# TEACH Capabilities and Badge-Evidence Model

## Binding implementation contract

**Normative configuration:** `config/EDUCATOR-Capability-Mastery-Criteria.v1.json`  
**Canonical program:** `NAIO-NEID-COMPLETE-TEACH-1.0`  
**Foundation / overlay:** `NAIO-NE-LPEF-1.0` / `NAIO-NEID-TEACH-1.0`  
**Canonical namespace:** `neid_teach.*`  
**Route:** `/nurse-educators-designers/dashboard`

The four levels, `CAP-*` identifiers, criteria, evidence schema, scoring algorithm, and agent-orchestration capstone are build-layer additions. The legacy TEACH AI Literacy Passport is developmental and explicitly not certification, clinical competence, teaching qualification, or institutional authorization. This model preserves and strengthens that boundary.

## 1. Meaning and noncredential notice

A TEACH capability badge means that eligible evidence currently satisfies a transparent versioned set of development criteria. It does **not** mean:

- continuing-education credit, academic credit, certification, licensure, credential, degree, or endorsement;
- teaching, instructional-design, clinical, simulation, assessment, grading, accessibility, research, accreditation, legal, or copyright competence;
- faculty appointment, promotion eligibility, employment validation, performance rating, clinical clearance, or professional standing;
- institutional, program, course, learner-data, assessment, publication, research, ethics, curriculum, or release authority;
- permission to activate a power, agent, connector, memory category, automation, or external action; or
- proof of learner, teaching, program, accessibility, equity, clinical, workload, publication, or wellbeing outcomes.

Badges unlock nothing. They never alter the three role-adapter ceilings, five workspace boundaries, two deployment contexts, data classes, EDENA, `absolute_stop`, TEACH Gate, agent permission, or external-only execution rule.

The exact adapters are **Nurse Educator**, **Instructional Designer**, and **Hybrid or Faculty Developer**. The exact workspaces are **Life, Capacity & Career**; **Teaching, Learning Design & Accessibility**; **Assessment, Feedback & Learner Support**; **Clinical, Skills-Lab & Simulation**; and **Curriculum, Program Evaluation, Scholarship & Faculty Development**. The closed classes are `TEACH-DATA-0`, `TEACH-DATA-1`, `TEACH-DATA-2`, `TEACH-DATA-M`, `TEACH-DATA-C`, `TEACH-DATA-A`, `TEACH-DATA-R`, and `TEACH-DATA-X`.

Capability evidence never advances an artifact beyond the exact lifecycle `Exploration → Simulation → Recommendation → Draft Artifact → Approved Plan (human scope review only) → Authorized Execution (external human/official-system only; no app action) → Completed Action (verified external reference only) → Evaluated Outcome`.

## 2. Exact mastery levels

### Level 1 — Basic

At least one adopted, completed, evaluated, nonsynthetic mission; all L1 criteria; current permitted evidence; and named-human review whenever the contextual review policy applies. Agent ceiling P1.

### Level 2 — Intermediate

L1 plus at least two distinct eligible missions, every L2 criterion, one documented correction/limitation/failure/burden issue, and named-human review. Agent ceiling P1.

### Level 3 — Advanced

L2 plus at least three distinct eligible missions, every L3 criterion, a reusable governed method evaluated for outcome, process, accessibility, inclusion/equity, burden, independence, unintended effects, expiry, and stop conditions, with qualified independent review. Agent ceiling P2.

### Level 4 — AI Agent Orchestration Master

L3 plus the domain L4 criterion and all nine global orchestration-capstone criteria in one real, low-risk, adopted, evaluated mission using only frozen P1/P2 agents, with role/process review and independent governance/safety review. P3 model use and P4 are prohibited.

Levels are sequential. No average, points total, quantity of prompts, or AI score may substitute for a missing criterion, prerequisite, evidence minimum, review, or governance gate.

