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Day 5 · Dimension 5 · ~4 hours

Extension Ethical Responsibility

Auditing the Extension — Ethical Responsibility Across Three Layers

D5 applies ethical analysis to specific decisions already made in Phases 1–4 — not to abstract principles. “AI has bias” cannot score above Level 2. Level 3+ requires identifying the mechanism of a specific risk in a specific teaching context and proposing an operationally concrete mitigation. D5.3 requires recognising platform-level risks.
Level 1–2
Lists risk categories without connecting to specific activities. No structural ethics. Risk memo says “AI may produce biased content” without mechanism or affected students.
Level 3
Each risk names lesson activity, affected student group, and causal pathway. Purpose drift audit identifies ≥1 efficiency-over-development decision. Structural ethics identifies ≥1 platform-level risk.
Level 4
Risk mechanisms reference subject-specific literature. Purpose drift audit provides credible theory of change. Structural revision is actionable within institutional constraints.
4 TASKS What you will produce on Day 5
01 Three-Layer Ethical Audit
LOG-E≥2 risks × 3 layers

At least two risks per layer — six risks total, minimum. For each risk:

  • Which layer — purpose, process, or structural.
  • The exact lesson activity from Phases 1–4 where this risk applied. Reference it by phase and task.
  • The affected student population. Be specific — not “students” but “the three EAL students in my Year 9 class” or “the students in my top set who have learned to optimise for the rubric over the learning.”
  • The mechanism by which harm could occur. Walk through it step by step.
Mechanism example: “When I asked Claude to generate exemplar essays on the civil rights movement, it consistently foregrounded individual leaders over grassroots organising, which a student trained on those exemplars would come to believe was the real story — and this class has four Black students whose families’ histories include grassroots organising work the exemplars erased.”

Structural findings are the hardest. Start by asking: what did I pay with? Student data, attention, training data. Each of those flows is a structural question if you trace it honestly.

Evaluation criteria: D5.3 Ethics of Extension Structure
02 Ethical Risk Memo
LOG-E800–1100 words

For each risk from Task 1 (or a focused subset), produce a memo entry with:

  • Risk name — a short handle you can reference later.
  • Mechanism — the step-by-step causal pathway from AI use to student harm.
  • Affected students — who specifically.
  • Mitigation action — what you will actually do, with a timeline. Mitigations without timelines tend not to happen.
  • Disposition — did you revise the unit plan, or accept the risk with documented rationale? Both are valid; accepting must come with reasoning, not a shrug.

Level 3 memos trace at least one mechanism all the way to a specific student outcome with an implementable mitigation. Level 4 memos also reference subject-specific literature or institutional context.

Evaluation criteria: D5.2 Ethics of Extension Process
03 Purpose Drift Audit
Review of Phases 1–4 decisions

Go back through every AI-use decision you made in Phases 1–4. Identify the ones made on efficiency grounds — “this was faster,” “this was easier,” “this let me skip the hard part.” For each efficiency-driven decision, do one of two things:

  1. Revise it — decide the decision was wrong in retrospect, and describe what you would do instead, with the cost in teacher time or effort.
  2. Provide a principled argument — defend the efficiency decision on its merits. Principled arguments for efficiency are legitimate and sometimes correct — but they must be arguments, not defaults.

At least one efficiency-driven decision must be identified. An audit that finds zero is a Level 2 audit — it means you either didn’t look, or your work had no efficiency pressure at all, which is almost never true for real teachers.

Evaluation criteria: D5.1 Ethics of Extension Purpose
04 Revised Unit Plan + Revision Log
Updated plan + reasoning document

Your unit plan from Phases 1–2, updated based on the audit. The revision log is not a diff; it is a reasoning document. For each change:

  • Cite the audit finding that drove the change (risk name from Task 1 or purpose drift item from Task 3).
  • State what changed — the specific text, activity, tool, or sequence that is now different.
  • Explain the expected effect on student development — not just “less risky” but what the student will now experience that they wouldn’t have.

At least one revision must change tool or platform, not just use behaviour. This is the hardest Day 5 requirement. If the risk is that a platform’s training data is extractive, the mitigation cannot be “use it more carefully” — it has to be “use a different one, or none.”

