Curriculum alignment · Europe / OECD

OECD–European Commission AI Literacy Framework and European guidance

We checked the LittleAIMaster Grade 1–12 AI course against all 76 rated requirements of this framework: 44 fully covered, 18 mostly covered, 14 partly covered. Open a requirement to see what learners do and the exact lessons.

82%
Fully covered 44Mostly covered 18Partly covered 14of 76 requirements22 taught earlier than the framework asks

Source documents (5)

Review draft May 2025; finalised framework launched 18 June 2026
2.2, published March 2022
AI Leap 2025 / TI-Hüpe (national AI-in-schools programme)
Estonian Ministry of Education and Research
Announced Feb 2025; launched 1 Sept 2025 (grades 10-11 pilot)
National recommendations on the use of AI in early childhood, primary and lower-secondary education
Finnish Ministry of Education and Culture / Finnish National Agency for Education (OPH)
March 2025
Adopted 10 Oct 2024; redaktionell überarbeitete Fassung März 2025

2 of 76 requirements are worded from secondary summaries or could not be checked against the primary document; they are marked.

Alignment chart

Every OECD–EC (Europe) requirement, rated

5Taught at this grade5Some material5Grades the framework asks for
Open a requirement to see what learners do and the exact lessons.

Showing 76 of 76 requirements

AILit

Engage with AI

Identify AI's presence in everyday life (Engage 1, basic level).

Framework grades 1–2, 3–5
Mostly covered
Our Grades 1–2 Mostly covered
What learners do

In Grade 2 'Choosers Pick for Me', learners compare narrowed recommendations, choose a wider item and make a personal viewing plan.

Still to add

Reflection uses supplied situations; analysis of the learner's own real feed is not required.

Our Grades 3–5 Fully covered
What learners do

In Grade 5 'AI and Friends' and 'AI and the Planet', learners work out why an advert appeared on their screen, name the pull and set their own limit, and see that a mistakenly watched video steers suggestions.

Share examples of how AI shapes personal choices (Engage 1, intermediate level).

We start Grade 5 · framework Grade 6Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 6 'AI Recommends For You', learners order the filter-bubble loop and write which of their own actions this week changed their recommendations.

Analyse how AI shapes beliefs and behaviours (Engage 1, advanced level).

Framework grades 9–10, 11–12
Partly covered
Our Grades 9–10 Partly covered
What learners do

In Grade 10 'Deepfakes and Misinformation', learners study framing, dark patterns and social proof, judge six given screens or AI replies as neutral or steering, and take a quiz.

Still to add

Learners do not examine a feed or app they use for effects on their own choices, and they never write their own analysis of how a system shapes behaviour.

Our Grades 11–12 Partly covered
What learners do

In Grade 11 'AI in Entertainment' and Grade 12 'How Does It Know?', learners study filter bubbles, feedback loops, popularity bias and engagement-driven feeds, applied to scenarios and a library recommender design.

Still to add

Learners never look at their own feed, recommendations or usage to see how their choices, beliefs or behaviour were shaped.

Where it is taught

Understand AI as a nonhuman tool that lacks authentic understanding, addressing common misconceptions (Engage 2, basic).

Framework grades 1–2, 3–5
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 2 'What Is AI?', 'Machines That Hear' and 'Teach Byte', learners see that a parrot can say hello without meaning it, a robot's sad face is not sadness, and reading is not understanding.

Our Grades 3–5 Fully covered
What learners do

In Grade 3 'AI at Work' and Grade 4 'Thinking Well', learners see that a robot with a sad face is not sad, that machines cannot know, choose or care, and that chat AI writes feelings it does not have.

Accurately describe how a specific AI tool operates (Engage 2, intermediate).

We start Grade 4 · framework Grade 6Framework grades 6–8
Fully covered
Our Grades 6–8 Covered earlier · Grade 4
What learners do

In Grade 4 'A Plant Doctor That Knows Facts', learners sort facts from rules, match IF and THEN halves, order the engine's steps, build the doctor, and match its parts to their lunch-box machine.

Where it is taught

Explain how AI systems perform tasks without attributing human traits to them (Engage 2, advanced).

We start Grade 2 · framework Grade 9Framework grades 9–10, 11–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

Grade 6 'AI Makes Mistakes' and earlier chapters name anthropomorphism and teach that AI has no feelings and is human-built, not alive. This is repeated across Grades 2, 3 and 6.

Our Grades 11–12 Fully covered
What learners do

Grade 12 'Universal Translator' and 'The Unsolved Mysteries' state that AI is a pattern matcher that does not understand. Earlier chapters, from Grade 2 on, teach that AI has no feelings.

Understand why verifying an AI output is necessary (Engage 3, basic).

Framework grades 1–2, 3–5
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 2 'Check Byte's Work', learners check Byte's bee claim against a pictured reference, reject the wrong leg count and explain why.

Our Grades 3–5 Fully covered
What learners do

In Grade 4 'Checking' and 'Made-Up Answers', learners ask, read, check and use, find a genuine second source, and reject an answer that cites a source that does not exist.

Verify AI outputs against trusted sources (Engage 3, intermediate).

We start Grade 4 · framework Grade 6Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 6 'When to Trust AI' and 'Real vs Fake: Deepfakes', learners use the SIFT and STOP methods and three verification questions, sort jobs into 'just use it' versus 'check it first', and do not pass on unverified AI facts.

Evaluate whether an AI output should be accepted, revised, or rejected, and justify the decision (Engage 3, advanced).

We start Grade 6 · framework Grade 9Framework grades 9–10, 11–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 10 'Deepfakes and Misinformation', learners pause, check and verify before sharing; this builds on risk-based checking in Grade 6 and verifying AI-generated information in Grade 8.

Our Grades 11–12 Fully covered
What learners do

In Grade 12 'AI That Writes Code', learners verify AI maths and review AI-generated code before shipping; Grade 6 lessons teach SIFT-style source checking.

Where it is taught

Know that AI uses personal/user data to generate recommendations (Engage 4, basic).

Framework grades 1–2, 3–5
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 2 'Choosers Pick for Me', learners identify the records behind recommendations, compare a narrowing feed and choose a way to widen it.

Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Why Am I Seeing This?', learners explain a narrowing feed, choose a wider recommendation and connect commercial selection to a user's need.