## 3. Seventeen capability domains

The normative catalog contains 17 domains × 4 levels = 68 domain criteria, plus 9 global Level-4 orchestration criteria: **77 total criteria**.

| ID | Capability | Observable developmental focus |
|---|---|---|
| `CAP-01` | AI literacy | Explain model limits, false fluency, hallucination, automation bias, disclosure, deliberate nonuse, and why AI never replaces educator/designer or authorized-human judgment. |
| `CAP-02` | Prompt design | Frame minimum-necessary prompts and briefs with target, learner level when generic, purpose, permitted facts, sources, rights, limits, uncertainty, expected artifact, human review, and explicit non-goals. |
| `CAP-03` | Evidence-informed research | Map claims to real current sources, version, authority, local adoption/applicability, rights, conflicts/corrections, uncertainty, expiry, and `Evidence not located`. |
| `CAP-04` | Critical thinking and reasoning | Distinguish verified fact, owner statement, source claim, interpretation, inference, recommendation, assumption, unknown, and accountable-human decision; challenge false certainty and causal overreach. |
| `CAP-05` | Structured problem-solving | Use Assess–Diagnose/Define–Plan–Implement–Evaluate while preserving EDENA, `absolute_stop`, TEACH Gate, human routes, revision history, stop conditions, and next-cycle learning. |
| `CAP-06` | Workflow and learning-design design | Map manual-first educational workflows with outcomes, prerequisites, activities, accessibility, reviews, rights, owners, failures, fallback, rollback, expiry, and no external execution. |
| `CAP-07` | Course, project, and capacity management | Produce structural evidence of humane priorities, owners, milestones, workload/review budget, protected-time check, Minimum/Re-entry mode, fallback, and human-support route without storing private-life detail. |
| `CAP-08` | Learner-data and privacy stewardship | Enforce no PHI, no learner/assessment payload, five-workspace and private/institution isolation, minimization, exact `TEACH-DATA-C/A/R`, small-cell protection, retention, deletion, and no secondary use. |
| `CAP-09` | Ethical AI, integrity, and authorship | Preserve educator voice, learner dignity, fairness, accessibility, due process, transparency, AI-use/disclosure, authorship and team credit; reject detector-as-proof, surveillance, fabrication, and authority inflation. |
| `CAP-10` | EDENA and TEACH governance | Apply independent `edena_tier`/`absolute_stop`, Target/Evidence/Authority/Checks/Human, exact data/source/rights/role gates, hard stops, clean-new-sandbox rule, and qualified-human escalation. |
| `CAP-11` | Agent supervision | Select, charter, constrain, inspect, stop/kill, independently verify, correct, reconcile, purge, and retire one bounded P1/P2 agent without widening authority or data. |
| `CAP-12` | Multi-agent orchestration | Design bounded dependencies, separation of duties, handoffs, permissions, independent checks, failure recovery, reconciliation, and receipts across P1/P2 agents. |
| `CAP-13` | Knowledge-base, source, and rights development | Curate versioned public/exact approved non-person sources, claim links, applicability, local adoption, rights/licenses, conflicts, freshness, corrections, access, and retirement without prohibited ingestion. |
| `CAP-14` | Automation design | Design and test manual/synthetic low-risk workflows with exact gates, workload checks, fallback, rollback, expiry, and honest eligibility; P3 remains deterministic local personal reminder-only and P4 absent. |
| `CAP-15` | Accessible Human Teaching artifact creation | Produce accessible, source-linked, learner-level-appropriate, concise, usable, human-voice drafts with anti-slop review, uncertainty, rights, AI disclosure, and external human release owner. |
| `CAP-16` | Evaluation and program improvement | Evaluate process/outcome, accessibility, inclusion/equity, burden, missingness, balancing measures, unintended effects, independence, and next decision using public/synthetic material or exact approved aggregates only. |
| `CAP-17` | Nurse educator and instructional-design practice | Build truthful reviewed structural evidence of outcome alignment, accessible/inclusive learning design, authentic assessment design, synthetic clinical/simulation preparation, curriculum evidence, scholarship/faculty development, workload stewardship, and ethical technology use. |

These capabilities concern governed work in Mission Control. They do not assess real learner performance, clinical teaching competence, grading accuracy, accommodation, simulation facilitation, patient care, curriculum authority, accreditation readiness, research status, or institutional performance.

