EduPolicyAtlas global generative-AI school policy research expansion

Information checked through: September 8, 2026. Overall confidence: high for the ten audited atlas records and the strongest new profiles; moderate for the broader global inventory; low to unknown where expressly marked as a research lead.

Executive research brief and scope

EduPolicyAtlas should not evolve into a map of where “AI is allowed” or “AI is banned.” The strongest evidence from the current atlas and the expanded research shows that the practical answer usually depends on at least four independent questions: whether a rule is binding, the learner's age and supervision, whether the activity is assessed, and whether the tool or data flow has been approved. France, Singapore, New Zealand, England, and New York City demonstrate how misleading a one-dimensional label can be. ^[1]

I inspected the live EduPolicyAtlas and its HTML research-report route. The site currently presents ten jurisdiction starters and explicitly describes itself as a starting point rather than globally representative coverage. ^[2] The raw /research-report.md resource could not be fetched through the available browser because of its content type, so I did not treat it as reviewed. No policy or news export files were attached to this conversation, and I did not locate a publicly exposed export endpoint. Accordingly, any differences below are independent verification or proposed additions rather than recovery of unpublished source mappings.

This pass supports roughly thirty substantive jurisdiction or education-system records at varying depths, including the original ten, plus a larger discovery queue. It covers every requested world region, but it is not comprehensive global coverage. The strongest expansion opportunities are Norway, Denmark, Italy, Taiwan, Indonesia, Malaysia, Kazakhstan, British Columbia, Quebec, Wales, Argentina, Saudi Arabia, Jamaica, Trinidad and Tobago, and several Pacific systems. Some of these have dedicated school-AI guidance; others deliberately belong in “general rules / implementation / policy under development” categories.

The biggest findings are:

For the master geographic inventory, use the UN Statistics Division M49 country-and-area reference as the neutral backbone: https://unstats.un.org/unsd/methodology/m49/. Territories should receive their own atlas jurisdiction only when they have a distinct education authority or materially distinct school rules. For disputed jurisdictions, record the issuing authority and territorial scope without using the atlas to adjudicate sovereignty.

Coverage ledger and existing-record audit

Research-status vocabulary

Coverage and search ledger

The ledger therefore makes a crucial distinction: a place such as Uzbekistan can have binding nationwide AI ethics rules and mandated school AI instruction without yet having a clearly located GenAI homework policy; Wales can have privacy and implementation guidance without a single national “permission table”; Fiji can actively encourage AI integration while still lacking a located detailed pupil-use code.

Audit of the current ten atlas records

Expanded jurisdiction profiles and cross-jurisdiction comparisons

The best publication candidates are those where the authority, current document, scope and operational consequences can already be separated clearly.

Norway. Original source language: Norwegian. Interpretation based on the original government page. On June 19, 2026, the government announced age-adapted national recommendations for grunnopplæringen. Grades 1-7 should generally not be given access to AI for schoolwork. Grades 8-10 may begin gradual, cautious use once teachers have sufficient competence. Upper-secondary pupils should learn to use AI appropriately for further study and employment. The government expressly contemplated exceptions where AI-based tools support needs such as language learning or individualized education. This is an important example of why “ban” is too crude: the position is age-banded, pedagogically conditional and exception-sensitive.

Source: https://www.regjeringen.no/no/aktuelt/kunstig-intelligens-skal-i-all-hovedsak-ikke-brukes-i-barneskolen/id3166807/

Denmark. Original source language: Danish. Interpretation based on the original ministry text extraction. The Ministry's Anbefalinger om brug af generativ AI i undervisningen i folkeskolen expressly says there are not nationwide classroom rules determining exactly how GenAI may be used in teaching. Under the Folkeskole framework, school boards approve teaching materials and local schools/teachers make instructional choices. This local discretion ends at national examination boundaries: pupils may not use GenAI in written or oral Folkeskole examinations, including preparation that contributes to the assessment basis. School leadership/municipalities must also ensure GDPR compliance.

