Curriculum alignment · Abu Dhabi (ADEK)

ADEK AI Literacy Curriculum, Abu Dhabi (built with MIT Day of AI)

We checked the LittleAIMaster Grade 1–12 AI course against all 74 rated requirements of this framework: 49 fully covered, 13 mostly covered, 12 partly covered. Open a requirement to see what learners do and the exact lessons.

84%
Fully covered 49Mostly covered 13Partly covered 12of 74 requirements12 taught earlier than the framework asks

Source documents (2)

ADEK AI Literacy Curriculum (built with MIT Day of AI)
Abu Dhabi Department of Education and Knowledge (ADEK), in partnership with Day of AI / MIT RAISE
Launched October 2025; rollout across 170+ private schools, KG-Grade 12
MIT Day of AI / RAISE Curriculum (international, PreK-12)
MIT RAISE (Responsible AI for Social Empowerment and Education) and i2 Learning, via Day of AI
Ongoing annual curriculum, expanded editions through 2025-2026

Alignment chart

Every ADEK requirement, rated

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

Showing 74 of 74 requirements

ADEK Grades K–2 (ages 5–7)

Sort human-made vs natural; machines are tools people build

Framework grades K–2
Fully covered
Our course Fully covered
What learners do

In Grades 1-2, the words 'machine' and 'smart machine' are introduced as things that notice and answer, so children learn that machines are tools people build.

Our Grades 1–2 · outcome review Fully covered
What learners do

In Grade 1 'Alive and Not Alive' and 'Machines All Around', learners sort living things from machines by rules and see machines as powered tools built by people for a job. Grade 2 'What Is AI?' continues this.

Compare human thinking/sensing with how a machine processes info; what "smart" means

Framework grades K–2
Fully covered
Our course Fully covered
What learners do

In Grade 2 'Machines That See' and 'Machines That Hear', children compare an eye with a camera and an ear with a microphone, comparing human and machine sensing.

Our Grades 1–2 · outcome review Fully covered
What learners do

In Grade 2 'Can a Machine Think?' and 'Rules or Examples?', learners compare a child's thinking and checking steps with a machine's data, rules and output, and challenge the label 'smart'.

Decide whether an everyday technology is AI or not

Framework grades K–2
Mostly covered
Our course Fully covered
What learners do

In Grade 1 'Smart or Just Its Buttons?' and Grade 2 'Learns, or Follows Its Buttons', children sort everyday machines into those that learn from examples or get to know you, and those that follow buttons.

Our Grades 1–2 · outcome review Mostly covered
What learners do

In Grade 2 'Can a Machine Think?' and 'Rules or Examples?', learners tell a machine that learns from one that only follows buttons, and explain their choice when the teacher revisits it.

Still to add

The repeated definition equates AI with learning from examples; rule-based AI is not clearly included in it.

Point out AI in daily life and name what it does

Framework grades K–2
Fully covered
Our course Fully covered
What learners do

In Grades 1-2, children spot AI in their day, sorting machines by what each does; the feedback names it, for example the speaker answers you and the phone knows your face.

Our Grades 1–2 · outcome review Fully covered
What learners do

In Grade 2 'Machines That Talk' and 'What Is AI?', learners trace a day through a smart speaker, news app, map app, shop, game character and TV recommender, and match each to its job.

Locate sensors, compare computer vs human sensing

Framework grades K–2
Mostly covered
Our course Fully covered
What learners do

In Grade 1 'My Senses Notice Things' and Grade 2 'Count It and Chart It', children look at what each machine notices and compare an eye with a camera and an ear with a microphone.

Our Grades 1–2 · outcome review Mostly covered
What learners do

In Grade 2 'Notice, Then Do' and Grade 1 'Parts and Apps', learners identify input parts of devices and compare tipping and shaking signals with human sensing.

Still to add

Learners are not explicitly asked to locate the camera or microphone on their own device.

AI is trained on examples and finds patterns

Framework grades K–2
Fully covered
Our course Fully covered
What learners do

In Grade 2 'Names and Labels', children teach a machine by sorting examples into groups and testing it on a held-back item, learning that AI learns from examples.

Our Grades 1–2 · outcome review Fully covered
What learners do

In Grade 2 'Learning From Examples', learners watch Byte guess better as it sees two, then ten cats, and then guess new pictures it has not seen.

Prepare data, train, test, report performance

Framework grades K–2
Mostly covered
Our course Fully covered
What learners do

In Grade 2, children sort examples to teach a machine and test it on a held-back item, which is a simple train and test split. Grade 3 continues with data-building activities.

Our Grades 1–2 · outcome review Mostly covered
What learners do

In Grade 2 'Notice, Then Do' and 'Check Byte's Work', learners train on movement samples or labelled tiles, test fresh examples and discuss a real miss.

Still to add

Preparing a dataset, training a model and writing a performance report are not joined into one documented workflow at this level.

Build/debug a simple sequence

Framework grades K–2
Fully covered
Our course Fully covered
What learners do

In Grade 2 'Robot Friend Codes', children remove a step from a robot's instructions, predict the failure, repair the order and retest, in a teacher-led session.

Where it is taught
Our Grades 1–2 · outcome review Fully covered
What learners do

In Grade 2 'Robo Repeats' and 'Check Byte's Work', learners run a grid route, repair the command sequence and explain the missing pickup or wrong turn to a partner.

