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About this framework
This page maps how well a school is integrating AI, across 6 areas of school life and 5 levels (Emerging → Leading). The school's leadership team, department heads and representative teachers self-assess once a year, coordinated by the school's AI Integration Coordinator. The Eduversal Academic Directorate reads the shared profiles in AI Maturity Self-Assessments to plan support — it does not validate the ratings.
For · Directors · Coordinators · Principals · Foundation Reps
Read-only reference
AI Competency Framework 26-27 · Part Three
Cadence · annual self-assessment
Six area levels · no single overall score
School Appraisal does not score this framework. Where AI practice is relevant, appraisal teams look at the same evidence inside the five School Appraisal domains, using the Evidence Guide and Rating Rubric. Network reporting may summarise patterns but never ranks schools. The level text comes from the repo copy in docs/research/eduversal/ai-competency-framework/ (taken from version 1.0); where it differs, the live AI Competency Framework 26-27 is the reference.
How to use this page
Five levels (Emerging → Leading) across six areas. Each box (5 × 6 = 30) says what the school looks like at that level in that area. Click a box to read the full description plus what to do next, what to never do, and which other pages to read.
Be honest and reflective. This is a developmental self-assessment, done once a year, usually at the start of the academic year. Keep the six area levels separate — the framework is never reduced to one overall score. Then identify the school's priority actions for the coming year from the results.
Maturity Levels at a Glance
The Five Levels
1
Emerging
Beginning to engage. Awareness limited. No systematic approach.
2
Developing
Initiated with basic policies. Inconsistent implementation. Building capabilities.
3
Established
Coherent integration. Majority staff engagement. Consistent practices.
4
Advanced
Sophisticated integration. Strong impact. Contributes to network learning.
5
Leading
Outstanding. Network model. Contributes to broader practice.
Assessment Domains
Six Domains
Click a domain to expand the 5-level descriptors. Click any level cell for the verbatim text.
Domain 1: Strategy and Leadership
Strategic planning, leadership commitment, governance.
1
Emerging
No formal AI strategy. Ad-hoc initiatives. Not in school improvement planning.
level1
2
Developing
Initial plan exists. Mentioned in improvement docs but not systematic.
level2
3
Established
Clear strategy aligned with priorities and Cambridge. Leadership champions actively.
level3
4
Advanced
Comprehensive, well-implemented. Sophisticated leadership. Embedded in culture.
level4
5
Leading
Network-recognised. Contributes to network strategy. Supports other schools.
level5
Domain 2: Policy and Compliance
AI policies, data privacy (UU PDP), academic integrity, responsible use.
1
Emerging
Limited or no formal policies. Staff uncertain. Ad-hoc data privacy.
level1
2
Developing
Basic policies exist. Uneven awareness. Compliance not systematically monitored.
level2
3
Established
Comprehensive policies. Clearly communicated. Data privacy compliance maintained.
level3
4
Advanced
Well-implemented, regularly reviewed. Strong adherence. Robust data governance.
level4
5
Leading
Exemplary policy framework. Network model. Evolves proactively.
level5
Domain 3: Staff Capability
Extent and depth of staff AI competency; quality of professional development.
1
Emerging
Few staff with AI knowledge. No systematic PD. Significant gaps, no plan.
level1
2
Developing
Some staff developed skills. Uneven distribution. Initial PD provided.
level2
3
Established
Majority at Foundation level. Systematic PD programme. Clear progression pathways.
level3
4
Advanced
Strong majority at Practitioner. Differentiated PD. Teacher leaders active.
level4
5
Leading
Outstanding capability. Network-model PD. Teacher leaders contribute network-wide.
level5
Domain 4: Teaching and Learning
Quality and consistency of AI integration in classroom practice and curriculum.
1
Emerging
Minimal or absent integration. Ad-hoc use. No student AI literacy approach.
level1
2
Developing
Some teachers integrate well. Inconsistent across school. Limited impact evidence.
level2
3
Established
Consistent across departments. Clear pedagogical rationale. Student AI literacy systematic.
level3
4
Advanced
Sophisticated, consistently impactful. Strategic targeting. Strong student literacy.
level4
5
Leading
Outstanding integration. Innovative approaches shared network-wide.
level5
Domain 5: Student Outcomes
Student AI literacy development; impact of AI integration on learning outcomes.
1
Emerging
Not systematically developed or assessed. Limited student understanding. Inconsistent responsible use.
level1
2
Developing
Initial attention. Basic AI awareness. Responsible use developing.
level2
3
Established
Age-appropriate literacy across strands. Responsible use is the norm. Prepared for further study.
level3
4
Advanced
Strong literacy exceeding expectations. Sophisticated responsible use. Clear outcome impact.
level4
5
Leading
Exemplary. Network model. Outstanding critical thinking. Exceptionally well-prepared.
level5
Domain 6: Infrastructure and Resources
Technical infrastructure, approved tools, budget, technical support.
1
Emerging
Insufficient infrastructure. Few or no approved tools. Minimal budget.
level1
2
Developing
Basic infrastructure. Some approved tools. Limited but growing budget.
level2
3
Established
Reliable infrastructure. Appropriate tools accessible. Adequate budget. Support meets needs.
level3
4
Advanced
Robust. Comprehensive tool ecosystem. Strong budget. Responsive proactive support.
level4
5
Leading
Outstanding. Network model. Tool ecosystem exemplary. Enables innovation and leadership.
level5
Assessment process
Step 1
School self-assesses
Annual self-assessment at the start of the academic year. Involves leadership team, department heads, representative teachers. Honest baseline.
Step 2
Eduversal plans support
The Eduversal Academic Directorate uses the profile to identify support needs, professional-learning priorities and network trends. It does not validate the ratings or turn them into a School Appraisal rating.