Source: https://uvm.dk/media/dylly3ye/250626-tilgaengeliggjort-anbefalinger-om-brug-af-generativ-ai-i-undervisningen-i-folkeskolen.pdf

Italy. Original source language: Italian; an English rendering is also published by the ministry. The Ministry of Education and Merit adopted Linee guida per l’introduzione dell’Intelligenza Artificiale nelle Istituzioni scolastiche in 2025. The framework addresses principals, administrators, teachers and students across school grades, with emphasis on governed institutional adoption rather than consumer-chatbot permission. Schools acting as data controllers must examine necessity and proportionality, transparency and vendor arrangements; the guidance calls for a DPIA before relevant AI personal-data processing and links high-risk use to AI Act fundamental-rights assessment requirements. It is therefore especially useful for EduPolicyAtlas's procurement/privacy dimension.

Sources:
https://www.mim.gov.it/documents/20182/0/MIM_Linee+guida+IA+nella+Scuola_09_08_2025-signed.pdf/b70fdc45-4b75-1f7e-73bf-eab12989b928?t=1756468797694&version=1.0
https://www.mim.gov.it/documents/20182/0/m_pi.AOOGABMI.Registro+Decreti%28R%29.0000166.09-08-2025.pdf/1062ccd9-d807-060e-5aa8-368e75980744?t=1756745377675&version=1.0

Taiwan. Original source language: Traditional Chinese. The Ministry's current portal lists 中小學使用生成式人工智慧注意事項2.1, rendered here as “Precautions for the Use of Generative Artificial Intelligence in Primary and Secondary Schools v2.1.” A separate ministry line of guidance addresses GenAI in student learning assessment and homework. Available ministry-linked notices stress teacher/parent guidance, service-age and account requirements, source verification, privacy, acknowledgment and avoiding overdependence. The coexistence of general-use guidance and assessment/homework guidance makes Taiwan a strong model for the atlas document-to-rule architecture. ^[20]

Portal: https://pads.moe.edu.tw/download.php

Indonesia. Original source language: Indonesian. Ministerial Decision No. 127/P/2025 established implementation guidance for coding and AI from early-childhood through secondary education beginning in the 2025-26 school year, including curriculum and teacher-capacity arrangements. In March 2026 a seven-ministry joint decision created a broader framework for AI and digital technology across formal, non-formal and informal education, with child protection, human-centered adoption, privacy, family involvement and monitoring. A reported red/yellow/green model for AI in assessment is highly promising but should not yet be published as a primary-source-verified rule until the exact clauses are checked directly in the joint decision.

Sources:
https://smk.kemendikdasmen.go.id/produk-hukum/keputusan-menteri-pendidikan-dasar-dan-menengah-republik-indonesia-nomor-127p2025-tentang-pedoman-implementasi-koding-dan-kecerdasan-artifisial-pada-pendidikan-anak-usia-dini-jenjang-pendidikan-dasar-dan-jenjang-pendidikan-menengah
https://www.menpan.go.id/site/berita-terkini/berita-daerah/pemerintah-terbitkan-skb-tujuh-menteri-tentang-pemanfaatan-ai-dan-teknologi-digital-di-dunia-pendidikan

Malaysia. Original source language: Malay. The Ministry of Education's 2026 Buku Panduan Literasi Kecerdasan Buatan (AI), “Artificial Intelligence Literacy Guide,” addresses institutions under the ministry and covers AI governance, teaching and learning, ethics, privacy/data security, academic honesty, bias and cyber safety. It is a stronger national source than generic “Malaysia is adopting AI” reporting, but the PDF still needs page-image verification before the atlas publishes fine-grained quotations or exact locators.

Source: https://www.moe.gov.my/storage/files/shares/Pengumuman/BUKU%20PANDUAN%20LITERASI%20KECERDASAN%20BUATAN%20AI%209.6.26.pdf

Kazakhstan. Original source languages: Kazakh and Russian; an English government rendering exists for the presidential program. Kazakhstan is one of the most significant discoveries in this pass. The President ordered a 2026-29 plan for AI in secondary education, including personalized learning, infrastructure, teacher preparation and protection of student personal data, with standards covering educational-content generation, assessment and academic integrity. The Ministry subsequently described a national conceptual framework for AI in secondary and vocational education. The National Academy of Education has also issued model rules defining GenAI, approved-tool “white lists,” acceptable and prohibited uses, special treatment for children under 13 or a service's applicable age, personal-data restrictions, assessment controls and procedural protections around AI-detector evidence. The Academy document says local rules take effect when approved by the head of the educational organization, so its individual provisions should not be mislabeled as automatically binding national law for every pupil.

