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v4-HOT/explanatory/pilot/20260524_111423.json

@@ -4,7 +4,7 @@
   "condition": "explanatory",
   "run_mode": "pilot",
   "started_at": "2026-05-24T11:14:23.811480+10:00",
-  "ended_at": "2026-05-24T11:15:43.691770+10:00",
+  "ended_at": "2026-05-24T11:15:51.948241+10:00",
   "model": "gpt-5.4-nano-2026-03-17",
   "case_id": "ai-predictive-analytics",
   "perfect_answer": {
@@ -64,6 +64,14 @@
       "directive": "Turn-back request. Give a single, concrete example from a teacher’s perspective of early intervention that helps a student adjust study habits without taking away autonomy. Keep it to one short scenario: what the teacher sees, what they do next, and what the student does in response. Then end by asking the participant which part of that scenario best matches what “meaningful support” would look like to them.",
       "reason": "participant asks for an example of how a teacher would provide feedback and resources effectively.",
       "latency_ms": 2495
+    },
+    {
+      "live_subprobe_id": 2,
+      "advanced_this_turn": true,
+      "turn_type": "transition-due",
+      "directive": "ENGAGED_MODE. Briefly recap the concrete “fine-tuning” idea: teachers treat early signals as adjustable, give the student voluntary options, and revise suggestions after the student tries them—so the label doesn’t silently take over. Then pivot to the next angle: whether teachers actually need to see the prediction (vs it being replaced by study tips inside the student-facing tool). Land on one open prompt asking what would change if teachers couldn’t see the prediction.",
+      "reason": "sub-trigger 2b: prior engaging-default count on sub-probe 1 = 3 (turns: 1, 4, 5); ENGAGED_MODE",
+      "latency_ms": 3556
     }
   ],
   "turns": [
@@ -220,6 +228,32 @@
         "main_ms": 4111,
         "total_ms": 6616
       }
+    },
+    {
+      "n": 7,
+      "timestamp": "2026-05-24T11:15:51.948214+10:00",
+      "user_message": "When you mentioned that the teacher fine-tunes the suggestions based on the student's feedback, what do you mean by \"fine-tunes\"? Could you give a concrete example of how that adjustment might look in practice?",
+      "manager_verdict": {
+        "live_subprobe_id": 2,
+        "advanced_this_turn": true,
+        "turn_type": "transition-due",
+        "directive": "ENGAGED_MODE. Briefly recap the concrete “fine-tuning” idea: teachers treat early signals as adjustable, give the student voluntary options, and revise suggestions after the student tries them—so the label doesn’t silently take over. Then pivot to the next angle: whether teachers actually need to see the prediction (vs it being replaced by study tips inside the student-facing tool). Land on one open prompt asking what would change if teachers couldn’t see the prediction.",
+        "reason": "sub-trigger 2b: prior engaging-default count on sub-probe 1 = 3 (turns: 1, 4, 5); ENGAGED_MODE",
+        "latency_ms": 3556
+      },
+      "plan_control": "=== PLAN CONTROL (host-managed, authoritative) ===\nturn_type: transition-due\nlive_subprobe_id: 2\nadvanced_this_turn: True\n\nLIVE SUB-PROBE (work this one this turn):\nid: 2\nfamily: Rights, privacy, and real choice (consent and how labels shape treatment)\nprobe: Think about the difference between giving study tips and sharing a prediction with teachers. Is it necessary for teachers to see the prediction, or is that built into the plan?\nrationale: The draft hints that predictions may influence how teachers see students. This pushes toward the key insight: changing teacher perception is a separate ethical step, not a side effect.\nanchor: \"influence how teachers perceive students\"\n\nDIRECTIVE for this turn:\nENGAGED_MODE. Briefly recap the concrete “fine-tuning” idea: teachers treat early signals as adjustable, give the student voluntary options, and revise suggestions after the student tries them—so the label doesn’t silently take over. Then pivot to the next angle: whether teachers actually need to see the prediction (vs it being replaced by study tips inside the student-facing tool). Land on one open prompt asking what would change if teachers couldn’t see the prediction.\n=== END PLAN CONTROL ===",
+      "assistant_response": "“Fine-tunes” here means the teacher updates the support to match what the student actually tried and what the student experienced, instead of just repeating the same advice that was suggested based on early signals. For a concrete example, after a week of following a recommended quiz practice schedule, the student reports that the questions feel too hard at once and they ran out of time, so the teacher switches to fewer questions per session, uses a shorter practice set first, and sets a new check-in date with a revised, still-optional routine. That keeps the label from silently becoming the plan.",
+      "scratch": {
+        "chosen_target": 2,
+        "response_summary": "I explained “fine-tunes” as adjusting support based on what the student tried and reported, with a brief example of changing the routine and check-in.",
+        "directive_followed": "no"
+      },
+      "latency_s": {
+        "manager_ms": 3557,
+        "ttft_ms": 2897,
+        "main_ms": 4686,
+        "total_ms": 8251
+      }
     }
   ],
   "feedback": [],
@@ -315,17 +349,21 @@
   },
   "upload_errors": [],
   "session_summary": {
-    "n_turns": 6,
-    "n_manager_verdicts": 6,
-    "n_transitions": 0,
+    "n_turns": 7,
+    "n_manager_verdicts": 7,
+    "n_transitions": 1,
     "n_plan_completes": 0,
     "subprobe_coverage": {
       "1": {
         "engaging_default": 3,
         "other": 3
+      },
+      "2": {
+        "engaging_default": 0,
+        "other": 1
       }
     },
-    "last_subprobe_visited": 1,
+    "last_subprobe_visited": 2,
     "total_subprobes_in_plan": 8,
     "outcome": "ran-without-plan-complete"
   }