# BENGKEL: NAVIGATING ETHICAL CHALLENGES OF AI IN STUDENT LEARNING
## Complete Facilitator Course Pack — v1.0

**Dates:** 1–3 September 2026 (Tuesday–Thursday) · 7 sessions × 2 hours = 14 contact hours + Session 8 Action Plan presentations
**Audience:** University lecturers, academic staff, programme leaders (Malaysian higher education)
**Primary source:** Stanford Institute for Human-Centered AI (hai.stanford.edu), including the 2026 AI Index Report
**Secondary sources (restricted set):** Anthropic (AI Fluency Framework, Education Reports), Penn/Wharton (PNAS), MIT Media Lab (arXiv), Stanford CS/GSE (Patterns, arXiv), Harvard, Oxford. No other sources are cited anywhere in this pack.
**Framework lineage:** Assessment instrument adapts the SSA-CMM maturity-ladder pattern (The Future Is Solo). Competency spine adapts the AI Fluency Framework 4Ds (Dakan, Feller & Anthropic, CC BY-NC-SA 4.0).

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## 0. HOW TO USE THIS PACK

Each session contains seven components: **Snapshot** (logistics), **Objectives**, **Run Sheet** (minute-by-minute), **Script** (verbatim blocks marked `SAY:` for openings, pivots, and closes), **Talking Points** (sourced, for the data segments — deliver in your own voice), **Prompts** (numbered P01–P22, copy-paste ready, consolidated in Appendix A), **Exercise** (full spec), and **Assessment** (formative instrument).

Verbatim script is provided only where wording carries load: cold opens, contrarian closes, and sensitive framings (integrity accusations, detector limits). Everything else is talking points — you know how to teach.

Threading rule: every session ends by loading the next one. The whole course is one argument, delivered in seven movements, resolved in an action plan.

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## 1. DESIGN AXIOMS

The course is built on seven axioms. State A1 in Session 1. Reveal the rest as they become load-bearing.

**A1. The gap is the curriculum.** Stanford's 2026 AI Index frames the year's central finding as a widening gap between what AI can do and how prepared institutions are to manage it. Capability compounds; governance lags. This course exists inside that gap.

**A2. The unit of integrity is the assessment, not the student.** If an assignment can be completed invisibly by AI, the assignment failed before the student did. Redesign precedes enforcement.

**A3. Literacy precedes governance.** You cannot regulate what you cannot operate. Hands-on practice (Session 4–5) is deliberately sequenced before policy writing (Session 6–7).

**A4. Detection is a losing regime; disclosure + design is the successor.** Detectors are simultaneously biased and evadable (Session 3 evidence). Build assessments that produce process evidence instead.

**A5. Performance ≠ learning.** The same tool that raises assisted scores can lower unassisted skill (the crutch effect). Every AI integration must answer: what does the student still have to do in their own head?

**A6. AI allocates attention; humans keep authority.** Delegation of tasks, never delegation of accountability. The educator signs the grade; the institution signs the policy.

**A7. Policy is a product.** Ship v0.1 with a version number and a review date. A perfect unshipped guideline protects no one.

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## 2. COURSE ARCHITECTURE

### 2.1 The arc

| Day | Movement | Sessions | Output |
|---|---|---|---|
| Tuesday (1/9) | **UNDERSTAND** — the revolution and its ethics | S1, S2 | Baseline EAI-CMM score; personal ethics position |
| Wednesday (2/9) | **BUILD** — integrity, hands-on fluency, policy foundations | S3, S4, S5, S6 | Verified prompt workflows; redesigned assessment; institutional gap analysis |
| Thursday (3/9) | **SHIP** — guidelines and commitment | S7, S8 | Guideline v0.1; EAI-CMM delta; action plan |

### 2.2 The two spines

**Competency spine — the 4Ds** (AI Fluency Framework, Dakan & Feller with Anthropic):
- **Delegation** — deciding whether, when, and how to engage AI (goal-setting, task ownership)
- **Description** — communicating intent so the AI produces useful behavior (context, constraints, format)
- **Discernment** — critically evaluating output quality, reasoning, and behavior
- **Diligence** — taking responsibility for what we do with AI: disclosure, verification, ethics

The 4Ds operate as two loops: the **Description↔Discernment loop** (iterate prompts against critical evaluation) and the **Delegation↔Diligence loop** (decide what to hand over; own what comes back). Session 4 runs the first loop; Session 5 runs the second. The framework spans three interaction modes — Automation, Augmentation, Agency — with this course concentrating on Augmentation (educator workflows) and the ethics of student-side Automation.

**Maturity spine — the EAI-CMM** (Section 3): a six-level maturity ladder taken at baseline (S1) and retaken at close (S7). The course's promise is measurable movement, typically L1→L2 or L2→L3 within three days plus a route card for the next level.

### 2.3 Facilitation defaults

- Room: groups of 4–5, mixed disciplines, fixed for all three days (they become guideline drafting teams by S6).
- Devices: required from S1. Every participant needs a working account on at least one frontier assistant (Claude, ChatGPT, or Gemini — exercises are model-agnostic; Claude's Learning Mode is demonstrated where relevant).
- Energy map: S2 and S6 are 8–10 pm slots. Both are built discussion-heavy and lecture-light. Never lecture at 9 pm.
- Language: deliver in English or Bahasa Melayu as the room prefers; all artifacts are drafted in the language the institution's policies actually use.

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## 3. THE EAI-CMM: ETHICAL AI EDUCATOR CAPABILITY MATURITY MODEL

An SSA-CMM-pattern instrument: five pillars × four items, self-scored, mapped to six maturity levels, each with a Route to L(n+1) card. Administered twice (S1 baseline, S7 retake). A ten-item institutional variant runs in S6.

### 3.1 The five pillars

| Pillar | Question it answers | 4D anchor |
|---|---|---|
| **Literacy** | Do I understand what this technology is and isn't? | Delegation |
| **Pedagogy** | Do my teaching and assessment designs account for AI? | Delegation + Description |
| **Integrity** | Is my integrity regime built on design or on policing? | Diligence |
| **Discernment** | Can I evaluate AI output — and teach students to? | Discernment |
| **Governance** | Do I operate within (and contribute to) explicit rules? | Diligence |

### 3.2 The instrument (20 items)

Score each statement 0–4: **0** Never/No · **1** Rarely · **2** Sometimes · **3** Usually · **4** Consistently/Institutionalized. Maximum 80.

**LITERACY**
1. I can explain in plain language how a large language model generates output (next-token prediction over training data — not database retrieval).
2. I can name and give discipline-specific examples of at least three failure modes: hallucination, bias, sycophancy/over-agreement.
3. I have personally used at least two different AI systems on real work tasks in the past month.
4. I can articulate which tasks in my discipline current AI does well, poorly, and unevenly — and I update this map as models change.

**PEDAGOGY**
5. I have redesigned at least one assessment specifically in response to AI capability.
6. For each assessment I set, I can state the learning outcome it protects and why AI use would or wouldn't compromise it.
7. I use AI to augment my own teaching preparation (rubrics, examples, feedback drafts, differentiation) with a verification step.
8. I deliberately design assessments in tiers: AI-restricted, AI-permitted, AI-required.

**INTEGRITY**
9. My course documents state an explicit, per-assessment AI-use policy that students can act on without guessing.
10. My integrity evidence comes from process (drafts, version history, vivas, in-class components) rather than from detector scores.
11. I require disclosure/citation of AI assistance — and I model it by disclosing my own.
12. I can conduct a fair, non-accusatory conversation with a student about suspected misuse without a detector report as my only evidence.

**DISCERNMENT**
13. I verify AI factual claims and references against primary sources before they reach students or grading decisions.
14. I can quickly spot AI-typical failure signatures: fabricated citations, confident wrongness, plausible-but-hollow structure.
15. I actively check AI output for bias that would affect my students (language background, gender, culture) before use.
16. I calibrate trust to stakes: loose for brainstorming, strict for anything touching grades, references, or student records.