Examine a dataset for gaps/bias

Framework grades K–2
Fully covered
Our course Fully covered
What learners do

In Grade 2 'Which Pictures Are Missing?' and 'Project: Teach Byte Fairly', children predict a wrong answer, find missing kinds in a set, add examples and retest the machine on a new item.

Where it is taught
Our Grades 1–2 · outcome review 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.

Check an AI output before trusting it

Framework grades K–2
Fully covered
Our course Fully covered
What learners do

In Grade 2 'Yes, Change It, or No', a teacher-led session asks children why a checked suggestion is reasonable and what new evidence makes them reject a route.

Where it is taught
Our Grades 1–2 · outcome review 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.

Build a chart from data

Framework grades K–2
Fully covered
Our course Fully covered
What learners do

In Grades 1-2, children read a two-bar chart in Grade 1 and build a two-column chart in Grade 2.

Our Grades 1–2 · outcome review Fully covered
What learners do

In Grade 1 'Tally It, Chart It', learners choose tally marks or bars for a purpose, collect actual counts and show a bar chart to classmates.

Robotics / physical device

Framework grades K–2
Partly covered
Our course Partly covered
What learners do

In Grade 1 'Robot Friend' and Grade 2 'Robot Friend Codes', children write instruction cards that a friend acts out as a robot. In 'My Tablet Notices' they match the tablet's touch screen, microphone and camera to what they sense.

Still to add

No required learner program drives a physical device that acts on the environment; sensing on the tablet exists, but screen robots and lights are the only outputs.

Where it is taught
Our Grades 1–2 · outcome review Partly covered
What learners do

In Grade 2 'Notice, Then Do', learners choose a rule driven by real tablet tilt or shake and watch the screen movement and light change.

Still to add

The output is on screen; no external actuator changes the physical environment.

Build an AI solution to a real, student-chosen problem

Framework grades K–2
Partly covered
Our course Partly covered
What learners do

In Grade 2 'Project: Plan, Make, Test, Fix', children choose one of two problems, sort examples, teach a helper, test it on new items and add a missing kind. Off-screen they design a helper for a real person's problem.

Still to add

Children build and test an in-app helper only for one of two preset problems; the problem they find themselves is only drawn, not taught to or tested on the machine.

Our Grades 1–2 · outcome review Partly covered
What learners do

In Grade 2 'A Helper for Everyone', learners choose a supplied problem, train and retest its helper, then rehearse a presentation.

Still to add

Learners do not identify the problem themselves, and the cardboard model does not sense or act, so a learner-found problem is not linked to a working AI build.

Research an SDG and design/evaluate an AI solution

Framework grades K–2
Partly covered
Our course Mostly covered
What learners do

In Grade 2 'A Helper for Everyone', children build, test, repair and show a sick-leaf helper, thinking about farmer time and missed young shoots.

Still to add

The task is prescribed; independent research and an evidenced impact evaluation of a learner-chosen Global Goal are thin.

Where it is taught
Our Grades 1–2 · outcome review Partly covered
What learners do

In Grade 1 'Machines Help Farms and Water' and Grade 2 'A Helper for Everyone', learners study crop needs and test a supplied leaf helper for a farmer.

Still to add

Research and evaluation of real societal impact stay separate from the supplied helper's card score.

AI governance / civic participation

Framework grades K–2
Partly covered
Our course Partly covered
What learners do

In Grade 2 'Rules for AI' and Grade 1 'Our Own Rule', children learn that people make rules for AI, choose between preset wordings of class rules and vote on them.

Still to add

Children only choose between preset wordings of preset rules; no step has them propose a rule of their own, apart from optional take-home extensions.

Where it is taught
Our Grades 1–2 · outcome review Mostly covered
What learners do

In Grade 1 'Our Own Rule', learners discuss class wellbeing, select a rule proposal and vote with classmates.

Still to add

The two starting proposals are supplied, taking a proposal to school leadership is optional, and independent policy drafting is limited.

Footprint / resource cost of AI

Framework grades K–2
Fully covered
Our course Fully covered
What learners do

In Grade 1 'Big Computers Get Hot', children explain smart-speaker data centres, power, cooling water and device materials, then make and share a saver card.

Where it is taught
Our Grades 1–2 · outcome review 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.

Careers

Framework grades K–2
Fully covered
Our course Fully covered
What learners do

In Grade 1 'Helpers and Jobs', children compare the machine and human parts of changing jobs, identify builder and checker tasks, and choose a personal job-interest and skill-growth card.

Where it is taught
Our Grades 1–2 · outcome review Fully covered
What learners do

In Grade 1 'Helpers and Jobs', learners match builder and checker tasks to skills and choose a personal job-and-skill card.

Our Grades 1–2 · outcome review Fully covered
What learners do

In Grade 1 'Helpers and Jobs', learners compare the tasks a post worker keeps with what a machine sorts, predict a new robot job and choose a skill to develop.

Generative AI / chatbots / deepfakes

Framework grades K–2
Fully covered
Our course Fully covered
What learners do

In Grade 2 'Ask, Look, Ask Again', children order how a talking machine responds, judge whether a next-word guess knows it is right, and check whether a picture matches their ask.

Where it is taught

ADEK Grades 3–5 (ages 8–10)

Compare human vs machine "smart"

We start Grade 1 · framework Grade 3Framework grades 3–5
Fully covered
Our course Fully covered
What learners do

Grades 3-4 teach the contrast between rule machines and learning machines in child-friendly words, which compares human and machine smartness.