Consider whether an AI recommendation is helpful or narrows perspective (Engage 4, intermediate).

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 6 'AI Recommends For You', learners trace recommenders from implicit signals to ranked output, compare collaborative and content-based filtering, and see how a feedback loop narrows what is shown.

Weigh the benefits and drawbacks of data-shaped access to information from predictive AI systems (Engage 4, advanced).

We start Grade 6 · framework Grade 9Framework grades 9–10, 11–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 9 'Conditional Probability', learners apply conditional probability to Netflix and Spotify recommendations, building on collaborative and content-based filtering, implicit signals and filter bubbles taught in Grade 6.

Our Grades 11–12 Fully covered
What learners do

In Grade 12 'How Does It Know?' and Grade 11 'AI in Entertainment', learners study collaborative filtering, implicit signals and cold start, and weigh filter bubbles against diversity and serendipity fixes.

Recognise that computers and AI require physical resources to run (Engage 5, basic).

Framework grades 1–2, 3–5
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 1 'Big Computers Get Hot', learners trace the hardware, power and cooling that remote computers need, choose a saver card and communicate a family action.

Our Grades 3–5 Fully covered
What learners do

In Grade 5 'AI's Footprint', learners account for materials, power and water, chart suitable tools, plan an evidence-based footprint message for an audience and say it to a friend or adult.

Describe environmental impacts of AI such as hardware, data centres, water and energy use (Engage 5, intermediate).

We start Grade 5 · framework Grade 6Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 5 'Being Kind' and Grade 6 'AI in 2030' and 'AI for Good', learners cover energy cost, data centres and model right-sizing, and weigh a project's benefit against the electricity and water it uses.

Analyse how design and deployment decisions affect an AI system's resource consumption (Engage 5, advanced).

Framework grades 9–10, 11–12
Partly covered
Our Grades 9–10 Partly covered
What learners do

In Grade 10 chapters such as 'GPU vs CPU', 'Early Stopping' and 'Model Optimization', learners read quantified energy, water and hardware figures and levers such as smaller models and early stopping, with a couple of multiple-choice energy items.

Still to add

Learners never design or communicate a way to reduce AI's footprint, accounting is mostly read, and water is never computed; the task exists in Grades 8 and 11.

Our Grades 11–12 Mostly covered
What learners do

In Grade 12 'Smart Grid Hero', learners work through chips, cooling water and a CO2 formula, and compute a footprint by hand; in Grade 11 'SDG Research-and-Build Project' they use a footprint calculator for a school-wide chatbot.

Still to add

Learners do not design and communicate their own footprint-reduction plan with before and after numbers, water is quoted but not estimated, and one sorting item has a wrong answer key.

Where it is taught

Understand that biases in training data can be perpetuated by AI systems (Engage 6, basic).

Framework grades 1–2, 3–5
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 2 'Which Pictures Are Missing?' and 'Project: Teach Byte Fairly', learners sort what is missing from examples, compare bar lengths for imbalance, predict wrong outcomes, add missing examples and retest.

Our Grades 3–5 Fully covered
What learners do

In Grade 4 'Wrong, and How Sure', learners see a red-apple-only model fail on yellow apples and a child-only app miss a grandad. They spot the gap, take photos, add them and test again.

Explain how biased data or design choices create unfair outcomes (Engage 6, intermediate).

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 6 'Teach the Teacher' and Grade 7 'Good Examples vs Bad Examples', learners judge ten dataset scenarios, match flaws to the damage they cause, and create an imbalance on purpose to see it bias a model.

Analyse how institutional deployment of AI can serve particular interests and amplify societal bias (Engage 6, advanced).

Framework grades 9–10, 11–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 10 'Bias in AI Systems' and Grade 9 'The Biased Hiring Model', learners see COMPAS, the healthcare algorithm, Amazon hiring and Apple Card as documented unequal impact by demographic group.

Our Grades 11–12 Fully covered
What learners do

In Grade 12 'The AI Radiologist' and 'The Algorithm That Discriminated', learners study healthcare AI that helps some patients while misdiagnosing women or failing rural clinics, plus COMPAS and hiring cases, using group-level metrics.

Where it is taught

Recognise that ethical AI use depends on design, development and user intentions (Engage 7, basic).

Framework grades 1–2, 3–5
Mostly covered
Our Grades 1–2 Mostly covered
What learners do

In Grade 2 'Fair and Good for Everyone?', learners apply simplified, named UNESCO principles to Talky and give a reason.

Still to add

Talky is fictional; applying the principles to a real documented case is not yet included.

Our Grades 3–5 Mostly covered
What learners do

In Grade 5 'Judge One Machine' and 'Check a Real Rule', learners apply UNESCO criteria to a structured fictional case and critique real UNESCO guidance.

Still to add

Applying the named framework to a documented case of a real deployed system is not explicitly assigned.

Identify unintended impacts of an AI use that differ from its immediate goals (Engage 7, intermediate).

Framework grades 6–8
Mostly covered
Our Grades 6–8 Mostly covered
What learners do

In Grades 6-8, learners apply named frameworks such as the UNESCO Recommendation and the UAE Charter to cases like a school-gate face camera (Grade 7), an exam proctor and a hiring filter (Grade 8), choosing the broken principle and the fix.

Still to add

Framework verdicts are always picked from options; learners do not write their own justification, and bioethics is not used.

Where it is taught

Evaluate how well an AI use aligns with ethical principles and human values across trade-offs (Engage 7, advanced).

Framework grades 9–10, 11–12
Mostly covered
Our Grades 9–10 Mostly covered
What learners do

In Grade 10 'Building Your AI Ethics Framework' and Grade 9 'The Biased Hiring Model', learners place a hiring AI in EU AI Act risk tiers and name the missing UNESCO principle, then write principles their own project meets.

Still to add

Applications to real cases are fixed-answer with pre-written feedback; learners never write a justified judgement on a real case.

Where it is taught
Our Grades 11–12 Fully covered
What learners do

In Grade 12 'The Council Decision' and 'The Ethics Question', learners apply consequentialist, deontological, virtue and care frameworks to a lethal-drone dilemma, and apply FATE and IRB frameworks to cases.

Create with AI

Explore how AI tools can expand thinking and spark ideas (Create 1, basic).

