Curriculum alignment · The course
Grade 6: units, topics and lessons
Every Grade 6 topic in the LittleAIMaster AI course, its lessons, and the requirements from 14 AI curricula it helps meet. Open a topic to see its lessons; lessons cited as evidence are in bold.
6units60topics1,032lessons236requirements met
Looking for the learner view? See the Grade 6 learning path.
Unit 1: The AI Story 10 topics
What is AI?17 lessons· meets 17 requirements inADEKCBSECISCE + statesEngland + Cambridge+5
Lessons
- Maya's Mystery
- Meet Byte!
- The Big Secret
- How Face Unlock Works
- Word Match
- Super Learner
- Humans vs AI: Face Recognition
- Learns or Rules?
- Myths Busted!
- How TikTok Knows You
- AI All Around
- Talk to an AI!
- Spot the AI!
- Checkpoint Quiz!
- Explain AI to a Friend
- New Words!
- Champion!
Requirements this topic helps meet
- ADEKAI vs non-AI decision, AI in daily life
- ADEKTrained-on-examples / rule-vs-learned
- CBSESummarise basic ideas and concepts of AI and its applications; describe key differences between machine intelligence and human intelligence; explain the difference between automation and AI using real-world cases; differentiate supervised, unsupervised, and reinforcement learning (Class 6).
- CBSECommunicate effectively about AI concepts and applications; describe the historical development of AI; differentiate types and domains of AI including their applications; recognise key terminologies/concepts related to machine learning and deep learning; formulate informed opinions on benefits and limitations of AI (Unit 1).
- CISCE + statesDefine artificial intelligence, give a brief history since John McCarthy coined the term in 1956, and explain the Turing Test and its importance
- CISCE + statesDefine AI, trace its evolution and applications across fields, and explain common AI applications (decision making, remote patient monitoring, data analysis, complex problem-solving)
- England + CambridgeExplore Artificial Intelligence (computers taking information from surroundings to produce outputs), machine learning, autonomous robotics, and augmented reality applications
- CSTA (US)Predict how computational innovations that have revolutionized aspects of our culture might evolve.
- CSTA (US)AI content is integrated (not a separate strand): students learn rule-based vs. data-driven approaches, machine learning basics, and related societal impacts such as bias, within the Algorithms & Design strand.
- CSTA (US)Students study the history of computing and AI, emerging technologies, human-AI relationships, and responsible decision-making about technology use.
- OECD–EC (Europe)Identify the benefits and drawbacks of each AI approach for a task (Manage 2, intermediate).
- UNESCOUse examples of AI tools (facial recognition, recommenders, self-driving cars, loan-default prediction, etc.) to explain the definition and scope of AI — what AI is and is not — and identify the main categories of AI technologies in daily use (CG4.1.3.1).
- SingaporeDevelop a basic understanding of what AI is, as part of the core Code for Fun curriculum (before taking the AI for Fun elective).
- SingaporeUnderstand what AI is.
- SingaporeLearn about AI: understand what AI is and how it works.
- Korea, Japan, HKExplain the concept and characteristics of artificial intelligence and distinguish AI software from non-AI software ([9정04-01]).
- Korea, Japan, HKDeepen understanding and skills for realising diverse communication and using information systems and diverse (including large-scale/multimedia) data, and understand the development of information technology and social change including its effect on intellectual activity.
Robots in History17 lessons· meets 8 requirements inCBSECISCE + statesIBCSTA (US)+2
Lessons
- History Project
- Ancient Dreams
- The Turk's Secret
- Myth or Machine?
- Inventor Match!
- Al-Jazari's Magic
- The First Programmer
- Machines Go Global
- World Tour!
- Old vs New
- Why Robots?
- Ancient Ideas, Modern Robots
- Timeline!
- History Quiz!
- Al-Jazari's Genius
- New Words!
- Time Traveler!
Requirements this topic helps meet
- CBSECommunicate effectively about AI concepts and applications; describe the historical development of AI; differentiate types and domains of AI including their applications; recognise key terminologies/concepts related to machine learning and deep learning; formulate informed opinions on benefits and limitations of AI (Unit 1).
- CISCE + statesDefine AI, trace its evolution and applications across fields, and explain common AI applications (decision making, remote patient monitoring, data analysis, complex problem-solving)
- IBDevelop an appreciation of the impact of design and technology innovations on life, global society, and environments, including their historical, political, and social contexts
- CSTA (US)Predict how computational innovations that have revolutionized aspects of our culture might evolve.
- CSTA (US)Students study the history of computing and AI, emerging technologies, human-AI relationships, and responsible decision-making about technology use.
- SingaporeUnderstand what a smart/intelligent robot is and how AI enables it to respond to inputs.
- SingaporeLearn about AI: understand what AI is and how it works.
- Korea, Japan, HKDeepen understanding and skills for realising diverse communication and using information systems and diverse (including large-scale/multimedia) data, and understand the development of information technology and social change including its effect on intellectual activity.
Birth of Computers16 lessons· meets 12 requirements inCBSECISCE + statesIBCSTA (US)+3
Lessons
- Emma's Discovery
- Why Computers?
- Human Computers!
- ENIAC - Giant Brain
- First Computers!
- Computing Pioneers
- The First 'Bug'
- Computer Timeline
- ENIAC vs Phone!
- Spot the Old Tech!
- Computer Quiz!
- Ada's Vision
- New Words!
- ENIAC vs Your Phone
- Turing's Dream Came True
- History Explorer!
Requirements this topic helps meet
- CBSECommunicate effectively about AI concepts and applications; describe the historical development of AI; differentiate types and domains of AI including their applications; recognise key terminologies/concepts related to machine learning and deep learning; formulate informed opinions on benefits and limitations of AI (Unit 1).
- CISCE + statesDefine AI, trace its evolution and applications across fields, and explain common AI applications (decision making, remote patient monitoring, data analysis, complex problem-solving)
- IBDevelop an appreciation of the impact of design and technology innovations on life, global society, and environments, including their historical, political, and social contexts
- IBUse AI-generated content as an object of inquiry within Theory of Knowledge to examine how knowledge is produced and evaluated
- CSTA (US)Predict how computational innovations that have revolutionized aspects of our culture might evolve.
- CSTA (US)Debate what differences do or should exist between human and artificial intelligence, sentience, consciousness, rights, and responsibilities.
- CSTA (US)Students study the history of computing and AI, emerging technologies, human-AI relationships, and responsible decision-making about technology use.
- AI4K12 (US)Progress from demonstrating ways intelligent agents don't understand things the way people do, e.g. no frustration at repeated questions (K-2), to demonstrating what a 'broad AI'/general-AI system could do that current narrow AI cannot (3-5), to defining criteria for consciousness (self-awareness, personal history) and evaluating AI systems or fictional AI characters against them (6-8), to debating alternative perspectives on human vs. artificial intelligence, including the Turing test and artificial superintelligence (9-12).
- SingaporeDevelop computational thinking as one of the nine core NDLP digital competencies.
- SingaporeLearn about AI: understand what AI is and how it works.
- Korea, Japan, HKDeepen understanding and skills for realising diverse communication and using information systems and diverse (including large-scale/multimedia) data, and understand the development of information technology and social change including its effect on intellectual activity.
- Korea, Japan, HKTrace the history and background of AI (from the 1943 perceptron and 1950 Turing Test through the Dartmouth Conference, AI winters, big data/deep learning and generative AI) and describe how AI, machine learning and deep learning relate as nested concepts.
AI Gets Its Name16 lessons· meets 8 requirements inCBSECISCE + statesCSTA (US)AI4K12 (US)+2
Lessons
- Priya's Team Name
- Summer 1956
- The Magic Name
- Match the Pioneers!
- The Name That Changed Everything
- Their Big Dreams
- The 10-Year Prediction
- Dreams vs Reality!
- Dartmouth's Dream in Your Pocket
- AI's Birthday!
- Name Your Own AI Field
- Why So Hard?
- Dartmouth Today!
- AI History Quiz!
- New Words!
- AI Birthday Expert!
Requirements this topic helps meet
- CBSECommunicate effectively about AI concepts and applications; describe the historical development of AI; differentiate types and domains of AI including their applications; recognise key terminologies/concepts related to machine learning and deep learning; formulate informed opinions on benefits and limitations of AI (Unit 1).
- CISCE + statesDefine AI, trace its evolution and applications across fields, and explain common AI applications (decision making, remote patient monitoring, data analysis, complex problem-solving)
- CSTA (US)Predict how computational innovations that have revolutionized aspects of our culture might evolve.
- CSTA (US)Students study the history of computing and AI, emerging technologies, human-AI relationships, and responsible decision-making about technology use.
- AI4K12 (US)Progress from giving commonsense explanations of human behavior useful for a robot to know, e.g. choosing a plausible story ending (K-2), to explaining what everyday cultural and naive-physics knowledge a computer needs to understand a story (3-5), to explaining the naive-physics inference rules (mass, volume, conservation, forces) needed to solve a physics reasoning problem (6-8), to explaining the cultural, naive-physics, and folk-psychology knowledge needed to interpret a fable, fairytale, or Winograd-schema sentence (9-12).
- SingaporeLearn about AI: understand what AI is and how it works.
- Korea, Japan, HKDeepen understanding and skills for realising diverse communication and using information systems and diverse (including large-scale/multimedia) data, and understand the development of information technology and social change including its effect on intellectual activity.
- Korea, Japan, HKTrace the history and background of AI (from the 1943 perceptron and 1950 Turing Test through the Dartmouth Conference, AI winters, big data/deep learning and generative AI) and describe how AI, machine learning and deep learning relate as nested concepts.
Early AI Dreams17 lessons· meets 10 requirements inCBSECISCE + statesCSTA (US)AI4K12 (US)+3
Lessons
- Ryan's Computer Lab
- The Golden Age!
- Could ELIZA Do It?
- The ELIZA Effect
- Run ELIZA's Trick
- More Golden Age Firsts!
- Early AI Match!
- From Hours to Milliseconds
- Meet SHAKEY!
- 1970s vs Today!
- From 'Rotten Meat' to 100+ Languages
- Predictions vs Reality
- Be ELIZA!
- New Words!
- Golden Age Quiz!
- Why We Believe
- Golden Age Expert!
Requirements this topic helps meet
- CBSECommunicate effectively about AI concepts and applications; describe the historical development of AI; differentiate types and domains of AI including their applications; recognise key terminologies/concepts related to machine learning and deep learning; formulate informed opinions on benefits and limitations of AI (Unit 1).
- CISCE + statesDefine AI, trace its evolution and applications across fields, and explain common AI applications (decision making, remote patient monitoring, data analysis, complex problem-solving)
- CSTA (US)Predict how computational innovations that have revolutionized aspects of our culture might evolve.
- CSTA (US)Debate what differences do or should exist between human and artificial intelligence, sentience, consciousness, rights, and responsibilities.
- CSTA (US)Students study the history of computing and AI, emerging technologies, human-AI relationships, and responsible decision-making about technology use.
- AI4K12 (US)Progress from demonstrating ways intelligent agents don't understand things the way people do, e.g. no frustration at repeated questions (K-2), to demonstrating what a 'broad AI'/general-AI system could do that current narrow AI cannot (3-5), to defining criteria for consciousness (self-awareness, personal history) and evaluating AI systems or fictional AI characters against them (6-8), to debating alternative perspectives on human vs. artificial intelligence, including the Turing test and artificial superintelligence (9-12).
- OECD–EC (Europe)Accurately describe how a specific AI tool operates (Engage 2, intermediate).
- SingaporeUnderstand what a smart/intelligent robot is and how AI enables it to respond to inputs.
- SingaporeLearn about AI: understand what AI is and how it works.
- Korea, Japan, HKDeepen understanding and skills for realising diverse communication and using information systems and diverse (including large-scale/multimedia) data, and understand the development of information technology and social change including its effect on intellectual activity.
The AI Winter16 lessons· meets 8 requirements inCBSECISCE + statesCSTA (US)Singapore+1
Lessons
- Jamie's Science Fair
- What is AI Winter?
- Summer vs Winter
- When the Money Froze
- The Golden Summer
- Promise vs Reality!
- The $400 Million Failure
- Why AI Failed
- The Lighthill Report
- AI's Oldest Habit: Overpromising
- Easy or Hard for AI?
- Who Kept Going?
- AI Winter Quiz!
- Lessons from Failure
- New Words!
- Winter Survivor!
Requirements this topic helps meet
- CBSECommunicate effectively about AI concepts and applications; describe the historical development of AI; differentiate types and domains of AI including their applications; recognise key terminologies/concepts related to machine learning and deep learning; formulate informed opinions on benefits and limitations of AI (Unit 1).
- CISCE + statesDefine AI, trace its evolution and applications across fields, and explain common AI applications (decision making, remote patient monitoring, data analysis, complex problem-solving)
- CSTA (US)Predict how computational innovations that have revolutionized aspects of our culture might evolve.
- CSTA (US)Students study the history of computing and AI, emerging technologies, human-AI relationships, and responsible decision-making about technology use.
- SingaporeUnderstand what a smart/intelligent robot is and how AI enables it to respond to inputs.
- SingaporeLearn about AI: understand what AI is and how it works.
- Korea, Japan, HKDeepen understanding and skills for realising diverse communication and using information systems and diverse (including large-scale/multimedia) data, and understand the development of information technology and social change including its effect on intellectual activity.
- Korea, Japan, HKTrace the history and background of AI (from the 1943 perceptron and 1950 Turing Test through the Dartmouth Conference, AI winters, big data/deep learning and generative AI) and describe how AI, machine learning and deep learning relate as nested concepts.
AI Wakes Up Again16 lessons· meets 10 requirements inCBSECISCE + statesEngland + CambridgeCSTA (US)+4
Lessons
- The Chess Match
- The Perfect Storm
- Moore's Magic Law
- If Cars Were Like Computers
- Internet Boom
- Data Needs
- The Internet's Data Explosion
- Machine Learning
- Old vs New AI
- Netflix's Billion-Dollar Brain
- Spot the ML!
- Do What Deep Blue Couldn't
- Checkpoint Quiz!
- Smart at One Thing
- Word Match!
- Champion!
Requirements this topic helps meet
- CBSECommunicate effectively about AI concepts and applications; describe the historical development of AI; differentiate types and domains of AI including their applications; recognise key terminologies/concepts related to machine learning and deep learning; formulate informed opinions on benefits and limitations of AI (Unit 1).