Primary sources:
https://www.akorda.kz/ru/o-merah-po-vnedreniyu-iskusstvennogo-intellekta-v-sistemu-srednego-obrazovaniya-respubliki-kazahstan-1242737
https://www.gov.kz/memleket/entities/edu/press/news/details/1070018?lang=kk
https://uba.edu.kz/storage/app/media/22%20FD%20FD%20RS%20RS.pdf

Uzbekistan. Original source language: Uzbek, with Russian and English legal-database renderings. Presidential Decree PF-189 of October 22, 2025 requires AI topics in general-secondary curricula from 2026-27 and establishes an “AI Day” program, textbooks/materials and teacher preparation. Separately, 2026 AI ethics rules impose broader principles of legality, human oversight, transparency, privacy, nondiscrimination and final human responsibility. These are material overlapping rules, but the research did not locate a dedicated national GenAI-homework/exam permission code. The correct atlas classification is therefore existing national AI rules and curriculum mandates apply; dedicated classroom GenAI policy not located, not “no policy.”

Sources:
https://www.lex.uz/en/docs/7790236
https://www.lex.uz/en/docs/8083233

Saudi Arabia. Original source language: Arabic. The Ministry of Education and Saudi Data and AI Authority issued a guide for generative AI in general education in January 2025, explicitly addressing students, teachers and parents and responsible/ethical use. This is a strong Middle East addition, but a second pass should extract exact age, assessment and data provisions page by page before public-facing rule claims go beyond that verified scope.

Source: https://www.moe.gov.sa/ar/mediacenter/MOEnews/Pages/news1_24012025.aspx ^[21]

Argentina. Original source language: Spanish. The national PaideIA program was launched May 12, 2025 to integrate AI across primary and secondary education. Its principles include computational thinking in primary education, critical and ethical AI use across primary/secondary levels, and more advanced AI development at secondary level. The ministry explicitly frames AI as support rather than replacement for teachers and rejects treating AI as infallible or neutral. This is best classified as a nationally led curricular and pedagogical implementation program, not a simple permission code.

Source: https://www.argentina.gob.ar/noticias/capital-humano-lanza-el-programa-paideia-para-integrar-inteligencia-artificial-en-el ^[22]

British Columbia. The province's “Digital literacy and the use of AI in education” resource provides provincial K-12 guidance within a human-centered approach. It is a useful Canadian counterexample to treating Canada as a single jurisdiction: education authority sits substantially at provincial level, so British Columbia and Quebec should be independent atlas records. ^[23]

BC: https://www2.gov.bc.ca/gov/content/education-training/k-12/administration/program-management/ai-in-education

Quebec. Original source language: French. Quebec provides L’utilisation pédagogique, éthique et légale de l'intelligence artificielle générative, with provincial resources spanning preschool, primary, secondary and other education sectors. Local organizations remain important for tool-selection and operating conditions. That combination of provincial guidance and local implementation belongs explicitly in the authority model. ^[24]

Source: https://www.quebec.ca/education/numerique/intelligence-artificielle/reseau-education/documents-outils-ia

Wales. Hwb guidance instructs schools to address data protection and consider DPIA responsibilities when integrating GenAI. Estyn's October 2025 inspection/research work found adoption was still largely staff-driven and uneven, with perceived benefits but concerns around training, bias, privacy and uncertainty over obligations. The Welsh Government accepted the need for a more coordinated strategic response. ^[25]

Sources:
https://hwb.gov.wales/school-improvement-and-leadership/education-digital-standards/generative-artificial-intelligence-in-education
https://estyn.gov.wales/improvement-resources/new-era-how-artificial-intelligence-ai-is-supporting-teaching-and-learning/

Jamaica. Government reporting says the Education Ministry has been developing draft guidelines for AI in schools while training educators, leaders and students. Jamaica's February 2025 national AI task-force recommendations call for AI/coding curriculum, teacher training for responsible AI and assessment, and stronger privacy/ethics governance. These recommendations are not themselves a final school-use code. The correct status is policy under development plus pilots and general data-protection rules.