Our Grades 3–5 · outcome review Fully covered
What learners do

In Grade 3 'AI at Work', learners see that a calculator beating a person is fast but not clever, map robot parts to body parts, and learn that reading symbols is not understanding.

AI vs non-AI decision

We start Grade 2 · framework Grade 3Framework grades 3–5
Fully covered
Our course Fully covered
What learners do

Grade 4 teaches the formal terms 'rule machine' and 'learning machine', which children use to decide whether a technology is AI.

Our Grades 3–5 · outcome review Fully covered
What learners do

In Grade 3 'Robots' and 'AI at Work', learners sort machines into those told the steps and those shown examples, see that a toaster needs no AI, and learn AI as a machine that learns.

Sensors, human vs computer sensing

Framework grades 3–5
Fully covered
Our course Fully covered
What learners do

The word 'sensor' is introduced in Grade 3, where children compare how humans and computers sense.

Our Grades 3–5 · outcome review Fully covered
What learners do

In Grade 3 'Senses: Mine, Animals', Machines'', learners look around their own tablet or phone for camera and microphone openings, find the hidden motion sensor, and compare human and machine sensing strengths.

Trained-on-examples / rule-vs-learned distinction

We start Grade 2 · framework Grade 3Framework grades 3–5
Fully covered
Our course Fully covered
What learners do

Grades 3-4 teach the difference between machines that follow written rules and machines that learn from examples.

Our Grades 3–5 · outcome review Fully covered
What learners do

In Grade 3 'Training', Byte is shown examples round after round, tested on unseen pictures, and predicts on new input from the patterns it found.

Our Grades 3–5 · outcome review 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.

Train a no-code classifier, vary the training set

Framework grades 3–5
Fully covered
Our course Fully covered
What learners do

In Grades 3-5, children train a no-code classifier by sorting examples into groups, with sets that must include enough variety, and see how changing the training set changes results.

Our Grades 3–5 · outcome review Fully covered
What learners do

In Grade 3 'Change the Examples, Change the Guess' and Grade 5 'Program a Gadget', learners train a motion classifier from labelled movements, test fresh movements and add varied examples after a miss.

Our Grades 3–5 · outcome review Fully covered
What learners do

In Grade 5 'Demo Day: My Own Helper', learners carry their chosen dataset into training, test unseen cards, report the actual counts and misses, and keep the results in a presentation.

Build a chart / dashboard

Framework grades 3–5
Fully covered
Our course Fully covered
What learners do

Grades 3-5 build up chart skills, finishing in Grade 5 with children charting their own data.

Our Grades 3–5 · outcome review Fully covered
What learners do

In Grade 3 'Pick the Right Chart', learners choose bars or a time-series line, enter a collected chart and use its saved counts in a poster for an audience.

Examine dataset gaps/bias, predict wrong outcomes

Framework grades 3–5
Fully covered
Our course Fully covered
What learners do

In Grade 4, children meet the word 'biased' and build a training set that lacks variety to see the effect; Grade 5 refines this.

Our Grades 3–5 · outcome review 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.

Check AI output, accept/revise/reject

Framework grades 3–5
Fully covered
Our course Fully covered
What learners do

In Grade 4, children learn that a machine can be very sure and still wrong, using a sureness number and misses to decide whether to accept an output.

Our Grades 3–5 · outcome review 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.

Take part in shaping AI policy — draft class AI rules

Framework grades 3–5
Fully covered
Our course Fully covered
What learners do

In Grade 5 'What I Will Build', learners draft class AI rules using a four-part build screen.

Our Grades 3–5 · outcome review Fully covered
What learners do

In Grade 5 'Check a Real Rule', learners draft a policy position with a concrete requirement and a checker, defend it to a partner, and turn real classroom cases into rules in their own words, then vote and date later revisions.

Note

Actual adoption of a policy is not claimed; the work is classroom policy drafting.

Explain governance as distinct from building AI

Framework grades 3–5
Fully covered
Our course Fully covered
What learners do

In Grade 5 'Who Decides the Rules?', children sort building tasks from rule-deciding tasks, learn who makes rules (families, schools, companies, countries), order rule-making steps and plan a rule with who makes and checks it.

Where it is taught

Point out AI in daily life

We start Grade 1 · framework Grade 3Framework grades 3–5
Fully covered
Our course Fully covered
What learners do

In Grade 3 'Which One Is This?', Grade 4 'Rules or Examples?' and Grade 5 'Two Machines, One Job', children sort everyday machines by whether they follow rules or learn from examples.

Our Grades 3–5 · outcome review Fully covered
What learners do

In Grade 2 'Machines That Talk' and Grade 3 'Sorting and Deciding', learners name assistants, face recognition, recommenders, route planning and game characters and what each does for the user.

Debug an algorithm

Framework grades 3–5
Fully covered
Our course Fully covered
What learners do

In Grade 3 'Debug the Algorithm' and Grade 5 'Debug the Steps' and 'Debug AND, OR, NOT', children find wrong, missing and misordered steps, fix them and test again, saying what kind of bug it is and why one test is not enough.

Where it is taught
Our Grades 3–5 · outcome review Fully covered
What learners do

In Grade 3 'Debug the Algorithm' and Grade 5 'Debug the Steps', learners spot the wrong step, fix it, sort bug kinds and run a debugging loop. Written reasoning is recorded in the unplugged classroom sheets.

Human-made vs AI-generated art, ethical questions

Framework grades 3–5
Mostly covered
Our course Fully covered
What learners do

In Grade 4 'AI and Creativity', children compare a human idea with an AI drawing and ask whether copying an artist's style is fair, using a style-credit check. No generative tool is used.