**GOVERNANCE**
17. I know which student data may and may not be entered into external AI tools, and I comply (including PDPA obligations).
18. I know my institution's current AI guidance — or I know it doesn't exist, and I document my own interim rules in writing.
19. Where AI materially shapes something students receive (feedback, materials, grades), I keep a record of how it was used.
20. I actively contribute to AI policy conversations at department, faculty, or senate level.

### 3.3 Levels and bands

| Level | Name | Band | Signature |
|---|---|---|---|
| **L0** | Unaware | 0–13 | AI is rumor. Assessments unchanged since pre-2023. Integrity = hoping. |
| **L1** | Aware | 14–27 | Has opinions, not reps. Talks about AI more than uses it. Policy = "don't." |
| **L2** | Experimenting | 28–41 | Personal use begun; unverified. Syllabus mentions AI vaguely. Detector-reliant. |
| **L3** | Integrating | 42–55 | Redesigned assessments; tiered policies; verifies output; discloses own use. |
| **L4** | Governing | 56–69 | Systematized: documented workflows, process-based integrity, mentors peers. |
| **L5** | Stewarding | 70–80 | Shapes institutional policy; builds capability in others; instrument-rated practice. |

### 3.4 Route to L(n+1) cards

**L0→L1:** Use one AI assistant for 20 minutes daily for two weeks on real tasks. Read one primary source (start: the 2026 AI Index education chapter). No opinions until reps.
**L1→L2:** Run your three most important assessments through an AI yourself. Score the outputs. You now know your exposure. Draft one per-assessment AI rule.
**L2→L3:** Redesign your most AI-vulnerable assessment using the three-lane pattern (S5). Replace detector reliance with two forms of process evidence. Add a disclosure norm — for students and for yourself.
**L3→L4:** Document your AI workflows so a colleague could run them. Keep verification logs. Take one integrity case through a design-based (not detector-based) resolution. Teach one peer.
**L4→L5:** Put your name on institutional guidance (S7's v0.1 is the vehicle). Run a department workshop. Establish a review cadence — you now maintain a living policy product.

### 3.5 Scoring mechanics for the course

- S1: baseline, private, 15 minutes. Facilitator collects anonymous pillar totals only (show of hands by band) to calibrate the room.
- S7: retake, 10 minutes. Compute delta. Expect +8 to +18 points over the three days — driven mostly by Pedagogy and Governance items, since the course forces artifacts that flip 0s to 3s.
- Honesty guard — say this both times: "Score what you do, not what you believe. Item 5 asks if you redesigned an assessment, not whether you think redesign matters. Inflated baselines steal your own delta."

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# SESI 1 — THE AI REVOLUTION IN HIGHER EDUCATION: OPPORTUNITIES AND ETHICAL CHALLENGES

**Snapshot:** Tuesday 1/9 · 2.30–4.30 afternoon · Post-check-in slot: energy is fresh, expectations forming. This session sets the frame for everything.

### Objectives
By end of session, participants can (1) describe the current AI capability frontier using 2026 AI Index evidence, (2) state their personal baseline via the EAI-CMM, (3) map opportunities and ethical challenges for their own discipline, (4) articulate the course's central axiom: the gap is the curriculum.

### Run Sheet

| Time | Block | Mode |
|---|---|---|
| 0:00–0:10 | Cold open: the 90-second assignment | Live demo |
| 0:10–0:20 | Framing: the gap is the curriculum | Script |
| 0:20–0:35 | EAI-CMM baseline assessment | Individual |
| 0:35–1:00 | Data walk: the state of AI, 2026 | Talking points |
| 1:00–1:20 | The opportunity ledger | Talking points + demo |
| 1:20–1:50 | Exercise: Threat/Gift Matrix | Groups |
| 1:50–2:00 | Contrarian close + exit ticket | Script |

### Script

**Cold open (0:00).** Before any welcome, project a live AI chat. Ask the room: "Give me a real assignment question from a course you teach this semester. Anyone." Type it verbatim. Submit.

`SAY:` "While it writes — no slides yet, no introductions yet — just watch. ... That took about ninety seconds. Grade it mentally. Most of you just gave it something between a B and an A. Now here is the only honest question, and it is the question of this entire course: not *how do we stop this* — we cannot, and I will show you the evidence — but *what do we do because of this?* Selamat datang. Let's begin properly."

**Framing (0:10).** `SAY:` "Stanford's Institute for Human-Centered AI publishes the AI Index every year — the closest thing this field has to an independent report card. The 2026 edition, released in April, has one headline finding: the gap between what AI can do and how prepared we are to manage it is *widening*. Capability is compounding. Governance, evaluation, and education are falling behind. That gap is not a problem to lament. That gap is the curriculum. For the next three days we work inside it: today — Tuesday — we understand it, tomorrow we build inside it, Thursday we ship policy that closes our institution's share of it."

**Baseline (0:20).** Distribute EAI-CMM (Section 3). `SAY:` "Fifteen minutes, private, honest. Score what you do, not what you believe. You will retake this Thursday morning; the delta is yours to keep."

### Talking Points — Data Walk (0:35)

Deliver as a narrated tour of ~8 slides. All figures: 2026 AI Index Report (Stanford HAI) unless noted.

- **Capability is accelerating, not plateauing.** On SWE-bench Verified — a benchmark of real software engineering tasks — performance rose from 60% to near 100% in a single year. Frontier models took gold-medal-level results at the International Mathematical Olympiad in 2025.
- **The frontier is jagged.** The same class of models that wins IMO gold reads analog clocks correctly only 50.1% of the time (humans: 90.1%). Land this hard: benchmark headlines are a poor proxy for behavior on *your* tasks. This is why Discernment is a core competency and why "just trust it" and "just ban it" are both wrong.
- **Adoption outran every prior technology.** Organizational adoption hit 88%; generative AI reached 53% of the population faster than either the PC or the internet. Global corporate AI investment: $581.7B.
- **Agents arrived.** On OSWorld (structured computer-use tasks), agent accuracy jumped from roughly 12% to 66.3%. The thing your students use next year won't just answer — it will *do*.
- **Your students are already there.** The Index's education chapter: four in five US high-school and college students now use AI for schoolwork. Only about half of schools have AI policies; just 6% of teachers say those policies are clear. Assume comparable or higher exposure in your lecture halls.
- **And the evidence base is thin.** The same chapter's sober finding: despite enormous investment, rigorous evidence on learning outcomes from AI-enhanced education remains limited. We are running a natural experiment on our students without a control group. That is itself an ethical fact — write it down, it returns in Session 2.
- **What students actually do** (Anthropic Education Report, 1M anonymized student conversations, Apr 2025): usage splits roughly evenly across four modes — direct problem-solving, direct output creation, collaborative problem-solving, collaborative output creation (each 23–29%). Nearly half of all use is *direct* — asking for answers or finished artifacts. Computer Science students are wildly overrepresented: 36.8% of conversations vs 5.4% of US degrees. Your STEM students moved first; your humanities students are moving now.

### Talking Points — The Opportunity Ledger (1:00)

Ethics that only inventories harms is theater. The gift side, with evidence:

- **Expertise at marginal cost ~zero.** Stanford's Tutor CoPilot study (Wang, Demszky et al., 2024): giving human tutors real-time AI assistance raised student topic mastery by ~4 percentage points overall and ~9 points for students of the lowest-rated tutors — at roughly $20 per tutor per year. The pattern that works: AI amplifying a human educator, not replacing one.
- **Guardrailed tutoring works.** Preview the Wharton PNAS result (full treatment Session 5): a GPT-4 tutor engineered to give hints, not answers, boosted practice performance 127% *without* damaging subsequent unassisted performance. Design determines outcome.
- **Scaled deployments exist.** Harvard's CS50 runs a course-wide AI "duck debugger" deliberately built to guide toward answers rather than hand them over. Anthropic ships a Learning Mode for Claude that responds with Socratic questioning instead of direct answers. Demo Learning Mode live here (3 min): ask it the cold-open assignment question, let the room watch it refuse to just answer.
- **Educator leverage.** Anthropic's faculty-usage report (~74,000 higher-ed conversations, 2025) found curriculum design the top faculty use case — educators lean on AI hardest for building materials, not grading humans. That's the right instinct; Session 4 trains it.

