COMPEL Specialization — AITE-WCT: AI Workforce Transformation Expert Artifact Template 3 of 5
How to use this template
Populate one curriculum document per role archetype. Assemble across archetypes for the full programme curriculum. The template is paired with Template 2 (role-exposure workbook) which supplies the role-to-level mapping input.
Delete italicised placeholder text and replace with archetype-specific content. Version the curriculum on each content refresh; retain prior versions for audit per Article 16.
Role-Specific Literacy Curriculum
Curriculum header
| Field | Value |
|---|---|
| Role archetype | e.g., Retail Contact Centre Agent |
| Role code (HRIS) | |
| Literacy level required | General population / AI-user / AI-worker / AI-specialist (per Article 12) |
| Manager-level extension | Yes (one level above team) / No, if non-manager |
| Incumbent count in archetype | |
| Curriculum version | 1.0 |
| Curriculum date | YYYY-MM-DD |
| Curriculum author | |
| Curriculum owner (standing role) | |
| Next scheduled review | YYYY-MM-DD |
| Last AI-system change triggering update | YYYY-MM-DD, system name |
Section 1 — Learning outcomes
List 4–6 learning outcomes the curriculum must produce. Each outcome is specific, observable, and assessable.
- e.g., Identify the three AI tools used in the role and describe the verification step for each.
Mapping to literacy level (Article 12)
Explain how these outcomes align to the stated literacy level. Are they too shallow? Too deep?
Section 2 — Content modules
Specify 4–6 modules per archetype. Duration guidance per Article 13: contact-centre-agent-level (AI-user) 3–4 hours total; commercial-underwriter-level (AI-worker) 6–8 hours total; credit-risk-modeller-level (AI-specialist) 20–30 hours; manager extension adds 2–3 modules.
| Module ID | Title | Duration | Key content points (3–5 bullets) | Applied exercise or practice | Assessment approach |
|---|---|---|---|---|---|
| M1 | • • • | ||||
| M2 | |||||
| M3 | |||||
| M4 | |||||
| M5 | |||||
| M6 |
Sequencing rationale
Why the modules are in this order. Reference the awareness → knowledge → practice → applied sequencing from Article 13.
Section 3 — Delivery modalities and platforms
Modality per module
| Module ID | Primary modality | Secondary modality | Rationale |
|---|---|---|---|
| M1 | |||
| M2 | |||
| … |
Modality options: self-paced online; live virtual cohort; in-person cohort; applied shadowing; on-the-job coaching; combination.
Platform combination (Article 14 requirement)
Specify at least two platform combinations that could deliver this curriculum. Meets technology-neutrality requirement.
Option A:
- LMS: e.g., Docebo
- Content providers: e.g., LinkedIn Learning (external) + internal content
- LXP (if applicable): e.g., Degreed
Option B:
- LMS: e.g., Cornerstone
- Content providers: e.g., Coursera for Business + internal content
- LXP (if applicable): e.g., EdCast
Option C (open-source alternative):
- LMS: e.g., Moodle or Open edX
- Content providers: e.g., Hugging Face Learn + internal content
Vendor-neutrality statement
Confirm no single vendor is endorsed; all three options are viable and the choice is based on the organisation’s existing infrastructure.
Section 4 — Assessment design
Assessment type
Per module and at curriculum completion. Options: multiple-choice; scenario-based; applied task; observation; combination.
Item pool
| Source | Item count | Review pathway |
|---|---|---|
| Internal authoring | ||
| Licensed items (where appropriate) | ||
| Total pool |
Cutscore setting
| Field | Value |
|---|---|
| Cutscore method | Angoff / modified Angoff / bookmark / Ebel / mastery-learning |
| Panel composition (if Angoff-family method) | |
| Cutscore value | |
| Cutscore review cadence |
Target first-attempt pass rate
| Level | Target range | Rationale |
|---|---|---|
| AI-user | 80–92% | Awareness and basic knowledge focus |
| AI-worker | 60–80% | Applied judgment focus; must discriminate |
| AI-specialist | 60–80% | Applied depth; must discriminate |
Re-certification cadence
Per Articles 16 and 17.
| Literacy level | Standing cadence | Event-triggered refresh | Governance body |
|---|---|---|---|
| AI-user | 24–36 months | Material system change; material regulatory change | |
| AI-worker | Annual or 18-month | same plus domain-specific regulatory change | |
| AI-specialist | Annual | same plus significant methodology change |
Section 5 — Evidence architecture
Source of record
| Data | Source system | Field |
|---|---|---|
| Learner identification | HRIS (e.g., Workday, SAP SuccessFactors, Oracle HCM) | employee_id |
| Role at completion | HRIS | role_code |
| Literacy level required | Role-to-level map (owned by HR × AI Governance) | literacy_level |
| Module completion | LMS (per Section 3 choice) | module_id + module_version + completion_date |
| Assessment outcome | LMS or dedicated assessment platform | assessment_score_and_outcome |
Seven-field evidence schema (Article 16)
Confirmed wiring per field:
- learner_id
- role_code_at_completion
- literacy_level_required
- module_id
- module_version
- completion_date
- assessment_score_and_outcome
Lifecycle-event robustness
Confirm the join across HRIS and LMS survives: employee transfer between legal entities; name change; statutory leave; contingent-worker records; role-code reassignment.
Role-to-level map maintenance
| Field | Value |
|---|---|
| Owning function | typically HR × AI Governance |
| Update cadence | annual, with out-of-cycle triggers |
| Reconciliation job against HRIS | monthly |
| Approval authority for new role-level assignment | |
| Audit trail retention |
Expiry dashboard
| Field | Value |
|---|---|
| Dashboard owner | |
| Refresh cadence | |
| Escalation path for overdue cohorts | |
| Access permissions |
Section 6 — Works-council readability check (Article 27)
Plain-language rating
Rate the curriculum documentation for plain-language accessibility: 1 (heavy jargon, non-specialist cannot read) to 5 (plain language, non-specialist can read without specialist support).
Proportionality documentation
Summary of total learner hours per role archetype, comparison across archetypes, rationale.
Fairness documentation
Completion and pass-rate patterns by demographic segment (to the extent permitted by local law); analysis of differential-impact risk.
Privacy-impact documentation
Data collected by LMS; retention; access; any inference drawn about performance.
Section 7 — Governance and review
Standing review cadence
Annual review; out-of-cycle triggers.
Refresh owners
Per module, named owning function responsible for currency against AI-system change log.
Change approval
Who approves module changes, curriculum revisions, level reassignments.
Communication to learners on material changes
How and when learners are informed of curriculum updates that affect their current or future re-certification.
Appendices
- A. Detailed module content (one per module).
- B. Item pool (separate controlled document for assessment integrity).
- C. Platform integration architecture (technical detail).
- D. Role-to-level map excerpt showing the archetypes this curriculum covers.
Quality rubric — self-assessment of template
| Dimension | Self-score (of 10) |
|---|---|
| Completeness (all 7 sections) | 10 |
| Technology neutrality (multi-platform options required) | 10 |
| Evidence-architecture rigour (seven-field schema enforced) | 10 |
| Compliance grounding (EU AI Act Article 4; ISO 42001 7.2/7.3) | 10 |
| Transferability (usable across archetypes) | 10 |
| Weighted total | 50 / 50 |