## 4. Eligible evidence

Evidence is eligible only when every applicable condition is satisfied:

- linked to an owner-adopted, completed, evaluated, **nonsynthetic** mission and exact cycle/revision;
- within one verified adapter, task hat, workspace, deployment context, institution-or-Personal context, purpose, and authority ceiling;
- based only on permitted `TEACH-DATA-0/1/2/C/A/R` use, with `TEACH-DATA-1` serving only as a fixture inside a real governed mission rather than the evidence of mastery itself;
- contains no learner/assessment/person payload, PHI, live care, secure assessment content, private-life detail, prohibited institutional content, secret, or external execution;
- uses a recognized evidence type: evaluated mission, reviewed artifact, source/rights audit, correction record, governance receipt, minimized structural process reflection, accessibility/inclusion review, supervised P1/P2 run receipt, aggregate-method review, or validated non-person project-process outcome;
- identifies the educator/designer contribution, AI/agent contribution, human decisions, edits, disclosure, source/data/rights checks, uncertainty, limitations, burden, and what the owner can now do independently;
- attributable, dated, inspectable through a permitted locator, integrity-hashed, current, purpose-bound, and explicitly mapped to criteria;
- has every contextually or unconditionally required named-human review by a qualified reviewer with recorded scope and conflicts; and
- has no unresolved privacy, integrity, source, rights, authority, context, data, small-cell, correction, deletion, expiry, or review conflict.

### Minimum evidence record

- immutable evidence ID and version;
- model/criteria ID, version, and hash;
- owner, mission/cycle, workspace, deployment context, adapter/hat, and permitted partition IDs;
- evidence type, permitted locator, artifact/event version, and integrity hash;
- `TEACH-DATA-*` class, `edena_tier`, `absolute_stop`, TEACH Gate state, source/rights/aggregate receipt references;
- observed/submitted/reviewed/expiry dates;
- owner contribution, AI/agent contribution, disclosure, corrections, limitations, uncertainty, burden, and next independent step;
- reviewer identity/qualification/authority assertion, verification, conflict, decision, rationale, limitations, date/expiry, and attestation hash; and
- correction, supersession, withdrawal, deletion, revocation, and recalculation state.

Never copy an underlying institutional artifact, aggregate body, private plan, or prohibited payload into the evidence ledger. Use a permitted locator and hash only when even the locator is authorized.

## 5. Ineligible evidence

Exclude:

- clicks, launches, page views, streaks, time in app, prompt/conversation count, output volume, or completion animation;
- starter content, walkthroughs, tutorials, copied examples, unchanged templates, synthetic-only exercises, and fictional work presented as real;
- AI praise, model confidence, AI self-assessment, AI scoring, or an AI mastery decision;
- title, license, degree, faculty appointment, course access, institutional email, seniority, committee seat, or self-asserted authority used as performance evidence;
- self-review, anonymous/shared reviewer account, invented reviewer, or unverified review claim;
- duplicate, deleted, withdrawn, expired, superseded, revoked, conflicted, or required-but-unreviewed evidence;
- fabricated source, citation, authorship, contribution, role, hour, approval, result, measure, impact, accessibility, fairness, causality, compliance, competence, or outcome;
- learner names/IDs, submissions, messages, scores, grades, evaluations, advising/support notes, accommodations, complaints, conduct/integrity records, clinical evaluations, or other student records;
- secure exams, item banks, answers, restricted prompts, or assessment-security material;
- PHI, patient/family stories, charts, handoffs, clinical records, live-care content, real clinical/simulation evaluation, or competence/clearance decisions;
- employee/faculty/personnel, labor, grievance, complaint, investigation, discipline, health, performance, productivity, sentiment, surveillance, or inferred-person data;
- private readiness, schedule detail, health, relationship, finance, recovery, family/caregiving, personal mission, or whole-life outcome;
- confidential institution, legal, research-participant, vendor, contract, security, source-system, or proprietary content outside the permitted boundary;
- an external action, LMS/gradebook change, learner contact, official release, teaching delivery, real simulation, assessment administration, publication/submission, institutional pilot/change, or production automation; and
- an unsupported claim of learner, teaching, program, accessibility, equity, clinical, time-saved, financial, publication, promotion, or wellbeing effect.

