Amid the Russian invasion, Ukraine's Ministry of Education and Ministry of Digital Transformation issued joint AI guidance for schools in May 2024, since analysed as a four-layer soft-law system with EU AI Act-aligned risk rules and inclusion uses for displaced and special-needs
Details
Maturity
Scaling
Promoter
Ministry of Education and Science of Ukraine (with the Ministry of Digital Transformation)
Period
May 2024–ongoing
Keywords
AI policy, wartime education, teacher guidance, soft-law governance, inclusion
Context
On 23 May 2024, Ukraine's Ministry of Education and Science, working with the Ministry of Digital Transformation, published recommendations for teachers and students on using artificial intelligence in schools. The guidance was issued as Ukraine continued to operate its education system under wartime conditions, including mass school displacement and disrupted access to in-person instruction.
Objectives
Give teachers and students practical guidance on classroom AI integration — including prompt-writing techniques, safety protocols and curated lists of recommended AI tools — while establishing risk-based rules for higher-stakes uses.
Activities
A 2026 academic analysis characterises Ukraine's approach as a deliberate choice of non-binding soft law over formal legislation, built in four layers: national guidance from the two ministries; sector-level codes translating principles into institutional templates; institutional policies governing data handling and procurement; and course-level rules such as syllabus clauses. The guidance applies an EU AI Act-aligned risk classification that flags high-stakes uses — admissions, grading, behavioural monitoring — as high-risk, and sets age thresholds of 13 for independent use, with parental consent required between ages 13 and 18. Ukraine has used AI-based tools such as speech-to-text and visual generation specifically to support students with special educational needs and children displaced by the conflict.
Results
The four-layer soft-law framework is publicly documented and has been independently reported by a news agency and analysed academically, with concrete inclusion applications (speech-to-text and visual generation for displaced and special-needs students) distinct from generic guidance.
Conclusions
The soft-law approach is explicitly a trade-off rather than a claim of comprehensiveness: the analysis frames it as pragmatic given wartime capacity constraints, while advising institutions not to wait for a formal statute or outsource pedagogical decisions to technology vendors — an acknowledgement that formal, binding regulation has not yet followed the initial guidance.
Implementation
Indicative cost
Low (< €50k) — Issuing guidance and analysis is low direct cost relative to infrastructure-heavy interventions; implementation cost is distributed across sector bodies and individual institutions building sector/institutional layers.
Time to results
Medium (1–3 years) — Guidance issued May 2024; the four-layer soft-law framework (sector codes, institutional policies, course-level rules) is still being built out under wartime capacity constraints as of the most recent (2026) academic analysis.
Staffing & skills
Ministry of Education and Science of Ukraine, Ministry of Digital Transformation, individual institutions (developing institutional policies), teachers (implementing course-level rules)
Conditions for success
multi-layer implementation from national guidance down to course-level rules
EU AI Act-aligned risk classification to flag high-stakes uses
age-based consent thresholds (13, and 13-18 with parental consent)
institutions not deferring pedagogical decisions to technology vendors
Common failure modes
deliberately non-binding soft law risks incomplete institutional follow-through, especially under wartime capacity constraints
formal, binding regulation has not yet followed the initial guidance
Where it fits
Governance type
national ministry soft-law guidance
Scale
national
Income level
lower-middle-income, wartime conditions
Commonly funded by
National / regional programmes
Indicative funding routes for practices of this type — always check each programme's current calls and eligibility rules.
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Claim it —
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Data sources
Where this practice's information was retrieved from, and when.
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