- CISCE + statesDifferentiate broad vs narrow AI and strong vs weak AI, and describe expert systems (e.g. Eliza), computer vision (CV), natural language processing (NLP) and neural networks (NN)
- CISCE + statesDefine AI, trace its evolution and applications across fields, and explain common AI applications (decision making, remote patient monitoring, data analysis, complex problem-solving)
- England + CambridgeEvaluate models and simulators for real-life scenarios; create relational databases with linked tables; understand Big Data and describe its applications
- CSTA (US)Predict how computational innovations that have revolutionized aspects of our culture might evolve.
- CSTA (US)Students study the history of computing and AI, emerging technologies, human-AI relationships, and responsible decision-making about technology use.
- OECD–EC (Europe)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).
- UNESCOGain interdisciplinary foundational knowledge for AI (e.g. artificial neural networks, strong vs weak AI, connections to STEM, languages and social studies) and reflect on AI's reciprocal impact on those subjects (CG4.1.3.3).
- SingaporeLearn about AI: understand what AI is and how it works.
- Korea, Japan, HKDeepen understanding and skills for realising diverse communication and using information systems and diverse (including large-scale/multimedia) data, and understand the development of information technology and social change including its effect on intellectual activity.
The Deep Learning Revolution25 lessons· meets 20 requirements inUAEADEKCBSECISCE + states+6
Lessons
- Smart Speaker Magic
- The Magic Recipe
- Deep = Many Layers
- The 2-GPU Shock
- Layer Match!
- Input, Hidden, Output
- Which Layer Does This Job?
- AND, OR and the Tricky XOR
- AND, OR or XOR?
- Try It: The XOR Challenge
- What Did the Playground Show?
- Million-Photo Practice
- Before or After the Leap?
- GPU Power!
- CPU vs GPU
- Voice Assistants
- Google Photos Sees Layers
- Voice Timeline!
- Spot Deep Learning!
- AI Beats Humans!
- The Move No Human Imagined
- Checkpoint Quiz!
- Explain the AI Leap
- New Words!
- Champion!
Requirements this topic helps meet
- UAEHow advanced models process information (neural networks)
- UAEUse generative AI models to create new content
- ADEKFootprint / resource cost of AI
- CBSECommunicate effectively about AI concepts and applications; describe the historical development of AI; differentiate types and domains of AI including their applications; recognise key terminologies/concepts related to machine learning and deep learning; formulate informed opinions on benefits and limitations of AI (Unit 1).
- CISCE + statesDefine AI, trace its evolution and applications across fields, and explain common AI applications (decision making, remote patient monitoring, data analysis, complex problem-solving)
- England + CambridgeUnderstand the hardware and software components that make up computer systems, and how they communicate with one another and with other systems
- CSTA (US)Predict how computational innovations that have revolutionized aspects of our culture might evolve.
- CSTA (US)Investigate the positive and negative environmental impacts of AI (e.g., minimizing deforestation via application of AI, energy use by AI).
- CSTA (US)Students examine security implications and environmental impacts of AI systems within Systems & Security.
- CSTA (US)Students study the history of computing and AI, emerging technologies, human-AI relationships, and responsible decision-making about technology use.
- AI4K12 (US)Progress from analyzing a decision-tree-building game and how the learning algorithm adds/replaces nodes (3-5), to comparing how decision-tree learning (add a node) differs from neural-network learning (adjust every weight) (6-8), to describing how decision-tree, neural-net, regression, and reinforcement-learning algorithms each adjust their own type of internal representation (9-12). K-2 is marked N/A.
- AI4K12 (US)Progress from illustrating that a 1-3 neuron network is a function that computes weighted-sum/threshold outputs (3-5), to illustrating the layered structure (input/hidden/output) of a neural network and its transfer functions (6-8), to describing feed-forward, 2D convolutional, recurrent, and generative adversarial network architectures and their typical uses, e.g. deepfakes (9-12). K-2 is marked N/A.
- AI4K12 (US)Progress from demonstrating how weights are assigned in a neural network to produce a desired input/output behavior (3-5), to demonstrating a learning rule (e.g. perceptron rule) that adjusts weights of a one-layer network in response to errors (6-8), to training a multilayer neural network with the backpropagation algorithm and describing how weights and hidden-unit outputs change during learning (9-12). K-2 is marked N/A.
- OECD–EC (Europe)Describe environmental impacts of AI such as hardware, data centres, water and energy use (Engage 5, intermediate).
- OECD–EC (Europe)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).
- SingaporeDevelop device and software operation skills as one of the nine core NDLP digital competencies.
- SingaporeLearn about AI: understand what AI is and how it works.
- Korea, Japan, HKDeepen understanding and skills for realising diverse communication and using information systems and diverse (including large-scale/multimedia) data, and understand the development of information technology and social change including its effect on intellectual activity.
- Korea, Japan, HKTrace the history and background of AI (from the 1943 perceptron and 1950 Turing Test through the Dartmouth Conference, AI winters, big data/deep learning and generative AI) and describe how AI, machine learning and deep learning relate as nested concepts.
- Korea, Japan, HKExplain how a neural network is built from perceptron units (inputs, weightings, bias, activation function) and how it "learns" via backpropagation to adjust weights to fit training data.
AI Today - ChatGPT & Beyond19 lessons· meets 12 requirements inUAECBSECISCE + statesCSTA (US)+5
Lessons
- Maya's Study Buddy
- ChatGPT Explosion!
- Inside an LLM
- Meet the AI Squad!
- 175 Billion Knobs
- AI Tools Match!
- Tiny Pixel Puzzle
- AI Creates Art!
- AI in Your Day
- Create or Suggest?
- Spotify's 30-Song Guess
- Smart AI Use
- AI Milestones!
- Spot AI Today!
- AI Challenges
- Checkpoint Quiz!
- Explain Smart AI Use
- New Words!
- Champion!
Requirements this topic helps meet
- UAEUse generative AI models to create new content
- CBSECommunicate effectively about AI concepts and applications; describe the historical development of AI; differentiate types and domains of AI including their applications; recognise key terminologies/concepts related to machine learning and deep learning; formulate informed opinions on benefits and limitations of AI (Unit 1).
- CISCE + statesDefine AI, trace its evolution and applications across fields, and explain common AI applications (decision making, remote patient monitoring, data analysis, complex problem-solving)
- CSTA (US)Predict how computational innovations that have revolutionized aspects of our culture might evolve.
- CSTA (US)Students study the history of computing and AI, emerging technologies, human-AI relationships, and responsible decision-making about technology use.
- OECD–EC (Europe)Compare AI-generated suggestions to one's own ideas during brainstorming (Create 1, intermediate).
- UNESCOInterrogate how automation of literature reviews, writing and artistic creation by AI may undermine human thinking and intellectual development, and identify concrete actions to protect themselves and peers from over-reliance on AI outputs (CG4.2.1.3).
- SingaporeLeverage AI tools effectively for learning and for daily life.
- SingaporeLearn about AI: understand what AI is and how it works.
- SingaporeLearn with AI: apply AI to support and enhance one's own learning process.
- Korea, Japan, HKDeepen understanding and skills for realising diverse communication and using information systems and diverse (including large-scale/multimedia) data, and understand the development of information technology and social change including its effect on intellectual activity.
- Australia + NZStudents and educators use generative AI tools in ways designed to enhance teaching and learning outcomes rather than replace core learning processes
Unit 1 Review - The AI Journey15 lessons· meets 7 requirements inCBSECISCE + statesCSTA (US)Singapore+1
Lessons
- Jordan's Journey
- Pioneer Match!
- Two Months to 100 Million
- Timeline Order!
- 3,000-Year AI Dream
- Era Sort!
- Complete Timeline
- 5 Big Ideas!
- Fun Facts Match!
- Google Lens Uses the Whole Journey
- Ultimate Spot AI!
- Unit 1 Final Quiz!
- Explain the AI Journey
- Master Vocabulary!
- AI Explorer Champion!
Requirements this topic helps meet
- CBSECommunicate effectively about AI concepts and applications; describe the historical development of AI; differentiate types and domains of AI including their applications; recognise key terminologies/concepts related to machine learning and deep learning; formulate informed opinions on benefits and limitations of AI (Unit 1).
- CISCE + statesDefine AI, trace its evolution and applications across fields, and explain common AI applications (decision making, remote patient monitoring, data analysis, complex problem-solving)
- CSTA (US)Predict how computational innovations that have revolutionized aspects of our culture might evolve.
- CSTA (US)Students study the history of computing and AI, emerging technologies, human-AI relationships, and responsible decision-making about technology use.
- SingaporeLearn about AI: understand what AI is and how it works.
- Korea, Japan, HKDeepen understanding and skills for realising diverse communication and using information systems and diverse (including large-scale/multimedia) data, and understand the development of information technology and social change including its effect on intellectual activity.
- Korea, Japan, HKTrace the history and background of AI (from the 1943 perceptron and 1950 Turing Test through the Dartmouth Conference, AI winters, big data/deep learning and generative AI) and describe how AI, machine learning and deep learning relate as nested concepts.
Unit 2: How Computers Think 10 topics
Patterns Are Everywhere16 lessons· meets 3 requirements inEngland + CambridgeAI4K12 (US)Singapore
Lessons
- Pattern Detective
- What's a Pattern?
- Patterns All Around
- Faster Than a Blink
- Pattern Match!
- The PIN Pattern Puzzle
- Pattern Power
- Train a Pattern-Finder
- Pattern Sorter
- AI Pattern Master
- Google Maps Spots Traffic Patterns
- Pattern Challenge!
- Pattern Quiz!
- Explain Pattern Power
- Pattern Words
- Pattern Pro!
Requirements this topic helps meet
- England + CambridgeDevelop and apply analytic, problem-solving, design and computational thinking skills
- AI4K12 (US)Progress from explaining how images are digitally represented as 2D pixel arrays (3-5), to explaining how sounds are digitally encoded via waveform sampling (6-8), to explaining how radar, lidar, GPS, and accelerometer data are represented and computed (9-12). K-2 is marked N/A for this topic.
- SingaporeDevelop computational thinking as one of the nine core NDLP digital competencies.
Your Brain Finds Patterns15 lessons· meets 5 requirements inUAEAI4K12 (US)
Lessons
- The Cricket Prediction
- 86 Billion Neurons!
- Flash or Work It Out?
- 100 Trillion Connections
- What Gave It Away?
- Half-Second Prediction
- The Climb to 'DOG!'
- Brain vs Computer
- Teach It a Face
- Face ID Copies Brain Tricks
- Safety Patterns
- Brain Power Quiz!
- Explain Brain Patterns
- Brain Words
- Brain Genius!
Requirements this topic helps meet
- UAECompare AI with humans (Cycle 2 part)
- UAEHow advanced models process information (neural networks)
- AI4K12 (US)Progress from explaining how images are digitally represented as 2D pixel arrays (3-5), to explaining how sounds are digitally encoded via waveform sampling (6-8), to explaining how radar, lidar, GPS, and accelerometer data are represented and computed (9-12). K-2 is marked N/A for this topic.
- AI4K12 (US)Progress from describing and giving examples of how people vs. computers learn (K-2), to differentiating human flexible learning from computer pattern-finding/trial-and-error learning (3-5), to contrasting human learning characteristics with narrowly-defined machine learning problems (6-8), to defining supervised, unsupervised, and reinforcement learning and giving human-learning analogues for each (9-12).
- AI4K12 (US)Progress from demonstrating how computers recognize emotions in faces via mouth/eye/eyebrow shape (K-2), to illustrating how sentiment analysis judges the emotional tone of text, e.g. movie/restaurant reviews, social media (3-5), to describing how computers combine facial expression, gaze, gesture, body language, tone of voice, and word choice to recognize emotional states (6-8), to identifying ways AI applications modify their behavior in response to detected human emotional states, e.g. adaptive tutoring systems (9-12).
What is Data?16 lessons· meets 7 requirements inCBSECISCE + statesAI4K12 (US)OECD–EC (Europe)+2
Lessons
- The Question at Dinner
- Recorded Answers!
- Three Data Types
- A Photo Is a Million Clues
- Data Sorter!
- Emoji Data Trail
- Data Everywhere!
- Data Match!
- Collecting Data
- YouTube Reads Data Signals
- Data Power-Up!
- Data Hunt!
- Data Quiz!
- Explain Data
- Data Words
- Data Detective!
Requirements this topic helps meet
- CBSEOrganise and represent data in various forms (text, numbers, images, sounds); recognise simple patterns in data and make decisions based on observations (Class 6); collect/organise structured data and create bar/line/pie charts (Class 7).
- CISCE + statesClassify types of data (audio, visual, numeric, text) and explain how data becomes information within the AI Project Cycle
- AI4K12 (US)Progress from explaining how images are digitally represented as 2D pixel arrays (3-5), to explaining how sounds are digitally encoded via waveform sampling (6-8), to explaining how radar, lidar, GPS, and accelerometer data are represented and computed (9-12). K-2 is marked N/A for this topic.
- OECD–EC (Europe)Consider whether an AI recommendation is helpful or narrows perspective (Engage 4, intermediate).
- OECD–EC (Europe)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).
- SingaporeDevelop data competencies as one of the nine core NDLP digital competencies.
- ChinaStudents collect, organize and analyze data, and solve real-world problems through project-based learning
Sorting and Organizing Data15 lessons· meets 2 requirements inEngland + CambridgeAI4K12 (US)
Lessons
- Library Chaos!
- Before vs After
- Five Sorting Ways
- A Million Down to One
- Method Match!
- The Shelf Shortcut
- Sorting All Around
- Netflix Sorts the Shelf
- Google's Secret
- Sort It Out!
- Sorting Challenge!
- Sorting Quiz!
- Explain Sorting
- Sorting Words
- Sorting Master!
Requirements this topic helps meet
- England + CambridgeUnderstand several key algorithms that reflect computational thinking (e.g. sorting and searching); use logical reasoning to compare the utility of alternative algorithms for the same problem
- AI4K12 (US)Progress from explaining how images are digitally represented as 2D pixel arrays (3-5), to explaining how sounds are digitally encoded via waveform sampling (6-8), to explaining how radar, lidar, GPS, and accelerometer data are represented and computed (9-12). K-2 is marked N/A for this topic.
Following Instructions - Algorithms16 lessons· meets 3 requirements inUAEAI4K12 (US)Singapore
Lessons
- Sandwich Disaster!