Our Grades 3–5 · outcome review Mostly covered
What learners do

In Grade 3 'Make a Tune With a Machine', learners compare human and machine-example music, vary request cards and debate credit and effects on musicians.

Still to add

The activity does not generate a new tune from a model; the experiments use prepared outputs.

Robotics / physical device

Framework grades 3–5
Partly covered
Our course Partly covered
What learners do

In Grade 5 'Devices That Sense and Act', children match sensors, programs and actuators, order an automatic-door chain and design a plant waterer as steps played out with friends. Grade 3 'Robots: Sense, Think, Act' covers robot rules.

Still to add

No required learner program drives a physical device that acts on the environment; screens do not program a device, and tablet sensing outputs only change a screen robot or light.

Where it is taught
Our Grades 3–5 · outcome review Partly covered
What learners do

In Grade 5 'Program a Gadget', learners configure a live tablet sensor rule, see a screen response and tell it apart from learned motion.

Still to add

There is no external actuator or physical robot affecting the environment.

Build AI solution to a real problem

Framework grades 3–5
Mostly covered
Our course Mostly covered
What learners do

In Grade 5 'Build It, Test It, Improve It', children pick a school problem, gather examples, build and test a helper, use feedback and test again. In Grade 4 'Present Your Project' they plan a talk.

Still to add

A learner's own problem is built only on paper with a classmate acting as the machine; the in-app helper is always one of three preset problems.

Where it is taught
Our Grades 3–5 · outcome review Mostly covered
What learners do

In Grade 5 'Demo Day: My Own Helper', learners build and present a saved-data helper, and separately interview a willing person and trial a paper helper for their own need.

Still to add

The learner-found need is not connected to the app's two supplied AI problem branches, and a paper checklist is not itself a learned model.

Research an SDG, build/evaluate an AI solution

Framework grades 3–5
Mostly covered
Our course Mostly covered
What learners do

In Grade 5 'Did It Really Help?', children read six labelled facts about a problem, pick a goal, build a branched helper, evaluate time saved and misses, and decide to keep, change or stop.

Still to add

The impact numbers and choices are supplied; independent research and a measured impact evaluation of the learner's own helper are not required.

Where it is taught
Our Grades 3–5 · outcome review Mostly covered
What learners do

In Grade 5 'Did It Really Help?', learners research attributed fact cards, design a goal and evaluate benefits, misses and stopping thresholds.

Still to add

Impact data are supplied stories rather than measured effects of the child's own AI solution.

Stakeholder debate / ethical matrix

Framework grades 3–5
Fully covered
Our course Fully covered
What learners do

In Grade 5 'What I Will Build', learners fill a who-is-affected grid and debate a lost-dog street camera case under 'What Could Go Wrong, and Who I Would Ask'.

Our Grades 3–5 · outcome review Fully covered
What learners do

In Grade 5 'Check a Real Rule', learners fill a stakeholder want-and-worry row, swap roles, explain the opposing view and debate a requirement.

Footprint / resource cost

Framework grades 3–5
Fully covered
Our course Fully covered
What learners do

In Grade 5 'AI's Footprint', children account for energy, water and materials, choose an audience and one change, chart evidence with a one-week check, and share the message with a friend or grown-up.

Where it is taught
Our Grades 3–5 · outcome review 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.

Careers

Framework grades 3–5
Fully covered
Our course Fully covered
What learners do

In Grade 5 'What I Will Build', children name the jobs that build and check a helper. Grade 3 'Sorting and Deciding' and Grade 4 'Bias in Words' also cover AI at work and AI and jobs.

Our Grades 3–5 · outcome review Fully covered
What learners do

In Grade 4 lessons such as 'Who Was Left Out', learners match roles such as engineer, designer, checker, safety person, tester and data collector to their work, meet a fairness checker and robot mender, and pick a job for themselves.

Our Grades 3–5 · outcome review Fully covered
What learners do

In Grade 4 'Then, Now, Next', learners compare retained and changing farm tasks, forecast how a sector will change next, and name a skill and an uncertainty.

Chatbot NLP, generative AI concepts

Framework grades 3–5
Mostly covered
Our course Fully covered
What learners do

In Grade 4 'Made-Up Answers' and Grade 5 'AI and Friends', children learn that generative AI writes answers and builds pictures, why it makes things up, and how chat replies are built. This is concept only, with no hands-on tool.

Our Grades 3–5 · outcome review 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.

ADEK Grades 6–8 (ages 11–13)

AI vs non-AI decision, AI in daily life

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

In Grade 6, learners drag eight everyday items into 'Uses AI' or 'No AI' groups.

Our Grades 6–8 · outcome review Fully covered
What learners do

In Grade 6 'What is AI?' and 'AI in Your Home', learners define AI and sort real gadgets as AI or not AI, using three levels, five clues and rule-based versus learning-based tests, giving reasons.

Our Grades 6–8 · outcome review Fully covered
What learners do

In Grade 6 'AI in Your Home', 'AI in Your Phone' and 'AI Recommends For You', learners hunt for AI at home, on the phone and in games, travel, shopping, health and school, naming what each does for the user.

Sensors / perception

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

Grades 6-8 cover perception in many lessons, and Grade 8 includes webcam-based image-classifier chapters.

Our Grades 6–8 · outcome review Fully covered
What learners do

In Grade 6 'Input and Output', learners sort nine devices as input, output or both and match six input devices to what each detects. Grade 8 and Grade 7 chapters name smart-home sensors and compare human and computer sensing.

