AI Literacy Course

An AI literacy course studies the competences in Empowering Learners for the Age of AI, the AI literacy framework the OECD and the European Union published in 2026. The publication defines AI literacy as the technical knowledge, durable skills, and future-ready attitudes required to thrive in a world influenced by AI: learners engage with AI, create with it, manage it, and shape it, and they critically evaluate its benefits, risks, and ethical implications. What AI literacy means in 2026 separates that work from training models. The framework is written for primary and secondary education and asks practitioners to adapt it. It is non-binding, and the publication says it is not guidance on enforcing the EU Artificial Intelligence Act.

The competences sit in four domains, in an order the text calls a possible learning path. Engage with AI is first. Create with AI and Manage AI come next, and the publication treats those two as parallel once the foundation is in place. Shape AI is last. Each competence has learner expectations at Basic, Intermediate, and Advanced. Those levels are not ages or grades. The publication also says simply interacting with AI tools neither develops nor depends on the competences below.

Engage with AI

The domain line is: become a critical and responsible participant in a world marked by AI. The framework weaves ethics through the materials: learner agency, transparency and explainability, fair, inclusive, and accountable use, privacy, environmental stewardship, and who benefits or is disadvantaged.

1. Recognise AI's role and influence in different contexts.
2. Describe how AI systems perform tasks using language that addresses and clarifies common misconceptions.
3. Evaluate whether AI outputs should be accepted, revised or rejected.
4. Examine how predictive AI systems provide recommendations that can inform or limit perspectives.
5. Compare how AI systems consume energy and natural resources.
6. Explain how AI could be used to amplify societal biases.
7. Analyse how well the use of an AI system aligns with ethical principles and human values.

The knowledge section tied to competence 5 states that AI systems require energy, minerals, and water. Competence 2 asks learners to describe programmed processes, and to drop phrases that treat a system as if it thinks or understands.

Create with AI

The domain line is: use AI as a creative partner while maintaining human agency. Learners explore open questions and develop ideas by brainstorming, prompting, reflecting, and revising. The publication keeps the learner's own ideas in charge, and it includes originality, intellectual property, and fair use.

1. Use AI systems to explore new perspectives and approaches that build upon original ideas.
2. Visualise, prototype and combine ideas using different types of AI systems.
3. Direct generative AI systems to elicit feedback, refine results and support reflection.
4. Analyse how AI can safeguard or violate content authenticity and intellectual property.

Manage AI

The domain line is: divide work intentionally between humans and AI. Learners compare approaches, break a problem into parts, and decide when a system should automate or augment a step so that human effort stays on judgement, creativity, relationship building, and domain expertise. They then monitor that split.

1. Decide whether to use AI systems based on the nature of the task.
2. Choose an appropriate AI approach for a task by comparing how different AI systems operate and what they are best suited to do.
3. Decompose a problem to determine when and how AI systems should be used to automate or augment tasks.
4. Monitor and evaluate AI use throughout a problem-solving process.

If you approve tools for other people, deciding whether to use AI, and whether that use aligns with ethical principles, is the same decision at team scale. Questions to ask before approving AI tools is the workplace checklist for data, wrong outputs, and fit to a real task.

Shape AI

The domain line is: improve AI systems to reflect human values. Learners connect how a system works with the human choices behind its behaviour. The stated goal is to create or improve systems so they reflect human values, diverse perspectives, and the common good. The publication's goal for this domain stops short of developing a commercial product or putting a system into service, and it says the domain is feasible once learners have the first three.

1. Investigate how an AI system is intended to work, whom it is designed for and what its limitations are.
2. Evaluate AI systems using defined criteria, expected outcomes, test cases and user feedback.
3. Design AI systems with attention to how data sources, selection and information flow influence behaviour and outputs.
4. Improve AI systems to address and promote human well-being and societal benefit.

Ailurn does not issue an accredited certificate. Basic, Intermediate, and Advanced are progressions for teachers planning lessons. Finishing a course here does not assign those labels or a qualification from the OECD or the European Union.

Ask for this sequence

Keep the framework's order: notice where a system is acting, judge one output, decide how to split the work, then read what a system was built to do.

Ailurn turns a prompt, a PDF, a GitHub repo, or a docs URL into a course you take in the same workspace. It does not watch a YouTube lecture. Open the AI course builder and ask for a course on the AILit sequence: recognise where an AI system is influencing a task you already do; describe how it produces an output without treating it as a person; accept, revise, or reject one output against a source you trust; choose which steps stay with you and which a system may augment, including whether the fit is rules or patterns from data; then read one model card and state what the system was designed to do, whom it is for, and what its limitations are.