Sources:
https://jis.gov.jm/education-ministry-unveils-initiative-to-integrate-ai-and-adaptive-technology-into-school-curriculums/
https://opm.gov.jm/wp-content/uploads/2025/02/National-Artificial-Intelligence-Task-Force-Policy-Recommendations-Final-1.pdf

Trinidad and Tobago. The education system is actively deploying AI-enabled learning and enterprise tools, but its strategic plan treats development of a National AI in Education Policy as a distinct future project. That is important evidence against inferring “policy exists” merely because a government has rolled out AI tools. The atlas should record both implementation and the still-developing governance instrument.

Sources:
https://www.moe.gov.tt/educationpolicy2023-2027/
https://storage.moe.gov.tt/wpdevelopment/2026/07/National-Strategic-Plan-Ministry-of-Education-2025-2023.pdf

Pacific systems. Fiji's 2024-33 education-policy framework explicitly includes AI, VR and AR solutions, digital learning, digital literacy and cybersecurity, but no detailed public GenAI classroom permission code was located. Tonga's 2025-35 education framework similarly embeds technology without resolving GenAI homework/examination questions. Samoa's located ICT-in-Education Policy covered 2018-23, so it must not be presented as a current 2026 AI policy absent renewal evidence. Papua New Guinea's 2026 sovereign AI strategy creates broad whole-of-government AI and data-governance direction, including education as a sector, but is not equivalent to K-12 classroom guidance.

Sources:
https://www.education.gov.fj/wp-content/uploads/2024/02/2023-Denarau-Declration.pdf
https://www.education.gov.fj/?p=14554
https://www.education.gov.to/images/TESFP%20-%20Full%20version%20Final%20-%20Printers%20marks%201.pdf
https://www.mesc.gov.ws/wp-content/uploads/2019/09/MESC-ICT-in-Education_Policy_2018-2023-30.10.2018_FINAL.pdf
https://www.ict.gov.pg/wp-content/uploads/2026/03/National-Sovereign-AI-Strategy-v15.pdf

Cross-jurisdiction comparison

This comparison suggests a compact atlas classification should use four separate fields, not a red/green badge:

A fifth, independent evidence-confidence label should describe the evidence rather than the conclusion: High means current primary source inspected with authority/scope established; Moderate means primary source exists but a consequential field or version remains unresolved; Low means only partial or secondary evidence is available; Unknown means the claim should remain unpublished.

Policy evolution, implementation evidence, and news

The timeline shows three broad phases. Late 2022 and 2023 were dominated by emergency responses, academic-integrity concern and early guidance. During 2024-25, governments moved toward frameworks, AI literacy and procurement/privacy controls. In 2026, several systems became markedly more specific about age, assessment, approved tools and deployment.

Implementation and outcome evidence

The evidence base does not support ranking jurisdictions by “AI-policy effectiveness.” England's current DfE guidance explicitly says evidence remains limited on effects on learner development, educational outcomes and safety. Ireland likewise describes the research/practice base as early and its guidance as an initial, reviewable step. ^[47]

Critical evidence conclusion: the atlas can confidently compare what governments require and how implementation is organized much sooner than it can compare whether one policy improves learning more than another. Self-reported teacher time savings, vendor claims, adoption counts and pilot launches should therefore never be displayed as learning-effectiveness evidence.

Verified news/update dataset

Press releases, inspection findings and news reports should all remain separate from policy_documents. A news row may point to a policy event, but it must never silently become the evidence for the rule when the primary document exists.

Practical classroom scenario guide

These scenarios should become a second navigation layer in EduPolicyAtlas. A parent usually starts with “Can my child use ChatGPT?”, not “What is the administrative bindingness classification of document X?” The scenario page can ask the necessary factual questions and then resolve them through the underlying structured rules.

Research method, monitoring, community review, and structured data

The atlas's strongest maintainable research model is field-level evidence, not document-level citation. A document can be authoritative yet fail to support a particular claim. Every consequential public field should therefore point to a source ID and locator independently.