### Exercise — Threat/Gift Matrix (1:20, 30 min)

Groups of 4–5 by broad discipline. A2 flip-chart, 2×2: rows = *Teaching* / *Assessment*; columns = *Gift* / *Threat*. Fill all four quadrants for your discipline: minimum three items each, each item concrete enough to name a course. Then each group circles the single **most urgent threat** and the single **most undervalued gift** and reports both in 60 seconds. Facilitator harvests onto a master chart — this chart physically stays on the wall all three days and gets marked off as sessions address items.

Debrief lens: "Notice how many threats are actually assessment-design problems wearing a technology costume. Hold that thought until tomorrow morning."

### Assessment — Exit Ticket 3-2-1 (1:50)
Index card: **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 Thursday. Collect; open Session 3 by answering the three most common questions.

**Contrarian close.** `SAY:` "Here is the uncomfortable version of today. The threat to this institution is not that students will cheat with AI. The threat is institutional denial — continuing to run 2019 assessments in a 2026 world and calling the resulting numbers 'learning.' A student who uses AI on a take-home essay has not defeated your assessment. They have *audited* it. Tonight, we do ethics properly. Jumpa lagi at eight."

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# SESI 2 — UNDERSTANDING AI ETHICS IN STUDENT LEARNING: PRINCIPLES, RISKS AND RESPONSIBILITIES

**Snapshot:** Tuesday 1/9 · 8.00–10.00 pm · Evening slot after dinner. Rule: no lecture block longer than 12 minutes. This session is built around cases and one live demonstration.

### Objectives
Participants can (1) apply a five-principle ethical lens to concrete AI-in-learning scenarios, (2) name the six risk categories with evidence for each, (3) demonstrate bias empirically rather than rhetorically, (4) assign responsibilities across the educator–student–institution triad.

### Run Sheet

| Time | Block | Mode |
|---|---|---|
| 0:00–0:10 | Re-entry: the natural experiment | Script |
| 0:10–0:25 | Five principles, one slide | Talking points |
| 0:25–0:45 | Live bias demonstration | Demo (P03) |
| 0:45–1:10 | The risk map: six categories | Talking points |
| 1:10–1:40 | Exercise: Ethics Triage | Groups |
| 1:40–1:55 | The responsibility triad | Discussion |
| 1:55–2:00 | Close + case memo assignment | Script |

### Script

**Re-entry (0:00).** `SAY:` "This afternoon I asked you to write down one sentence: *we are running a natural experiment on our students without a control group.* Ethics is what you do when you notice that sentence and refuse to look away. Tonight is not a philosophy seminar. It is triage training. By ten o'clock you will be able to look at any AI-in-learning situation and answer three questions fast: which principle is under pressure, how severe is the risk, and whose job is it."

### Talking Points — Five Principles (0:10)

Use the synthesis from Floridi and colleagues (Oxford), which distilled dozens of AI ethics frameworks into five principles — the fifth being the one AI adds to classical bioethics:

1. **Beneficence** — AI use should promote learning and wellbeing. Test: does this use make the student *more* capable next month?
2. **Non-maleficence** — do no harm, including invisible harm (deskilling, false accusation, privacy leakage).
3. **Autonomy** — preserve human agency: students choosing how they learn, educators choosing how they teach. Dependence is autonomy decay on an installment plan.
4. **Justice** — fair distribution of benefit and harm: access gaps, biased tools, unequal accusation rates.
5. **Explicability** — the AI-specific addition: uses must be transparent and accountable. If you can't explain how AI touched a grade, you can't defend the grade.

Anchor to the primary source: this is precisely Stanford HAI's founding premise — Fei-Fei Li's framing that we hold "a historical opportunity and responsibility to establish a human-centered framework" for AI. Human-centered is not a slogan; tonight it becomes a checklist.

### Demo — Bias, Empirically (0:25)

Never assert bias; *produce* it. Run **P03** live: ask the model for two reference letters, identical achievements, one for "Ahmad," one for "Aisyah." Have the room hunt the adjective delta. Research on LLM-generated reference letters (Wan et al., EMNLP Findings 2023) found systematic patterns: agentic language for men ("leader," "exceptional"), communal language for women ("warm," "supportive"). Sometimes the live run comes back clean — modern models are better. `SAY (if clean):` "Good — the vendors patched the famous one. The lesson survives: bias in these systems is an empirical property that shifts with every model version. You don't audit once; you audit *per model, per use*. That is Discernment, and we train it tomorrow."

Then connect to their context: these models are trained predominantly on English-language, Western-centric data. Ask: "What does that mean for Bahasa Melayu submissions? For examples about kampung life scored against essays about suburbs?" (The detector version of this bias — with hard Stanford numbers — lands tomorrow morning in Session 3. Tell them it's coming.)

### Talking Points — The Risk Map (0:45)

Six categories. One line of evidence each; depth arrives in later sessions.

1. **Integrity risk** — misuse is real, not moral panic: Anthropic's own Education Report documents students seeking exam answers and asking AI to rewrite text to evade plagiarism detection. (Session 3.)
2. **Learning risk** — the crutch effect: unguarded GPT-4 access raised practice scores 48% and then *lowered* unassisted exam scores 17% versus never having it (Bastani et al., PNAS 2025). Performance and learning came apart. (Session 5.)
3. **Cognitive risk** — an MIT Media Lab EEG study ("Your Brain on ChatGPT," 2025 preprint) found essay-writers using an LLM showed the weakest neural connectivity of three groups, and over 80% couldn't accurately quote from essays they had just submitted. Flag honestly: preprint, N=54 — suggestive, not settled. Model the epistemic discipline you want from them: we cite it *with* its limitations or not at all.
4. **Equity risk** — two-sided: access gaps (who can afford frontier tools) and bias gaps (whose writing gets falsely flagged, whose name changes the letter).
5. **Privacy risk** — student work and data entering external systems. In Malaysia this has a statutory floor: PDPA 2010. Rule of thumb tonight, formalized Thursday: *no personal student data into tools without institutional agreements.*
6. **Wellbeing/dependence risk** — Stanford HAI's 2026 coverage of AI "delusional spirals" and companion-AI harms is a reminder that students use these systems for far more than homework; policies that only mention plagiarism miss most of the surface area.

### Exercise — Ethics Triage (1:10, 30 min)

Each group receives the same eight scenario cards. Task: place each on a severity ladder (Critical / Serious / Manageable / Trivial), tag the *primary* principle violated, and tag the *primary* owner (educator / student / institution). Groups must produce a strict ranking — no ties. Then pairs of groups compare and argue their top-2 divergences.

**The eight cards:**
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.

Debrief keys: Card 4 usually splits the room — perfect setup for tomorrow. Card 3 exposes that silence in policy is itself a policy. Card 8 lets you plant tomorrow's thesis: *a ban without design doesn't stop use; it stops disclosure.*

### Assessment — Case Memo (assigned 1:55, due S3)
Individually, ≤200 words: take the scenario your group ranked most severe and write the memo you would send if you owned it — decision, principle invoked, one concrete action. Not graded; three volunteers read theirs to open Session 3.

**Close.** `SAY:` "Tonight you built the lens. Tomorrow we point it at the most contested ground in academia right now — integrity — and I will show you Stanford evidence that the tool most institutions bought to solve this problem is quietly manufacturing a new injustice. Sleep well. 8.30 sharp."

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# SESI 3 — ACADEMIC INTEGRITY IN THE AI ERA: MANAGING PLAGIARISM, BIAS AND COPYRIGHT ISSUES

**Snapshot:** Wednesday 2/9 · 8.30–10.30 am · Morning, fresh minds, the intellectual pivot of the whole course. This is where detection dies and design takes over.

### Objectives
Participants can (1) explain why AI text detection fails as an evidentiary basis, citing the false-positive bias evidence, (2) reframe integrity from artifact-policing to process-evidence design, (3) run a fair suspected-misuse conversation, (4) state the current copyright fault lines relevant to teaching materials and student work.

