Boost Azure Demo

Mock-Data/README.md

@@ -20,12 +20,20 @@ assignment_assignees.json   85 (student × assignment) rows with status/scores
 student_answers.json        593 per-question answer records — THE primary file
 activity_logs.json          593 activity log rows (timestamps, durations, solve_mode)
 dataset.json                All of the above bundled into one object
+bonus_early_warning_input.json   3 classes × 10 students for the bonus EWS challenge
+bonus_early_warning_output.json  Expected morning alert output (top 3 per class)
 generate.py                 Deterministic generator (re-run anytime)
 ```
 
 The agent's main input is `dataset.json` (or `student_answers.json` plus
 `question_bank.json` if you'd rather load files separately).
 
+For the bonus challenge shown in the brief, use
+`bonus_early_warning_input.json` as a class-level monitor input with ordered
+topics plus per-student topic understanding scores, and
+`bonus_early_warning_output.json` as the expected ranked risk list with a
+specific weak topic and recommended action for each student.
+
 ## Schema fidelity
 
 Field names and enum values match the production models exactly:
@@ -59,6 +67,13 @@ you ever seed this data into the real DB.
 | `_question_topic` / `_sub_topic` / `_difficulty` | `student_answers.json` | Denormalised from `question_bank` for convenience |
 | `_answered_at` | `student_answers.json` | Same as `created_at`, just clearer name |
 
+The mock exports now also include newer review-style fields used by the current app schema, such as:
+
+- per-question: `is_correct`, `ai_feedback`, `review_needs_attention`, `review_issue_reason`, `review_correctness_score`, `review_understanding_score`, `review_question_score`, `review_confidence`, `review_tags`
+- per-assignee: `overall_score`, `ai_feedback`, `next_step_outcome`
+
+These make it easier to seed or backfill historical closed homework into the newer review surfaces.
+
 ## The 12 students
 
 Five students have engineered misconception personas; the other seven are