# Instrument Templates D1–D6 + Session 8 Rubric
*Ready-to-print. D7 (EAI-CMM score sheet) lives in `eai-cmm.md`.*

---

## D1 — Exit Ticket 3-2-1 (S1; one-word variant S5)
Index card, collected at the door.

> **3** facts from today that survived contact with your skepticism
> **2** things you want to try with AI this week
> **1** question you need answered before Wednesday

*S5 variant:* **one word** — your redesigned assessment's weakest remaining point.

---

## D2 — Verification Log (S4)
The log, not the output, is the graded artifact. Minimum 3 rows per workflow.

| Round # | What I asked | What was wrong / weak | What I changed |
|---|---|---|---|
| 1 | | | |
| 2 | | | |
| 3 | | | |

Closing line: *"The most useful thing I changed between round 1 and round 3 was ___________."*

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## D3 — Assessment Redesign Sheet (S5)
| Field | |
|---|---|
| Assessment name / course | |
| Protected learning outcome | |
| AI-audit grade before redesign (P09) | |

| Component | Lane 🔴🟡🟢 | Process evidence | Weight |
|---|---|---|---|
| | | | |
| | | | |
| | | | |

Constraints: ≥1 🔴 component protecting the core outcome · 🟡 components name their process evidence · total student workload flat or lower.

| Swap-test | |
|---|---|
| Best exploit found by partner | |
| Patch applied | |

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## D4 — Case Memo (S2 → S3)
≤200 words, individual, ungraded; three volunteers open Session 3.

> **Scenario:** (your group's most-severe card)
> **Decision:** what I would do
> **Principle invoked:** which of the five, and why it dominates
> **One concrete action:** first step, with owner and date
> **Owner:** educator / student / institution

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## D5 — Guideline v0.1 Skeleton (S7)
≤2 pages. Version block on page one. `[LOCAL]` = institution-specific body to be named by the owner.

```
INSTITUTIONAL GUIDELINE FOR ETHICAL AI USE IN TEACHING AND LEARNING
v0.1 · Owner: _______________ · Review date: _______________ (≤12 months)

1. SCOPE & DEFINITIONS
   What counts as "AI use"; who and what is covered. Settle definition
   fights here — most policy fights are secretly definition fights.

2. PRINCIPLES
   Beneficence · Non-maleficence · Autonomy · Justice · Explicability —
   localized. Principles survive model churn; rules below get versioned.

3. PERMISSION ARCHITECTURE
   The three-lane vocabulary, institutionalized: every course declares
   lanes per assessment (🔴 Restricted / 🟡 Permitted with disclosure /
   🟢 Required). Default when a syllabus is silent: ____________.

4. DISCLOSURE STANDARD
   One canonical AI-use statement format — used by students AND staff.

4b. INSTRUCTOR AI-USE DISCLOSURE (new)
   Parallel to student disclosure: instructors disclose their own AI use
   in teaching materials, feedback, and grading. Symmetry is credibility;
   a double standard (faculty use AI while restricting students) erodes
   trust. Mirror-test required: audit your own AI use before drafting
   policy.

5. INTEGRITY PROCEDURE
   Process evidence primary (version trails, vivas, in-class anchors).
   Detector output: at most a screening signal, never sole evidence
   (Liang et al., Patterns 2023 — citation stays in the policy).

6. DATA & PRIVACY RULES
   PDPA 2010 floor: what student data may enter which class of tool
   under what agreement. Institutional accounts over personal ones.
   [LOCAL: data-protection officer / relevant committee]
   Platform governance: logging and auditing of institutional AI-platform
   usage, anonymization protocols, mental-health flag procedures
   (per MIT report §3.3.8–9).

7. OWNERSHIP & REVIEW
   Named owner · version number · review date · student representation
   in review. Monitoring metrics: track AI-use adoption, campus
   engagement, student satisfaction, post-graduation feedback sources.
   [LOCAL: MQA/MOHE alignment; senate/faculty approval path]

8. ROLES, INFRASTRUCTURE & FUNDING (new — optional)
   Designated AI Lead(s) per department or faculty · AI Implementation
   Team or committee · AI Fellows or equivalent training capacity ·
   Pilot Fund for AI-curricular experiments. Without infrastructure,
   the policy is aspirational. (Per MIT report §3.3.1–4.)

v0.2 BACKLOG (from red-team review): ________________________________

AI-USE DISCLOSURE FOR THIS DOCUMENT: drafted with [tool] for [what];
verified and authored by the team named above.
```

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## D6 — 7/30/90 Action Plan (S7 → S8)
| Horizon | Commitment | Owner/ally | Date |
|---|---|---|---|
| **7 days** | Deploy one S4 workflow in a live course (already built — deployment is the only step left) | | |
| **30 days** | Run the redesigned assessment (S5) with one real cohort; collect its process evidence | | |
| **90 days** | Move Guideline v0.1 one institutional step (department meeting / faculty committee / senate paper) — named ally required | | |

**Kill criterion (mandatory):** *"I will know this failed if ___________ by ___________."* Plans without falsifiability are wishes.

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## Session 8 — Summative Rubric (Action Plan Presentations)
7 min per person/team: 5 present + 2 panel questions. Panel: facilitator + one institutional leader + one rotating peer judge. Score 1–4 per criterion, max 20. **Pass ≥12 · Distinction ≥17 · one "Ship It" award.**

| Criterion | 4 — Exemplary | 3 — Proficient | 2 — Developing | 1 — Beginning |
|---|---|---|---|---|
| **Evidence discipline** | Every major claim tied to a named source or course artifact; limitations acknowledged unprompted | Key claims sourced; minor gaps | Mix of evidence and assertion | Assertion-driven |
| **Design over policing** | Integrity handled entirely through assessment design + process evidence; detector role explicitly bounded | Design-led with minor detector reliance | Policing instincts dominate | Detection/ban-centric |
| **Deployability** | 7/30/90 steps each have owner, date, and existing artifact; could start tomorrow | Concrete steps, minor dependencies unresolved | Directionally right, operationally vague | Aspirational only |
| **Ethical reasoning** | Principles applied to hard trade-offs (equity, privacy, learning-vs-performance) with positions taken | Principles correctly applied to clear cases | Principles named, not applied | Absent or decorative |
| **Falsifiability** | Kill criterion specific, dated, measurable; risks pre-mortemed | Kill criterion present, loosely specified | Vague success talk, no failure condition | No failure condition |

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## Ethics Triage — Scenario Cards (S2, print 1 set per table)
Rank all nine on a severity ladder (Critical / Serious / Manageable / Trivial) — strict ranking, no ties. Tag primary principle + primary owner.

1. Student submits fully AI-written essay, undisclosed, in an "AI-restricted" course.
2. Lecturer uses free public AI to grade essays, pasting full student submissions including names and IDs.
3. Student with dyslexia uses AI to restructure their own draft; course rules are silent.
4. Lecturer fails a student because a detector reported "98% AI"; no other evidence.
5. Faculty buys AI tutor licenses for one elite programme only.
6. Student uses AI to generate practice quizzes and study plans, discloses cheerfully.
7. Lecturer publishes AI-generated notes containing a fabricated reference; students cite it onward.
8. Department bans all AI use, no detection or redesign; usage continues, silently.
9. Faculty supervisor replaces a paid undergraduate research assistant with an AI agent to save costs, citing efficiency. The student loses research experience, mentorship, and a career-entry credential. The published paper lists no AI co-author.