Framework grades 1–2, 3–5
Mostly covered
Our Grades 1–2 Mostly covered
What learners do

In Grade 1 'Me and the Smart Machine', learners state their own idea, compare prepared machine ideas and combine selected contributions in a paper plan.

Still to add

The AI ideas are authored examples and the app plan is closed-choice; no original solution is made with an actual AI tool.

Our Grades 3–5 Mostly covered
What learners do

In Grade 4 'Make It With Two Machines', learners write their own idea first, keep, change or drop supplied AI suggestions, and fold selected ideas into a paper design.

Still to add

The machine suggestions are authored examples and the digital composition is constrained; open AI brainstorming is not available.

Compare AI-generated suggestions to one's own ideas during brainstorming (Create 1, intermediate).

We start Grade 4 · framework Grade 6Framework grades 6–8
Mostly covered
Our Grades 6–8 Mostly covered
What learners do

In Grade 4 learners choose their own ideas first, keep, change or drop a machine's ideas, and build a plan that tags each part. In Grade 7 they keep or cut ideas for a story.

Still to add

The machine's ideas are written in advance and no Grade 6-8 lesson uses live AI ideas, so open brainstorming is not practised.

Purposefully integrate AI outputs with original ideas to build a comprehensive solution (Create 1, advanced).

Framework grades 9–10, 11–12
Mostly covered
Our Grades 9–10 Fully covered
What learners do

In Grade 10 'Project Ideation', learners write five ideas of their own, ask Prompt Lab for five more under the same limits, compare all ten, merge one useful angle and label the AI contribution.

Where it is taught
Our Grades 11–12 Mostly covered
What learners do

In Grade 11 'Capstone Planning', learners write three capstone ideas first, ask Prompt Lab for five more, compare all eight with reasons, merge one angle, reject one unsafe idea, and credit which parts came from the AI.

Still to add

The live AI request can be replaced by a worked example when free tool opens run out, so the comparison with real AI output is not guaranteed.

Where it is taught

Use AI to generate images, stories, or models representing an idea (Create 2, basic).

Framework grades 1–2, 3–5
Partly covered
Our Grades 1–2 Partly covered
What learners do

In Grade 2 'Ask, Look, Ask Again', learners select picture and story request cards and assemble the prepared outputs.

Still to add

The activity routes authored branches; no generative tool produces a new output in response to the learner's request.

Our Grades 3–5 Partly covered
What learners do

In Grade 4 'Make It With Two Machines', learners combine supplied picture and text outputs and explain what each source contributed.

Still to add

The branches are authored rather than live generative tools, and selecting supplied outputs is not the same as using a tool.

Experiment with multiple AI tools to develop diverse ideas (Create 2, intermediate).

Framework grades 6–8
Partly covered
Our Grades 6–8 Partly covered
What learners do

Prompt Lab is reached in Grades 6-8 through optional capstones such as 'Prompt Engineering 101'. Grade 8 'Liam's Art Dilemma' and 'The AI Challenge' cover prompts and matching project tool combinations.

Still to add

No lesson has learners produce an output and combine outputs from two tools into a finished piece; the one live generator is optional and there is no image generation.

Select and integrate outputs from multiple AI systems into a final product (Create 2, advanced).

Framework grades 9–10, 11–12
Partly covered
Our Grades 9–10 Partly covered
What learners do

In Grade 10 'Choosing and Judging AI Tools' and Grade 9 'The Right Question', learners use Prompt Lab for a real goal, produce a plan and a message, check and revise them, and score four models' fixed outputs.

Still to add

Learners make text with one tool only; they never produce images or models, or combine outputs from more than one tool into a finished piece.

Where it is taught
Our Grades 11–12 Mostly covered
What learners do

In Grade 11 'Journey Complete', learners draft a script and captions through a live Prompt Lab request, make two toy cover versions, choose between versions with reasons, assemble one page and write a credit line stating what each tool did.

Still to add

The second tool is a labelled toy picture maker, not a trained image generator, so no real second generative tool output is combined.

Know how to prompt an AI system to get accurate feedback and helpful responses (Create 3, basic).

Framework grades 1–2, 3–5
Mostly covered
Our Grades 1–2 Mostly covered
What learners do

In Grade 2 'Ask, Look, Ask Again', learners compare the result of a first request with a colour-only revision and explain what changed.

Still to add

The exchanges follow authored branches; there is no open generative or conversational exchange.

Our Grades 3–5 Mostly covered
What learners do

In Grade 4 'Better Asks, Better Answers', learners refine a structured request, compare how the answers change and check against a reference.

Still to add

The exchange is a finite authored simulation rather than an open generative or conversational system.

Refine work through iterative exchanges with an AI system (Create 3, intermediate).

Framework grades 6–8
Partly covered
Our Grades 6–8 Partly covered
What learners do

In Grade 8 'Liam's Art Dilemma' and 'Carlos's Writing Helper', learners build layered prompts, sort vivid from vague ones and compare simulated runs. In an optional capstone they refine prompts against real output.

Still to add

Refining prompts against real output happens only in one optional capstone, and improving work through follow-up exchanges first appears in Grade 9.

Where it is taught

Critically evaluate how AI feedback shapes one's ideas and reflect on the creative process (Create 3, advanced).

We start Grade 2 · framework Grade 9Framework grades 9–10, 11–12
Mostly covered
Our Grades 9–10 Fully covered
What learners do

Learners practise prompt clarity and refinement from Grade 2 'When AI Says No', prompt engineering in Grade 8 'Maya's Discovery', and deepen it in Grade 11 'Welcome, AI Specialists!'.

Our Grades 11–12 Mostly covered
What learners do

In Grade 11 'Building Your App' and Grade 12 'The Blueprint Before the Building', learners write a system prompt, run test cases in Prompt Lab, change a line when a test fails and re-run all tests.

Still to add

Learners never improve a separate piece of their own work over several rounds with a model, and the in-chapter Prompt Lab runs can be skipped.

Where it is taught

Know that AI-generated content may reuse or copy protected work (Create 4, basic).

Framework grades 1–2, 3–5
Mostly covered
Our Grades 1–2 Mostly covered
What learners do

In Grade 1 'Find It, Make It, Share It' and Grade 2 'Ask, Look, Ask Again', learners attach an origin statement to a mixed picture and story piece and explain what the child and the machine example each contributed.

Still to add

An origin label is practised, but a separate end-credit or source list is not; the outcome asks for credit in both the work and the bibliography.

Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Who Owns It?' and Grade 5 'My Digital Toolkit', learners credit mixed materials and AI contributions in source notes, keep evidence on a poster and explain what was actually used.

Consider when attribution, permission, or avoiding AI use is the appropriate choice (Create 4, intermediate).

We start Grade 3 · framework Grade 6Framework grades 6–8
Fully covered
Our Grades 6–8 Covered earlier · Grade 3
What learners do

In Grade 3 'Who Owns It?', learners write a sources list entry for AI help with tool, date, request and what was used, and finish a poster with an honest source note. In Grade 4 they label which parts a machine made.

Assess the ethical implications and justify how an AI-assisted work maintains authenticity (Create 4, advanced).

Framework grades 9–10, 11–12
Mostly covered
Our Grades 9–10 Mostly covered
What learners do

In Grade 10 'Choosing and Judging AI Tools', learners learn that AI-written work handed in is not theirs, see what a good AI note includes (tool, date, prompt, what was kept or changed), judge sample notes, and write their own AI note.

Still to add

Disclosure is practised only as a separate AI note; no in-text acknowledgement or reference entry for an AI tool is written, and MLA and APA formats are named but not shown.

Where it is taught
Our Grades 11–12 Mostly covered
What learners do

In Grade 12 'Crediting AI in Your Paper', learners study APA and MLA forms for citing an AI tool, fix reference entries, then write an AI-use statement with a reference and in-text citation for their own capstone report.

Still to add

Learners who used no AI practise on a labelled example, and the statement is kept in their notes rather than checked inside a finished report.

Manage AI

Identify AI systems and the tasks they support (Manage 1, basic).

Framework grades 1–2, 3–5
Mostly covered
Our Grades 1–2 Mostly covered
What learners do

In Grade 1 'Who Should Do It?', learners give card and reminder tasks to people or tools and defend the choices using personal care, ability and checking needs.

Still to add

Choices mainly concern printing, reminders and calculation; a judgement about AI suitability against complexity and ethics is not fully shown.

Our Grades 3–5 Fully covered
What learners do

In Grade 5 'Being Kind' and Grade 4 'AI and Jobs', learners decide between a calculator and AI for a simple sum, which answers are fine to use versus check first, and asking AI to explain rather than do.

Determine whether AI is appropriate for a specific task (Manage 1, intermediate).

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 8 'The AI Hammer', learners run a suitability checklist over data, complexity, cost and ethics, sort green-light from red-flag cases and write a justification.

Compare task complexity, the need for human judgment, and ethical implications when deciding whether to use AI (Manage 1, advanced).

We start Grade 8 · framework Grade 9Framework grades 9–10, 11–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 8 'Maya's Discovery', learners use an AI suitability checklist, overkill test and cost-benefit analysis; Grade 10 'Spark of Inspiration' asks whether a project needs AI.

Our Grades 11–12 Fully covered
What learners do

In Grade 8 lessons such as 'The AI Hammer' and 'The Wrong Solution', learners use an AI suitability checklist and overkill warnings; Grades 11-12 add sorting needs-AI versus simple-rule cases and judging reinforcement learning against direct programming.

Recognise the difference between rules-based and pattern-learning AI systems (Manage 2, basic).

Framework grades 1–2, 3–5
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 2 'Rules or Examples?', learners choose a written rule for an exact doorbell task and examples for varied cat pictures, and explain why.

Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Robots' and 'AI at Work', learners sort machines as told-the-steps or shown-examples and see that nobody could write every step for spotting a cat.

Identify the benefits and drawbacks of each AI approach for a task (Manage 2, intermediate).

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

Grade 8 'What Makes AI Intelligent?' is a whole chapter on rule-based versus learning-based AI with app sorting. Learners also sort jobs into needs-a-rule or needs-training.

Evaluate the effectiveness of an AI approach considering context, data, efficiency, transparency, and impact (Manage 2, advanced).

We start Grade 3 · framework Grade 9Framework grades 9–10, 11–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 9 'Welcome, AI Engineers!', learners sort eight tasks into traditional programming or machine learning and get feedback on which approach suits each.

Our Grades 11–12 Fully covered
What learners do

Grade 8 chapters such as 'What Makes AI Intelligent?' teach rule-based versus learning-based AI with code. Grade 12 sorts product ideas into needs-AI versus needs-a-rule.

Where it is taught

Identify problem types and consider whether AI might help solve them (Manage 3, basic).

Framework grades 1–2, 3–5
Mostly covered
Our Grades 1–2 Mostly covered
What learners do

In Grade 1 'Who Should Do It?', learners break birthday-card work into personal ideas, drawing, copying and reminders, assign the parts and explain the split.

Still to add

The split mainly gives printing and reminders to ordinary digital tools; an equally concrete human versus AI-specific division is not fully shown.

Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Sorting and Deciding', learners sort hospital tasks into machine-does-this-well versus needs-a-person, for example AI flags odd x-rays and the doctor decides.

Break a problem into parts and consider AI support for specific steps (Manage 3, intermediate).

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 6 'AI for Good' and Grade 8 'Carlos's Writing Helper', learners split eight project steps between machine and person, hospital tasks between AI's job and human's job, and writing tasks between AI's job and their own.

Deliberately assign parts of a task to human strengths versus AI capabilities (Manage 3, advanced).

We start Grade 6 · framework Grade 9Framework grades 9–10, 11–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 10 'AI and the Future of Work', learners split jobs into AI-automated tasks versus human creativity, empathy and judgement, building on task-versus-whole-job in Grade 6 and centaur chess in Grade 7.

Our Grades 11–12 Fully covered
What learners do

In Grade 12 'The AI Radiologist' and 'Jobs Transformed', learners sort clinical tasks into AI-excels, human-excels and best-together, and study automation versus augmentation in jobs and workflows.

Recognise the need for decisions that support accountability and fairness when using AI (Manage 4, basic).

Framework grades 1–2, 3–5
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 1 'Test It Many Ways' and Grade 2 'Check Byte's Work', learners check a route each round against its goal and choose keep, change or stop based on observed mistakes.

Our Grades 3–5 Fully covered
What learners do

In Grade 5 'Did It Really Help?', learners set a checkpoint of more than two misses in ten, inspect weekly results and choose a pause with a named human checker.