The recommended lifecycle is:

Discovered → source authenticated → extracted → translated if needed → rule-level verified → conflict checked → editorially approved → published → monitored → superseded/corrected.

Automated link checking should never silently advance a source from “current” to “verified.” A 200 response proves only that a URL returned content. It does not prove the document is still controlling, unchanged, or correctly interpreted.

Date handling should separate at least: publication_date, revision_date, announcement_date, effective_date, superseded_date, source_retrieved_at, and rule_verified_at. Never turn “June 2025” into 2025-06-01 simply to satisfy a database field.

Translations should record: source language, original title, English rendering, translation method (original-language interpretation, official translation, machine translation, human-reviewed translation), and interpretation notes. “Official English translation” should be reserved for an issuing authority's translation. Japan is a useful test case because MEXT itself labels its English material tentatively. ^[69]

Authority conflicts should resolve downward to the practical layer: law or regulation can constrain ministry guidance; ministry guidance can coexist with examination-board rules; district policy can narrow national discretion; a school's local policy can further restrict use where higher-level rules permit local choice. Vendor terms are a separate constraint and must never be described as government policy.

Monitoring watchlist

Because RSS/API availability was not separately verified for most sources, the atlas should not claim these are automated feeds. The safe initial architecture is a scheduled URL/change monitor plus a mandatory human-review queue for semantic changes.

A monitored change should create a revised policy record when controlling text or scope changes; a timeline event when the change materially shifts practical rules; a news item when it is publicly newsworthy; a correction notice when prior atlas content was wrong at the time it was published; and a pending-review flag whenever the system detects changed source content that has not yet been interpreted.

Community-submission rubric

A lightweight reviewer form should ask: What authority issued this? What exact jurisdiction and school population does it cover? Is it current? Is it policy, guidance, examination rule, news, research or commentary? What sentence or section supports the proposed rule? Is the source in the original language? How was it translated? What earlier atlas record would this change?

Structured-data package

No downloadable files were written before the research execution window closed. The research itself can nevertheless be normalized directly into the requested files. The recommended relationships are:

jurisdictions.csv
  jurisdiction_id PK
  parent_jurisdiction_id FK nullable
  country_or_area
  jurisdiction_name
  administrative_level
  education_authority
  iso_or_m49_reference
  research_status
  verification_date

policy_documents.csv
  document_id PK
  jurisdiction_id FK
  authority
  original_title
  english_title
  source_language
  document_type
  bindingness
  publication_date
  revision_date
  effective_date
  superseded_date
  current_status
  translation_status

policy_rules.csv
  rule_id PK
  document_id FK
  jurisdiction_id FK
  rule_dimension
  population
  age_min
  age_max
  grades
  school_types
  rule_summary
  conditions
  exceptions
  authority_level
  exact_locator
  source_id FK
  verification_date
  evidence_limitation

sources.csv
  source_id PK
  source_type
  issuing_body
  title
  url
  language
  publication_date
  retrieved_at
  primary_or_secondary
  access_status
  archive_url
  notes

policy_events.csv
  event_id PK
  jurisdiction_id FK
  document_id FK nullable
  event_date
  date_type
  announcement_date
  effective_date
  event_type
  previous_position
  new_position
  source_id
  confidence
  unresolved_questions

news.csv
  news_id PK
  jurisdiction_id FK
  headline
  publisher
  publication_date
  event_date
  category
  summary
  url
  primary_source_id
  related_document_ids
  establishes_policy_change
  dedupe_key

research_evidence.csv
  evidence_id PK
  jurisdiction_id FK nullable
  title
  authors_or_body
  population
  education_level
  sample_size
  design
  intervention
  comparison
  data_collection_dates
  findings
  limitations
  funding_conflicts
  peer_review_status
  policy_relevance
  source_id

coverage_gaps.csv
  gap_id PK
  jurisdiction_id FK
  gap_dimension
  status
  searches_completed
  missing_evidence
  next_authority_to_check
  priority

monitoring_sources.csv
  monitor_id PK
  organization
  jurisdiction_id
  url
  language
  content_type
  frequency
  rss_api_status
  meaningful_change_definition
  automation_suitability
  human_review_required

existing_record_corrections.csv
  correction_id PK
  jurisdiction_id
  existing_statement
  finding
  proposed_replacement
  source_ids
  reason
  remaining_uncertainty
  editorial_status

atlas_research.json should preserve the same IDs and expose nested views for the website without making the JSON itself the canonical database. Unknown values should be null, not "unknown" or empty strings. “Not applicable,” “not addressed in this document,” “not located,” and “not verified” should remain separate enumerated states.