- Algorithm Revealed!
- Algorithm or Not?
- Fast Does Not Mean Right
- Algorithm Match!
- Recipe Code
- Four Rules
- The Rules Inside a Roblox Game
- Written or Learned?
- Order the Steps!
- Followable or Vague?
- Order Challenge!
- Algorithm Quiz!
- Explain Algorithms
- Algorithm Words
- Algorithm Master!
Requirements this topic helps meet
- UAELearn from data; algorithms make decisions
- AI4K12 (US)Progress from explaining how images are digitally represented as 2D pixel arrays (3-5), to explaining how sounds are digitally encoded via waveform sampling (6-8), to explaining how radar, lidar, GPS, and accelerometer data are represented and computed (9-12). K-2 is marked N/A for this topic.
- SingaporeDevelop computational thinking as one of the nine core NDLP digital competencies.
Making Decisions - IF-THEN15 lessons· meets 5 requirements inUAEEngland + CambridgeCSTA (US)AI4K12 (US)
Lessons
- Smart Door
- IF-THEN Magic!
- Split the Rule!
- A Million Tiny Choices
- Logic Match!
- Password Logic
- AND & OR Power!
- AND vs OR!
- Decision Trees
- Rules and Learning in Your Inbox
- Logic Challenge!
- Decision Quiz!
- Explain IF-THEN Logic
- Logic Words
- Logic Master!
Requirements this topic helps meet
- UAELearn from data; algorithms make decisions
- England + CambridgeUnderstand simple Boolean logic (AND, OR, NOT) and its uses in circuits and programming; understand binary representation and carry out simple binary operations
- CSTA (US)Understand that representation includes modalities (text, speech, audio, image, video) and symbolic mappings (text, graphs).
- AI4K12 (US)Progress from explaining how images are digitally represented as 2D pixel arrays (3-5), to explaining how sounds are digitally encoded via waveform sampling (6-8), to explaining how radar, lidar, GPS, and accelerometer data are represented and computed (9-12). K-2 is marked N/A for this topic.
- AI4K12 (US)Progress from giving examples of symbols encountered in daily life such as restroom icons and card suits (K-2), to explaining hierarchical tree structures such as ancestry trees and taxonomies (3-5), to translating a structure like a game board, road map, or mind map into a labeled graph of nodes and links (6-8), to translating the premises of a syllogism from English into predicate-logic notation and completing the inference (9-12).
How Computers Count - Binary15 lessons· meets 5 requirements inCBSEEngland + CambridgeCSTA (US)AI4K12 (US)
Lessons
- Binary Birthday!
- Binary Revealed!
- Count Like a Computer!
- Eight Switches, 256 Choices
- Match Binary!
- Binary Birthday Code
- Run the Recipe!
- Binary World
- QR Codes Are Binary Grids
- Bits & Bytes!
- Binary Challenge!
- Binary Quiz!
- Explain Binary
- Binary Words
- Binary Master!
Requirements this topic helps meet
- CBSEInterpret and solve multi-step, multi-layered problems using advanced 3-D transformations, algebraic/multi-rule numerical patterns, interdependent-clue decomposition, and rule-based algorithms with conditional branching - increasing in complexity from Class 6 through Class 8 (e.g., Class 8 adds number-system generalisation across decimal/binary/ternary/Roman/Chinese numerals, and optimisation problems).
- England + CambridgeUnderstand simple Boolean logic (AND, OR, NOT) and its uses in circuits and programming; understand binary representation and carry out simple binary operations
- England + CambridgeUnderstand how instructions are stored and executed, and how data of various types (text, sound, pictures) can be represented and manipulated digitally in binary
- CSTA (US)Represent data using multiple encoding schemes.
- AI4K12 (US)Progress from explaining how images are digitally represented as 2D pixel arrays (3-5), to explaining how sounds are digitally encoded via waveform sampling (6-8), to explaining how radar, lidar, GPS, and accelerometer data are represented and computed (9-12). K-2 is marked N/A for this topic.
Memory and Storage15 lessons· meets 5 requirements inIBEngland + CambridgeAI4K12 (US)Singapore
Lessons
- Priya's Big Mistake
- Two Types of Memory
- One Game Night, In Order
- RAM Forgets in a Flash
- Memory Match!
- Tiny RAM, Giant Storage
- Where Storage Really Lives
- Sort the Storage!
- Who Is Doing the Saving?
- Google Photos: Storage That Sorts Itself
- RAM or Storage?
- Memory Master Quiz
- Explain Memory
- Memory Words
- Memory Champion!
Requirements this topic helps meet
- IBExplain how core computer hardware, operating systems, and cloud computing concepts underpin computing systems
- England + CambridgeUnderstand the hardware and software components that make up computer systems, and how they communicate with one another and with other systems
- England + CambridgeShow understanding of operating system process management (multi-tasking, process states, scheduling algorithms) and virtual memory/paging/segmentation
- AI4K12 (US)Progress from explaining how images are digitally represented as 2D pixel arrays (3-5), to explaining how sounds are digitally encoded via waveform sampling (6-8), to explaining how radar, lidar, GPS, and accelerometer data are represented and computed (9-12). K-2 is marked N/A for this topic.
- SingaporeDevelop device and software operation skills as one of the nine core NDLP digital competencies.
Input and Output15 lessons· meets 7 requirements inADEKIBEngland + CambridgeAI4K12 (US)+1
Lessons
- Raj's Video Call
- The Three Steps
- Input Devices
- Six Parts in One Call
- Match the Input!
- Touchscreens Do Double Duty
- Output Devices
- Sort the Devices!
- The Future of I/O
- A Video Call Runs Both Ways at Once
- Input or Output?
- I/O Expert Quiz
- Explain Input and Output
- I/O Vocabulary
- I/O Champion!
Requirements this topic helps meet
- ADEKSensors / perception
- IBExplain how core computer hardware, operating systems, and cloud computing concepts underpin computing systems
- England + CambridgeUnderstand the hardware and software components that make up computer systems, and how they communicate with one another and with other systems
- England + CambridgeShow understanding of operating system process management (multi-tasking, process states, scheduling algorithms) and virtual memory/paging/segmentation
- AI4K12 (US)Progress from locating and identifying computer sensors such as cameras and microphones on everyday devices (K-2), to illustrating how computer sensing differs from human sensing (3-5), to giving examples of intelligent agents (e.g. self-driving cars) combining data from multiple sensors (6-8), to describing the limitations and advantages of specific sensor types such as cameras, microphones, radar, lidar, GPS, and accelerometers (9-12).
- AI4K12 (US)Progress from explaining how images are digitally represented as 2D pixel arrays (3-5), to explaining how sounds are digitally encoded via waveform sampling (6-8), to explaining how radar, lidar, GPS, and accelerometer data are represented and computed (9-12). K-2 is marked N/A for this topic.
- SingaporeDevelop device and software operation skills as one of the nine core NDLP digital competencies.
Unit 2 Review - Thinking Like a Computer16 lessons· meets 3 requirements inEngland + CambridgeAI4K12 (US)Singapore
Lessons
- Raj's Question
- Data or Process?
- The Big Ideas
- Nine Chapters, One Search
- Concept Match!
- The 0101 Shortcut
- It's All Connected!
- Brain vs Computer
- All in One Search!
- Google Search Uses the Whole Stack
- Order the Search!
- True or False?
- Unit 2 Final Quiz!
- Explain Computer Thinking
- Master Vocabulary
- Unit 2 Champion!
Requirements this topic helps meet
- England + CambridgeUnderstand the hardware and software components that make up computer systems, and how they communicate with one another and with other systems
- AI4K12 (US)Progress from explaining how images are digitally represented as 2D pixel arrays (3-5), to explaining how sounds are digitally encoded via waveform sampling (6-8), to explaining how radar, lidar, GPS, and accelerometer data are represented and computed (9-12). K-2 is marked N/A for this topic.
- SingaporeDevelop device and software operation skills as one of the nine core NDLP digital competencies.
Unit 3: AI Around Us 10 topics
AI in Your Home15 lessons· meets 16 requirements inUAEADEKCBSECISCE + states+6
Lessons
- Maya's Smart Morning
- Who Decided?
- What's a Smart Home?
- A House That Learns
- Smart Device Match!
- Two-Second Voice Helper
- Hey Google, How?
- Regular vs Smart!
- AI Room by Room
- Alexa Connects the Home
- Spot the Smart!
- Smart Home Quiz
- Explain Smart Home AI
- Smart Home Words
- Home AI Expert!
Requirements this topic helps meet
- UAEAI applied across sectors
- ADEKAI vs non-AI decision, AI in daily life
- CBSESummarise basic ideas and concepts of AI and its applications; describe key differences between machine intelligence and human intelligence; explain the difference between automation and AI using real-world cases; differentiate supervised, unsupervised, and reinforcement learning (Class 6).
- CISCE + statesGain foundational awareness of AI concepts, robotics and digital tools beyond conventional computer education, using SCERT-prepared bilingual textbooks
- England + CambridgeUnderstand that robots can work autonomously and describe robotics benefits in manufacturing/food production
- England + CambridgeExplore Artificial Intelligence (computers taking information from surroundings to produce outputs), machine learning, autonomous robotics, and augmented reality applications
- England + CambridgeUnderstand what is meant by Artificial Intelligence (AI)
- AI4K12 (US)Progress from demonstrating figure/ground segmentation in an image (K-2), to illustrating how occlusion changes the outlines of objects (3-5), to describing how edge detectors compose into more complex feature detectors such as letter/shape detectors (6-8), to demonstrating how perceptual reasoning at a higher abstraction level (objects, surfaces, boundaries) draws on lower levels (contours, edges, pixels) (9-12).
- AI4K12 (US)Progress from identifying devices in daily life that use AI, e.g. smartphones, smart assistants (K-2), to describing how AI-powered services (search, recommenders, autonomous vehicles) are used in daily life (3-5), to examining an aspect of daily life predicted to change due to AI, e.g. transportation and self-driving cars (6-8), to explaining the kinds of cultural debates that arise as AI evolves, e.g. AI-generated art, LLM use in homework, data rights (9-12).
- OECD–EC (Europe)Weigh the benefits and drawbacks of data-shaped access to information from predictive AI systems (Engage 4, advanced).
- UNESCOUse examples of AI tools (facial recognition, recommenders, self-driving cars, loan-default prediction, etc.) to explain the definition and scope of AI — what AI is and is not — and identify the main categories of AI technologies in daily use (CG4.1.3.1).
- SingaporeDevelop a basic understanding of what AI is, as part of the core Code for Fun curriculum (before taking the AI for Fun elective).
- SingaporeUnderstand what AI is.
- SingaporeLearn about AI: understand what AI is and how it works.
- SingaporeDescribe Artificial Intelligence (AI) as the ability of a computer to perform complex tasks without constant human guidance and improve its performance as more data is collected.
- Korea, Japan, HKExplain the concept and characteristics of artificial intelligence and distinguish AI software from non-AI software ([9정04-01]).
AI in Your Phone17 lessons· meets 8 requirements inADEKCISCE + statesEngland + CambridgeAI4K12 (US)+2
Lessons
- Raj's Photo Magic
- Camera AI Secrets
- The Puppy Photo Mystery
- Which Keyboard AI?
- 30,000 Face Clues
- Camera AI Match!
- Keyboard Guessing Game
- Hey Siri! Hey Google!
- Old vs Smart Phone!
- Face Unlock, In Order
- Face Match Lab
- Google Photos Finds Bruno
- Phone AI Challenge!
- Phone AI Quiz
- Explain Phone AI
- Phone AI Words
- Phone AI Master!
Requirements this topic helps meet
- ADEKAI vs non-AI decision, AI in daily life
- CISCE + statesGain foundational awareness of AI concepts, robotics and digital tools beyond conventional computer education, using SCERT-prepared bilingual textbooks
- England + CambridgeUnderstand data security during transmission and different user authentication methods (password, fingerprint, facial recognition)
- AI4K12 (US)Progress from demonstrating figure/ground segmentation in an image (K-2), to illustrating how occlusion changes the outlines of objects (3-5), to describing how edge detectors compose into more complex feature detectors such as letter/shape detectors (6-8), to demonstrating how perceptual reasoning at a higher abstraction level (objects, surfaces, boundaries) draws on lower levels (contours, edges, pixels) (9-12).
- AI4K12 (US)Progress from discussing why intelligent agents need to understand languages other than English (K-2), to discussing why domain knowledge must be broad enough to cover accents, dialects, and musical genres (3-5), to describing how a vision system can exhibit cultural bias if it lacks knowledge of objects outside its creators' culture, e.g. non-US traffic signs (6-8), to describing technical difficulties (e.g. low-contrast faces, children's speech) in making perception systems work well for diverse groups (9-12).
- AI4K12 (US)Progress from identifying devices in daily life that use AI, e.g. smartphones, smart assistants (K-2), to describing how AI-powered services (search, recommenders, autonomous vehicles) are used in daily life (3-5), to examining an aspect of daily life predicted to change due to AI, e.g. transportation and self-driving cars (6-8), to explaining the kinds of cultural debates that arise as AI evolves, e.g. AI-generated art, LLM use in homework, data rights (9-12).
- OECD–EC (Europe)Weigh the benefits and drawbacks of data-shaped access to information from predictive AI systems (Engage 4, advanced).
- SingaporeLearn about AI: understand what AI is and how it works.
AI Plays Games17 lessons· meets 6 requirements inAI4K12 (US)OECD–EC (Europe)Singapore
Lessons
- The Smart Opponent
- Game AI Secrets
- The Boss Fight Mystery
- What is Game AI?
- Bots Can Play Millions
- Who Controls Who?
- Fake Mistakes Feel Real
- Finding the Path
- Easy vs Hard AI!
- Secret Difficulty!
- Difficulty Tuning Lab
- Roblox NPCs Find Paths
- Spot the Game AI!
- Game AI Quiz
- Explain Game AI
- Game AI Words
- Game AI Master!
Requirements this topic helps meet
- AI4K12 (US)Progress from demonstrating figure/ground segmentation in an image (K-2), to illustrating how occlusion changes the outlines of objects (3-5), to describing how edge detectors compose into more complex feature detectors such as letter/shape detectors (6-8), to demonstrating how perceptual reasoning at a higher abstraction level (objects, surfaces, boundaries) draws on lower levels (contours, edges, pixels) (9-12).
