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

@@ -4,11 +4,18 @@
   "condition": "explanatory",
   "run_mode": "pilot",
   "started_at": "2026-05-24T11:14:23.811480+10:00",
-  "ended_at": "2026-05-24T11:14:23.811881+10:00",
+  "ended_at": "2026-05-24T11:14:42.099164+10:00",
   "model": "gpt-5.4-nano-2026-03-17",
   "case_id": "ai-predictive-analytics",
-  "perfect_answer": null,
-  "discussion_plan": null,
+  "perfect_answer": {
+    "text": "A university should not put in place an AI system that predicts each student’s likely success in a course and then guides who gets extra attention. Even if the system is described as support, the way it works turns student behavior into a label that can shape teaching. That risks harming students, especially when the university has not shown the needed accuracy, limits on access, and respect for students’ choice.\n\nThe first problem is consent and respect. Privacy is not just about keeping data secret. It is also about whether students understand what is being inferred from their activity and whether that inference will affect how they are treated. In this case, teachers may use the predictions to decide who needs follow-up. That means the system can change a student’s experience without the student having a real say. A quick notice, or relying on the fact that students already use the learning platform, does not make the new use fair.\n\nThere is also a deeper issue with the meaning of “extra support.” Support sounds kind, but sorting people into “likely to succeed” and “likely to struggle” can turn into differential treatment. Some students may want help early, but others may feel singled out and studied. The system can also nudge students in a way that does not match how they learn. Students who engage offline, work in study groups, or rely on print materials can look disengaged in the platform data. If the university then acts on that mismatch, it may take away time and attention from students who need it most.\n\nThe second problem is the long-term effects, not just the first weeks. When teachers see a prediction, their expectations can shift, even subtly. That can lead to students getting different feedback, different opportunities, and a different “story” about what their answers mean. Over time, students may start acting in ways that fit what the platform measures, not what they truly understand. The system can end up rewarding the appearance of engagement, while missing real learning.\n\nWrong calls can also land hardest on students who are already under extra pressure. If the system is less accurate for some groups, those students will be tagged more often for “extra support” even when they are not struggling in the way the model suggests. And when students are repeatedly told, in effect, that they are expected to fail, it can damage confidence and study habits. The case also points to measurable outcomes, like later course success numbers, but it does not show whether those gains truly come from the AI. Some improvements could come from other changes at the university. Meanwhile, the harms, like stigma and lowered expectations, may be harder to count.\n\nFinally, there is a responsibility issue. Teaching is a relationship. A teacher should respond to the student in front of them, based on what the student shows in discussion, work, and questions. When an AI prediction enters that relationship, teachers may trust the model more than they should, or treat the prediction as evidence about the student’s abilities. If something goes wrong, it is also unclear who is accountable. The university controls the system, teachers implement it, and students pay the price, but accountability is fuzzy.\n\nSo the ethical choice is to refuse to put this system in place as described. If the university wants to help students, it can use approaches that do not require labeling students by predicted success. For example, offer voluntary, clearly explained support resources, encourage early meetings, and let students opt into personal study help. Those options can help learning without turning students into predictions that follow them through the course.",
+    "generated_at": "2026-05-24T11:14:42.099099+10:00",
+    "char_count": 3700
+  },
+  "discussion_plan": {
+    "text": "1) Rights, privacy, and real choice (consent and how labels shape treatment):\n   - id: 1\n     probe: Look at what real choice a student has, beyond just using the learning platform. Could a student refuse this use in a way that actually stops predictions from guiding anything?\n     rationale: The draft worries that “something feels wrong” but it stays vague. This probe turns that into a clear check: privacy and consent are not just about data access, but about whether the student can opt out in a meaningful way.\n     anchor: \"Something about it feels wrong.\"\n   - id: 2\n     probe: 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?\n     rationale: 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.\n     anchor: \"influence how teachers perceive students\"\n   - id: 3\n     probe: 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?\n     rationale: 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.\n     anchor: \"The school says it's for support\"\n2) Results over time and who pays the cost (harms, fairness, and measurable vs hidden effects):\n   - id: 4\n     probe: Consider the short-term benefits versus the hidden long-term downsides. What changes after teachers and students learn the system exists?\n     rationale: The draft focuses on bias and privacy fears, but it needs a clear story about time. This probe helps turn gut discomfort into a timeline of teacher expectations, student behavior shifts, and lasting effects.\n     anchor: \"I've heard people talk about algorithmic bias\"\n   - id: 5\n     probe: Look at fairness by asking: are predictions equally accurate for students with different learning styles and life situations? What happens when the system is wrong?\n     rationale: The draft does not fully understand bias, so this probe gives a concrete way to test it. It shifts from a vague concern to who is harmed by wrong calls and how often.\n     anchor: \"algorithmic bias\"\n   - id: 6\n     probe: Separate “success improved” from “the AI caused it.” What evidence would actually show the AI is responsible, and what harms are not likely to show up in reports?\n     rationale: The draft feels unsure, which is a strength. This probe helps refine that uncertainty into a demand for better evidence and for counting harms that dashboards may miss.\n     anchor: \"I'd want to understand the tradeoffs better\"\n3) Good judgment, accountability, and teaching as a relationship (whether the institution is acting with integrity):\n   - id: 7\n     probe: Ask whether using prediction labels fits the kind of teaching the university claims to value. Does it help teachers respond to students, or does it replace that relationship?\n     rationale: The draft mentions “labeling students.” This probe connects labeling to professional judgment, not just privacy. It frames the issue as a mismatch between teaching and prediction.\n     anchor: \"Maybe it's the idea of labeling students?\"\n   - id: 8\n     probe: Think about responsibility if students are harmed. Who takes accountability, and what can students do to challenge a wrong call that affects their support or how they are treated?\n     rationale: The draft wants to understand tradeoffs before deciding. This probe adds a key missing piece: an ethics plan needs a clear way to fix harms, not just promise good intentions.\n     anchor: \"I have a feeling there's more going on.\"",
+    "extracted_at": "2026-05-24T11:14:42.099159+10:00"
+  },
   "manager_history": [],
   "turns": [],
   "feedback": [],