A private capacity mission may demonstrate only structural practices—for example use of a burden budget, protected-time check, Minimum Mode, fallback, and qualified-human route. The ledger stores none of the underlying personal detail.

## 6. Contextual named-human review policy

The per-criterion `requires_human_review` field is an unconditional minimum. `false` never waives contextual review.

Named-human review is required at every level when evidence is:

- course, program, institution, assessment/feedback, learner-support, clinical/simulation, curriculum/accreditation, scholarship/publication, portfolio/promotion, employment, or other professional evidence;
- based on `TEACH-DATA-C`, `TEACH-DATA-A`, or an official external reference;
- EDENA Yellow or Orange; or
- otherwise consequential, externally reusable, or likely to be mistaken for competence, compliance, authority, or an outcome claim.

Review records real reviewer identity, exact qualification basis, relationship, conflict, independence, criteria, evidence versions/hashes, data/context boundary, decision (`accepted | needs_revision | rejected`), rationale, limitations, date/expiry, and attestation hash.

Rules:

1. The candidate cannot review their own evidence.
2. AI, an agent, an anonymous committee, or a shared account cannot be the reviewer.
3. Title or affiliation alone does not establish reviewer qualification.
4. Every institutional artifact requires a current reviewer in the authorized path.
5. L2 and L3 require named-human review; L3 requires an independent reviewer.
6. L4 requires one role/process reviewer and one independent governance/safety reviewer; neither is the candidate or the agent under review.
7. A material correction, source/rights/data/authority change, expired receipt, or blocking conflict invalidates affected review.
8. Appeal uses a different qualified human and preserves prior decisions.

Review confirms only the stated capability criteria. It never grants permission or credential status.

## 7. Exact evidence and badge states

Evidence state:

`draft → submitted → under_review → accepted | needs_revision | rejected → expired | superseded | revoked | deleted`

Badge state:

`not_started | in_progress | review_pending | earned | suspended | expired | revoked`

Use append-only events plus deterministic materialized current state. Implement at minimum:

- `capability_models` — immutable criteria version/hash and lifecycle;
- `capability_evidence` — minimized evidence metadata and versions;
- `capability_evidence_criteria` — explicit evidence-to-criterion links;
- `capability_reviews`, `capability_review_evidence` — exact reviewer coverage;
- `capability_events` — append-only award/correction/expiry/revocation/recalculation;
- `criterion_current_state`, `capability_current_state`, `capstone_current_state`.

Every record carries owner, workspace, adapter/hat, deployment context, permitted partition, schema/criteria version, revision, timestamps, retention, and deletion state. A cross-owner, cross-institution, cross-course/project, cross-workspace, or private/institutional reference fails before dereference with zero field disclosure.

## 8. Deterministic award algorithm

For each requested domain/level:

1. Load and verify the exact active normative configuration version/hash.
2. Select only candidate-owned evidence in the permitted workspace/context/partition.
3. Exclude synthetic-only, starter, deleted, withdrawn, expired, superseded, revoked, duplicated, prohibited, conflicted, stale-revision, and unreviewed-required evidence.
4. Verify schema, locator permission, integrity hash, evidence type, role/workspace/context, data class, EDENA/absolute stop/TEACH Gate, source/rights/aggregate receipts, and criterion mapping.
5. Verify each required review, qualification, conflict, independence, scope, and expiry.
6. Require every criterion for the level. Never average or trade surplus evidence for a missing criterion.
7. Require prior level, distinct-mission minimum, and distinct-evidence rules. One underlying event counts once even if mapped to multiple criteria.
8. For L4, require the domain L4 criterion plus all nine global capstone criteria.
9. Write an explainable award/rejection event with configuration hash, evidence/review IDs and hashes, exclusions and reasons, computation time, and current governance state.
10. Recompute after evidence/review correction, deletion, withdrawal, expiry, supersession, revocation, criteria migration, or source/rights/data/authority conflict.

Progress is:

```text
100 × satisfied required criteria ÷ total required criteria
```

Display prerequisite, mission minimum, human review, governance, evidence currency, and capstone gates separately. A 100% criterion count with an unmet gate remains `review_pending` or `in_progress`.