Trained-on-examples / rule-vs-learned

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

Machine learning is the most widely covered strand across Grades 6-12, including the difference between rules and learning from examples.

Our Grades 6–8 · outcome review Fully covered
What learners do

In Grade 7 'What is Training?' and 'Learning from Examples', learners follow a guess, compare and nudge training loop, find clues from labelled photos, and draw to train, testing on unseen examples.

Our Grades 6–8 · outcome review 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.

Train/test a model, report performance

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

In Grade 7 'Teaching by Showing', and in Raise Your AI and Python Lab, learners train a model, test it and report performance. A Grade 7 capstone asks them to read the model's report card.

Our Grades 6–8 · outcome review Fully covered
What learners do

In Grade 7 chapters such as 'Lily's Big Moment' and 'Refinement Week!', learners plan data, shoot balanced photos, train in Teachable Machine, hold out a test set, and report accuracy per category and failures.

Explain why generative AI can fabricate/hallucinate

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

In Grade 6, learners are taught that AI can be wrong while sounding completely confident, so they should always double-check.

Our Grades 6–8 · outcome review Fully covered
What learners do

In Grade 6 'When to Trust AI', Grade 7 'AI Writing Stories' and Grade 8 'Carlos's Writing Helper', learners study training cut-offs and hallucination, sort what a model could and could not know, and judge real passages before trusting them.

Chatbot mechanics, build a simple conversational agent

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

Grades 6-8 include chatbot lessons, and Grade 8 Unit 5 was rebuilt around a chatbot-building sequence.

Our Grades 6–8 · outcome review 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.

Build a chart/dashboard

Framework grades 6–8
Partly covered
Our course Fully covered
What learners do

Learners in Grades 6-8 build charts and dashboards as part of their data work.

Our Grades 6–8 · outcome review Partly covered
What learners do

In Python Lab Module 5 'Draw a Text Bar Chart', learners write a loop that builds a labelled text bar chart from supplied activity data.

Still to add

The dataset, chart form and scaling are prescribed, and independent chart selection for a real pattern and audience is thin.

Debug an algorithm/program

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

In Python Lab graded modules, which start in Grades 6-8, learners write and debug Python programs.

Where it is taught
Our Grades 6–8 · outcome review Fully covered
What learners do

In Grade 8 'First Feature Victory' and 'The Demo Disaster', learners follow a five-step debugging process, meet four named Python errors and hunt down a zero-score bug. Most programming chapters include true/false bug hunts.

Examine dataset gaps/bias

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

Grades 6-8 contain bias-detective style lessons, and the Bias Detective tool lets learners examine datasets for gaps and bias.

Where it is taught
Our Grades 6–8 · outcome review 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.

Check AI output before trusting

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

Grades 6-8 teach learners to check AI output before trusting it.

Our Grades 6–8 · outcome review 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.

Careers

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

Career lessons appear across every grade in Grades 6-8.

Our Grades 6–8 · outcome review Fully covered
What learners do

In Grade 6 'AI and Jobs' and 'AI Jobs of Tomorrow', learners match shrinking jobs to the new jobs beside them, sort eight jobs as mostly gone or newly created, and write and justify a future skill.

Human/AI-generated art + ethics

Framework grades 6–8
Mostly covered
Our course Fully covered
What learners do

In Grade 7 'AI Writing Stories' and Grade 8 'Liam's Art Dilemma', learners see how an image generator makes a picture and test art cases for consent, labelling and effect, with rules from several countries.

Our Grades 6–8 · outcome review Mostly covered
What learners do

In Grades 6-8 learners compose a tune or paint half a picture, let a small pattern model finish it, run it three times and edit one, then judge person or model blind. They also argue credit and artists' rights in Grade 8.

Still to add

The generator is a small pattern model trained on the learner's own taps, not a full music or image generator.

Where it is taught

Build an AI solution to a real problem

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

In Grade 7 'The Great Project Choice' and Grade 8 'Capstone Project', learners choose their own problem with a four-part scoping check; in Grade 7 Unit 6 they build and test their own classifier.

Our Grades 6–8 · outcome review Fully covered
What learners do

In Grade 7 'The Great Project Choice' and 'Your Building Week!', learners choose their own problem and build it over a structured week. They present at a showcase in 'The Big Day!' or the Grade 8 'Innovation Fair' with a rehearsed demo.

Research an SDG, build/evaluate a solution

Framework grades 6–8
Mostly covered
Our course Fully covered
What learners do

In Grade 8 'The Global Goals Project', learners go from a Global Goal to a local problem they can count, a non-AI baseline, a helper design, a Raise Your AI build, a footprint estimate and a fair trial plan.

Our Grades 6–8 · outcome review Mostly covered
What learners do

In Grade 8 'The Global Goals Project', learners match local problems to the 17 goals, scope ideas, train a mini Bin Buddy in Raise Your AI, plan evidence and pitch their own SDG project.

Still to add

The learner's own research and impact judgement are planned but never carried out, and the build is for the worked example rather than the learner's chosen problem.

Where it is taught

Footprint / resource cost of AI

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

In Grade 8 'The Global Goals Project', learners weigh the footprint of their own helper; footprint arithmetic also appears in many other chapters.

Our Grades 6–8 · outcome review 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.

Draft/commit to ethical-use guidelines

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

In Grade 8 'Reflection & Growth', learners sort draft rules into keep or drop, build a rule set from its parts, and write their own rule set.