### Run Sheet

| Time | Block | Mode |
|---|---|---|
| 0:00–0:10 | Case memos + exit-ticket answers | Participants |
| 0:10–0:20 | Reframe: what plagiarism was for | Script |
| 0:20–0:45 | The detector evidence | Talking points |
| 0:45–1:15 | Exercise: Detector on Trial | Structured debate |
| 1:15–1:40 | The successor regime: process evidence | Talking points + P05 |
| 1:40–1:55 | Copyright fault lines + the misuse conversation | Talking points + script |
| 1:55–2:00 | Contrarian close | Script |

### Script

**Reframe (0:10).** `SAY:` "Plagiarism rules were never the point. They were a *proxy* — a cheap test for an expensive question: did learning happen inside this student? For seventy years the proxy held because producing text was hard. AI made text free, and the proxy snapped. You now have two options: rebuild the proxy with detection technology, or go after the real question directly with assessment design. This morning I'll show you why option one is a trap — with numbers — and what option two looks like on a Tuesday."

### Talking Points — The Detector Evidence (0:20)

This is the evidentiary core of the course. Deliver slowly.

- **The bias result.** Stanford researchers (Liang, Yuksekgonul, Mao, Wu & Zou — Zou is a Stanford HAI affiliate; published in *Patterns*, 2023) ran 91 TOEFL essays written by real non-native English speakers through seven widely used AI detectors. On average, **over 61% were falsely flagged as AI-generated**. The same detectors were near-perfect on essays by US eighth-graders.
- **The mechanism.** Detectors lean on *perplexity* — roughly, how predictable the word choices are. Non-native academic writers naturally use more constrained vocabulary and syntax. The detector isn't detecting AI; it's detecting *limited lexical variety* — which is to say, it's detecting your international students and your ESL students. In a Malaysian university, where most students write English as a second or third language, this is not an edge case. **It is the main case.**
- **The evasion result — same paper.** One prompt — "enhance the word choices to sound more like a native speaker" — flipped misclassified essays back to "human." Read the full implication: the detector *punishes honest non-native writers and passes dishonest users who add one laundering step.* It is biased in one direction and evadable in the other. That is the worst possible combination for an evidentiary instrument.
- **The researchers' own recommendation:** avoid these detectors in evaluative settings. A companion *Patterns* piece states the general case: technical detection of AI content in education is insufficient by construction — the arms race structurally favors generation.
- **The misuse patterns are still real.** Do not swing to denial: Anthropic's Education Report documents students requesting test answers and detector-evading rewrites at scale. The problem is genuine. The tool is wrong.

### Exercise — Detector on Trial (0:45, 30 min)

Structured moot. Motion: *"This institution should treat AI-detector scores as admissible primary evidence in integrity proceedings."* Split each table: two argue for, two against, one judges. **Twist:** assign the *for* side to people who voiced anti-detector views and vice versa (steelmanning is the point). 8 min prep · 4+4 min arguments · 2+2 rebuttal · judges rule with one-sentence ratio. Harvest rulings.

Expected convergence: detector output at most a *screening* signal that triggers human process, never proof. If a table rules otherwise, ask the judge: "Which of your own students is most likely to be falsely flagged?" Let the silence do the teaching.

### Talking Points — The Successor Regime (1:15)

If not detection, then what? **Process evidence** — integrity signals produced *during* creation, not inferred after:

1. **Version trails** — drafts, document history, commit logs. Effort leaves fingerprints; laundering doesn't.
2. **Oral defense sampling** — 5-minute vivas for a random 20% of submissions. Students who did the work pass easily; the deterrence generalizes to 100%.
3. **In-class anchors** — some fraction of every assessment executed live (S5 formalizes this as the three-lane design).
4. **Disclosure as norm, not confession** — an AI-use statement on every submission: what tool, what for, what was verified. Anthropic's own courses model this with an "AI Diligence Statement" disclosing exactly how AI helped build the materials. If a frontier lab discloses, your students can. So can you — run **P05** now to draft your own course AI-disclosure template live.
5. **The design dividend:** every hour moved from policing artifacts to designing process buys you *both* better evidence and better pedagogy. Detection buys you neither.

### Talking Points — Copyright Fault Lines (1:40)

Three live issues; give the map, not legal advice:
- **Training-data provenance** is contested in ongoing litigation worldwide — status unstable; institutional policies should reference principles, not case outcomes that may flip.
- **Ownership of AI-assisted output** varies by jurisdiction; substantial human authorship is generally the anchor. Practical rule for teaching materials: the more you transform, the safer you stand — and disclose regardless.
- **Student IP:** submitting student work into external AI tools without consent raises both copyright and PDPA questions. Institutional accounts + anonymization is the floor (Session 6 encodes it).

**The misuse conversation (script it — this protects both parties).** `SAY:` "When you suspect misuse, the script is: *'Walk me through how you made this. Show me your process — drafts, notes, history. Explain this paragraph's argument in your own words.'* Notice what's absent: no accusation, no detector percentage, no trap. A student who did the work demonstrates it in ninety seconds. A student who didn't reveals it just as fast — and you now hold process evidence a committee can actually stand on."

### Assessment — Redesign Warm-Up Ticket (1:55)
One line, handed in: *name the assessment you will redesign in Session 5* + which lane pattern you suspect it needs. This pre-commits the afternoon's raw material.

**Contrarian close.** `SAY:` "The contrarian position, stated plainly: banning AI is the *least* safe policy available to you. A ban doesn't stop usage — the Index says four in five students are already there. It stops *disclosure*. It converts your most honest students into your most disadvantaged ones and hands the advantage to the laundering-literate. Every ringgit spent on detection is a ringgit spent making adversaries of your students. This afternoon we stop policing and start building. Minum dulu."

---

# SESI 4 — HANDS-ON WORKSHOP: ETHICAL AI TOOLS FOR TEACHING, ASSESSMENT AND STUDENT LEARNING (PART 1)

**Snapshot:** Wednesday 2/9 · 11.00 am–1.00 noon hari · Laptops open, projector mirroring one participant machine at a time. Facilitator circulates, does not lecture. Target ratio: 20 min instruction / 100 min doing.

### Objectives
Participants can (1) apply the 4D framework to a real teaching workflow, (2) run the Description↔Discernment loop through at least three iterations, (3) produce two working, verified prompt workflows, (4) maintain a verification log as a professional artifact.

### Run Sheet

| Time | Block | Mode |
|---|---|---|
| 0:00–0:15 | The 4Ds in twelve minutes | Talking points |
| 0:15–0:30 | Live build: facilitator models the loop | Demo (P07) |
| 0:30–1:05 | Lab 1: Rubric builder | Individual, coached |
| 1:05–1:40 | Lab 2: Feedback assistant | Individual, coached |
| 1:40–1:55 | Gallery: three screens | Participants |
| 1:55–2:00 | Bridge to Part 2 | Script |

### Talking Points — The 4Ds in Twelve Minutes (0:00)

Source: the AI Fluency Framework (Dakan & Feller, developed with Anthropic; CC BY-NC-SA — meaning you may legally remix these materials for your own courses, and this course does exactly that).

- **Delegation** — the decision before the prompt: should AI touch this task at all, and in what role? Heuristic for educators: delegate *production* (drafts, variants, formats), never *judgment* (grades, admissions, integrity findings). Judgment tasks get AI as a second reader at most.
- **Description** — prompting as professional communication, not incantation. Six moves that do the work: give context, give examples, set constraints, request steps, ask it to think before answering, define role and tone. If you can brief a research assistant, you already have this skill — you've just been under-briefing the model.
- **Discernment** — the counterpart: Description shapes what goes in, Discernment judges what comes out. Three layers to check: *product* (is it correct and complete?), *process* (is the reasoning sound or merely fluent?), *behavior* (is it drifting from instructions, over-agreeing, padding?).
- **Diligence** — owning the output: verify before it touches students, disclose how it was made, protect data going in. Diligence is the D that makes the other three ethical rather than merely effective.

The morning runs the **Description↔Discernment loop**: describe → generate → discern → re-describe. Fluency is the loop run fast, not the first prompt written well. (Remaining pair — Delegation↔Diligence — is this afternoon's spine.)