Compare AI outputs to desired results and redirect the approach as needed (Manage 4, intermediate).

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 6 'AI in Healthcare' and 'Decision Tree Fun' and Grade 8 'Arjun's Smart Home', learners order TB X-ray oversight steps, tag decide-alone versus suggest-only by stakes, and require a manual override on every automation.

Establish checkpoints, monitor AI use against criteria, and adjust roles throughout a problem-solving process (Manage 4, advanced).

Framework grades 9–10, 11–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 10 'Responsible AI Development' and 'Deployment Basics', learners study continuous monitoring after launch, production red flags and drift, and ethics boards with authority to stop harmful projects.

Our Grades 11–12 Fully covered
What learners do

In Grade 12 'The Safety Lab' and 'AI That Takes Action', learners study and judge agent guardrails such as least privilege, human approval, budget limits and logging, plus kill switches and human sign-off gates.

Shape AI

Identify what different AI systems are designed to do (Shape 1, basic).

Framework grades 1–2, 3–5
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 2 'Fair and Good for Everyone?', learners weigh a talking toy's useful functions, its misleading feelings and who is responsible for an unchecked error.

Our Grades 3–5 Fully covered
What learners do

In Grade 4 'How AI Is Built' and Grade 3 'Predicting', learners see that Byte is wrong one time in a hundred, that confidence is not correctness, pick the smallest tool that works, and sort uses into fine-to-use and check-first.

Describe an AI system's purpose, intended users, and basic constraints (Shape 1, intermediate).

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 8 'The AI Hammer', learners judge whether AI fits a stated purpose using a suitability checklist. Grade 6 'AI Makes Mistakes' and 'When to Trust AI' cover mistakes, limits and risks.

Assess an AI system's strengths and limitations considering its purpose and potential impacts (Shape 1, advanced).

Framework grades 9–10, 11–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

Learners sort the pros and cons of K-Nearest Neighbors and decision trees in Grade 9, judge real versus hyped AI capability in Grade 10 'AI for Global Good', and check whether eight project ideas can be built in 'Spark of Inspiration'.

Our Grades 11–12 Fully covered
What learners do

Grade 12 'The Unsolved Mysteries' and Grade 11 'AI in Healthcare' and 'Testing & Evaluation' cover open problems, confidently wrong answers, risks by sector and fitness judged with metrics.

Define criteria for what counts as successfully accomplishing a task with AI (Shape 2, basic).

Framework grades 1–2, 3–5
Partly covered
Our Grades 1–2 Partly covered
What learners do

In Grade 2 'Check Byte's Work', learners compute an out-of-four score and tell apart two kinds of mistake.

Still to add

Learners do not build a confusion matrix from their own model or apply precision and recall to its results.

Our Grades 3–5 Mostly covered
What learners do

In Grade 4 'Two Kinds of Mistake' and Grade 5 'Demo Day: My Own Helper', learners build a confusion matrix from saved predictions, calculate accuracy and tell apart the user costs of false alarms and misses.

Still to add

Precision, recall and F1 calculations and a fully protected final test set inside the app are not complete; a paper final-check covers only part of this.

Assess an AI system's performance using defined criteria and benchmark tests (Shape 2, intermediate).

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 7 'Testing What AI Learned' and Grade 8 'Testing AI Solutions', learners use train, validation and test splits, compute accuracy by hand, drag results into a confusion matrix, and use the precision and recall formulas.

Note

The F1 score is not covered at this level; it arrives in Grade 9.

Design evaluation criteria for an AI system, compare its performance across inputs/users, and propose improvements (Shape 2, advanced).

Framework grades 9–10, 11–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 9 'Model Evaluation Basics', 'Accuracy and Errors' and 'Confusion Matrix', learners use a train/test split, the accuracy, precision, recall and F1 formulas and confusion matrices, and consider the user cost of each error type.

Our Grades 11–12 Fully covered
What learners do

In Grade 11 'Testing & Evaluation' and Grade 9 chapters such as 'Confusion Matrix', learners study precision, recall, F1, the confusion matrix and train/test splits, using priority scenarios and honest reporting of what the numbers mean.

Where it is taught

Identify examples of the data used to train an AI system (Shape 3, basic).

Framework grades 1–2, 3–5
Fully covered
Our Grades 1–2 Fully covered
What learners do

In Grade 2 'Which Pictures Are Missing?', 'Label It Right' and 'A Helper for Everyone', learners judge size, coverage and labels, choose their own or consented pictures for a build and connect missing kinds to errors.

Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Data' and 'Counting, Charts and Scores' and Grade 4 'Wrong, and How Sure', learners ask who collected the data, whose it is, whether there was consent, what is missing, how many examples there are and what failures follow.

Compare the importance of data selection and collection methods for an AI system (Shape 3, intermediate).

Framework grades 6–8
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 6 'Teach the Teacher', Grade 7 'Good Examples vs Bad Examples' and Grade 8 'Data for AI', learners judge data on clarity, accuracy, representativeness, variety, consent and bias, and match each flaw to the model behaviour it causes.

Evaluate how dataset properties affect an AI system's performance and impact, factoring in data source and selection (Shape 3, advanced).

Framework grades 9–10, 11–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 9 'The Data Hunter' and 'The Biased Hiring Model' and Grade 10 'The Data Hunt', learners evaluate source, size, coverage, consent, quality versus quantity and bias, using five data-quality signs and how they shape behaviour.

Our Grades 11–12 Fully covered
What learners do

In Grade 12 'Data is Everything', Grade 11 'Finding Your Data' and Grade 9 chapters such as 'The Unsinkable Ship', learners evaluate source, coverage, cleanliness, consent and bias and judge how data properties will shape model behaviour.

Where it is taught

Recognise that AI can be designed to better support individuals and communities (Shape 4, basic).

Framework grades 1–2, 3–5
Mostly covered
Our Grades 1–2 Mostly covered
What learners do

In Grade 2 'A Helper for Everyone', learners plan for missed groups and consent, label the machine, test it and add a missing kind after feedback.

Still to add

Ethical checkpoints sit mainly in the plan and are not revisited at every stage of a learner-designed build.

Our Grades 3–5 Mostly covered
What learners do

In Grade 5 'Demo Day: My Own Helper' and 'The Helper's Report Card', learners record affected users, consent, limits and review responsibility, and make test-driven changes.