- AI4K12 (US)Progress from illustrating a next possible board state in tic-tac-toe (K-2), to illustrating how a computer represents game play as a linear sequence of board positions connected by legal moves (3-5), to illustrating how a computer solves a maze, finds a map route, or reasons over a knowledge graph by drawing a search tree (6-8), to identifying real-world problems (task planning, scheduling, resource allocation) as search problems and describing their states and operators (9-12).
- AI4K12 (US)Progress from demonstrating how training data are labeled by class in a tool like Teachable Machine (3-5), to explaining the difference between training a reasoning model and using it on new data (6-8), to illustrating all steps of building a classifier/predictor with machine learning, including choosing features, labeling, cross-validation, and test-set evaluation (9-12). K-2 is marked N/A.
- AI4K12 (US)Progress from identifying devices in daily life that use AI, e.g. smartphones, smart assistants (K-2), to describing how AI-powered services (search, recommenders, autonomous vehicles) are used in daily life (3-5), to examining an aspect of daily life predicted to change due to AI, e.g. transportation and self-driving cars (6-8), to explaining the kinds of cultural debates that arise as AI evolves, e.g. AI-generated art, LLM use in homework, data rights (9-12).
- OECD–EC (Europe)Weigh the benefits and drawbacks of data-shaped access to information from predictive AI systems (Engage 4, advanced).
- SingaporeLearn about AI: understand what AI is and how it works.
AI Recommends For You18 lessons· meets 13 requirements inUAEADEKCISCE + statesCSTA (US)+4
Lessons
- Magical Music App
- AI Everywhere!
- The Different Feed Mystery
- How AI Knows You
- A Feed Just for You
- What Gets Recommended?
- Skipping Is a Signal
- Two Magic Methods
- Which Method?
- The Filter Bubble
- Recommendation Signal Lab
- YouTube Learns Your Next Video
- What Does AI Learn?
- Recommendation Quiz
- Explain Recommendations
- Log Your Week, Chart It
- Recommendation Words
- Recommendation Master!
Requirements this topic helps meet
- UAEHow AI generates outputs from learned patterns
- ADEKAI vs non-AI decision, AI in daily life
- ADEKBuild a chart/dashboard
- CISCE + statesGain foundational awareness of AI concepts, robotics and digital tools beyond conventional computer education, using SCERT-prepared bilingual textbooks
- CSTA (US)Identify the kinds of AI models (e.g., classifier, predictor, recommender) people interact with in their daily lives.
- AI4K12 (US)Progress from demonstrating figure/ground segmentation in an image (K-2), to illustrating how occlusion changes the outlines of objects (3-5), to describing how edge detectors compose into more complex feature detectors such as letter/shape detectors (6-8), to demonstrating how perceptual reasoning at a higher abstraction level (objects, surfaces, boundaries) draws on lower levels (contours, edges, pixels) (9-12).
- AI4K12 (US)Progress from identifying devices in daily life that use AI, e.g. smartphones, smart assistants (K-2), to describing how AI-powered services (search, recommenders, autonomous vehicles) are used in daily life (3-5), to examining an aspect of daily life predicted to change due to AI, e.g. transportation and self-driving cars (6-8), to explaining the kinds of cultural debates that arise as AI evolves, e.g. AI-generated art, LLM use in homework, data rights (9-12).
- OECD–EC (Europe)Share examples of how AI shapes personal choices (Engage 1, intermediate level).
- OECD–EC (Europe)Consider whether an AI recommendation is helpful or narrows perspective (Engage 4, intermediate).
- OECD–EC (Europe)Weigh the benefits and drawbacks of data-shaped access to information from predictive AI systems (Engage 4, advanced).
- OECD–EC (Europe)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).
- SingaporeLearn about AI: understand what AI is and how it works.
- Korea, Japan, HKDistinguish skill-based reasoning (direct task response), rule-based reasoning (decisions from learned patterns) and knowledge-based reasoning (applying machine learning to find the best solution) in a hands-on robot-reasoning experiment (e.g. a moral-dilemma or line-following task).
AI Helps You Travel17 lessons· meets 4 requirements inAI4K12 (US)OECD–EC (Europe)Singapore
Lessons
- Raj Trusts the Blue Line
- What a Map App Really Watches
- The Traffic Mystery
- Turning Clues Into a Time
- A Map That Keeps Changing Its Mind
- Match the Words!
- Traffic Has Patterns
- How a Ride App Picks Your Driver
- Live or Learned?
- Which Safety Feature Is Working?
- Arrival Time Lab
- Why the App Sends You the Long Way
- Spot the Travel AI!
- Navigation AI Quiz
- Explain Travel AI
- Travel AI Vocabulary
- Navigation AI Expert!
Requirements this topic helps meet
- AI4K12 (US)Progress from demonstrating figure/ground segmentation in an image (K-2), to illustrating how occlusion changes the outlines of objects (3-5), to describing how edge detectors compose into more complex feature detectors such as letter/shape detectors (6-8), to demonstrating how perceptual reasoning at a higher abstraction level (objects, surfaces, boundaries) draws on lower levels (contours, edges, pixels) (9-12).
- AI4K12 (US)Progress from identifying devices in daily life that use AI, e.g. smartphones, smart assistants (K-2), to describing how AI-powered services (search, recommenders, autonomous vehicles) are used in daily life (3-5), to examining an aspect of daily life predicted to change due to AI, e.g. transportation and self-driving cars (6-8), to explaining the kinds of cultural debates that arise as AI evolves, e.g. AI-generated art, LLM use in homework, data rights (9-12).
- OECD–EC (Europe)Weigh the benefits and drawbacks of data-shaped access to information from predictive AI systems (Engage 4, advanced).
- SingaporeLearn about AI: understand what AI is and how it works.
AI in Shopping17 lessons· meets 7 requirements inAI4K12 (US)OECD–EC (Europe)SingaporeAustralia + NZ
Lessons
- Maya's Four Invisible Helpers
- AI Powers in Online Shopping
- The Photo Search Mystery
- Who Is That Suggestion For?
- Your Shop Looks Different
- Match the Shopping AI!
- A Cart Can Teach AI
- Shopping Chatbots
- Traditional vs AI Shopping
- Visual Search: Ask First, Then Snap
- Why Is THIS Product First?
- Amazon Finds the Art Set
- Spot the Shopping AI!
- Shopping AI Quiz
- Explain Shopping AI
- Shopping AI Vocabulary
- Shopping AI Expert!
Requirements this topic helps meet
- AI4K12 (US)Progress from demonstrating figure/ground segmentation in an image (K-2), to illustrating how occlusion changes the outlines of objects (3-5), to describing how edge detectors compose into more complex feature detectors such as letter/shape detectors (6-8), to demonstrating how perceptual reasoning at a higher abstraction level (objects, surfaces, boundaries) draws on lower levels (contours, edges, pixels) (9-12).
- AI4K12 (US)Progress from identifying devices in daily life that use AI, e.g. smartphones, smart assistants (K-2), to describing how AI-powered services (search, recommenders, autonomous vehicles) are used in daily life (3-5), to examining an aspect of daily life predicted to change due to AI, e.g. transportation and self-driving cars (6-8), to explaining the kinds of cultural debates that arise as AI evolves, e.g. AI-generated art, LLM use in homework, data rights (9-12).
- OECD–EC (Europe)Weigh the benefits and drawbacks of data-shaped access to information from predictive AI systems (Engage 4, advanced).
- OECD–EC (Europe)Weigh the benefits and disadvantages of using AI-driven search engines, including privacy and commercial-interest trade-offs (AI example 31, competence 1.1).
- OECD–EC (Europe)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).
- SingaporeLearn about AI: understand what AI is and how it works.
- Australia + NZStudents understand that their privacy and personal data must be protected when using generative AI tools, and are taught not to input sensitive personal or others' data into such tools without appropriate safeguards
AI in Healthcare17 lessons· meets 8 requirements inAI4K12 (US)OECD–EC (Europe)UNESCOSingapore
Lessons
- Grandma Gets Help
- Where AI Really Helps
- The Scan Mystery
- Teach Your Own Scan Spotter
- A Second Pair of Eyes
- Match the Healthcare Words!
- Tiny Wrist Signals
- Order the Symptom Checker
- Healthcare Without vs With AI
- What Your Watch Really Knows
- Health Signal Lab
- A Watch That Watches Patterns
- Spot the Healthcare AI!
- Healthcare AI Quiz
- Explain Healthcare AI
- Healthcare AI Vocabulary
- Healthcare AI Expert!
Requirements this topic helps meet
- AI4K12 (US)Progress from giving examples of intelligent vs. non-intelligent machines and discussing what makes a machine 'intelligent' (K-2), to demonstrating a machine perception tool (e.g. speech transcription, object recognition) and explaining why it is perception rather than mere sensing (3-5), to giving examples of specialized perception algorithms (face detection, obstacle detection, speech recognition, etc.) (6-8), to explaining how perception algorithms power real-world applications like license plate readers and Google Lens (9-12).
- AI4K12 (US)Progress from demonstrating figure/ground segmentation in an image (K-2), to illustrating how occlusion changes the outlines of objects (3-5), to describing how edge detectors compose into more complex feature detectors such as letter/shape detectors (6-8), to demonstrating how perceptual reasoning at a higher abstraction level (objects, surfaces, boundaries) draws on lower levels (contours, edges, pixels) (9-12).
- AI4K12 (US)Progress from identifying devices in daily life that use AI, e.g. smartphones, smart assistants (K-2), to describing how AI-powered services (search, recommenders, autonomous vehicles) are used in daily life (3-5), to examining an aspect of daily life predicted to change due to AI, e.g. transportation and self-driving cars (6-8), to explaining the kinds of cultural debates that arise as AI evolves, e.g. AI-generated art, LLM use in homework, data rights (9-12).
- OECD–EC (Europe)Weigh the benefits and drawbacks of data-shaped access to information from predictive AI systems (Engage 4, advanced).
- OECD–EC (Europe)Compare AI outputs to desired results and redirect the approach as needed (Manage 4, intermediate).
- UNESCOUnderstand the necessity of exercising sufficient human control over AI, including the consequences when human oversight fails or is absent (CG4.1.1.2).
- UNESCOEvaluate the consequences of institutional use of AI to make high-stakes decisions about people (e.g. profiling students, hiring, loan/assessment scoring) and understand why human accountability must not be ceded to AI in such decisions (CG4.2.1.2).
- SingaporeLearn about AI: understand what AI is and how it works.
AI in Schools17 lessons· meets 7 requirements inAI4K12 (US)OECD–EC (Europe)SingaporeAustralia + NZ
Lessons
- Raj and the Fractions
- Five Jobs: Which One Is It?
- The Fraction Clue Mystery
- What is Adaptive Learning?
- A Record for Every Student
- Match the Learning AI!
- Wrong Answers Teach Too
- Myth or Backed by Evidence?
- One Lesson, or Tuned to One Learner?
- Homework Helper, In Order
- Practice Plan Lab
- The App That Guesses What You Forgot
- Spot the Education AI!
- Education AI Quiz
- Explain Learning AI
- Education AI Vocabulary
- Education AI Expert!
Requirements this topic helps meet
- AI4K12 (US)Progress from demonstrating figure/ground segmentation in an image (K-2), to illustrating how occlusion changes the outlines of objects (3-5), to describing how edge detectors compose into more complex feature detectors such as letter/shape detectors (6-8), to demonstrating how perceptual reasoning at a higher abstraction level (objects, surfaces, boundaries) draws on lower levels (contours, edges, pixels) (9-12).
- AI4K12 (US)Progress from identifying devices in daily life that use AI, e.g. smartphones, smart assistants (K-2), to describing how AI-powered services (search, recommenders, autonomous vehicles) are used in daily life (3-5), to examining an aspect of daily life predicted to change due to AI, e.g. transportation and self-driving cars (6-8), to explaining the kinds of cultural debates that arise as AI evolves, e.g. AI-generated art, LLM use in homework, data rights (9-12).
- OECD–EC (Europe)Weigh the benefits and drawbacks of data-shaped access to information from predictive AI systems (Engage 4, advanced).
- SingaporeLeverage AI tools effectively for learning and for daily life.
- SingaporeLearn about AI: understand what AI is and how it works.
- SingaporeLearn with AI: apply AI to support and enhance one's own learning process.
- Australia + NZStudents and educators use generative AI tools in ways designed to enhance teaching and learning outcomes rather than replace core learning processes
AI Innovations Around the World17 lessons· meets 5 requirements inEngland + CambridgeAI4K12 (US)OECD–EC (Europe)Singapore
Lessons
- The Global Science Fair
- Six Countries, Six Problems
- The Local Problem Mystery
- Why Different Countries Build Different AI
- One Breakthrough, Millions Helped
- Match Countries and Innovations!
- Small Teams Can Matter
- Amazing AI Breakthroughs
- Big Tech vs Rising Stars
- How Countries Collaborate on AI
- Global Impact Lab
- Google Translate Crosses Borders
- Spot Global AI Innovations!
- Global AI Quiz
- Explain Global AI
- Global AI Vocabulary
- Global AI Expert!
Requirements this topic helps meet
- England + CambridgeUnderstand that robots can work autonomously and describe robotics benefits in manufacturing/food production
- AI4K12 (US)Progress from demonstrating figure/ground segmentation in an image (K-2), to illustrating how occlusion changes the outlines of objects (3-5), to describing how edge detectors compose into more complex feature detectors such as letter/shape detectors (6-8), to demonstrating how perceptual reasoning at a higher abstraction level (objects, surfaces, boundaries) draws on lower levels (contours, edges, pixels) (9-12).
- AI4K12 (US)Progress from identifying devices in daily life that use AI, e.g. smartphones, smart assistants (K-2), to describing how AI-powered services (search, recommenders, autonomous vehicles) are used in daily life (3-5), to examining an aspect of daily life predicted to change due to AI, e.g. transportation and self-driving cars (6-8), to explaining the kinds of cultural debates that arise as AI evolves, e.g. AI-generated art, LLM use in homework, data rights (9-12).
- OECD–EC (Europe)Weigh the benefits and drawbacks of data-shaped access to information from predictive AI systems (Engage 4, advanced).
- SingaporeLearn about AI: understand what AI is and how it works.
Unit 3 Review - AI Scavenger Hunt!16 lessons· meets 14 requirements inADEKCBSECISCE + statesEngland + Cambridge+6
Lessons
- The Ultimate AI Hunt
- Recognise or Predict?
