("err"),
+                func.count().label("cnt"),
+            )
+            .join(
+                StudyQuestion,
+                and_(
+                    StudyQuestion.id == StudyQuestionJob.study_question_id,
+                    StudyQuestion.study_topic_id == topic_id,
+                    StudyQuestion.user_id == user.id,
+                ),
+            )
+            .where(
+                StudyQuestionJob.user_id == user.id,
+                StudyQuestionJob.created_at >= recent_cutoff,
+            )
+            .group_by("st", "err")
+        )
+    ).all()
+    recent_jobs: dict[str, int] = {}
+    for r in job_rows:
+        key = f"{r.st}:{r.err}" if r.err else r.st
+        recent_jobs[key] = int(r.cnt)
+
     return StatsResponse(
         questions=StatsQuestions(
             total=total_q, attempted=attempted, unattempted=unattempted
@@ -625,4 +719,10 @@ async def topic_stats(
         session_trend=session_trend,
         daily_attempts_30d=daily_attempts_30d,
         subject_breakdown=subject_breakdown,
+        ai_explanation=StatsAiExplanation(
+            status_distribution=ai_status_distribution,
+            target_total=target_total,
+            target_ready=target_ready,
+            recent_jobs=recent_jobs,
+        ),
     )