Still to add

Ethical criteria are not explicitly revisited at every step of the learner's own model build.

Propose specific design changes to improve an AI system for human well-being (Shape 4, intermediate).

We start Grade 5 · framework Grade 6Framework grades 6–8
Mostly covered
Our Grades 6–8 Mostly covered
What learners do

In Grade 5 helper projects, learners check photos for consent before gathering, ask who a helper might miss, set a pause rule and decide keep, change or stop. In Grade 6 and 8 plans they name one decision the AI must not make.

Still to add

The step-by-step checks are on worked projects; learners do not apply ethical criteria at each step of their own Grade 6-8 build.

Where it is taught

Design improvements to an AI system based on technical constraints, ethics, and user needs to promote societal benefit (Shape 4, advanced).

Framework grades 9–10, 11–12
Partly covered
Our Grades 9–10 Partly covered
What learners do

In Grade 10 'Spark of Inspiration', learners sort a sample app's design choices against values and apply principles from their own country's list to their own idea, with one failing choice and its fix.

Still to add

Ethics is applied only at idea, problem and data definition; there is no affected-groups list for the learner's build and no well-being-focused design change.

Where it is taught
Our Grades 11–12 Fully covered
What learners do

In Grade 12 'The Big Idea', learners keep an ethics log for their own assistant: one row each for problem, data, prompt, test and launch, naming affected people, a principle, a check, a design change and a re-test, with two well-being changes.

Where it is taught

DigComp 2.2

1.1 Browsing, searching and filtering data, information and digital content

Know how to formulate unambiguous search queries so a conversational agent or smart speaker can respond as required (AI example 29, competence 1.1).

Framework grades 6–8, 9–10
Partly covered
Our Grades 6–8 Partly covered
What learners do

In Grade 8 'Liam's Art Dilemma' and 'Carlos's Writing Helper', learners build layered prompts, sort vivid from vague ones and compare simulated runs. In an optional capstone they refine prompts against real output.

Still to add

Refining prompts against real output happens only in one optional capstone, and improving work through follow-up exchanges first appears in Grade 9.

Where it is taught
Our Grades 9–10 Fully covered
What learners do

Learners practise prompt clarity and refinement from Grade 2 'When AI Says No', prompt engineering in Grade 8 'Maya's Discovery', and deepen it in Grade 11 'Welcome, AI Specialists!'.

Weigh the benefits and disadvantages of using AI-driven search engines, including privacy and commercial-interest trade-offs (AI example 31, competence 1.1).

Framework grades 9–10, 11–12
Partly covered
Our Grades 9–10 Mostly covered
What learners do

In Grade 9 'Rank the Search Results', learners act as the search engine and rank five pages for a query by word importance. In Grade 10 'Neutral or Steering?' they sort screens and AI replies by whose interest they serve.

Still to add

Search technique (quotes, refining a query, cross-checking results) is not taught, and judging is done on prepared snippets rather than a real results page or feed.

Where it is taught
Our Grades 11–12 Partly covered
What learners do

In Grade 12 'The Research Hunt' and 'How Does It Know?', learners order a literature search, judge what they found, and study how a two-stage recommender ranks items, including filter bubbles. Advertising lessons show the user and the payer differ.

Still to add

Learners never judge whether a search page or feed serves the user or a commercial interest, and search practice covers academic databases only, not open-web ranking.

Where it is taught

1.2 Evaluating data, information and digital content

Be aware that AI algorithms may embody a commercial or political message rather than only serving the user's information need, with possible negative consequences such as reproducing stereotypes or spreading misinformation (AI example 48, competence 1.2).

Framework grades 9–10, 11–12
Partly covered
Our Grades 9–10 Mostly covered
What learners do

In Grade 9 'Rank the Search Results', learners act as the search engine and rank five pages for a query by word importance. In Grade 10 'Neutral or Steering?' they sort screens and AI replies by whose interest they serve.

Still to add

Search technique (quotes, refining a query, cross-checking results) is not taught, and judging is done on prepared snippets rather than a real results page or feed.

Where it is taught
Our Grades 11–12 Partly covered
What learners do

In Grade 12 'The Research Hunt' and 'How Does It Know?', learners order a literature search, judge what they found, and study how a two-stage recommender ranks items, including filter bubbles. Advertising lessons show the user and the payer differ.

Still to add

Learners never judge whether a search page or feed serves the user or a commercial interest, and search practice covers academic databases only, not open-web ranking.

Where it is taught

Be aware that the data on which AI depends may include biases which can be automated and worsened by AI use (AI example 49, competence 1.2).

We start Grade 6 · framework Grade 9Framework grades 9–10, 11–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 10 'Bias in AI Systems' and Grade 9 'The Biased Hiring Model', learners examine real skewed outputs in hiring, healthcare, credit and photo tagging, with the system cause explained each time.

Our Grades 11–12 Fully covered
What learners do

In Grade 12 'The Algorithm That Discriminated' and Grade 10 'Bias in AI Systems', learners examine the Amazon hiring and COMPAS cases and trace skewed output back to the training data or objective.

Where it is taught

Know that 'deep-fakes' are AI-generated images, video, or audio of events or people that did not really happen, and may be indistinguishable from the real thing (AI example 51, competence 1.2).

We start Grade 6 · framework Grade 9Framework grades 9–10, 11–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 10 'Deepfakes and Misinformation' and 'Building Your AI Ethics Framework', learners define deepfakes, check tells such as eyes, edges, lip sync and lighting, sort harms and apply a pause, verify, share rule.

Our Grades 11–12 Fully covered
What learners do

In Grade 6 'Real vs Fake: Deepfakes', learners study what deepfakes are and the limits of detection. Grade 11 'The Art Controversy' and 'AI in Entertainment' have learners order and write responsible-use rules for synthetic media.

1.3 Managing data, information and digital content

Be aware that AI systems use statistics and algorithms to analyse data and generate outcomes, such as predicting what content a user might like (AI example 16, competence 1.3).

Framework grades 3–5, 6–8
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Why Am I Seeing This?', learners explain a narrowing feed, choose a wider recommendation and connect commercial selection to a user's need.

Our Grades 6–8 Fully covered
What learners do

In Grade 6 'AI Recommends For You', learners trace recommenders from implicit signals to ranked output, compare collaborative and content-based filtering, and see how a feedback loop narrows what is shown.