- The Eleven That Got Crossed Out
- AI Detection Checklist
- Maya's Twelve
- Match the AI to its Job
- One Checklist, Every Country
- Guess vs Truth
- Voice AI, In Order
- AI Detector Lab
- Google Assistant Uses the Steps
- The Board
- Unit 3 Final Quiz
- Explain AI Around Us
- Unit 3 Key Terms Review
- Unit 3 Complete!
Requirements this topic helps meet
- ADEKAI vs non-AI decision, AI in daily life
- CBSESummarise basic ideas and concepts of AI and its applications; describe key differences between machine intelligence and human intelligence; explain the difference between automation and AI using real-world cases; differentiate supervised, unsupervised, and reinforcement learning (Class 6).
- CISCE + statesGain foundational awareness of AI concepts, robotics and digital tools beyond conventional computer education, using SCERT-prepared bilingual textbooks
- England + CambridgeExplore Artificial Intelligence (computers taking information from surroundings to produce outputs), machine learning, autonomous robotics, and augmented reality applications
- CSTA (US)Identify the kinds of AI models (e.g., classifier, predictor, recommender) people interact with in their daily lives.
- AI4K12 (US)Progress from demonstrating figure/ground segmentation in an image (K-2), to illustrating how occlusion changes the outlines of objects (3-5), to describing how edge detectors compose into more complex feature detectors such as letter/shape detectors (6-8), to demonstrating how perceptual reasoning at a higher abstraction level (objects, surfaces, boundaries) draws on lower levels (contours, edges, pixels) (9-12).
- AI4K12 (US)Progress from identifying devices in daily life that use AI, e.g. smartphones, smart assistants (K-2), to describing how AI-powered services (search, recommenders, autonomous vehicles) are used in daily life (3-5), to examining an aspect of daily life predicted to change due to AI, e.g. transportation and self-driving cars (6-8), to explaining the kinds of cultural debates that arise as AI evolves, e.g. AI-generated art, LLM use in homework, data rights (9-12).
- OECD–EC (Europe)Weigh the benefits and drawbacks of data-shaped access to information from predictive AI systems (Engage 4, advanced).
- UNESCOUse examples of AI tools (facial recognition, recommenders, self-driving cars, loan-default prediction, etc.) to explain the definition and scope of AI — what AI is and is not — and identify the main categories of AI technologies in daily use (CG4.1.3.1).
- SingaporeDevelop a basic understanding of what AI is, as part of the core Code for Fun curriculum (before taking the AI for Fun elective).
- SingaporeUnderstand what AI is.
- SingaporeLearn about AI: understand what AI is and how it works.
- Korea, Japan, HKExplain the concept and characteristics of artificial intelligence and distinguish AI software from non-AI software ([9정04-01]).
- Korea, Japan, HKDistinguish skill-based reasoning (direct task response), rule-based reasoning (decisions from learned patterns) and knowledge-based reasoning (applying machine learning to find the best solution) in a hands-on robot-reasoning experiment (e.g. a moral-dilemma or line-following task).
Unit 4: Being Smart About AI 10 topics
AI Makes Mistakes17 lessons· meets 38 requirements inADEKIBEngland + CambridgeCSTA (US)+7
Lessons
- The Wrong Diagnosis
- Why AI Makes Mistakes
- Train Me, Then Trick Me
- Everyday AI Fails
- 95% Still Misses
- Match the Mistakes!
- New Examples Matter
- Be AI Smart!
- Low vs High Risk!
- Who Has the Last Word?
- Accuracy Risk Lab
- Where Translation AI Still Slips
- Spot the Mistake!
- AI Mistakes Quiz!
- Explain AI Mistakes
- Mistake Words!
- AI-Mistake Aware!
Requirements this topic helps meet
- ADEKDeepfakes: explain, spot flaws, set responsible-use rules
- ADEKCareers, future of work
- IBAct with integrity and honesty and take responsibility for one's own actions when making design decisions
- IBUnderstand the bias inherent in content produced by an AI tool and be able to critically review AI-generated output
- England + CambridgeYoung people should understand how AI works, its capabilities and limitations, and learn to use it effectively without becoming dependent on it; this should be delivered primarily through Computing rather than assumed to develop automatically
- England + CambridgeMedia literacy (taught mainly via Citizenship) should cover critically engaging with messages conveyed through different media channels, including AI, and identifying/protecting against misinformation and disinformation
- CSTA (US)Explain the privacy concerns related to the collection and generation of data through automated processes that may not be evident to users.
- AI4K12 (US)Progress from describing jobs that no longer exist due to past technology advances, e.g. farriers replaced by auto mechanics (K-2), to describing how a specific job will change due to AI/robotic technology, e.g. warehouse workers alongside robots (3-5), to predicting a new job type that might arise or an existing job that might change/disappear due to AI adoption (6-8), to investigating the specific skills needed for AI-enabled careers across the workforce, not just for programmers (9-12).
- OECD–EC (Europe)Explain how AI systems perform tasks without attributing human traits to them (Engage 2, advanced).
- OECD–EC (Europe)Evaluate whether an AI output should be accepted, revised, or rejected, and justify the decision (Engage 3, advanced).
- OECD–EC (Europe)Deliberately assign parts of a task to human strengths versus AI capabilities (Manage 3, advanced).
- OECD–EC (Europe)Compare AI outputs to desired results and redirect the approach as needed (Manage 4, intermediate).
- OECD–EC (Europe)Describe an AI system's purpose, intended users, and basic constraints (Shape 1, intermediate).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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.
- OECD–EC (Europe)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.
- UNESCOUnderstand the necessity of exercising sufficient human control over AI, including the consequences when human oversight fails or is absent (CG4.1.1.2).
- UNESCOArticulate personal and social responsibilities of citizens in an AI society — from both user and designer perspectives — including ensuring human control and accountability over every step of the AI life cycle (CG4.3.1.2).
- SingaporeUnderstand AI's uses, risks and limitations, and apply ethical considerations (e.g. academic integrity, proper data handling) when using AI to support learning.
- SingaporeIdentify and explain AI's potential benefits, limitations and risks.
- SingaporeLeverage AI tools effectively for learning and for daily life.
- SingaporeLearn with AI: apply AI to support and enhance one's own learning process.
- SingaporeThink critically about AI's biases, question AI-generated outputs, and discern or identify false or misleading information produced by AI.
- Korea, Japan, HKUnderstand the roles information technology plays for people and society and its effects, explicitly including how AI and robots change the nature of work and raise cybercrime/digital-divide/health concerns.
- Korea, Japan, HKScientifically understand the characteristics of media and communication methods, including how they have changed over time, and the role information design plays for people and society.
- Korea, Japan, HKCultivate an attitude of participating proactively in, and contributing to the development of, the information society while creating new value through appropriate use of information and information technology and adherence to information morals (law, security).
- Korea, Japan, HKFindings from FY2023 pilot schools showed some students uncritically accepted AI answers as fact or could not judge appropriate situations for use on their own — confirming an explicit target competency: judge the appropriateness/veracity of generative-AI output rather than accept it uncritically.
- ChinaStudents practice social responsibility, weighing technological innovation against societal risk when using or designing AI
- ChinaStudents understand the ethics and challenges of a smart/AI-driven society, strengthening information-security awareness, responsibility, and dialectical (balanced) views of AI
- Australia + NZBuild knowledge about the function and purpose of AI systems (Years 5-6), progressing to evaluating the advantages and disadvantages of AI and other emerging technologies (Years 7-8)
- Australia + NZStudents and educators use generative AI tools in ways designed to enhance teaching and learning outcomes rather than replace core learning processes
- Australia + NZStudents develop an understanding of how generative AI tools work in broad terms, how they can be used appropriately, and when/how such tools are affecting the content and information they see (transparency about AI-generated or AI-assisted content)
- Australia + NZStudents are taught that generative AI tools must be used, and their outputs treated, in ways that are accessible, fair and respectful to all users, being alert to potential bias in AI outputs
- Australia + NZStudents understand that their privacy and personal data must be protected when using generative AI tools, and are taught not to input sensitive personal or others' data into such tools without appropriate safeguards
AI Can Be Unfair19 lessons· meets 33 requirements inUAEADEKCBSEIB+8
Lessons
- The App That Can't See Priya
- Where Bias Sneaks In
- The Missing Faces Mystery
- Five Shapes of Unfairness
- Fair for 90 Is Not Fair
- Match the Bias!
- The Data Mirror
- The Woman Who Measured It
- Cause or Result?
- When AI Stands at a Door
- Fairness Test Lab
- Photo Apps Need Fair Face AI
- Spot the Bias!
- AI Fairness Quiz!
- Explain AI Fairness
- Fairness Words!
- Risk-Tier Check!
- Write the Rule!
- Bias Aware!
Requirements this topic helps meet
- UAEEthics, cultural values and inclusion guide AI decisions
- ADEKExamine dataset gaps/bias
- ADEKDeepfakes: explain, spot flaws, set responsible-use rules
- ADEKCareers, future of work
- CBSEDemonstrate understanding of ethics and digital responsibility (digital footprints, privacy, responsible technology use), practice internet safety protocols, and explain what bias in AI means and identify situations where AI can give unfair results (Class 6-7); recognise how bias leads to unfair conclusions and understand accountability, privacy, and fairness/inclusivity strategies (Class 8).
- IBUnderstand the bias inherent in content produced by an AI tool and be able to critically review AI-generated output
- CSTA (US)Describe the ways that bias can be introduced and mitigated in an AI model.
- CSTA (US)Explore strategies to turn ethical considerations into actions, such as mitigating bias in datasets.
- AI4K12 (US)Progress from discussing why intelligent agents need to understand languages other than English (K-2), to discussing why domain knowledge must be broad enough to cover accents, dialects, and musical genres (3-5), to describing how a vision system can exhibit cultural bias if it lacks knowledge of objects outside its creators' culture, e.g. non-US traffic signs (6-8), to describing technical difficulties (e.g. low-contrast faces, children's speech) in making perception systems work well for diverse groups (9-12).
- AI4K12 (US)Progress from examining a labeled dataset to identify problems that could cause incorrect predictions, e.g. an all-green 'healthy foods' set (K-2), to examining training-data features/labels for potential sources of bias such as Amazon's biased resume sorter (3-5), to explaining how unbalanced training data biases a classifier, e.g. faces or loan applications (6-8), to investigating demographic imbalances in training data using data-visualization tools such as histograms or Pandas (9-12).
- AI4K12 (US)Progress from evaluating the ways a decision impacts different people differently (K-2), to evaluating how an AI technology, e.g. speech or facial recognition, has different effects on different demographic groups (3-5), to evaluating how various stakeholders' conflicting goals and values shape AI system design decisions (6-8), to explaining how the use of AI systems (e.g. health-risk algorithms) has led to documented disparate impacts on different groups (9-12).
- AI4K12 (US)Progress from describing jobs that no longer exist due to past technology advances, e.g. farriers replaced by auto mechanics (K-2), to describing how a specific job will change due to AI/robotic technology, e.g. warehouse workers alongside robots (3-5), to predicting a new job type that might arise or an existing job that might change/disappear due to AI adoption (6-8), to investigating the specific skills needed for AI-enabled careers across the workforce, not just for programmers (9-12).
- OECD–EC (Europe)Explain how AI systems perform tasks without attributing human traits to them (Engage 2, advanced).
- OECD–EC (Europe)Evaluate whether an AI output should be accepted, revised, or rejected, and justify the decision (Engage 3, advanced).
- OECD–EC (Europe)Explain how biased data or design choices create unfair outcomes (Engage 6, intermediate).
- OECD–EC (Europe)Identify unintended impacts of an AI use that differ from its immediate goals (Engage 7, intermediate).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- UNESCODiscuss age-appropriate real-world cases around six core AI ethical principles — do no harm, proportionality, non-discrimination, sustainability, human determination, and transparency/explainability — and their personal implications (CG4.1.2.2).
- UNESCOInternalise ethical principles on AI (human rights, data privacy, human agency, equity, inclusion, social justice, environmental sustainability) into reflective practices, e.g. advocating for inclusive AI interfaces and reporting discriminatory bias found in AI tools (CG4.1.2.3).
- UNESCOArticulate personal and social responsibilities of citizens in an AI society — from both user and designer perspectives — including ensuring human control and accountability over every step of the AI life cycle (CG4.3.1.2).
- SingaporeLearn with AI: apply AI to support and enhance one's own learning process.
- Korea, Japan, HKCultivate an attitude of participating proactively in, and contributing to the development of, the information society while creating new value through appropriate use of information and information technology and adherence to information morals (law, security).
- Korea, Japan, HKIdentify AI ethical principles and analyse ethical issues raised by real AI applications (bias, fairness, accountability), as a dedicated unit distinct from general 'societal impact'.
- ChinaStudents understand the ethics and challenges of a smart/AI-driven society, strengthening information-security awareness, responsibility, and dialectical (balanced) views of AI
- Australia + NZConsider the possible bias or harm to others that may occur when inputting data into AI systems, and explore how AI systems use predictive strategies based on large amounts of data to perform tasks or solve problems
- Australia + NZStudents and educators use generative AI tools in ways designed to enhance teaching and learning outcomes rather than replace core learning processes
- Australia + NZStudents develop an understanding of how generative AI tools work in broad terms, how they can be used appropriately, and when/how such tools are affecting the content and information they see (transparency about AI-generated or AI-assisted content)
- Australia + NZStudents are taught that generative AI tools must be used, and their outputs treated, in ways that are accessible, fair and respectful to all users, being alert to potential bias in AI outputs
- Australia + NZStudents understand that their privacy and personal data must be protected when using generative AI tools, and are taught not to input sensitive personal or others' data into such tools without appropriate safeguards
AI and Privacy18 lessons· meets 32 requirements inUAEADEKCBSEIB+9
Lessons
- Raj's App Surprise
- Does It Really Need That?
- The Racing Game Mystery
- Can You Rub It Out?
- Tiny Clicks Build a Profile
- Match the Privacy Terms!
- Free Apps Still Cost Data
- Privacy Problems
- Good vs Bad Privacy Practices
- Same Photo, Different Rules
- Whose Rules?
- Permission Risk Lab
- YouTube Puts the History Switch in Your Hands
- Spot the Privacy Risk!
- Privacy Quiz
- Explain Privacy Choices
- Privacy Vocabulary
- Privacy Protector!