Step 3
Network learning and priorities
Network-level reporting may summarise patterns, strengths, needs and progress for shared learning. It never ranks schools. Each school sets its priority actions for the coming year.
Phase 2-3 (planned): annual self-assessment writes to Firestore
ai_maturity_assessments/{schoolId}_{academicYear};
Cloud Function maintains network-level aggregates; the Eduversal team adds developmental support notes from CH /ai-maturity-admin.
Other Eduversal pages to read alongside this one
- Digital Citizenship & AI: Implementation Toolkit 26-27 ↗ — The school's launch checklist, roles, 30-60-90 plan and Approved AI Tools List.
- Appendix B: AI Responsible Use Policy 26-27 ↗ — What staff and students may and may not do with AI.
- Appendix A: Academic Integrity Policy 26-27 ↗ — Academic integrity, including work that may be AI-written.
- Teacher Framework (Part 1) — Helps Area 3 (Staff Capability) most.
- Student Framework (Part 2) — Helps Areas 4 and 5 (Teaching/Learning and Student Outcomes).
- The Eduversal Academic Directorate uses this framework to plan support and professional learning. School Appraisal does not score it.
Evidence & Provenance · Part 3 · Institutional
A school-leadership framework should be auditable. Here are the sources.
The 5 institutional readiness areas above are anchored to international leadership standards, Indonesian regulation (UU PDP No. 27/2022 + Permendikdasmen 13/2025), Cambridge International policy, and the live working patterns of school leaders ahead of you. Every paragraph in the matrix has a citation behind it. Browse the wall in 60 seconds; click any card to read the original.
14
Sources cited
on this page
on this page
UU 27
Data-protection
law anchor
law anchor
8
Independent
publishers
publishers
2026
Freshness anchor
(latest source)
(latest source)
The three leadership pillars
If you read three sources before your next AI-policy meeting, read these. They cover the law, the global benchmark, and the operational ceiling.
Republik Indonesia
UU PDP No. 27/2022 — Personal Data Protection Law
Indonesia's GDPR-equivalent, enforceable since Oct 2024. Lembaga PDP (enforcement body) targeted operational 2026. Why "no student PII in free-tier AI tools" is law, not policy. The non-negotiable floor.
OECD
Digital Education Outlook 2026
Annual OECD digital-education report. 2026 edition focuses on AI integration outcomes across member countries. The international peer-school benchmark for your readiness assessment.
UNESCO
Recommendation on the Ethics of AI
The 193-member-state agreement that grounds the ethical floor underneath every leadership decision. Articulates the human-centred principles cited throughout the Eduversal framework.
Indonesia regulation
National law + curriculum context
-
Permendikdasmen 13/2025 — Koding & AI elective
National regulation adding Koding & AI as electives (SD k.5, SMP k.7, SMA k.10). Phased; schools self-assess readiness; NOT compulsory. Direct input into Area 1 strategy + Area 4 curriculum decisions.
-
Detik · Schools can now add Koding & AI from 2025-2026
Practitioner reporting on Permendikdasmen 13/2025 rollout. Useful for board / yayasan briefings.
UNESCO + United Nations
Global ethics + leadership frame
-
Guidance for Generative AI in Education & Research
UNESCO's operational guidance. Useful for school-level policy drafting — Leader Playbook references this implicitly throughout.
-
UNESCO AI Competency Framework for Teachers (2024)
Global teacher framework. Maps onto Part One of the Eduversal AI Competency Framework, which feeds Area 3 "Staff Capability" of this institutional framework.
Cambridge International
Curriculum + coursework authority
-
Cambridge Teaching, Learning, Assessment & AI Hub
School-level AI policy MUST defer to Cambridge guidance — see Red Line 8. Includes Exams Officers Guide Phase 3, plagiarism guidance, AI & environment.
-
The Cambridge Approach to Generative AI & Assessment
Cambridge's overall position statement. Frames every specific operational document Cambridge issues — read this before drafting your school AI policy.
AI provider educator-tier
The 3 most likely network-approved tools
-
Claude for Education
Anthropic's school-tier product. Learning Mode (Socratic). Strong Bahasa Indonesia. Cautious tone — preferred for HQ curriculum work.
-
ChatGPT for Teachers
OpenAI's K-12 product, Nov 2025. SAML SSO. Not trained on educator data by default. FERPA-aware.
-
Gemini for Education
Bundled with Workspace for Education. School-official under FERPA. COPPA-aware. Strongest Bahasa Indonesia per 2025-2026 benchmark.
Research + standards bodies
Peer-reviewed evidence + integrity
-
What Should Teachers Teach & Students Learn (OECD 2025)
Headline: teacher co-creation of purpose-built generative AI; human-in-the-loop assessment. Cited in Specialist + Leader Playbooks. Anchors Area 5 "Student Outcomes".
-
Why AI Literacy is Now a Core Competency in Education
WEF positioning paper for parent communication and strategic positioning of AI literacy investment. Useful for board / parent town halls.
-
AI Detection in Schools — NPR coverage
Reporting on the unreliability of AI detection. Multiple major universities disabled Turnitin AI detection by late 2025. Anchors Red Line 9 — no AI detectors for student work.
Practitioner + benchmarks
Real-world working data
-
Artificial Analysis — Multilingual Index (Indonesian)
Independent benchmark of LLM performance on Indonesian tasks. 2026 leaders: Gemini 3.x + Claude Opus 4.6. Annual review required — model versions change.
-
Anthropic Education Report — How Educators Use Claude
Empirical evidence: differentiation = ~61% of educator use. Useful for Area 3 staff-capability ROI conversations with the board.