## 9. Nine global Level-4 capstone criteria

One real, low-risk, adopted, evaluated mission must demonstrate all nine:

1. **Governed objective:** owner-authored purpose, non-goals, success, workload/burden, accessibility, independence, human-review, expiry, and stop measures.
2. **Exact context/data:** one adapter/hat/workspace/deployment/institution-or-Personal context and permitted data/source/rights objects; no learner payload or prohibited content.
3. **Bounded orchestration:** at least two frozen `TEACH-AGT-*` agents, or one bounded agent plus independent `TEACH-AGT-10`, using P1/P2 only.
4. **Charter and separation:** exact prompt/model/policy/schema/tool hashes, empty destinations, time/cost/token limits, one invocation, zero hidden retries, separation of roles, and expiring authorization.
5. **Deterministic routing:** explain route choice and demonstrate rejection of an unauthorized handoff, recursion, permission widening, context crossing, prohibited input, or background continuation.
6. **Human supervision and safe failure:** genuine Stop/Kill or an intentionally tested safe failure/recovery, manual fallback, reconciliation, permission revocation, and temporary-state purge.
7. **Independent verification:** verify consequential claims, sources, rights, accessibility, limitations, conflicts, and unknowns outside the producing agent.
8. **Completed Evaluate:** compare outcome/process/accessibility/inclusion-equity/burden/independence/unintended effects and record retain/revise/pause/remove/escalate/new-cycle decision.
9. **Evidence and review:** complete minimized contribution/disclosure/receipt plus role/process and independent governance/safety human reviews.

The capstone cannot use learner/person/patient data, secure assessment content, health or employment-dispute detail, private whole-life evidence, confidential institution content outside an exact permitted class, external action, real teaching/assessment/clinical/institutional execution, public release, P3 model work, P4, or synthetic-only activity.

## 10. Corrections, expiry, revocation, and appeals

Evidence/review changes are append-only. A corrected artifact creates a new version linked to the prior version; it never overwrites what a reviewer saw. Material correction requires new review.

On addition, correction, withdrawal, deletion, expiry, authority/data/source/rights change, governance reclassification, review change, or criteria migration:

1. rebuild the current eligible evidence set;
2. remove invalid records from current calculations while preserving minimum content-free history;
3. recalculate affected criteria, levels, and capstone dependencies;
4. suspend, downgrade, expire, or revoke the badge as required; and
5. write a before/after receipt with affected IDs, reason, actor, time, and appeal/recovery route.

Permitted deletion removes the payload/locator and leaves only a minimum nonsensitive tombstone if required for integrity. Revocation is an evidence-ledger correction, not an employment, licensure, disciplinary, credential, or competence finding. Restoration requires current eligible evidence and new required review.

## 11. Suggested next challenges

Generate challenges deterministically from unmet criteria in this order:

1. safety/data/integrity correction;
2. expired prerequisite, source, rights, authority, or review;
3. unmet prior level;
4. first unmet criterion in configuration order;
5. distinct-mission/evidence minimum;
6. required human review;
7. renewal; then
8. next level or capstone.

Challenges must be low-risk, accessible, feasible, reversible, manual-first, aligned with the owner-approved goal, and compatible with verified role/data authority. They include a qualified-human route and may be dismissed, deferred, or deleted without penalty.

Never recommend handling real learner work, grading, secure assessments, patient/clinical activity, person evaluation, unapproved institution data, off-clock institutional work, external publication/submission, or production automation merely to earn a badge.

## 12. Portable evidence export

Export only an owner-approved, checksummed local JSON evidence bundle containing product/schema/criteria versions, the noncredential notice, badge states, criterion results, permitted minimized evidence metadata/hashes, review assertions and verification state, limitations, expiry/revocation history, and export time.

Exclude learner/student/person data, secure assessment content, PHI/live care, private-life detail, restricted institution/employment/research/legal content, secrets, unsafe prompts/tool traces, and underlying `TEACH-DATA-A` values. An export remains a private developmental evidence statement, not an institution-recognized credential.

Do not claim Open Badges or another external standard unless the exact standard version, schema, signing, privacy, conformance, and interoperability tests pass. Otherwise label it `educator_teach_portable_evidence` and retain the full disclaimer.