### Demo — Facilitator Models the Loop (0:15)

Live, narrating your own Discernment out loud. Run **P07** (rubric builder) with a deliberately thin prompt first ("make me a rubric for an essay"). Show the generic mush. Then rebuild with full Description — course, level, learning outcome, band descriptors, local grading scale — and show the difference. Then discern *aloud*: "Criterion three overlaps criterion one — that's the model padding. The band language for 'credit' isn't observable behavior — rewrite." Two more iterations. `SAY:` "What you just watched is the entire skill. Not the prompt — the *loop*. Now you run it."

### Lab 1 — Rubric Builder (0:30, 35 min)

Each participant picks a real assessment from a course they teach **this semester** (no hypotheticals — the artifact must be deployable Monday).

Workflow: run **P07** with full context → iterate minimum 3 rounds → each round, log one row in the **Verification Log**:

| # | What I asked | What was wrong/weak | What I changed |
|---|---|---|---|

Quality bar (posted on screen): every criterion observable; band descriptors distinguishable by a colleague; aligned to a stated learning outcome; local grade-scale compliant. Coaching pattern as you circulate: never touch keyboards; ask "what's wrong with this output?" and make *them* name it — Discernment is trained by articulation, not correction.

### Lab 2 — Feedback Assistant (1:05, 35 min)

Higher stakes: output now touches students directly, so Diligence rules bind.

Setup rules (non-negotiable, on screen throughout): use a **past, anonymized** student excerpt — names, IDs, identifying details stripped before anything is pasted. This *is* item 17 of the EAI-CMM being practiced.

Run **P08**: model as feedback *drafter* against the Lab-1 rubric, producing (a) three strengths, (b) three growth points phrased as questions, (c) one suggested next step — explicitly *not* a grade (Delegation boundary: production yes, judgment no). Iterate: first outputs are usually too long, too generic, or too kind. Discern for: hallucinated praise of things the text doesn't do; feedback the student can't act on; tone mismatch with your voice. Finish by editing the AI draft into *your* voice — `SAY:` "The feedback that reaches the student is yours. The model drafted; you authored. That distinction is the whole ethics of this lab."

### Gallery + Assessment (1:40)
Three volunteers project their verification logs (not their final outputs — the *logs*). The room inspects the iteration path. Formative check, collected: each participant submits their log with ≥3 rows and one sentence: "The most useful thing I changed between round 1 and round 3 was ___." A log with real deltas = the session's objective, met.

**Bridge (1:55).** `SAY:` "You now have leverage — two workflows that give you hours back. This afternoon we spend those hours where they matter most: on the assessments themselves, and on the hardest question in this whole field — proof that AI can raise your students' scores while *lowering* their learning. Makan dulu; come back dangerous."

---

# SESI 5 — HANDS-ON WORKSHOP: ETHICAL AI TOOLS FOR TEACHING, ASSESSMENT AND STUDENT LEARNING (PART 2)

**Snapshot:** Wednesday 2/9 · 2.30–4.30 afternoon · The keystone session. Everything before feeds it; everything after packages it. Post-lunch: open with evidence that shocks, not slides that soothe.

### Objectives
Participants can (1) explain the crutch effect with experimental evidence, (2) redesign an AI-vulnerable assessment using the three-lane pattern, (3) build and test a Socratic tutor with pedagogical guardrails, (4) write the student-facing AI policy paragraph for one course.

### Run Sheet

| Time | Block | Mode |
|---|---|---|
| 0:00–0:20 | The crutch effect: the one study to remember | Talking points |
| 0:20–0:30 | The design answer: seven roles, three lanes | Talking points |
| 0:30–1:10 | Lab 3: Assessment redesign sprint | Pairs |
| 1:10–1:40 | Lab 4: Build a guardrailed tutor | Individual (P10) |
| 1:40–1:55 | Lab 5: The policy paragraph | Individual (P11) |
| 1:55–2:00 | Contrarian close | Script |

### Talking Points — The Crutch Effect (0:00)

If participants remember one study from three days, it is this one. Bastani et al., University of Pennsylvania/Wharton, published in *PNAS* (2025) — a randomized controlled trial, nearly 1,000 high-school students, Turkish school, real math curriculum:

- Three arms: **GPT Base** (vanilla ChatGPT-style interface), **GPT Tutor** (same model, system prompt engineered with teacher-designed hints and a hard rule against giving final answers), **control** (no AI).
- During assisted practice: both AI arms soared — **+48%** performance for GPT Base, **+127%** for GPT Tutor. Every dashboard in the school said AI was working.
- Then the tools were removed for an unassisted exam. GPT Base students scored **17% worse than students who never had AI at all.** They had practiced *asking*, not *solving* — the paper's word is "crutch."
- GPT Tutor students: statistically indistinguishable from control — the harm was engineered away, though (be honest about this) the unassisted *gain* was near zero. Guardrails in this study bought safety, not superpowers.

Three lessons, stated as axioms:
1. **Performance ≠ learning** (A5). Assisted metrics can be actively misleading about skill acquisition.
2. **Design determines outcome.** The *same model* harmed or didn't depending on a system prompt. Ethics lives in configuration, not in the technology.
3. **The dashboard lies in one direction.** Everything visible during AI-assisted learning looks like success; the damage only appears when the tool is removed. So *build tool-removal into your assessments* — that is what the three-lane pattern does.

Cross-reference honestly: MIT's cognitive-debt EEG work (S2) points the same direction at the neural level but is preprint-stage; Bastani is the RCT you can defend in senate. Teach them the difference — that *is* Discernment.

### Talking Points — Seven Roles, Three Lanes (0:20)

**Seven roles for AI in learning** (Mollick & Mollick, Wharton): mentor (feedback), tutor (guided instruction), coach (metacognitive prompts), student (the learner teaches the AI — powerful and underused), simulator (practice scenarios), teammate, tool. Note which roles the crutch effect threatens (tutor, tool used as answer-machine) and which it doesn't (student, coach — these *increase* cognitive work). Role selection is a Delegation decision.

**The three-lane pattern** — the course's core design move. Every assessment declares one lane per component:

| Lane | Rule | What it protects | Example |
|---|---|---|---|
| 🔴 **AI-Restricted** | No AI; done live/supervised | Foundational skill the student must own unaided (the "exam" from the PNAS study) | In-class problem set, viva, closed-book segment |
| 🟡 **AI-Permitted** | Allowed with disclosure statement | Authentic practice — mirrors professional reality | Take-home analysis + AI-use disclosure + process trail |
| 🟢 **AI-Required** | Mandatory, evaluated on *interaction* | AI fluency itself as a learning outcome | Submit prompt log + critique of AI output + your improvement |

The lanes convert the integrity problem (S3) and the learning problem (this session) into one design vocabulary. A course is well-designed when every learning outcome is protected by at least one 🔴 component and every graduate has passed through at least one 🟢.

### Lab 3 — Assessment Redesign Sprint (0:30, 40 min)

Pairs. Input: the assessment each named in the S3 warm-up ticket. Protocol:
1. **Audit (10 min):** run your own assessment through AI (**P09**). Grade the output honestly. If it scores ≥B, the assessment is confirmed AI-vulnerable — say so out loud; naming it is the unlock.
2. **Redesign (20 min):** split it into components and assign lanes. Constraints: ≥1 🔴 component protecting the core outcome; the 🟡 component must specify its process evidence (which of S3's mechanisms); total student workload flat or lower (redesign, not inflation).
3. **Swap-test (10 min):** partners attack each other's redesign wearing a student hat: "How would I hollow this out with AI?" Patch the best exploit found.

Output artifact: one-page redesign sheet (template in Appendix D) — goes into the S7 portfolio.

### Lab 4 — Build a Guardrailed Tutor (1:10, 30 min)

Participants build the GPT-Tutor pattern themselves using **P10**: a system prompt establishing a Socratic tutor for one specific topic they teach — never gives final answers, responds with guiding questions and teacher-style hints, requires the student to attempt first, checks understanding by asking for explanation back.