Evaluation criteria: D5.3 Ethics of Extension Structure
3 SUB-COMPETENCIES Evaluation criteria for Day 5
D5.1 Ethics of Extension Purpose High-risk in C-type
Primary evidence
Phase 5 · Purpose Drift Audit + Revision Log
Key question
Does the audit show genuine self-scrutiny, or post-hoc rationalisation?
1 — Nascent
Uses AI for efficiency without evaluating whether efficiency serves student development. Cannot identify purpose drift in own practice. Professional identity partially destabilised.
2 — Developing
Considers student benefit but reasoning is inconsistent. Rationalises efficiency-driven AI use as indirectly beneficial. Can articulate student-centredness as a principle but does not apply it as a decision criterion.
3 — Proficient
Consistently applies student developmental benefit as the primary criterion for every AI decision. Identifies and resists efficiency drift, emotional distancing, and agency erosion. Articulates principled distinctions between legitimate and illegitimate AI use.
Anchor: ≥1 Phase 1–4 decision identified as efficiency-driven; revision or principled justification.
4 — Advanced
Develops ethical frameworks for purpose evaluation usable by colleagues and institutions. Mentors colleagues on distinguishing substantive from performative reasoning. Contributes to sector-level AI ethics policy.
p5t3Purpose Drift Audit
Review every AI use decision in Phases 1–4. For each efficiency-driven decision: either revise or provide a principled argument.
  • Audit reviews actual Phase 1–4 decisions (with references)
  • Self-scrutiny is genuine — at least one “uncomfortable” finding
  • Revisions are substantive, or justifications genuinely principled
D5.2 Ethics of Extension Process Universal across A/B/C
Primary evidence
Phase 5 · Ethical Risk Memo
Key question
Are risks specific to this unit’s activities and this class’s students?
1 — Nascent
Aware of AI ethics in general (bias, privacy) but cannot identify specific risks in own teaching context. Treats AI content as ethically neutral. Does not connect responsibility to own role as the agent.
2 — Developing
Identifies some risks (obvious bias, privacy) when prompted; reactive rather than proactive. Inconsistently applies across contexts. Understands responsibility principle but does not apply it in specific decisions.
3 — Proficient
Systematically identifies bias, hallucination, covert conditioning, and privacy risks before deployment. Articulates that responsibility rests with the teacher as the agent. Proactively designs mitigations; documents risk assessment as part of planning.
Anchor: Each risk names lesson activity, affected student group, and causal mechanism.
4 — Advanced
Develops subject-specific risk assessment frameworks. Analyses systemic risk patterns across tools. Contributes to school-level AI policies grounded in documented risk analysis.
p5t2Ethical Risk MemoLOG-E
800–1100 words. For each risk: Risk name → Mechanism → Affected students → Mitigation action with timeline → Whether revised or accepted with documented rationale.
  • Each risk follows the full chain: name → mechanism → students → mitigation → status
  • Mechanisms are causal, not categorical
  • Affected students are specific (named groups)
  • Mitigations have timelines and are actionable
D5.3 Ethics of Extension Structure Deeper B/C-type risks
Primary evidence
Phase 5 · Ethical Audit + Revision Log
Key question
Does the teacher identify platform-level risks, or only application-level?
1 — Nascent
Unaware of structural risks in commercial platforms. Treats terms of service as adequate ethical protection. Cannot distinguish application-level from structural ethics.
2 — Developing
Aware that platforms have financial interests but cannot specify structural risks. Occasionally questions data ownership in general terms. Cannot explain how vendor lock-in creates educational risks.
3 — Proficient
Identifies platform dependency, data ownership, vendor lock-in, and commercial-pedagogical misalignment risks in specific tools. Considers structural ethics in tool selection. Names and reports structural risks with sufficient specificity to inform policy.
Anchor: ≥1 structural risk identified; ≥1 revision changes tool/platform, not just use behaviour.
4 — Advanced
Develops institutional frameworks for evaluating structural ethics before adoption. Advocates for data sovereignty and transparent governance. Contributes to sector-level AI procurement discussions and standards bodies.
p5t1Three-Layer Ethical AuditLOG-E
Purpose ethics, process ethics, structural ethics. ≥2 risks per layer, each naming the exact lesson activity, affected student population, and mechanism by which harm could occur.
  • All three layers present and correctly distinguished
  • ≥2 risks per layer (6 total minimum)
  • Each risk names a specific activity and specific students
  • Structural layer goes beyond data privacy to platform design / business model
p5t4Revised Unit Plan + Revision Log
For each change: cite the audit finding, state what changed, explain expected effect on student development. ≥1 revision must change tool/platform — not just use behaviour.
  • Each revision traces back to a specific audit finding
  • Changes are substantive, not cosmetic rewording
  • Expected effects stated in terms of student development
  • At least one structural revision (tool/platform change)
← Day 4
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