Take part in shaping AI policy

Framework grades 6–8
Mostly covered
Our course Fully covered
What learners do

In Grade 8 'Reflection & Growth', learners follow a draft, trial and review cycle and write their own class AI rule set.

Our Grades 6–8 · outcome review Mostly covered
What learners do

In Grade 6 'Draft a Class AI Policy' and Grade 8 'Write Your Class AI Rules', learners write checkable class AI rules in their own words. In Grade 8 'How the World Governs AI' they place features in real rulebooks and compare two approaches.

Still to add

Drafting is one learner's entry for their own class; there is no group or student-council step, and the two-week trial and revision is ordered but never run.

Where it is taught

Explain governance as distinct from building

Framework grades 6–8
Mostly covered
Our course Fully covered
What learners do

In Grade 8 'Reflection & Growth' and 'How the World Governs AI', learners study governance as distinct from building, comparing law, plans and principles across five places plus UNESCO and the OECD.

Our Grades 6–8 · outcome review Mostly covered
What learners do

In Grade 8 'How the World Governs AI', learners sort real laws, strategies and frameworks into three governance tools, place one app's features in each rulebook, order a launch plan and explain two places' approaches with a trade-off each.

Still to add

Societal impact beyond bias (jobs, people left out, environment) is not among the governance checks, and learners never split one system's tasks into building and governing.

Where it is taught

Stakeholder debate / ethical matrix

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

In Grade 7 'Computer Vision in the Real World', learners fill in an ethical matrix of stakeholders by fairness, accuracy and a say, then take and defend a position on a courtroom risk score.

Our Grades 6–8 · outcome review Fully covered
What learners do

In Grade 6 'AI Debate Club', learners build claim-evidence-reasoning arguments and practise steelmanning; Grade 7 and Grade 8 lessons sort benefits by who gains and contrast different users' needs.

Robotics / physical device

Framework grades 6–8
Partly covered
Our course Mostly covered
What learners do

In Grade 7 'How Robots Sense, Decide and Move', learners write micro:bit simulator rules from light readings that switch a pin on, and export rules that run on a real board; Python Lab reads live phone motion.

Still to add

Learners write no text program for a board, and running the export on a real board with an actuator is optional.

Our Grades 6–8 · outcome review Partly covered
What learners do

In Python Lab Module 48 'Sensors and Gestures', learners write code that reads a phone's live accelerometer and gyroscope and react to gestures. The Motion Trainer and micro:bit simulator export rules for a real board.

Still to add

Only sensing is met; outputs are on screen, learners do not write or upload device code, and no lesson covers driving a motor, servo or buzzer.

Generative image tools (GAN/diffusion), deepfakes

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

In Grade 6 'Real vs Fake: Deepfakes', Grade 7 'AI Writing Stories' and Grade 8 'Liam's Art Dilemma', learners study how image generators work, apply the STOP method and critique images in four layers. There is no image generator.

ADEK Grades 9–12 (ages 14–18)

Classification vs generation, how generative AI is trained

We start Grade 7 · framework Grade 9Framework grades 9–12
Fully covered
Our course Fully covered
What learners do

Grade 10 covers neural-network architectures including GANs and the forward pass, loss and backpropagation loop.

Our Grades 9–10 · outcome review Fully covered
What learners do

In Grade 7 'AI Understands Words', Grade 8 'Maya's Discovery' and Grade 10 'Transformer Preview', learners contrast generative with sorting AI, and study diffusion and text-to-image, and Transformers and generation.

Our Grades 11–12 · outcome review Fully covered
What learners do

In Grade 11 'The Creative Revolution' and 'Word by Word' and Grade 12 'Art Partner', learners contrast generative with discriminative AI, and study training on large datasets and text, image and audio generation through sorting and quizzes.

GAN/diffusion image generation, critique an image

Framework grades 9–12
Partly covered
Our course Mostly covered
What learners do

In Python Lab 'Module 50: Olympiad Extras', learners fit and sample a tiny denoiser in 'Diffusion in One Dimension' and inspect an eight-pixel brightness output and a mean-collapse failure.

Still to add

The example is a simple toy, expressly not a neural diffusion or GAN image generator, so image-generation use remains incomplete.

Where it is taught
Our Grades 9–10 · outcome review Partly covered
What learners do

In Grade 10 'Deepfakes and Misinformation', learners study how autoencoders, GANs and diffusion produce images, name the generator family for three cases and learn to spot flaws in generated images.

Still to add

Learners never generate an image at these grades or critique one they made, so generating and critiquing with a model is missing.

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

In Grade 11 'Pictures from Noise' and 'The AI Battle', learners study diffusion, the U-Net, guidance settings and the GAN game through explanations and sliders. Image Gen Critique capstone work involves critiquing images.

Still to add

Learners do not generate an image with a real diffusion or GAN model; the generation tools are a toy, so the live image-creation part is not fully met.

Where it is taught

Deepfakes: explain, spot flaws, set responsible-use rules

We start Grade 6 · framework Grade 9Framework grades 9–12
Fully covered
Our course Fully covered
What learners do

Grade 10 'Deepfakes and Misinformation' covers GAN-based deepfakes, spotting visual and audio tells, and a pause, verify, check response.

Our Grades 9–10 · outcome review 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 · outcome review 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.

Human/AI-generated art, ethics of generative tools

We start Grade 3 · framework Grade 9Framework grades 9–12
Mostly covered
Our course Fully covered
What learners do

Grade 11 has an ethics block on training-data consent, copyright of AI art and style copying that harms living artists, ending in a guideline list such as disclose AI use and do not copy living artists.