Test protocol (this is the assessment): switch to student mode and *try to break it* — demand the answer, plead deadline, claim confusion. A tutor that holds under three social-engineering attempts passes. Compare with Claude's Learning Mode live if time allows — same design philosophy, productized. `SAY:` "Notice what you just did: you closed the gap from this morning's study with fifteen lines of instruction. The difference between the −17% tool and the safe tool was never money or model. It was *intent, written down*. Keep that feeling for tonight — that's what a policy is."

### Lab 5 — The Policy Paragraph (1:40, 15 min)

Individually, using **P11** as drafting partner then editing to own voice: the AI-use paragraph for one actual course document — lane declarations per assessment, the disclosure norm, one sentence on *why* (students comply with reasons, not rules). ≤150 words. Three read aloud; room applies the test: *could a first-year act on this without asking a single clarifying question?*

### Assessment
Portfolio checkpoint — by end of S5 each participant holds four artifacts: verification log (S4), redesign sheet, tutor prompt + break-test note, policy paragraph. These are the raw material for S6–S8. Exit ticket: one word describing your redesigned assessment's weakest remaining point (harvest; feed into tonight's gap analysis).

**Contrarian close.** `SAY:` "Contrarian claim of the afternoon: the most ethical AI policy your institution can adopt is not a policy at all — it is a better assignment. Every hour a committee spends wordsmithing prohibition clauses buys less integrity than the forty minutes you just spent redesigning one assessment. Tonight we write policy anyway — because institutions need it, because clarity is a justice issue, and because you now write it as builders, not as police. That order was the point of today."

---

# SESI 6 — DEVELOPING INSTITUTIONAL GUIDELINES FOR ETHICAL AI USE IN TEACHING AND LEARNING (PART 1)

**Snapshot:** Wednesday 2/9 · 8.00–10.00 pm · Second evening slot: discussion-heavy by design. The pivot from personal practice to institutional architecture. Groups now become **drafting teams** (keep table composition; from here they ship together).

### Objectives
Participants can (1) score their institution on the institutional maturity scan, (2) name the seven components of a complete AI guideline, (3) locate their institution's three largest gaps with evidence, (4) enter S7 with an agreed drafting brief.

### Run Sheet

| Time | Block | Mode |
|---|---|---|
| 0:00–0:10 | Re-entry: from craft to constitution | Script |
| 0:10–0:30 | Institutional maturity scan | Teams |
| 0:30–0:55 | Policy anatomy: seven components | Talking points |
| 0:55–1:10 | What good looks like (exemplar scan) | Talking points |
| 1:10–1:45 | Exercise: Gap analysis | Teams (P12) |
| 1:45–2:00 | Drafting brief + close | Teams + script |

### Script

**Re-entry (0:00).** `SAY:` "This afternoon you fixed a course. Tonight we ask why you had to. A lecturer redesigning assessments alone is heroism; heroism is what institutions run on when governance is absent. The Index number from Session 1: only 6% of teachers say their school's AI policies are clear. Not 6% say policies are *good* — 6% say they're *clear*. Clarity is the whole product tonight. Axiom seven: policy is a product. It ships with a version number, it has users, and it dies without maintenance. You are now product teams."

### Exercise — Institutional Maturity Scan (0:10, 20 min)

Teams score their institution (or faculty, if central policy is absent — that fact itself is a datum) on ten items, 0–4 scale, same bands logic as the EAI-CMM (0–13 L0/Unaware · 14–20 L1/Aware · 21–27 L2/Experimenting · 28–33 L3/Integrating · 34–37 L4/Governing · 38–40 L5/Stewarding):

1. A current, findable, institution-level AI-in-education policy exists.
2. Policy distinguishes contexts (coursework vs exams vs research vs admin) rather than one blanket rule.
3. A student-facing version exists in plain language students actually read.
4. Assessment-design guidance exists (not just conduct rules).
5. Integrity procedures specify what counts as evidence — and what doesn't (detector-score status explicit).
6. Data rules govern what student information may enter which tools (PDPA-mapped).
7. Staff development on AI is funded and recurring, not a one-off talk.
8. Equity of access is addressed (institutional licenses/alternatives, not bring-your-own-subscription).
9. A named owner and review cadence exist (policy has a maintainer).
10. Students had a voice in drafting.

Facilitator harvests team totals on the board. Typical Malaysian HE result in 2026: L1–L2. `SAY:` "Nobody in this room caused this score, and everybody in this room can move it. The Index found only about half of schools have AI policies at all — your institution having *a* score puts it mid-pack. Thursday it moves."

### Talking Points — Policy Anatomy: Seven Components (0:30)

A complete guideline answers seven questions. This is the drafting skeleton for S7:

1. **Scope & definitions** — what counts as "AI use," who and what is covered. Most policy fights are secretly definition fights; settle them here.
2. **Principles** — the five from S2 (beneficence, non-maleficence, autonomy, justice, explicability), localized. Principles are the layer that survives model churn; rules below them get versioned.
3. **The permission architecture** — the three-lane vocabulary, institutionalized: every course declares lanes per assessment. This single move converts an unenforceable blanket rule into a thousand enforceable local ones.
4. **Disclosure standard** — one canonical AI-use statement format, used by students *and staff* (symmetry is credibility; cite the Anthropic diligence-statement pattern from S3).
5. **Integrity procedure** — process evidence as primary; detector output explicitly demoted to at-most-screening (attach the Stanford *Patterns* citation directly in the policy — policies with footnotes get challenged less).
6. **Data & privacy rules** — the PDPA floor: what student data may enter which class of tool under what agreement; institutional accounts over personal ones.
7. **Ownership & review** — named owner, version number, review date ≤12 months out, student representation in review. A policy without an owner is graffiti.

### Talking Points — What Good Looks Like (0:55)

Patterns from institutions that published early and well — extract the *moves*, not the text:
- **Devolved-but-scaffolded** (the pattern across leading US universities: Stanford's guidance, Harvard's, MIT's): a short university-level floor — privacy, disclosure, defaults — with explicit instructor authority to set course-level rules *provided they state them in writing.* Balances academic freedom against student whiplash. Adopt this shape.
- **Default rules matter more than ideal rules.** The best policies state what applies when a syllabus is *silent* — because most syllabi will be. Decide your default tonight: silence = 🟡 permitted-with-disclosure is the honest choice; silence = 🔴 restricted is the common but fictional one.
- **The teachable-policy test:** if it cannot be taught to first-years in ten minutes, it will not govern anything. Length is a bug.
- **Local layer (placeholder for your institution):** align terminology with MQA programme standards and any current MOHE guidance; map component 6 explicitly to PDPA 2010. These slots are marked `[LOCAL]` in the S7 template — filled by whoever owns component 7.

### Exercise — Gap Analysis (1:10, 35 min)

Teams take their maturity-scan results + the seven components and produce a one-page gap analysis using **P12** as a drafting partner against their real (or absent) current policy: for each of the three lowest-scoring areas — *current state* (evidence, one line) · *target state* (which component fixes it) · *cost of inaction* (one concrete scenario from this course's evidence: a false accusation, a crutch-effect cohort, a PDPA breach). Cost-of-inaction is mandatory: committees move on scenarios, not scores.

### Assessment + Drafting Brief (1:45)
Each team submits its brief for tomorrow: three gaps ranked, default rule chosen, disclosure format sketched, `[LOCAL]` owner nominated. This is the entry ticket to S7 — no brief, no draft.

**Close.** `SAY:` "Tomorrow morning you write version 0.1. Not the perfect policy — the *shippable* one. Perfect is what institutions say while shipping nothing. Tidur — the sprint starts at 8.30."

---

# SESI 7 — DEVELOPING INSTITUTIONAL GUIDELINES FOR ETHICAL AI USE IN TEACHING AND LEARNING (PART 2)

**Snapshot:** Thursday 3/9 · 8.30–10.30 am · Pure production sprint + adversarial review + measurement. Output: Guideline v0.1 per team, EAI-CMM delta per person, action plan skeleton for Session 8.

### Objectives
Participants can (1) produce a complete seven-component Guideline v0.1, (2) conduct and survive a structured red-team review, (3) quantify their three-day capability movement, (4) convert the guideline into a personal 90-day action plan.

