# EAI-CMM — Ethical AI Educator Capability Maturity Model
*Print two copies per participant (S1 baseline · S7 retake). Adapted from the SSA-CMM maturity-ladder pattern.*

**Honesty guard (read aloud 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."

**Scale:** 0 Never/No · 1 Rarely · 2 Sometimes · 3 Usually · 4 Consistently/Institutionalized. **Max 80.**

## LITERACY — Do I understand what this technology is and isn't?
| # | Statement | 0–4 |
|---|---|---|
| 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 — Do my teaching and assessment designs account for AI?
| # | Statement | 0–4 |
|---|---|---|
| 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 — Is my integrity regime built on design or on policing?
| # | Statement | 0–4 |
|---|---|---|
| 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 — Can I evaluate AI output, and teach students to?
| # | Statement | 0–4 |
|---|---|---|
| 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 — Do I operate within (and contribute to) explicit rules?
| # | Statement | 0–4 |
|---|---|---|
| 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. | |

## Score sheet
| Pillar | Subtotal /16 |
|---|---|
| Literacy | |
| Pedagogy | |
| Integrity | |
| Discernment | |
| Governance | |
| **Total /80** | |
| **Band/Level** | |
| **Delta (S7 only)** | |
| Biggest-moving pillar · stubbornest item (S7 only) | |

## Levels
| 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. |

## Route to the next level
- **L0→L1:** One AI assistant, 20 min daily, two weeks, real tasks. Read one primary source (start: 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 with the three-lane pattern. Replace detector reliance with two forms of process evidence. Add a disclosure norm — for students and yourself.
- **L3→L4:** Document your workflows so a colleague could run them. Keep verification logs. Take one integrity case through a design-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 maintain a living policy product.

## Institutional variant (S6, teams, 10 items × 0–4, max 40)
Score your institution (or faculty if central policy is absent — that absence is itself a datum):

1. A current, findable, institution-level AI-in-education policy exists.
2. Policy distinguishes contexts (coursework / exams / research / admin), not one blanket rule.
3. A student-facing plain-language version exists that 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, not bring-your-own-subscription).
9. A named owner and review cadence exist (the policy has a maintainer).
10. Students had a voice in drafting.

**Bands:** 0–13 L0 Unaware · 14–20 L1 Aware · 21–27 L2 Experimenting · 28–33 L3 Integrating · 34–37 L4 Governing · 38–40 L5 Stewarding.