Our Grades 9–10 · outcome review Fully covered
What learners do

In Grade 3 'AI in Games', Grade 8 'Maya's Discovery' and Grade 6 'Pattern Games', learners study style imitation, authorship and ownership of AI art, human versus AI creativity, and consent for using artists' work.

Our Grades 11–12 · outcome review Mostly covered
What learners do

In Art & Music Studio, learners compose a tune or half a picture, let a pattern model finish it, and take a blind person-or-model test; in Grades 11-12 'Art Partner' and 'AI Art' they weigh the ethics for artists.

Still to add

The creative tool is a small pattern-counting model trained on the learner's own taps, not a real generative AI, and no Grade 11-12 lesson links it.

Where it is taught

Facial recognition: privacy/safety/fairness tension

Framework grades 9–12
Fully covered
Our course Fully covered
What learners do

Facial-recognition ethics appear as case studies in Grade 7 (face-unlock reliability for twins and under-13s) and Grade 10 (accuracy gaps found in independent testing).

Our Grades 9–10 · outcome review Fully covered
What learners do

In Grade 10 'Face Recognition', learners judge face-recognition use cases as ethical or concerning, weighing consent, surveillance, bias and safety, and sort risk levels.

Our Grades 11–12 · outcome review Fully covered
What learners do

In Grade 10 'Face Recognition' and related computer-vision chapters, learners study face-recognition privacy and bias with liveness and embeddings, and weigh limiting facial recognition as a governance principle.

Where it is taught

Examine dataset bias, propose fixes

We start Grade 4 · framework Grade 9Framework grades 9–12
Fully covered
Our course Fully covered
What learners do

Grade 10 case studies cover racial bias in recidivism scoring, a healthcare algorithm that under-ranked Black patients' needs and resume screening bias against women, with fixes such as diverse data, audits and human oversight.

Our Grades 9–10 · outcome review Fully covered
What learners do

In Grade 9 'The Unsinkable Ship' and Grade 10 'Bias in AI Systems', learners examine missing-data bias and imbalance in Titanic data and trace landmark bias cases to data gaps.

Our Grades 11–12 · outcome review Fully covered
What learners do

Learners repair datasets in Grade 7 (such as 'Good Examples vs Bad Examples') and analyse bias types in real benchmarks in Grade 12 'Data is Everything', predicting failures and proposing data fixes.

Stakeholder debate / ethical matrix

Framework grades 9–12
Partly covered
Our course Fully covered
What learners do

In Grade 12 'The Council Decision' and Grade 9 'The Biased Hiring Model', learners match stakeholders to concerns, then write a council verdict naming those helped and at risk and answering the strongest opposing argument.

Where it is taught
Our Grades 9–10 · outcome review Partly covered
What learners do

In Grade 9 'The Biased Hiring Model', learners read a table of six stakeholders' arguments about a hiring screener, match arguments to stakeholders and pick the best rebuttal from options.

Still to add

Learners never represent stakeholders' goals themselves: no matrix is built, no steelman or reply is written, no debate is held and no outside views are sought.

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

In Grade 12 'The Council Decision', learners write a full ethical matrix of four stakeholders by three principles for a school AI case they choose, mark the worst-harm cell, then consult a real person and record which cell changed.

Where it is taught

Draft/commit to ethical-use guidelines

Framework grades 9–12
Fully covered
Our course Fully covered
What learners do

In Grade 11 'Draft Your Governance Clause' and Grade 12 'Draft Your School's AI-Use Guideline', learners draft ethical-use rules. A capstone asks for a one-page school guideline.

Our Grades 9–10 · outcome review Fully covered
What learners do

In Grade 10 'Building Your AI Ethics Framework', learners build a personal AI ethics framework from five core values and a question checklist, and commit to the PAUSE method.

Our Grades 11–12 · outcome review Fully covered
What learners do

In Grade 10 'Why AI Ethics Matters' and related ethics chapters, learners build a personal ethics framework and a pause-decision method; Grade 11 learners write responsible-use rules for AI art.

Where it is taught

Design a scalable AI system architecture

Framework grades 9–12
Partly covered
Our course Fully covered
What learners do

In Grade 12 'When AI Needs to Look Things Up' and 'From Chatbots to Agents', learners use the System Builder to assemble a retrieval pipeline and a two-tool agent loop. Python Lab also builds related pieces.

Our Grades 9–10 · outcome review Partly covered
What learners do

In Grade 10 'The AI Blueprint', 'Model Optimization' and 'Deployment Basics', learners meet the model, optimisation and serving layers separately, including a cloud, edge or browser choice and a deployment plan.

Still to add

Learners do not design an end-to-end architecture with data, model and interface layers, choose frameworks against a budget, or estimate cost, compute or energy for their own system.

Our Grades 11–12 · outcome review Partly covered
What learners do

Grade 11 'Beyond the Model' and Grade 12 'The Blueprint Before the Building' teach architecture layers, scaling, cost and energy per query, and learners answer design reviews of supplied systems. The 'Design an ML API' capstone covers the interface layer.

Still to add

Learners never specify data, model and interface layers for their own system, justify framework choices or set a compute, cost or energy budget.

Where it is taught

Python analysis of a people-rating/ranking dataset

Framework grades 9–12
Fully covered
Our course Fully covered
What learners do

In Python Lab 'Audit a Shortlist', learners write a function that reports accuracy, selection rates and their ratio for applicant groups, then interpret the screening flag on a changed table.