Requirements this topic helps meet
- UAEEthics, cultural values and inclusion guide AI decisions
- UAEPrivacy, accountability and personal rights
- ADEKDeepfakes: explain, spot flaws, set responsible-use rules
- ADEKCareers, future of work
- CBSEDemonstrate understanding of ethics and digital responsibility (digital footprints, privacy, responsible technology use), practice internet safety protocols, and explain what bias in AI means and identify situations where AI can give unfair results (Class 6-7); recognise how bias leads to unfair conclusions and understand accountability, privacy, and fairness/inclusivity strategies (Class 8).
- IBUnderstand the bias inherent in content produced by an AI tool and be able to critically review AI-generated output
- England + CambridgeUnderstand a range of ways to use technology safely, respectfully, responsibly and securely, including protecting online identity and privacy; recognise inappropriate content, contact and conduct and know how to report concerns
- England + CambridgeUnderstand data security during transmission and different user authentication methods (password, fingerprint, facial recognition)
- CSTA (US)Describe tradeoffs between allowing information to be public and keeping information private and secure.
- CSTA (US)Explain how physical and digital security measures protect electronic information.
- AI4K12 (US)Progress from discussing what makes a system fair vs. unfair through a fair-distribution exercise (K-2), to evaluating whether an AI system meets the design criteria of transparency and explainability (3-5), to evaluating whether an AI system meets the design criteria of accountability and respect for privacy (6-8), to analyzing whether an AI system satisfies the full set of ethical design criteria: fairness, transparency, explainability, accountability, respect for privacy, and adherence to societal values (9-12).
- AI4K12 (US)Progress from describing jobs that no longer exist due to past technology advances, e.g. farriers replaced by auto mechanics (K-2), to describing how a specific job will change due to AI/robotic technology, e.g. warehouse workers alongside robots (3-5), to predicting a new job type that might arise or an existing job that might change/disappear due to AI adoption (6-8), to investigating the specific skills needed for AI-enabled careers across the workforce, not just for programmers (9-12).
- OECD–EC (Europe)Explain how AI systems perform tasks without attributing human traits to them (Engage 2, advanced).
- OECD–EC (Europe)Evaluate whether an AI output should be accepted, revised, or rejected, and justify the decision (Engage 3, advanced).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- UNESCOCategorise general safety risks of AI and typical AI incidents, deepen knowledge of human rights to data protection and privacy and creators' legal responsibility to collect data only with informed consent, and practise strategies to ensure personal data is collected, used, shared, archived and deleted only with consent (CG4.2.2.3).
- UNESCOArticulate personal and social responsibilities of citizens in an AI society — from both user and designer perspectives — including ensuring human control and accountability over every step of the AI life cycle (CG4.3.1.2).
- SingaporeDevelop digital safety and security competencies as one of the nine core NDLP digital competencies.
- SingaporeDevelop digital responsibility as one of the nine core NDLP digital competencies.
- SingaporeLearn with AI: apply AI to support and enhance one's own learning process.
- Korea, Japan, HKExamine problems that can arise in the collection and use of data needed for AI learning (bias, privacy, consent) and propose legally, socially and ethically valid solutions ([9정04-05]).
- Korea, Japan, HKCultivate an attitude of participating proactively in, and contributing to the development of, the information society while creating new value through appropriate use of information and information technology and adherence to information morals (law, security).
- ChinaStudents understand the ethics and challenges of a smart/AI-driven society, strengthening information-security awareness, responsibility, and dialectical (balanced) views of AI
- Australia + NZPractise online safety skills and digital literacy behaviours (the Version 9.0 sub-strand that replaced the old ICT Capability general capability), including safe, respectful interaction with digital systems and other users
- Australia + NZStudents and educators use generative AI tools in ways designed to enhance teaching and learning outcomes rather than replace core learning processes
- Australia + NZStudents develop an understanding of how generative AI tools work in broad terms, how they can be used appropriately, and when/how such tools are affecting the content and information they see (transparency about AI-generated or AI-assisted content)
- Australia + NZStudents are taught that generative AI tools must be used, and their outputs treated, in ways that are accessible, fair and respectful to all users, being alert to potential bias in AI outputs
- Australia + NZStudents understand that their privacy and personal data must be protected when using generative AI tools, and are taught not to input sensitive personal or others' data into such tools without appropriate safeguards
Real vs Fake: Deepfakes15 lessons· meets 31 requirements inUAEADEKIBEngland + Cambridge+8
Lessons
- The Nine-Second Video
- Can a Faker Fake It?
- The Missing Announcement
- Why Your Eyes Keep Losing
- A Face That Never Existed
- Match the Fakes
- The Deliberate Speed Bump
- Trust It, or Suspect It?
- Is That Enough?
- Trust Check Lab
- The Label Only Appears If Someone Ticks It
- Truth Detective Quiz
- Explain Your Truth Check
- Truth Detective Words
- Truth Detective!
Requirements this topic helps meet
- UAEAI-manipulated media: safety and security
- ADEKCheck AI output before trusting
- ADEKGenerative image tools (GAN/diffusion), deepfakes
- ADEKDeepfakes: explain, spot flaws, set responsible-use rules
- ADEKCareers, future of work
- IBUnderstand the bias inherent in content produced by an AI tool and be able to critically review AI-generated output
- England + CambridgeMedia literacy (taught mainly via Citizenship) should cover critically engaging with messages conveyed through different media channels, including AI, and identifying/protecting against misinformation and disinformation
- CSTA (US)Explain how physical and digital security measures protect electronic information.
- CSTA (US)Identify the intended and unintended impacts of AI on society, including government, education, entertainment, culture, careers, and national security, while considering how these impacts may differ among diverse communities.
- AI4K12 (US)Progress from describing jobs that no longer exist due to past technology advances, e.g. farriers replaced by auto mechanics (K-2), to describing how a specific job will change due to AI/robotic technology, e.g. warehouse workers alongside robots (3-5), to predicting a new job type that might arise or an existing job that might change/disappear due to AI adoption (6-8), to investigating the specific skills needed for AI-enabled careers across the workforce, not just for programmers (9-12).
- OECD–EC (Europe)Explain how AI systems perform tasks without attributing human traits to them (Engage 2, advanced).
- OECD–EC (Europe)Verify AI outputs against trusted sources (Engage 3, intermediate).
- OECD–EC (Europe)Evaluate whether an AI output should be accepted, revised, or rejected, and justify the decision (Engage 3, advanced).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- UNESCOCarry out responsible AI practices in compliance with ethical principles and locally applicable regulations, iteratively building and following a personal checklist of lawful and responsible practices (e.g. protecting privacy/copyright, marking AI-generated content, avoiding disinformation, hate speech, or sensitive content about identifiable individuals) (CG4.2.2.1).
- UNESCOGain an age-appropriate understanding of personal, legal and ethical responsibilities when using AI and reinforce self-disciplined behaviours around sensitive personal data, copyrighted materials, deepfake/identifiable-person imagery, and the spread of misinformation, disinformation or hate speech (CG4.2.2.2).
- UNESCOArticulate personal and social responsibilities of citizens in an AI society — from both user and designer perspectives — including ensuring human control and accountability over every step of the AI life cycle (CG4.3.1.2).
- SingaporeLearn to use AI: use AI tools safely and responsibly.
- SingaporeLearn with AI: apply AI to support and enhance one's own learning process.
- SingaporeThink critically about AI's biases, question AI-generated outputs, and discern or identify false or misleading information produced by AI.
- Korea, Japan, HKCultivate an attitude of participating proactively in, and contributing to the development of, the information society while creating new value through appropriate use of information and information technology and adherence to information morals (law, security).
- Korea, Japan, HKEvaluate the societal impact of AI, including both opportunities and risks it poses to communities, drawing on real cases (e.g. AlphaFold's protein-folding breakthrough, AI-assisted astronomy, AI-generated music).
- ChinaStudents understand the ethics and challenges of a smart/AI-driven society, strengthening information-security awareness, responsibility, and dialectical (balanced) views of AI
- Australia + NZStudents and educators use generative AI tools in ways designed to enhance teaching and learning outcomes rather than replace core learning processes
- Australia + NZStudents develop an understanding of how generative AI tools work in broad terms, how they can be used appropriately, and when/how such tools are affecting the content and information they see (transparency about AI-generated or AI-assisted content)
- Australia + NZStudents are taught that generative AI tools must be used, and their outputs treated, in ways that are accessible, fair and respectful to all users, being alert to potential bias in AI outputs
- Australia + NZStudents understand that their privacy and personal data must be protected when using generative AI tools, and are taught not to input sensitive personal or others' data into such tools without appropriate safeguards
AI and Jobs15 lessons· meets 29 requirements inADEKIBEngland + CambridgeCSTA (US)+7
Lessons
- The Day the Arm Arrived
- Jobs Through Time
- Task, or Whole Job?
- Three Types of Job Changes
- A Job Is a Bundle of Tasks
- Old Jobs → New Jobs
- Tasks, Not Whole Jobs
- Easy vs Hard to Automate
- Future-Proof Skills Finder
- Automation Fit Lab
- YouTube Creator Tools
- Future Jobs Quiz
- Explain a Future Skill
- Career Words
- Future Ready!
Requirements this topic helps meet
- ADEKCareers
- ADEKDeepfakes: explain, spot flaws, set responsible-use rules
- ADEKCareers, future of work
- IBDevelop an appreciation of the impact of design and technology innovations on life, global society, and environments, including their historical, political, and social contexts
- IBUnderstand the bias inherent in content produced by an AI tool and be able to critically review AI-generated output
- England + CambridgeUnderstand that robots can work autonomously and describe robotics benefits in manufacturing/food production
- CSTA (US)Compare tradeoffs associated with computing technologies that affect people's everyday activities and career options.
- CSTA (US)Identify the intended and unintended impacts of AI on society, including government, education, entertainment, culture, careers, and national security, while considering how these impacts may differ among diverse communities.
- AI4K12 (US)Progress from evaluating the ways a decision impacts different people differently (K-2), to evaluating how an AI technology, e.g. speech or facial recognition, has different effects on different demographic groups (3-5), to evaluating how various stakeholders' conflicting goals and values shape AI system design decisions (6-8), to explaining how the use of AI systems (e.g. health-risk algorithms) has led to documented disparate impacts on different groups (9-12).
- AI4K12 (US)Progress from identifying current uses of AI and how they have impacted society (K-2), to identifying how sectors of society such as manufacturing, retail, and healthcare are changing due to AI (3-5), to comparing AI-driven societal change to previous industrial revolutions (6-8), to predicting how a specific sector of society is likely to change in the short and intermediate term as a result of AI, referencing Amara's Law (9-12).
- AI4K12 (US)Progress from describing jobs that no longer exist due to past technology advances, e.g. farriers replaced by auto mechanics (K-2), to describing how a specific job will change due to AI/robotic technology, e.g. warehouse workers alongside robots (3-5), to predicting a new job type that might arise or an existing job that might change/disappear due to AI adoption (6-8), to investigating the specific skills needed for AI-enabled careers across the workforce, not just for programmers (9-12).
- OECD–EC (Europe)Explain how AI systems perform tasks without attributing human traits to them (Engage 2, advanced).
- OECD–EC (Europe)Evaluate whether an AI output should be accepted, revised, or rejected, and justify the decision (Engage 3, advanced).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- UNESCOArticulate personal and social responsibilities of citizens in an AI society — from both user and designer perspectives — including ensuring human control and accountability over every step of the AI life cycle (CG4.3.1.2).
- SingaporeLearn with AI: apply AI to support and enhance one's own learning process.
- SingaporeLearn beyond AI: develop distinctly human capabilities and dispositions that AI cannot replicate.
- Korea, Japan, HKCultivate an attitude of participating proactively in, and contributing to the development of, the information society while creating new value through appropriate use of information and information technology and adherence to information morals (law, security).
- Korea, Japan, HKEvaluate the societal impact of AI, including both opportunities and risks it poses to communities, drawing on real cases (e.g. AlphaFold's protein-folding breakthrough, AI-assisted astronomy, AI-generated music).
- Korea, Japan, HKAnalyse how AI adoption is changing the nature of jobs and the skills workers need, and reflect on implications for the student's own future career choices.
- ChinaStudents understand the ethics and challenges of a smart/AI-driven society, strengthening information-security awareness, responsibility, and dialectical (balanced) views of AI
- Australia + NZStudents and educators use generative AI tools in ways designed to enhance teaching and learning outcomes rather than replace core learning processes
- Australia + NZStudents develop an understanding of how generative AI tools work in broad terms, how they can be used appropriately, and when/how such tools are affecting the content and information they see (transparency about AI-generated or AI-assisted content)
- Australia + NZStudents are taught that generative AI tools must be used, and their outputs treated, in ways that are accessible, fair and respectful to all users, being alert to potential bias in AI outputs
- Australia + NZStudents understand that their privacy and personal data must be protected when using generative AI tools, and are taught not to input sensitive personal or others' data into such tools without appropriate safeguards
Being a Good Digital Citizen15 lessons· meets 35 requirements inADEKCBSEIBEngland + Cambridge+7
Lessons
- The Group Chat Dilemma
- Six Pillars of Digital Citizenship
- Same Tool, Different Trouble
- AI Do's and Don'ts
- A Screenshot Takes Seconds
- Match the Habit!
- Kindness Travels Too
- OK or Not OK to Do?
- Good Netiquette Finder
- Post Check Lab
- ChatGPT as a Study Partner
- Digital Citizenship Quiz
- Explain Honest AI Use
- Citizenship Words
- Digital Citizen!
Requirements this topic helps meet
- ADEKDeepfakes: explain, spot flaws, set responsible-use rules
- ADEKCareers, future of work
- CBSEDemonstrate understanding of ethics and digital responsibility (digital footprints, privacy, responsible technology use), practice internet safety protocols, and explain what bias in AI means and identify situations where AI can give unfair results (Class 6-7); recognise how bias leads to unfair conclusions and understand accountability, privacy, and fairness/inclusivity strategies (Class 8).
