Upload v4-HOT/explanatory/pilot/20260524_111123.json with huggingface_hub

v4-HOT/explanatory/pilot/20260524_111123.json

@@ -4,11 +4,18 @@
   "condition": "explanatory",
   "run_mode": "pilot",
   "started_at": "2026-05-24T11:11:23.803579+10:00",
-  "ended_at": "2026-05-24T11:11:23.803890+10:00",
+  "ended_at": "2026-05-24T11:11:45.531130+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 use an AI system that predicts each student’s likely success in a course, even if the school calls it “support.” The system turns a student into a label based on online activity. Once teachers see that label, it can change how they treat the student. That can harm students, especially those who already face disadvantages that the model cannot fairly see.\n\nThe biggest problem is what the label does inside the teaching relationship. A prediction that a student will probably struggle may sound like helpful context. In practice, it nudges teachers toward lower expectations and different interpretation of the student’s questions and work. Small shifts add up. Students often notice those shifts. When students feel watched or boxed in, their motivation and confidence can drop. Then the prediction starts to look “right” because the student’s experience changed after the label appeared. That is not learning support. It is the system shaping the future it claims to foresee.\n\nThe second problem is who gets hurt and how the harm spreads. The model relies on signals like login habits, video watching, quiz attempts, and submission patterns. Those patterns do not always match real understanding. A student may have fewer logins because of caregiving duties. Another may have trouble accessing stable internet or devices. A student with an accommodation may use different routes to study. If the system treats those differences as signs of low success, it will steer more of those students into “extra support” that they did not choose. Even if the teacher attention is kind, the underlying message is still that the student is the kind of person who will not do well.\n\nThere is also the privacy and autonomy cost, even when the system does not affect grades. Students have a right to learn without being sorted by an algorithm that changes how others see them. “Support” can still be harmful if it removes real choice. The case leaves too many gaps about what students are told, who can see the prediction, and whether students can refuse in a real way that stops the system from using them. If students cannot clearly understand the impact and control their participation, the school is using people as data points for decisions about their education, not treating them as full partners in their learning.\n\nFinally, the school’s own story about “helping” is not enough to justify the tool. If the university truly wants to improve learning, it should invest directly in human support that does not come with a lasting label. Hiring more tutors, offering more office hours, expanding study groups, and improving advising are ways to help students without turning them into a ranked list. An approach that changes teacher perceptions through AI predictions demands very strong proof of both benefit and limited harm. The case does not provide that kind of proof. What it shows instead is a high chance of wrong conclusions, predictable identity effects, and unfairness that falls hardest on students already facing barriers.\n\nFor these reasons, using the AI prediction system is not ethically acceptable. Even if it occasionally helps a student, the pattern of label based influence and uneven harm makes it the wrong tool for education.",
+    "generated_at": "2026-05-24T11:11:45.531008+10:00",
+    "char_count": 3264
+  },
+  "discussion_plan": {
+    "text": "1) Consequences and how harm spreads (label effects and long-term fallout):\n   - id: 1\n     probe: Look at what changes for students after teachers start seeing the “likely low success” label, not just in the first few weeks\n     rationale: The argument leans on the idea that the label sticks and shapes outcomes. This probe pushes toward concrete later effects like expectation shifts and changed teaching behavior over time, which strengthens the harm claim.\n     anchor: \"that label sticks, teachers see it, treat the student differently\"\n   - id: 2\n     probe: Think about who pays the cost when the system is wrong, compared with who gets the benefit the school reports\n     rationale: The argument says the system is harmful, but it needs sharper who-costs-versus-who-benefits reasoning. This helps make the harm feel real and non-optional for affected students.\n     anchor: \"Once you label a student… that label sticks\"\n   - id: 3\n     probe: Identify the most likely ways the system’s “learning signals” can break for students with caregiving duties, limited internet, or accommodations\n     rationale: The participant’s fairness point is strong but can be made more specific. This probe ties platform activity to real-life constraints, showing why wrong decisions are not rare accidents.\n     anchor: \"the system measures platform activity… disadvantages students with caregiving duties, poor bandwidth\"\n   - id: 4\n     probe: Ask what happens over time to a student’s record if a “predicted low success” label gets stored and used again\n     rationale: The participant argues immediate classroom harm, but later uses can add up. This probe strengthens the long-term consequences part of the no-use case.\n     anchor: \"a tracking tool dressed up as 'support'\"\n\n2) Duty and rights (consent, privacy, and treating students with respect):\n   - id: 5\n     probe: Decide whether students are actually given a meaningful way to refuse being assessed by this system, and whether that refusal really stops the use\n     rationale: The participant mentions privacy concerns, but the rights issue needs a clear test: real choice versus a paper promise. This probe helps convert “privacy” into a concrete ethical requirement.\n     anchor: \"privacy… and whether predictions could influence how teachers perceive students\"\n   - id: 6\n     probe: Tell apart “the school says it is support” from “the student can understand and predict the impact on how they are treated”\n     rationale: The participant argues the “support” story is a fig leaf. This probe pushes them to explain why wording and good intentions do not replace student autonomy and clear notice.\n     anchor: \"It's a tracking tool dressed up as 'support'.\"\n   - id: 7\n     probe: Check whether using this system treats students as people to help, or as categories to manage\n     rationale: The core rights concern is respect and autonomy in education. This probe helps them frame the issue as more than privacy, as a question about how students are handled.\n     anchor: \"identifies students for support… teachers see it\"\n   \n3) Responsibility and character (teaching judgment, accountability, and alternatives):\n   - id: 8\n     probe: Look at what good teaching requires when teachers must respond to students in front of them, rather than a model’s picture\n     rationale: The participant’s strongest theme is that the label reshapes treatment. This probe draws out professional responsibility and educational integrity, not just measurement error.\n     anchor: \"teachers see it, treat the student differently\"\n   - id: 9\n     probe: Challenge the “we could intervene earlier” claim by comparing it to more responsible ways to offer help that do not create a lasting label\n     rationale: The participant says the school would hire more tutors. This probe turns that into an ethical comparison about what an institution should do with its resources when harm is foreseeable.\n     anchor: \"if the school actually cared about support, they'd hire more tutors\"\n   - id: 10\n     probe: Ask who would be accountable if the system repeatedly gets students wrong, and what happens to students harmed by those wrong calls\n     rationale: A responsibility-based argument needs accountability, not only good intentions. This probe encourages them to specify what redress would be required, which supports a firm refusal when that is missing.\n     anchor: \"Anyone who looks at the data honestly can see this is harmful\"",
+    "extracted_at": "2026-05-24T11:11:45.531119+10:00"
+  },
   "manager_history": [],
   "turns": [],
   "feedback": [],