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

In Grade 9 'The Unsinkable Ship' and Python Lab Module 30, learners analyse real Titanic passenger records, rank group survival rates and interpret the differences with a stated limit. Grade 10 'Fairness in Machine Learning' has them audit a shortlist in Python.

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

In Grade 9 chapters such as 'The Unsinkable Ship' and 'The Biased Hiring Model', learners use Python to analyse people data, including survival prediction, fairness metrics and proxy variables, and reflect on what the ranking implies.

Where it is taught

Build a data story (data + visuals + narrative for an audience)

Framework grades 9–12
Mostly covered
Our course Mostly covered
What learners do

In Python Lab 'Tell the Canteen Story with Context', learners complete a plotted chart with an event marker, shaded context and labelled axes, with a qualified narrative, alongside their own written data story.

Still to add

Audience, chart type, marks and finding sentence are specified for the learner; independent visual and audience design is still missing.

Where it is taught
Our Grades 9–10 · outcome review Mostly covered
What learners do

In Grade 9 'Graphs Tell Stories' and Grade 10 'Presentation and Demo', learners build plots with an annotation and a narrative for a supplied audience, such as 'Build It for the Principal'.

Still to add

The findings, chart forms, annotations and narrative are largely prescribed; independent visual storytelling for learners' own finding is not established.

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

Grade 9 projects such as 'The Failed Project', 'Science Fair Star' and 'Portfolio Opens Doors' teach storytelling structure, figures and tables, audience adaptation and honest reporting, and apply them to project communication.

Where it is taught

Careers, future of work

We start Grade 6 · framework Grade 9Framework grades 9–12
Fully covered
Our course Fully covered
What learners do

In Grade 9 'AI Careers: Who Builds, Shapes and Uses AI' and Grade 12 'Graduation Day', learners explore careers; Grade 12 also covers resume, portfolio and applicant-tracking lessons.

Our Grades 9–10 · outcome review Fully covered
What learners do

In Grade 9 'Welcome, AI Engineers!', learners see the work, skills and workplaces of AI engineers and score their own readiness. Grade 8 'Designing AI Solutions' covers technical, creative and ethics roles.

Our Grades 11–12 · outcome review Fully covered
What learners do

In Grade 11 'AI Careers' and Grade 12 'Your AI Path', learners match machine-learning engineer, data engineer, ethics specialist and product manager roles to their day-to-day work and skills, and assess themselves against their interests.

Our Grades 9–10 · outcome review 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 · outcome review 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.

Footprint / resource cost of AI

Framework grades 9–12
Partly covered
Our course Fully covered
What learners do

In Grade 11 'SDG Research-and-Build Project', learners estimate their own project's footprint from sourced per-use figures; footprint arithmetic appears elsewhere too.

Our Grades 9–10 · outcome review 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 · outcome review 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

AI governance/civic participation

We start Grade 5 · framework Grade 9Framework grades 9–12
Fully covered
Our course Fully covered
What learners do

In Grade 11 'AI Ethics in Practice' and Grade 12 'The Council Decision', learners study principle versus binding law, enforcement, exceptions and review, and draft a clause and a school guideline.

Our Grades 9–10 · outcome review Fully covered
What learners do

In Grade 10 'AI Governance and Regulation' and 'Responsible AI Development', governance is defined as privacy, oversight and societal impact handled by regulators, ethics boards and companies, separate from building.

Our Grades 11–12 · outcome review Fully covered
What learners do

In Grade 12 'The Council Decision', 'When AI Breaks the Law' and 'The Safety Lab', governance is taught as a concern separate from building: oversight, accountability gaps, privacy rules and societal impact.

Our Grades 9–10 · outcome review Covered earlier · Grade 8
What learners do

In Grade 8 'Rules for the Next Loop', learners follow a draft, trial and review cycle and write three class AI rules covering who, for what and a check. In Grade 5 they check real UNESCO school guidance and rewrite a proposed law.

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

In Grade 12 'The Council Decision', learners draft a policy proposal and a school AI-use guideline; a capstone requires consulting a student, a teacher and a parent. In Grade 11 'Draft Your Governance Clause' they write a clause.

Where it is taught

Robotics / physical device

Framework grades 9–12
Partly covered
Our course Partly covered
What learners do

In Python Lab 'Module 48: Sensors and Gestures', learners write Python for phone motion thresholds. A gesture rule from the Motion Trainer can be exported to a real micro:bit, with an in-app simulator.

Still to add

No Grades 9-12 lesson links to the micro:bit or Motion Trainer tools, and no required learner program drives a physical device that acts on the environment.

Our Grades 9–10 · outcome review Partly covered
What learners do

In Python Lab Module 48, learners write Python that reads a phone's live motion sensors. The Motion Trainer records real sensor data, and the micro:bit simulator exports rules that run on a real board.

Still to add

No learner-written code makes an actuator act on the environment, the micro:bit output is only an LED picture from generated code, and no Grade 9-10 lesson links to this work.

Our Grades 11–12 · outcome review Partly covered
What learners do

Python Lab Module 48 'Sensors and Gestures' and the Motion Trainer and micro:bit simulator are open to learners, and Grade 12 'AI on Tiny Devices' covers the idea. No Grade 11-12 lesson uses the sensor work.

Still to add

Learners do not write the program that runs on a device, and no actuator acts on the physical world; outputs are on-screen text or LED pictures.

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.