The accompanying README.md should document enumerations, dates, translation rules, primary-key patterns, source authority hierarchy, evidence confidence and the invariant that a jurisdiction is not a policy document, and a policy document is not a rule.

Publication recommendations and open limitations

Ready for editorial consideration

The following findings are sufficiently supported for atlas editorial review now:

Australia, Ireland, British Columbia, Quebec, Argentina and Wales are also publication-ready at the overview level, subject to normal editorial review. ^[76]

Needs verification or interpretation

Malaysia's 2026 ministry guide is highly promising but should receive page-level source verification before granular public rules are quoted. Indonesia's seven-ministry framework is strong primary evidence, but the reported red/yellow/green assessment taxonomy should be checked word-for-word against the controlling document before publication. Saudi Arabia's guide should receive detailed Arabic extraction for age, assessment and privacy fields. Chile needs a deeper Spanish rule audit. Washington State needs a final version-history reconciliation. Jamaica's school-specific guidance remains described as draft in the evidence located. Trinidad and Tobago is implementing AI at scale while its strategic plan separately calls for development of a National AI in Education Policy. Samoa needs confirmation of what replaced or extended its 2018-23 ICT policy.

Africa remains the largest geographic evidence gap. Rwanda, Nigeria and South Africa have meaningful AI strategy, curriculum, research or pilot activity, but this pass did not establish sufficiently detailed national K-12 GenAI-use codes. Kenya needs a full Ministry/KNEC investigation. South Asia outside the limited India/Sri Lanka discovery pass is also incomplete. Southeast Asian coverage beyond Indonesia and Malaysia requires deeper original-language work in the Philippines, Thailand, Vietnam, Brunei and Cambodia.

Do not publish as factual claims

Do not publish “Country X has no AI policy” merely because one was not found. Do not call Norway's national recommendation a criminal, statutory or absolute “AI ban.” Do not treat Denmark's local classroom discretion as permission to use AI in national examinations. Do not describe Trinidad and Tobago's AI-tool deployment as proof that its planned National AI in Education Policy is already in force. Do not extrapolate a provincial Canadian rule to Canada. Do not infer a ministry ban from a vendor's age restriction. Do not infer student permission from a teacher-training program. Do not state that an AI detector proves misconduct. Do not describe a pilot as system-wide authorization. Do not rank countries by educational effectiveness based on the evidence gathered here.

Open limitations

The most material unfinished work is the full original-language, page-level extraction for Japan, Chile, Malaysia, Saudi Arabia and selected Indonesia documents; examination-authority audits outside the strongest existing records; systematic state/province/Land/district sampling in decentralized systems; a deeper Sub-Saharan African pass; South Asia; additional Southeast Asian languages; Caribbean CXC implementation; and a larger empirical K-12 research review.

The original raw research-report Markdown and any current policy/news export files were not available in the materials I could inspect, so this package does not claim to have reconciled hidden atlas records against those exports. The live HTML site and report were inspected, and corrections are framed accordingly. ^[77]

The strongest immediate product decision is to make rule-level comparison the core data model: age/access, teacher authorization, assessment, data/privacy, accessibility, staff use, bindingness and implementation should each be independently filterable and independently cited. That architectural change would do more to reduce parent and educator confusion than adding another thirty country cards with a single “allowed/restricted” label.

First action: update the NYC, France, Singapore, New Zealand and England records before expanding the public map, because those five corrections add more practical user value with stronger evidence than a larger batch of shallow new-country summaries.

[2026-09-08]

Original report citation identifiers

The pasted report did not include the URL mapping for these ChatGPT references. They are preserved for traceability, not presented as verified clickable sources. The explicit official links in the report and atlas remain available.

1. [1] turn4view3, turn9view1, turn9view0, turn12view0, turn14view1
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