7 modules. Built against DigComp 3.0. Written for public service.
Every module is mapped to a specific DigComp 3.0 competence and proficiency level. Modules 1 to 6 take most learners from Foundation to Intermediate. Module 7 is for senior officials and targets Advanced levels.
What the curriculum covers
What AI is and is not
How large language models and ML systems actually work. What hallucination is and why it happens. The practical limits of AI in professional contexts. No maths, no code.
DigComp areas
Proficiency
Levels 3 to 4Domain
GeneralData and information in public service
What counts as good data for AI purposes. How training data quality affects outputs. Managing data in AI-enabled workflows. What public sector data obligations apply.
DigComp areas
Proficiency
Levels 3 to 4Domain
Data management, procurementLegal and procurement context
What EU AI Act Article 4 requires of deployers. How Annex III high-risk classification works. GDPR Article 22 on automated decisions. The GovTech Lab pre-commercial procurement pathway.
DigComp areas
Proficiency
Levels 3 to 4Domain
Law and procurementSafety, privacy, and cyber security
How AI systems create new attack surfaces. What data protection obligations change when AI is involved. How to protect staff and citizens from AI-enabled risks.
DigComp areas
Proficiency
Levels 3 to 4Domain
Cyber securityHuman rights and impact assessment
How AI systems can cause harm at scale. The Robodebt and SyRI cases. What a Fundamental Rights Impact Assessment covers and when one is required.
DigComp areas
Proficiency
Levels 4 to 5Domain
Human rights and impactUsing AI tools effectively
Prompt engineering for professional contexts. How to evaluate AI outputs critically. When not to use AI. Disclosure obligations when AI assists public communications.
DigComp areas
Proficiency
Levels 3 to 4Domain
GeneralAdvanced track: Leading AI adoption
Coming Q2 2027For senior officials leading AI adoption decisions. Institutional strategy, procurement governance, impact assessment oversight, and briefing non-technical stakeholders.
DigComp areas
Proficiency
Levels 5 to 6Domain
GeneralFrom Module 1: What AI is and is not
This excerpt is from the opening of Module 1. It gives you a sense of the level and tone. No prior technical knowledge is assumed.
Why "artificial intelligence" is a misleading name
The word "intelligence" implies understanding. When you understand something, you can explain it, apply it in new situations, notice when it does not apply, and know when you are wrong. AI systems do none of these things.
A large language model is a statistical pattern matcher. It is the kind of system that powers ChatGPT, Copilot, and most of the AI tools you will encounter at work. It was trained on very large amounts of text. Given a prompt, it produces output that is statistically plausible given that training. It does not know what it is saying. It cannot verify that what it says is true. It has no model of the world it can check its output against.
This matters because plausible and correct are not the same thing. A system that produces confident-sounding text is not the same as a system that produces accurate text. The practical implication of that gap is what most of Module 1 is about.
Module-to-competence mapping
Each module maps to specific DigComp 3.0 areas, competences, and proficiency levels. Lithuania's National AI Strategy 2026 to 2035 uses DigComp 3.0 as the national measurement standard for AI literacy.
| Module | DigComp areas | Competences | Levels |
|---|---|---|---|
| 1 | 1, 5 | 1.1, 1.2, 5.1, 5.4 | 3 to 4 |
| 2 | 1 | 1.1, 1.2, 1.3 | 3 to 4 |
| 3 | 4 | 4.2, 4.3 | 3 to 4 |
| 4 | 4 | 4.1, 4.2, 4.4 | 3 to 4 |
| 5 | 4, 5 | 4.3, 5.2 | 4 to 5 |
| 6 | 2, 3, 5 | 2.1, 3.1, 3.3, 5.1, 5.2 | 3 to 4 |
| 7 | 4, 5 | 4.3, 5.2, 5.4 | 5 to 6 |
Competence codes follow DigComp 3.0 (JRC publication JRC144121, November 2025). Full mapping documentation available on request.
Read the full alignment documentation
The alignment document covers Article 4 obligations, the full DigComp 3.0 mapping at the competence level, and the compliance evidence the platform produces. It is prepared for institutional review. Get in touch to request it.