2.1 Interacting through digital technologies

Be aware that some AI systems aim to provide human-like interaction (e.g. customer-service chatbots) and be able to interact with and give feedback to such a system to influence what it recommends next (AI examples 8 and 33, competence 2.1).

Framework grades 3–5, 6–8
Mostly covered
Our Grades 3–5 Mostly covered
What learners do

In Grade 5 'Build a Chatty Helper', learners build and repair keyword and reply rules and test how a changed message changes the reply.

Still to add

The agent is stateless and finite, and the multi-turn path is arranged rather than a live conversation that remembers earlier turns.

Our Grades 6–8 Fully covered
What learners do

In Grade 7 'How Chatbots Work' and Grade 8 'Choosing the Reply' and 'The Tech Fair', learners trace rule-based bot mechanics, design a paper bot, and build StudyBuddy in Python with intent scoring, context memory and fallbacks.

2.2 Sharing through digital technologies

Be aware that content shared publicly online can be used to train AI systems (e.g. facial-recognition training on shared photos), which may raise privacy concerns (AI example 45, competence 2.2).

We start Grade 3 · framework Grade 9Framework grades 9–10, 11–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 10 'Privacy and AI', learners survey what phones, apps and smart-home devices quietly collect, building on digital footprints in Grade 6 and data collection in Grade 3.

Our Grades 11–12 Fully covered
What learners do

In Grade 11 'The Art Controversy' and Grade 12 'How Does It Know?', learners study scraped training data and recommenders that infer preferences users never shared; Grade 6 lessons cover digital footprints and permissions.

2.3 Engaging in citizenship through digital technologies

Recognise that AI applications directly interacting with humans and making decisions about their lives (e.g. CV-sorting, exam scoring) can be controversial even where other AI applications are not (AI example 63, competence 2.3).

Framework grades 9–10, 11–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 10 'Why AI Ethics Matters' and 'Explainable AI (XAI)', learners sort loans, hiring, diagnosis and sentencing by stakes with mandatory human review, and critique a black-box loan denial.

Our Grades 11–12 Fully covered
What learners do

In Grade 12 'The Algorithm That Discriminated' and Grade 11 'AI in Finance' and 'AI in Education', learners study hiring, credit scoring and grading, including right to explanation, appeals and human accountability.

2.4 Collaborating through digital technologies

Be aware that AI-based technologies can replace some human functions, potentially causing job losses or reallocations while also creating new jobs (AI example 60, competence 2.4).

We start Grade 6 · framework Grade 9Framework grades 9–10, 11–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 10 'AI and the Future of Work', learners sort eight jobs as growing or shrinking, order five steps of how AI transforms a job, and identify five future-ready skills.

Our Grades 11–12 Fully covered
What learners do

In Grade 12 'Jobs Transformed' and Grade 11 'AI Careers', learners match jobs to automation, augmentation, creation or transformation, and identify the skills each change implies.

2.5 Netiquette

Be aware that AI systems are typically developed in English-speaking contexts and can work less accurately in non-English contexts, e.g. machine translation between less-used language pairs (AI example 55, competence 2.5).

We start Grade 8 · framework Grade 9Framework grades 9–10, 11–12
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 10 'Face Recognition' and 'Multilingual NLP', learners study skin-tone accuracy gaps, low-resource languages and the digital divide. Accent bias in speech data is covered in Grade 8.

Our Grades 11–12 Fully covered
What learners do

In Grade 12 'The Voice Revolution' and 'AI Eyes', learners study accent and dialect bias in speech, skin-tone failures in vision and low-resource language gaps, each with its data cause.

Where it is taught

3.1 Developing digital content

Be aware that AI can automatically create digital content (text, news, music, images) from existing content as its source, which can be difficult to distinguish from human creations, including in news media/journalism (AI examples 5 and 6, competence 3.1).

Framework grades 3–5, 6–8
Partly covered
Our Grades 3–5 Partly covered
What learners do

In Grade 4 'Make It With Two Machines', learners combine supplied picture and text outputs and explain what each source contributed.

Still to add

The branches are authored rather than live generative tools, and selecting supplied outputs is not the same as using a tool.

Our Grades 6–8 Partly covered
What learners do

Prompt Lab is reached in Grades 6-8 through optional capstones such as 'Prompt Engineering 101'. Grade 8 'Liam's Art Dilemma' and 'The AI Challenge' cover prompts and matching project tool combinations.

Still to add

No lesson has learners produce an output and combine outputs from two tools into a finished piece; the one live generator is optional and there is no image generation.

3.2 Integrating and re-elaborating digital content

Be aware that AI can help edit and process digital content (e.g. photo aging, text auto-suggestion), and know how to incorporate AI-edited or AI-generated content into one's own work while considering questions of credit (AI examples 7 and 38, competence 3.2).

Framework grades 3–5, 6–8
Fully covered
Our Grades 3–5 Fully covered
What learners do

In Grade 3 'Who Owns It?' and Grade 5 'My Digital Toolkit', learners credit mixed materials and AI contributions in source notes, keep evidence on a poster and explain what was actually used.

Our Grades 6–8 Covered earlier · Grade 3
What learners do

In Grade 3 'Who Owns It?', learners write a sources list entry for AI help with tool, date, request and what was used, and finish a poster with an honest source note. In Grade 4 they label which parts a machine made.

4.2 Protecting personal data and privacy

Know that processing of personal data by AI (e.g. voice interactions with a virtual assistant) is subject to regulations such as the EU GDPR and can carry data protection, privacy and security risks (AI example 39, competence 4.2).

We start Grade 6 · framework Grade 9Framework grades 9–10, 11–12
Mostly covered
Our Grades 9–10 Mostly covered
What learners do

In Grade 10 'Privacy and AI', learners study rights of access, erasure, opt-out and objection under GDPR, CCPA and India's DPDP Act, and match five situations to the right that applies.

Still to add

Duties of the organisation holding the data, such as breach notification and controller roles, appear only in Grade 12; learners do not apply the law to a whole data flow.

Our Grades 11–12 Fully covered
What learners do

In Grade 12 'When AI Breaks the Law' and Grade 11 'The Silent Failures', learners study DPDP Act and GDPR duties in detail, including consent, erasure within 30 days, children's data, processors and cross-border storage rules.