- IBAct with integrity and honesty and take responsibility for one's own actions when making design decisions
- IBUnderstand the bias inherent in content produced by an AI tool and be able to critically review AI-generated output
- England + CambridgeUnderstand a range of ways to use technology safely, respectfully, responsibly and securely, including protecting online identity and privacy; recognise inappropriate content, contact and conduct and know how to report concerns
- England + CambridgeUnderstand how changes in technology affect safety, including new ways to protect online privacy and identity, and how to identify and report a range of concerns
- AI4K12 (US)Progress from describing jobs that no longer exist due to past technology advances, e.g. farriers replaced by auto mechanics (K-2), to describing how a specific job will change due to AI/robotic technology, e.g. warehouse workers alongside robots (3-5), to predicting a new job type that might arise or an existing job that might change/disappear due to AI adoption (6-8), to investigating the specific skills needed for AI-enabled careers across the workforce, not just for programmers (9-12).
- OECD–EC (Europe)Explain how AI systems perform tasks without attributing human traits to them (Engage 2, advanced).
- OECD–EC (Europe)Evaluate whether an AI output should be accepted, revised, or rejected, and justify the decision (Engage 3, advanced).
- OECD–EC (Europe)Consider when attribution, permission, or avoiding AI use is the appropriate choice (Create 4, intermediate).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- UNESCOInterrogate how automation of literature reviews, writing and artistic creation by AI may undermine human thinking and intellectual development, and identify concrete actions to protect themselves and peers from over-reliance on AI outputs (CG4.2.1.3).
- UNESCOCarry out responsible AI practices in compliance with ethical principles and locally applicable regulations, iteratively building and following a personal checklist of lawful and responsible practices (e.g. protecting privacy/copyright, marking AI-generated content, avoiding disinformation, hate speech, or sensitive content about identifiable individuals) (CG4.2.2.1).
- UNESCOGain an age-appropriate understanding of personal, legal and ethical responsibilities when using AI and reinforce self-disciplined behaviours around sensitive personal data, copyrighted materials, deepfake/identifiable-person imagery, and the spread of misinformation, disinformation or hate speech (CG4.2.2.2).
- UNESCOArticulate personal and social responsibilities of citizens in an AI society — from both user and designer perspectives — including ensuring human control and accountability over every step of the AI life cycle (CG4.3.1.2).
- SingaporeDevelop digital safety and security competencies as one of the nine core NDLP digital competencies.
- SingaporeDevelop digital responsibility as one of the nine core NDLP digital competencies.
- SingaporeLearn to use AI: use AI tools safely and responsibly.
- SingaporeLearn with AI: apply AI to support and enhance one's own learning process.
- Korea, Japan, HKUnderstand laws/regulations on information, the importance of information security, and individual responsibility and information morals in the information society.
- Korea, Japan, HKCultivate an attitude of participating proactively in, and contributing to the development of, the information society while creating new value through appropriate use of information and information technology and adherence to information morals (law, security).
- ChinaStudents understand the ethics and challenges of a smart/AI-driven society, strengthening information-security awareness, responsibility, and dialectical (balanced) views of AI
- Australia + NZPractise online safety skills and digital literacy behaviours (the Version 9.0 sub-strand that replaced the old ICT Capability general capability), including safe, respectful interaction with digital systems and other users
- Australia + NZStudents and educators use generative AI tools in ways designed to enhance teaching and learning outcomes rather than replace core learning processes
- Australia + NZStudents understand that generative AI tools should be used in ways that benefit all members of the school community and support wellbeing, not just individual convenience
- Australia + NZStudents develop an understanding of how generative AI tools work in broad terms, how they can be used appropriately, and when/how such tools are affecting the content and information they see (transparency about AI-generated or AI-assisted content)
- Australia + NZStudents are taught that generative AI tools must be used, and their outputs treated, in ways that are accessible, fair and respectful to all users, being alert to potential bias in AI outputs
- Australia + NZStudents learn that use of generative AI outputs (e.g. in assessable work) must remain open to challenge, retain human agency, and that the student/user remains accountable for decisions made with AI assistance (e.g. academic integrity implications)
- Australia + NZStudents understand that their privacy and personal data must be protected when using generative AI tools, and are taught not to input sensitive personal or others' data into such tools without appropriate safeguards
When to Trust AI15 lessons· meets 31 requirements inADEKIBEngland + CambridgeAI4K12 (US)+6
Lessons
- Priya's Moon Problem
- Could It Even Know?
- Why Did It Sound So Sure?
- Priya's Three Questions (and SIFT)
- The Book That Never Existed
- Match the Smart Move
- Facts Move Faster Than Training
- Trust or Check?
- Would You Trust This Source?
- Priya's Trust Dial
- How Search Engines Decide Who to Believe
- AI Trust Quiz
- Explain Your Trust Test
- Trust & Verify Words
- Trust Expert!
Requirements this topic helps meet
- ADEKExplain why generative AI can fabricate/hallucinate
- ADEKCheck AI output before trusting
- ADEKDeepfakes: explain, spot flaws, set responsible-use rules
- ADEKCareers, future of work
- IBUnderstand the bias inherent in content produced by an AI tool and be able to critically review AI-generated output
- England + CambridgeYoung people should understand how AI works, its capabilities and limitations, and learn to use it effectively without becoming dependent on it; this should be delivered primarily through Computing rather than assumed to develop automatically
- England + CambridgeMedia literacy (taught mainly via Citizenship) should cover critically engaging with messages conveyed through different media channels, including AI, and identifying/protecting against misinformation and disinformation
- AI4K12 (US)Progress from describing jobs that no longer exist due to past technology advances, e.g. farriers replaced by auto mechanics (K-2), to describing how a specific job will change due to AI/robotic technology, e.g. warehouse workers alongside robots (3-5), to predicting a new job type that might arise or an existing job that might change/disappear due to AI adoption (6-8), to investigating the specific skills needed for AI-enabled careers across the workforce, not just for programmers (9-12).
- OECD–EC (Europe)Explain how AI systems perform tasks without attributing human traits to them (Engage 2, advanced).
- OECD–EC (Europe)Verify AI outputs against trusted sources (Engage 3, intermediate).
- OECD–EC (Europe)Evaluate whether an AI output should be accepted, revised, or rejected, and justify the decision (Engage 3, advanced).
- OECD–EC (Europe)Describe an AI system's purpose, intended users, and basic constraints (Shape 1, intermediate).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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.
- UNESCOArticulate personal and social responsibilities of citizens in an AI society — from both user and designer perspectives — including ensuring human control and accountability over every step of the AI life cycle (CG4.3.1.2).
- SingaporeUnderstand AI's uses, risks and limitations, and apply ethical considerations (e.g. academic integrity, proper data handling) when using AI to support learning.
- SingaporeIdentify and explain AI's potential benefits, limitations and risks.
- SingaporeLearn with AI: apply AI to support and enhance one's own learning process.
- SingaporeThink critically about AI's biases, question AI-generated outputs, and discern or identify false or misleading information produced by AI.
- Korea, Japan, HKCultivate an attitude of participating proactively in, and contributing to the development of, the information society while creating new value through appropriate use of information and information technology and adherence to information morals (law, security).
- ChinaStudents deepen ethical understanding and critically identify misinformation/hallucination risks in generative-AI outputs
- ChinaStudents understand the ethics and challenges of a smart/AI-driven society, strengthening information-security awareness, responsibility, and dialectical (balanced) views of AI
- Australia + NZBuild knowledge about the function and purpose of AI systems (Years 5-6), progressing to evaluating the advantages and disadvantages of AI and other emerging technologies (Years 7-8)
- Australia + NZStudents and educators use generative AI tools in ways designed to enhance teaching and learning outcomes rather than replace core learning processes
- Australia + NZStudents develop an understanding of how generative AI tools work in broad terms, how they can be used appropriately, and when/how such tools are affecting the content and information they see (transparency about AI-generated or AI-assisted content)
- Australia + NZStudents are taught that generative AI tools must be used, and their outputs treated, in ways that are accessible, fair and respectful to all users, being alert to potential bias in AI outputs
- Australia + NZStudents understand that their privacy and personal data must be protected when using generative AI tools, and are taught not to input sensitive personal or others' data into such tools without appropriate safeguards
AI for Good18 lessons· meets 28 requirements inUAEADEKIBEngland + Cambridge+8
Lessons
- The Ocean Cleanup
- AI Helping Our World
- The Turtle Rescue Mystery
- Machine's Job or Person's Job?
- A Camera Trap That Never Sleeps
- Match AI to Its Good Work
- Tiny Patterns Save Time
- Healthcare or Environment?
- Real, or Too Good to Be True?
- Maps From Space
- Warn, Map or Get Aid There?
- Good Plan or Risky Plan?
- Impact Finder Lab
- Google Maps Helps During Disasters
- AI for Good Quiz
- Explain AI for Good
- Good Impact Words
- AI Hero!
Requirements this topic helps meet
- UAEGlobal challenges: sustainability, humanitarian aid, social impact
- ADEKResearch an SDG, build/evaluate a solution
- ADEKFootprint / resource cost of AI
- ADEKDeepfakes: explain, spot flaws, set responsible-use rules
- ADEKCareers, future of work
- IBUnderstand the bias inherent in content produced by an AI tool and be able to critically review AI-generated output
- England + CambridgeUnderstand that robots can work autonomously and describe robotics benefits in manufacturing/food production
- CSTA (US)Investigate the positive and negative environmental impacts of AI (e.g., minimizing deforestation via application of AI, energy use by AI).
- CSTA (US)Students examine security implications and environmental impacts of AI systems within Systems & Security.
- AI4K12 (US)Progress from describing jobs that no longer exist due to past technology advances, e.g. farriers replaced by auto mechanics (K-2), to describing how a specific job will change due to AI/robotic technology, e.g. warehouse workers alongside robots (3-5), to predicting a new job type that might arise or an existing job that might change/disappear due to AI adoption (6-8), to investigating the specific skills needed for AI-enabled careers across the workforce, not just for programmers (9-12).
- AI4K12 (US)Progress from describing how AI can be used to solve a societal problem, e.g. Code.org's AI for Oceans classifying wildlife vs. debris (K-2), to designing a solution to a societal problem using AI technology, e.g. environmental protection, energy conservation (3-5), to researching a societal problem and describing how AI perception/reasoning/learning technologies could address it, referencing the UN Sustainable Development Goals (6-8), to evaluating an actual AI-for-social-good project in terms of the problem addressed and its real or potential impact (9-12).
- OECD–EC (Europe)Explain how AI systems perform tasks without attributing human traits to them (Engage 2, advanced).
- OECD–EC (Europe)Evaluate whether an AI output should be accepted, revised, or rejected, and justify the decision (Engage 3, advanced).
- OECD–EC (Europe)Describe environmental impacts of AI such as hardware, data centres, water and energy use (Engage 5, intermediate).
- OECD–EC (Europe)Break a problem into parts and consider AI support for specific steps (Manage 3, intermediate).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- UNESCOArticulate personal and social responsibilities of citizens in an AI society — from both user and designer perspectives — including ensuring human control and accountability over every step of the AI life cycle (CG4.3.1.2).
- SingaporeLearn with AI: apply AI to support and enhance one's own learning process.
- Korea, Japan, HKCultivate an attitude of participating proactively in, and contributing to the development of, the information society while creating new value through appropriate use of information and information technology and adherence to information morals (law, security).
- ChinaStudents understand the ethics and challenges of a smart/AI-driven society, strengthening information-security awareness, responsibility, and dialectical (balanced) views of AI
- Australia + NZStudents and educators use generative AI tools in ways designed to enhance teaching and learning outcomes rather than replace core learning processes
- Australia + NZStudents develop an understanding of how generative AI tools work in broad terms, how they can be used appropriately, and when/how such tools are affecting the content and information they see (transparency about AI-generated or AI-assisted content)
- Australia + NZStudents are taught that generative AI tools must be used, and their outputs treated, in ways that are accessible, fair and respectful to all users, being alert to potential bias in AI outputs
- Australia + NZStudents understand that their privacy and personal data must be protected when using generative AI tools, and are taught not to input sensitive personal or others' data into such tools without appropriate safeguards
You Control AI17 lessons· meets 34 requirements inUAEADEKIBCSTA (US)+7
Lessons
- Maya Takes Control
- The Power Is Yours
- The Suggestion Mystery
- Tool vs Boss
- AI Has No Feelings
- Tool or Boss?
- Your Pause Button
- What Makes YOU Special
- AI Help vs Yourself
- Your AI Rights
- Control Balance Lab
- YouTube Autoplay Is a Choice
- Healthy AI Use
- Control Quiz
- Explain Staying in Control
- Empowerment Words
- AI Controller Badge!
Requirements this topic helps meet
- UAEPrivacy, accountability and personal rights
- UAEHow AI is governed through national policies
- ADEKDeepfakes: explain, spot flaws, set responsible-use rules
- ADEKCareers, future of work
- IBAct with integrity and honesty and take responsibility for one's own actions when making design decisions
- IBUnderstand the bias inherent in content produced by an AI tool and be able to critically review AI-generated output
- CSTA (US)Debate when humans should or should not use AI to perform a specific task.
- CSTA (US)Explore the tradeoffs related to human agency (including privacy, safety, creativity, autonomy, and intellectual property) when AI is used.
- AI4K12 (US)Progress from analyzing deepfake images/videos to identify the flaws that currently reveal them, e.g. thispersondoesnotexist.com (3-5), to critiquing uses of AI technology for surveillance or privacy violation, e.g. face recognition, license-plate trackers (6-8), to identifying appropriate areas for AI regulation and evaluating proposed legislation, e.g. autonomous-vehicle laws (9-12). K-2 is marked N/A.
- AI4K12 (US)Progress from describing jobs that no longer exist due to past technology advances, e.g. farriers replaced by auto mechanics (K-2), to describing how a specific job will change due to AI/robotic technology, e.g. warehouse workers alongside robots (3-5), to predicting a new job type that might arise or an existing job that might change/disappear due to AI adoption (6-8), to investigating the specific skills needed for AI-enabled careers across the workforce, not just for programmers (9-12).
- OECD–EC (Europe)Share examples of how AI shapes personal choices (Engage 1, intermediate level).
- OECD–EC (Europe)Explain how AI systems perform tasks without attributing human traits to them (Engage 2, advanced).
- OECD–EC (Europe)Evaluate whether an AI output should be accepted, revised, or rejected, and justify the decision (Engage 3, advanced).
- OECD–EC (Europe)Compare AI-generated suggestions to one's own ideas during brainstorming (Create 1, intermediate).
- OECD–EC (Europe)Determine whether AI is appropriate for a specific task (Manage 1, intermediate).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- UNESCOExplain that AI is human-led and that decisions by AI creators shape how AI systems affect human rights, human-AI interaction, and society; recognise that AI cannot replace human thinking or intellectual development (CG4.1.1.1).
