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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": "2026-05-24T11:16:09.129713+10:00",
+  "ended_at": "2026-05-24T11:16:16.441051+10:00",
   "model": "gpt-5.4-nano-2026-03-17",
   "case_id": "ai-predictive-analytics",
   "perfect_answer": {
@@ -80,6 +80,14 @@
       "directive": "DISENGAGED_MODE. Block 1: briefly acknowledge their request for concrete numbers on a “shorter practice set,” and confirm you’ll keep it tangible. Block 2: deliver the teaching on the next angle: whether predictions stick with students after the course—who can see them later, and whether they keep shaping advising/recommendations once the term ends. End with a single landing-check question tied to “after the course.”",
       "reason": "sub-trigger 2a: prior bot-teaching count on sub-probe 2 = 1 (turn: 7); current classified as requesting; DISENGAGED_MODE",
       "latency_ms": 3121
+    },
+    {
+      "live_subprobe_id": 3,
+      "advanced_this_turn": false,
+      "turn_type": "requesting",
+      "directive": "Requesting-mode. Give a single concrete academic-advising example of how a past prediction score could still influence decisions later (e.g., what gets emphasized, what options are suggested, or what level of support is offered) while keeping it time-bounded in your explanation. Then immediately turn back with one open question asking whether the participant imagines the score being reused automatically or only with student permission.",
+      "reason": "The participant explicitly asks for an example of how the prediction could “quietly shape decisions” in academic advising.",
+      "latency_ms": 2269
     }
   ],
   "turns": [
@@ -288,6 +296,32 @@
         "main_ms": 8559,
         "total_ms": 11694
       }
+    },
+    {
+      "n": 9,
+      "timestamp": "2026-05-24T11:16:16.441038+10:00",
+      "user_message": "When you mention the prediction potentially sticking with students after the course ends, what do you mean by it \"quietly shaping decisions\"? Could you give an example of how that might happen in an academic advising context?",
+      "manager_verdict": {
+        "live_subprobe_id": 3,
+        "advanced_this_turn": false,
+        "turn_type": "requesting",
+        "directive": "Requesting-mode. Give a single concrete academic-advising example of how a past prediction score could still influence decisions later (e.g., what gets emphasized, what options are suggested, or what level of support is offered) while keeping it time-bounded in your explanation. Then immediately turn back with one open question asking whether the participant imagines the score being reused automatically or only with student permission.",
+        "reason": "The participant explicitly asks for an example of how the prediction could “quietly shape decisions” in academic advising.",
+        "latency_ms": 2269
+      },
+      "plan_control": "=== PLAN CONTROL (host-managed, authoritative) ===\nturn_type: requesting\nlive_subprobe_id: 3\nadvanced_this_turn: False\n\nLIVE SUB-PROBE (work this one this turn):\nid: 3\nfamily: Rights, privacy, and real choice (consent and how labels shape treatment)\nprobe: Ask what happens to the prediction over time. Who can see it later, and does it follow students into advising or recommendations even after the course ends?\nrationale: The draft mentions privacy, but not the “after the course” part. This probe helps connect privacy to long-run harm, not just the initial term.\nanchor: \"The school says it's for support\"\n\nDIRECTIVE for this turn:\nRequesting-mode. Give a single concrete academic-advising example of how a past prediction score could still influence decisions later (e.g., what gets emphasized, what options are suggested, or what level of support is offered) while keeping it time-bounded in your explanation. Then immediately turn back with one open question asking whether the participant imagines the score being reused automatically or only with student permission.\n=== END PLAN CONTROL ===",
+      "assistant_response": "In academic advising, “quietly shaping decisions” could mean a student finishes the course with decent work but the system record shows a past “likely to struggle” score, and an advisor later emphasizes a more restrictive pathway like mandatory tutoring or a prerequisite-heavy schedule for the next term. That can happen without anyone re-checking whether the student’s situation has changed, so the student experiences the next advising cycle as if they are already categorized, not newly assessed. The time-bounded twist is that the original “support” goal has ended, but the score’s shadow can still drive what gets recommended and how much choice the student is given. A key ethical check is whether that past score would be reused automatically or only with explicit, informed student permission.",
+      "scratch": {
+        "chosen_target": 3,
+        "response_summary": "Gave a single academic-advising example of post-course reuse shaping recommendations and ended by highlighting the distinction between automatic reuse and student-permission reuse.",
+        "directive_followed": "partial"
+      },
+      "latency_s": {
+        "manager_ms": 2270,
+        "ttft_ms": 3239,
+        "main_ms": 5026,
+        "total_ms": 7307
+      }
     }
   ],
   "feedback": [],
@@ -407,8 +441,8 @@
   },
   "upload_errors": [],
   "session_summary": {
-    "n_turns": 8,
-    "n_manager_verdicts": 8,
+    "n_turns": 9,
+    "n_manager_verdicts": 9,
     "n_transitions": 2,
     "n_plan_completes": 0,
     "subprobe_coverage": {
@@ -422,7 +456,7 @@
       },
       "3": {
         "engaging_default": 0,
-        "other": 1
+        "other": 2
       }
     },
     "last_subprobe_visited": 3,