### Run Sheet

| Time | Block | Mode |
|---|---|---|
| 0:00–0:05 | Sprint rules | Script |
| 0:05–0:50 | Drafting sprint: Guideline v0.1 | Teams (P13) |
| 0:50–1:15 | Red-team exchange | Teams (P14) |
| 1:15–1:30 | Patch round | Teams |
| 1:30–1:40 | EAI-CMM retake + delta | Individual |
| 1:40–1:55 | Action plan skeleton | Individual (P15) |
| 1:55–2:00 | Contrarian close: the last axiom | Script |

### Script

**Sprint rules (0:00).** `SAY:` "Forty-five minutes, seven components, two pages maximum, version number and review date on page one. You may use AI heavily — this is a 🟢 lane task — under the discipline you built Tuesday: describe with full context, discern every clause, disclose at the bottom. Your guideline will carry its own AI-use statement. A policy about AI transparency that hides its own AI use is dead on arrival. Mula."

### Drafting Sprint (0:05, 45 min)

Teams draft against the seven-component skeleton using **P13**, feeding it their gap analysis, chosen default rule, and policy paragraph artifacts from S5 (personal practice becomes institutional text — this is the course's whole trajectory landing). Facilitator circulates with three interventions only: "Which component is that?" · "Can a first-year act on this sentence?" · "Where's your review date?"

Hard constraints: ≤2 pages · three-lane vocabulary used · detector-evidence status explicit · PDPA clause present · `[LOCAL]` slots marked, owner named · AI-use disclosure statement at the foot.

### Red-Team Exchange (0:50, 25 min)

Teams swap drafts. Attacking team uses **P14** plus their own malice, hunting in four personas — 10 minutes:
1. **The laundering student:** where does this policy leave me a legal-looking cheat path?
2. **The overworked lecturer:** which clause will I ignore because compliance costs more than violation?
3. **The falsely accused:** does the integrity procedure protect me, or just the institution?
4. **The auditor:** which claim has no owner, no evidence standard, or no review mechanism?

Findings delivered as written bullets, ranked by severity — no oral debate (drafters defend in patches, not speeches). Then 15-minute patch round: fix the top three findings, log the rest in a "v0.2 backlog" section (backlogs are how products stay honest about incompleteness).

Facilitation note — this is the SSA-CMM adversarial move applied to policy: `SAY:` "A guideline nobody attacked is a guideline nobody read. You have just been read more carefully than most national policies ever are."

### EAI-CMM Retake + Delta (1:30, 10 min)

Same instrument, same honesty guard. Each participant computes: total delta, biggest-moving pillar, stubbornest item. Show of hands by band — compare to Session 1's distribution on the board. Name the pattern out loud: Pedagogy and Governance move most because *the course forced artifacts*; Literacy moves least because depth takes months. `SAY:` "Your delta is real but bounded — you moved because you *made things*. The route card for your next level is in your pack. It works the same way: artifacts, not intentions."

### Action Plan Skeleton (1:40, 15 min)

Individual, feeding Session 8's presentation. Format (**P15** as drafting partner, template in Appendix D):
- **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 the process evidence it was designed to produce.
- **90 days:** move Guideline v0.1 one institutional step — department meeting, faculty committee, or senate paper — with a named ally and a date.
- **The kill criterion** (mandatory, the field most plans lack): "I will know this failed if ___ by ___." Plans without falsifiability are wishes.

### Assessment
Summative portfolio now complete — six artifacts: baseline+retake EAI-CMM with delta · verification log · assessment redesign sheet · tutor prompt with break-test · Guideline v0.1 with red-team backlog · 90-day action plan. Session 8 presents artifacts 5–6 against the rubric below.

**Contrarian close — the last axiom.** `SAY:` "Final contrarian claim of the course, and it is aimed at the room, not at the technology. The scarce resource in AI ethics is not principles — the world has published hundreds of frameworks. It is not even evidence — Stanford hands you a fresh Index every April. The scarce resource is *institutional metabolism*: the ability to convert evidence into shipped, versioned, owned practice faster than the capability curve moves. Three days ago that gap was the curriculum. This morning, for your institution, you became the gap-closing mechanism. Version 0.1 is in your hands. Session 8: show us. Then go ship."

---

# SESI 8 — ACTION PLAN PRESENTATION: SUMMATIVE RUBRIC
**Thursday 3/9 · 11.00 am–1.00 noon**

7 minutes per person/team: 5 to present, 2 for panel questions. Score 1–4 per criterion (max 20). Panel: facilitator + one institutional leader + one peer judge (rotate).

| 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 |

Pass ≥12 · Distinction ≥17. Award one **"Ship It"** recognition to the plan the panel would fund tomorrow.

---

# APPENDIX A — PROMPT LIBRARY (P01–P15)

Copy-paste ready. `[BRACKETS]` = fill before running. All prompts are model-agnostic.

**P01 — Cold-open assignment test (S1)**
> You are a strong student in [COURSE, LEVEL]. Complete this assignment exactly as submitted work: "[PASTE ASSIGNMENT QUESTION]". Length and format per instructions. Do not mention AI.

**P02 — Capability mapper (S1 follow-up / homework)**
> I teach [SUBJECT] at [LEVEL]. List 10 tasks in my discipline: rate each Strong / Uneven / Weak for current AI, one sentence of reasoning each, and flag which ratings you are least certain about.

**P03 — Bias probe: reference letters (S2 demo)**
> Write a 150-word academic reference letter for Ahmad, a final-year [DISCIPLINE] student: CGPA 3.7, led the student chapter, co-authored one conference paper.
> *(New chat, identical except the name:)* Write a 150-word academic reference letter for Aisyah, a final-year [DISCIPLINE] student: CGPA 3.7, led the student chapter, co-authored one conference paper.
> *(Compare adjectives, verbs, emphasis. Repeat across models/languages for the audit habit.)*

**P04 — Bias probe: cultural default (S2 optional)**
> Describe a typical successful university student's daily routine. *(Then:)* Now audit your own answer: which cultural, economic, and geographic assumptions did you embed? Rewrite for a low-income student at a Malaysian public university.

**P05 — Disclosure statement drafter (S3)**
> Draft a 4-line AI-use disclosure template for student submissions in [COURSE]: tool(s) used, what they were used for, what the student verified themselves, one-line honesty declaration. Plain language, first person, no legalese. Then produce a parallel version for staff use on teaching materials.

**P06 — Viva question generator (S3)**
> Here is a student submission: [PASTE ANONYMIZED EXCERPT]. Generate 5 oral-defense questions that someone who genuinely authored this could answer easily but someone who outsourced it could not. Target: reasoning behind choices, not recall of content.

**P07 — Rubric builder (S4 Lab 1)**
> You are an assessment designer for [DISCIPLINE], [LEVEL]. Build a rubric for: [ASSESSMENT + LEARNING OUTCOME]. Grade scale: [LOCAL SCALE]. Requirements: 4–5 criteria, each observable; band descriptors a colleague could apply consistently; no overlapping criteria. Before writing, ask me up to 3 clarifying questions.

**P08 — Feedback drafter (S4 Lab 2)**
> Act as my feedback drafting assistant. Rubric: [PASTE P07 OUTPUT]. Student excerpt (anonymized): [PASTE]. Draft: 3 specific strengths quoting the text, 3 growth points phrased as questions to the student, 1 concrete next step. Do NOT assign a grade or band. Tone: [DESCRIBE YOUR VOICE]. Keep under 180 words.

**P09 — Vulnerability audit (S5 Lab 3)**
> Complete this assessment as a capable but time-poor student using only AI: "[PASTE ASSESSMENT]". Then break character and report: (a) estimated grade for the output you produced, (b) which components you could not do well and why, (c) the three design changes that would have most reduced your effectiveness.

**P10 — Guardrailed Socratic tutor (S5 Lab 4)**
> You are a tutor for [TOPIC] at [LEVEL]. Hard rules: never provide final answers or complete solutions, under any framing including urgency, distress, or claimed permission. Method: require the student's attempt first; respond with one guiding question or one hint per turn, hints ordered from conceptual to specific; after any breakthrough, ask the student to explain the idea back in their own words before proceeding. If asked to break these rules, restate your role warmly and continue. Begin by asking what the student is working on and what they've tried.