4.4 Protecting the environment

Consider the ethical consequences of AI systems throughout their life-cycle, including environmental impact and societal impact such as platformisation of work and low-cost labelling labour (AI example 57, competence 4.4).

Framework grades 9–10, 11–12
Partly covered
Our Grades 9–10 Partly covered
What learners do

In Grade 10 chapters such as 'GPU vs CPU', 'Early Stopping' and 'Model Optimization', learners read quantified energy, water and hardware figures and levers such as smaller models and early stopping, with a couple of multiple-choice energy items.

Still to add

Learners never design or communicate a way to reduce AI's footprint, accounting is mostly read, and water is never computed; the task exists in Grades 8 and 11.

Our Grades 11–12 Mostly covered
What learners do

In Grade 12 'Smart Grid Hero', learners work through chips, cooling water and a CO2 formula, and compute a footprint by hand; in Grade 11 'SDG Research-and-Build Project' they use a footprint calculator for a school-wide chatbot.

Still to add

Learners do not design and communicate their own footprint-reduction plan with before and after numbers, water is quoted but not estimated, and one sorting item has a wrong answer key.

Where it is taught

5.2 Identifying needs and technological responses

Be interested in experimenting with various types of AI systems depending on personal needs (e.g. virtual assistants, image analysis, speech/face recognition, robots) (AI example 71, competence 5.2).

Framework grades 6–8, 9–10
Partly covered
Our Grades 6–8 Partly covered
What learners do

In Grades 6-8, learners open several tools, mostly image and drawing classifiers, such as a drawing trainer in Grade 6 'How to Learn More', a face-layout detector in Grade 7 'Face Detection Magic' and 'Raise Your AI' in Grade 7.

Still to add

Learners never choose tools for a need of their own; speech recognition is not tried, face recognition is only a drawn layout, and robot tools are not linked from lessons.

Where it is taught
Our Grades 9–10 Fully covered
What learners do

In Grade 8 'Maya's Discovery' and Grade 7 'Aisha's Big Idea', learners try Teachable Machine, Scratch AI, text-to-image, background removal, style transfer, upscaling, chatbots, speech tools and IoT.

5.4 Identifying digital competence gaps

Have a disposition to keep learning and stay informed about AI, including how algorithms can be biased and the difference between narrow AI and (hypothetical) general AI (AI example 72, competence 5.4).

We start Grade 5 · framework Grade 9Framework grades 9–10, 11–12
Mostly covered
Our Grades 9–10 Covered earlier · Grade 5
What learners do

In Grade 5 'One Job, Any Job, Smarter Than Us?', learners sort eight real and story systems into narrow AI, general AI or superintelligence, and judge that today's chatbot is narrow. In Grade 6 they write why general AI is harder.

Where it is taught
Our Grades 11–12 Mostly covered
What learners do

In Grade 12 'Can AI Really Think?', learners work through definitions of narrow AI, AGI and superintelligence, apply a working test, and sort eight capabilities as narrow AI or AGI.

Still to add

The terms 'weak AI' and 'strong AI' are never named, and superintelligence is defined in one sentence but not used in a classification task.

Where it is taught

Estonia AI Leap / TI-Hüpe

AI tool use and critical thinking (upper secondary, grades 10-11 pilot)

Use AI tools within a guided national learning app designed to build critical thinking by prompting students to find answers and form their own conclusions rather than supplying direct answers.

We start Grade 6 · framework Grade 9Framework grades 9–10· secondary source
Fully covered
Our Grades 9–10 Fully covered
What learners do

In Grade 6 'AI Makes Mistakes' and Grade 7 'AI Understands Words', learners study honest AI use in schoolwork, tool-versus-boss dependence, and plan-and-check versus handover.

Germany KMK Handlungsempfehlung (2024)

Lernen über KI (learning about AI) / Medienbildung

States that a foundational understanding of AI's technical functioning and the ability to recognise chances, limits and risks are indispensable for students to use AI responsibly and reflectively ('digital mündig'); calls on the Länder to integrate AI-related informatics competences and 'Bildung in der digitalen Welt' competences into curricula for all subjects and school types, without itself specifying discrete grade-level student outcomes.

Framework grades 6–8, 9–10, 11–12· secondary source
Fully covered
Our Grades 6–8 Fully covered
What learners do

In Grade 8 'The AI Hammer', learners judge whether AI fits a stated purpose using a suitability checklist. Grade 6 'AI Makes Mistakes' and 'When to Trust AI' cover mistakes, limits and risks.

Our Grades 9–10 Fully covered
What learners do

Learners sort the pros and cons of K-Nearest Neighbors and decision trees in Grade 9, judge real versus hyped AI capability in Grade 10 'AI for Global Good', and check whether eight project ideas can be built in 'Spark of Inspiration'.

Our Grades 11–12 Fully covered
What learners do

Grade 12 'The Unsolved Mysteries' and Grade 11 'AI in Healthcare' and 'Testing & Evaluation' cover open problems, confidently wrong answers, risks by sector and fitness judged with metrics.

School and system rules (not rated)

These apply to schools and education systems (hours, delivery, assessment, staffing, age rules). A course cannot meet them on its own.

  • Explore real-world AI tool use cases through hackathons, co-creation sprints and workshops as part of the national programme; no discrete, published per-grade learning-outcome statements were found on the primary site.
  • National guidance directs early-childhood, primary and lower-secondary providers to use AI responsibly, safely and innovatively in teaching and learning, but does not itself publish student-facing learning-outcome statements by grade.
  • The Länder note it is worth discussing largely foregoing AI language models (chatbots/LLMs) in Grundschule (primary) and the early years of Sekundarstufe I, as the SWK (Standing Scientific Commission) recommends, reflecting a precautionary stance on generative AI for younger children.
  • Existing assessment formats should be reviewed and extended so that new task and examination formats also assess AI-related competences (alongside the '4K': communication, collaboration, creativity, critical thinking) needed for a well-functioning co-activity of humans and AI.

Bring the course to your school or region

Schools, school groups, resellers and partners: we will walk you through the alignment for your board and the lessons behind it.

For schoolsFor partners and resellersContact us

See how the course maps to the other 13 curricula on the curriculum alignment overview, or browse the course grade by grade.

Our own reading of our own course, ratings reviewed 22 October 2026. It is not an endorsement, certification or accreditation by any authority named here, and it makes no claim about learning results.