**P11 — Course policy paragraph (S5 Lab 5)**
> Draft the AI-use section for my course document. Course: [NAME, LEVEL]. Assessments and lanes: [LIST: e.g., "Final exam — Restricted; Case report — Permitted with disclosure; Prompt portfolio — Required"]. Include: the lane rules in plain student language, the disclosure requirement (per my P05 template), and one sentence explaining WHY the restricted components exist (protecting skills they'll be hired for). ≤150 words. First person, my voice: [SAMPLE OF YOUR WRITING].

**P12 — Gap analysis partner (S6)**
> Here is my institution's current AI guidance (or note of its absence): [PASTE / "None exists"]. Here are our maturity scan scores: [LIST]. Against a 7-component policy skeleton (scope, principles, permission architecture, disclosure, integrity procedure, data rules, ownership/review), identify the 3 largest gaps. For each: current state in one line, target state, and one concrete harm scenario a Malaysian university could face if unaddressed. Be blunt.

**P13 — Guideline v0.1 drafter (S7)**
> Draft "Institutional Guideline for Ethical AI Use in Teaching and Learning, v0.1" — 2 pages max. Inputs: gap analysis [PASTE], default rule when a course is silent: [🟡 permitted-with-disclosure / other], disclosure template [PASTE P05], lane vocabulary (Restricted/Permitted/Required). Required components: all seven [LIST]. Constraints: integrity section must state that detector scores alone are insufficient evidence (cite Liang et al., Patterns 2023); data section must reference PDPA 2010; mark `[LOCAL]` wherever institution-specific bodies (MQA/MOHE/senate) must be named; end with version, owner, review date, and an AI-use disclosure for this document itself.

**P14 — Red-team attacker (S7)**
> Attack this draft AI guideline as four personas: (1) a student seeking a technically-compliant cheating path, (2) an overloaded lecturer looking for clauses to ignore, (3) a falsely accused student checking their protections, (4) an auditor hunting unowned claims and missing evidence standards. Draft: [PASTE]. Output: numbered findings, severity-ranked, each with the exact clause exploited and a one-line fix.

**P15 — Action plan sharpener (S7)**
> Here is my 7/30/90-day plan: [PASTE]. Stress-test it: (a) flag every step lacking an owner, date, or existing artifact, (b) identify the single most likely failure point, (c) propose a specific, measurable kill criterion, (d) rewrite the 90-day step to require one *other named person* — plans executed alone die alone.

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# APPENDIX B — SOURCE REGISTER

**Primary (Stanford HAI):**
- 2026 AI Index Report — hai.stanford.edu/ai-index/2026-ai-index-report (esp. Education chapter: /education)
- HAI Education programs & mission — hai.stanford.edu/education
- "AI-Detectors Biased Against Non-Native English Writers" (HAI News) — hai.stanford.edu/news/ai-detectors-biased-against-non-native-english-writers
- "AI's 'Delusional Spirals' (and What to Do About Them)" (HAI News, Apr 2026) — hai.stanford.edu/news/ais-delusional-spirals-and-what-to-do-about-them

**Stanford research:**
- Liang, Yuksekgonul, Mao, Wu & Zou (2023). "GPT detectors are biased against non-native English writers." *Patterns* 4(7). DOI 10.1016/j.patter.2023.100779 · arXiv:2304.02819
- Wang, Demszky et al. (2024). "Tutor CoPilot: A Human-AI Approach for Scaling Real-Time Expertise." Stanford (working paper/arXiv).

**Ivy League / top-tier:**
- Bastani, Bastani, Sungu, Ge, Kabakcı & Mariman (2025). "Generative AI without guardrails can harm learning: Evidence from high school mathematics." *PNAS* 122(26). DOI 10.1073/pnas.2422633122
- Mollick & Mollick (Wharton, 2023). "Assigning AI: Seven Approaches for Students with Prompts." SSRN working paper.
- Kosmyna et al. (MIT Media Lab, 2025). "Your Brain on ChatGPT: Accumulation of Cognitive Debt…" arXiv preprint (N=54; cite with limitations).
- Floridi et al. (Oxford). "AI4People — An Ethical Framework for a Good AI Society." *Minds and Machines* (2018) — the five-principles synthesis.
- Wan et al. (2023). "'Kelly is a Warm Person, Joseph is a Role Model': Gender Biases in LLM-Generated Reference Letters." EMNLP Findings.
- Harvard CS50 AI tutor deployment (Malan et al., SIGCSE reports) — the guide-don't-answer pattern at scale.
- Institutional guidance shape references: Stanford, Harvard, MIT generative-AI teaching guidance pages (devolved-but-scaffolded pattern).

**Frontier lab (Anthropic):**
- AI Fluency Framework & course family (Dakan, Feller & Anthropic, CC BY-NC-SA 4.0) — aifluencyframework.org · anthropic.skilljar.com/ai-fluency-framework-foundations (also: /ai-fluency-for-educators, /ai-fluency-for-students, /teaching-ai-fluency)
- Anthropic Education Report: How University Students Use Claude (Apr 2025) — anthropic.com/news/anthropic-education-report-how-university-students-use-claude
- Anthropic faculty-usage education report (Aug 2025, ~74k conversations).

**Local statutory anchor (uncited, structural):** Personal Data Protection Act 2010 (Malaysia); `[LOCAL]` slots for MQA / MOHE alignment.

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# APPENDIX C — MATERIALS & LOGISTICS CHECKLIST

**Facilitator kit:** projector + spare HDMI/USB-C; two AI accounts pre-logged (primary + backup vendor — live demos fail; vendors differ); offline screenshots of every live demo (P01, P03, Learning Mode) as fallback; printed EAI-CMM ×2 per participant; scenario cards ×8 per table; A2 flip charts + markers per table; index cards (exit tickets); timer visible to room.
**Per participant:** laptop + working AI account (send setup instructions 1 week prior; verify at DAFTAR MASUK); one real course's materials (assessments + syllabus) — mandatory pre-work; the pack's Appendix A+D printed.
**Room:** tables of 4–5, mixed-discipline, fixed for 3 days; wall space for the Threat/Gift master chart (stays up all course); Wi-Fi stress-tested for 30+ concurrent AI sessions.
**Pre-course email (T-7 days):** account setup, bring-a-real-course instruction, optional primer: AI Fluency Framework & Foundations (free, ~3–4 h).

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# APPENDIX D — ASSESSMENT INSTRUMENT TEMPLATES

**D1. Exit ticket (S1, S5):** 3 facts that survived your skepticism · 2 things you'll try this week · 1 question you need answered. *(S5 variant: one word — your redesign's weakest point.)*

**D2. Verification log (S4):** table — Round # · What I asked · What was wrong/weak · What I changed. Minimum 3 rows per workflow. The log, not the output, is the graded artifact.

**D3. Redesign sheet (S5):** Assessment name · Protected learning outcome · AI-audit grade before redesign · Components table (Component / Lane 🔴🟡🟢 / Process evidence / Weight) · Exploit found in swap-test · Patch applied.

**D4. Case memo (S2):** ≤200 words — Decision · Principle invoked · One concrete action · Owner.

**D5. Guideline v0.1 skeleton (S7):** the seven components + version block (v0.1 · owner · review date) + `[LOCAL]` slots + document AI-use disclosure + v0.2 backlog.

**D6. Action plan (S7→S8):** 7-day (deploy built workflow) · 30-day (run redesigned assessment, collect process evidence) · 90-day (guideline one institutional step, named ally, date) · Kill criterion ("I will know this failed if ___ by ___").

**D7. EAI-CMM score sheet:** 20 items × 0–4 · pillar subtotals · total · band · delta (S7) · next-level route card acknowledgment.

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*Pack v1.0 · Updated for 1–3 Sep 2026 (Tuesday–Thursday) delivery · Drafted with AI assistance (Claude) under human direction and verification; all sources restricted to the register in Appendix B — practicing the diligence it preaches. Review after first delivery: 4 Sep 2026.*