- UNESCOCritically examine the dynamic relationship between human agency and machine agency, including cases where a degree of machine agency may be warranted (e.g. medical pattern detection) versus cases requiring human control (CG4.1.1.3).
- UNESCOCritically examine, from legal, ethical and logical perspectives, whether AI should or should not be used for a given real-world challenge, and define the boundaries, goals and constraints of a problem before attempting to train an AI model (CG4.1.4.1).
- UNESCOInterrogate how automation of literature reviews, writing and artistic creation by AI may undermine human thinking and intellectual development, and identify concrete actions to protect themselves and peers from over-reliance on AI outputs (CG4.2.1.3).
- UNESCOArticulate personal and social responsibilities of citizens in an AI society — from both user and designer perspectives — including ensuring human control and accountability over every step of the AI life cycle (CG4.3.1.2).
- SingaporeLearn with AI: apply AI to support and enhance one's own learning process.
- Korea, Japan, HKIdentify problems that can be solved with an AI system and apply an AI system suited to that problem ([9정04-04]).
- Korea, Japan, HKCultivate an attitude of participating proactively in, and contributing to the development of, the information society while creating new value through appropriate use of information and information technology and adherence to information morals (law, security).
- ChinaStudents understand the ethics and challenges of a smart/AI-driven society, strengthening information-security awareness, responsibility, and dialectical (balanced) views of AI
- Australia + NZStudents and educators use generative AI tools in ways designed to enhance teaching and learning outcomes rather than replace core learning processes
- Australia + NZStudents understand that generative AI tools should be used in ways that benefit all members of the school community and support wellbeing, not just individual convenience
- Australia + NZStudents develop an understanding of how generative AI tools work in broad terms, how they can be used appropriately, and when/how such tools are affecting the content and information they see (transparency about AI-generated or AI-assisted content)
- Australia + NZStudents are taught that generative AI tools must be used, and their outputs treated, in ways that are accessible, fair and respectful to all users, being alert to potential bias in AI outputs
- Australia + NZStudents understand that their privacy and personal data must be protected when using generative AI tools, and are taught not to input sensitive personal or others' data into such tools without appropriate safeguards
Unit 4 Review17 lessons· meets 19 requirements inADEKIBAI4K12 (US)OECD–EC (Europe)+5
Lessons
- Your AI Journey
- Your AI Toolkit
- The Suspicious Message
- The Big Ideas, Sorted
- The Six Cases That Never Existed
- Match the Lesson
- Putting It Together
- The $25 Million Video Call
- AI Safety Scenarios
- Achievement: You Can Run STOP
- How Hard Should I Check?
- YouTube's Safety AI
- Knowledge Check
- Unit 4 Final Quiz
- Your Most Important Tool
- Unit 4 Key Terms
- Unit 4 Champion!
Requirements this topic helps meet
- ADEKDeepfakes: explain, spot flaws, set responsible-use rules
- ADEKCareers, future of work
- IBUnderstand the bias inherent in content produced by an AI tool and be able to critically review AI-generated output
- AI4K12 (US)Progress from describing jobs that no longer exist due to past technology advances, e.g. farriers replaced by auto mechanics (K-2), to describing how a specific job will change due to AI/robotic technology, e.g. warehouse workers alongside robots (3-5), to predicting a new job type that might arise or an existing job that might change/disappear due to AI adoption (6-8), to investigating the specific skills needed for AI-enabled careers across the workforce, not just for programmers (9-12).
- OECD–EC (Europe)Explain how AI systems perform tasks without attributing human traits to them (Engage 2, advanced).
- OECD–EC (Europe)Evaluate whether an AI output should be accepted, revised, or rejected, and justify the decision (Engage 3, advanced).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- UNESCOArticulate personal and social responsibilities of citizens in an AI society — from both user and designer perspectives — including ensuring human control and accountability over every step of the AI life cycle (CG4.3.1.2).
- SingaporeLearn with AI: apply AI to support and enhance one's own learning process.
- Korea, Japan, HKCultivate an attitude of participating proactively in, and contributing to the development of, the information society while creating new value through appropriate use of information and information technology and adherence to information morals (law, security).
- ChinaStudents understand the ethics and challenges of a smart/AI-driven society, strengthening information-security awareness, responsibility, and dialectical (balanced) views of AI
- Australia + NZStudents and educators use generative AI tools in ways designed to enhance teaching and learning outcomes rather than replace core learning processes
- Australia + NZStudents develop an understanding of how generative AI tools work in broad terms, how they can be used appropriately, and when/how such tools are affecting the content and information they see (transparency about AI-generated or AI-assisted content)
- Australia + NZStudents are taught that generative AI tools must be used, and their outputs treated, in ways that are accessible, fair and respectful to all users, being alert to potential bias in AI outputs
- Australia + NZStudents understand that their privacy and personal data must be protected when using generative AI tools, and are taught not to input sensitive personal or others' data into such tools without appropriate safeguards
Unit 5: AI Activities 10 topics
Pattern Games19 lessons· meets 11 requirements inUAEADEKCISCE + statesCSTA (US)+4
Lessons
- The Pattern Detective
- Types of Patterns
- The Smart Playlist
- Finding the Rule
- Patterns in Your DNA
- Match Patterns to Rules
- How AI Uses Patterns
- Nature's Secret Code
- Sort by Pattern Type
- Create Your Own
- A Tune Is a Pattern
- Finish My Tune
- Pattern Difficulty Calculator
- Shazam's Pattern Magic
- Pattern Challenge
- Pattern Master Quiz
- Patterns in Your Life
- Pattern Words
- Pattern Master Badge!
Requirements this topic helps meet
- UAEUse generative AI models to create new content
- ADEKHuman/AI-generated art + ethics
- ADEKHuman/AI-generated art, ethics of generative tools
- CISCE + statesExplain types of data, data identification/acquisition/exploration, binary logic and conditional gates, and deterministic vs probabilistic nature of real-life problems with examples
- CISCE + statesExplain Machine Learning as computers learning to make predictions/decisions from data without explicit programming, with an example such as predicting house prices using a decision tree
- CSTA (US)AI content is integrated (not a separate strand): students learn rule-based vs. data-driven approaches, machine learning basics, and related societal impacts such as bias, within the Algorithms & Design strand.
- AI4K12 (US)Progress from giving examples of visual features (parts, textures, colors) used to recognize an object class such as cats (K-2), to illustrating how face detection extracts facial features (3-5), to simulating an edge detector to illustrate feature extraction from images (6-8), to explaining how features (formants, edges, intensity gradients) are extracted from waveforms and images at multiple scales (9-12).
- OECD–EC (Europe)Evaluate the effectiveness of an AI approach considering context, data, efficiency, transparency, and impact (Manage 2, advanced).
- SingaporeDevelop computational thinking as one of the nine core NDLP digital competencies.
- SingaporeDefine Machine Learning (ML) as a technique used in AI and explain the difference between ML and traditional programming.
- Korea, Japan, HKUnderstand the characteristics of machine learning versus traditional (rule-based) programming, judge which problems are suited to machine-learning-based AI, and collect data appropriate to the problem.
Sorting Challenge20 lessons· meets 8 requirements inCISCE + statesCSTA (US)OECD–EC (Europe)Korea, Japan, HK
Lessons
- The Messy Room
- Category or Feature?
- The Confused Photo App
- What is Classification?
- Classification Steps
- The World's Biggest Sorter
- Feature Clues
- Features: The Key
- The AI That Learned Only Half the World
- Clear Raj's Floor
- Spam or Safe?
- Easy or Tricky?
- Email Spam Detector
- AI Classification Match
- Gmail's Spam Detective
- Classification Challenge
- Classification Quiz
- The Tomato Problem
- Sorting Words
- Sorting Champion!
Requirements this topic helps meet
- CISCE + statesExplain types of data, data identification/acquisition/exploration, binary logic and conditional gates, and deterministic vs probabilistic nature of real-life problems with examples
- CISCE + statesExplain Machine Learning as computers learning to make predictions/decisions from data without explicit programming, with an example such as predicting house prices using a decision tree
- CSTA (US)Explain how physical and digital security measures protect electronic information.
- CSTA (US)Identify the kinds of AI models (e.g., classifier, predictor, recommender) people interact with in their daily lives.
- CSTA (US)AI content is integrated (not a separate strand): students learn rule-based vs. data-driven approaches, machine learning basics, and related societal impacts such as bias, within the Algorithms & Design strand.
- OECD–EC (Europe)Evaluate the effectiveness of an AI approach considering context, data, efficiency, transparency, and impact (Manage 2, advanced).
- Korea, Japan, HKUnderstand the characteristics of machine learning versus traditional (rule-based) programming, judge which problems are suited to machine-learning-based AI, and collect data appropriate to the problem.
- Korea, Japan, HKDistinguish skill-based reasoning (direct task response), rule-based reasoning (decisions from learned patterns) and knowledge-based reasoning (applying machine learning to find the best solution) in a hands-on robot-reasoning experiment (e.g. a moral-dilemma or line-following task).
Human Robot Game20 lessons· meets 8 requirements inCISCE + statesEngland + CambridgeCSTA (US)AI4K12 (US)+3
Lessons
- The Sandwich Disaster
- Human vs Robot Talk
- The Literal Robot Cleaner
- Can the Robot Check It?
- Human Superpowers
- Mars Needs Perfect Directions
- Sandwich Steps
- What is an Algorithm?
- Factory-Grade Accuracy
- Algorithm Examples
- Match the First Step
- Real Robot Precision
- Robot Arm Accuracy Lab
- Precision Challenge
- Amazon's Shelf Robots
- Good vs Bad Instructions
- Robot Instructions Quiz
- Robot-Ready Rewrite
- Robot Words
- Human Robot Champion!
Requirements this topic helps meet
- CISCE + statesExplain types of data, data identification/acquisition/exploration, binary logic and conditional gates, and deterministic vs probabilistic nature of real-life problems with examples
- CISCE + statesExplain Machine Learning as computers learning to make predictions/decisions from data without explicit programming, with an example such as predicting house prices using a decision tree
- England + CambridgeUnderstand that robots can work autonomously and describe robotics benefits in manufacturing/food production
- CSTA (US)AI content is integrated (not a separate strand): students learn rule-based vs. data-driven approaches, machine learning basics, and related societal impacts such as bias, within the Algorithms & Design strand.
- AI4K12 (US)Progress from giving commonsense explanations of human behavior useful for a robot to know, e.g. choosing a plausible story ending (K-2), to explaining what everyday cultural and naive-physics knowledge a computer needs to understand a story (3-5), to explaining the naive-physics inference rules (mass, volume, conservation, forces) needed to solve a physics reasoning problem (6-8), to explaining the cultural, naive-physics, and folk-psychology knowledge needed to interpret a fable, fairytale, or Winograd-schema sentence (9-12).
- OECD–EC (Europe)Evaluate the effectiveness of an AI approach considering context, data, efficiency, transparency, and impact (Manage 2, advanced).
- SingaporeUnderstand what a smart/intelligent robot is and how AI enables it to respond to inputs.
- Korea, Japan, HKUnderstand the characteristics of machine learning versus traditional (rule-based) programming, judge which problems are suited to machine-learning-based AI, and collect data appropriate to the problem.
Decision Tree Fun20 lessons· meets 15 requirements inCISCE + statesEngland + CambridgeCSTA (US)AI4K12 (US)+3
Lessons
- The Snack Decision
- What's a Decision Tree?
- The Mixed-Up Zoo Kiosk
- Parts of a Tree
- Tree Structure
- Tiny Choices, Huge Speed
- Build Umbrella Tree
- AI Uses Trees
- Hospital Triage Trees
- More AI Examples
- AI Decision Match
- Good vs Bad Questions
- Build a Better Tree Question
- Question Sorting
- Netflix's Next-Show Picker
- Tree Challenge
- Decision Tree Quiz
- Pick the First Question
- Tree Words
- Decision Tree Champion!
Requirements this topic helps meet
- CISCE + statesExplain types of data, data identification/acquisition/exploration, binary logic and conditional gates, and deterministic vs probabilistic nature of real-life problems with examples
- CISCE + statesExplain Machine Learning as computers learning to make predictions/decisions from data without explicit programming, with an example such as predicting house prices using a decision tree
- England + CambridgeFollow and create algorithms presented as flowcharts using standard symbols (start, stop, process, decision, connector), including multiple variables
- England + CambridgeFollow, edit and create algorithms using flowcharts and pseudocode; use selection statements and Boolean logic; progress from linear to binary search
- CSTA (US)Use flowcharts and/or pseudocode to address complex problems as algorithms.
- CSTA (US)Understand that representation includes modalities (text, speech, audio, image, video) and symbolic mappings (text, graphs).
- CSTA (US)Create and evaluate different abstract representations (e.g., subway map).
- CSTA (US)AI content is integrated (not a separate strand): students learn rule-based vs. data-driven approaches, machine learning basics, and related societal impacts such as bias, within the Algorithms & Design strand.
- AI4K12 (US)Progress from constructing a map of a home, school, or neighborhood and understanding that 'the map is not the territory' (K-2), to demonstrating how different map styles (geometric vs. topological) capture different aspects of the world (3-5), to showing how a game board (tic-tac-toe, chess) can be represented as a plain-language description of squares, relationships, and piece positions (6-8), to describing how to represent a concept as a schema.org-style schema with superconcepts and inherited properties (9-12).
- AI4K12 (US)Progress from giving examples of symbols encountered in daily life such as restroom icons and card suits (K-2), to explaining hierarchical tree structures such as ancestry trees and taxonomies (3-5), to translating a structure like a game board, road map, or mind map into a labeled graph of nodes and links (6-8), to translating the premises of a syllogism from English into predicate-logic notation and completing the inference (9-12).
- OECD–EC (Europe)Evaluate the effectiveness of an AI approach considering context, data, efficiency, transparency, and impact (Manage 2, advanced).
- OECD–EC (Europe)Compare AI outputs to desired results and redirect the approach as needed (Manage 4, intermediate).
- UNESCOUnderstand the necessity of exercising sufficient human control over AI, including the consequences when human oversight fails or is absent (CG4.1.1.2).
- UNESCOEvaluate the consequences of institutional use of AI to make high-stakes decisions about people (e.g. profiling students, hiring, loan/assessment scoring) and understand why human accountability must not be ceded to AI in such decisions (CG4.2.1.2).
- Korea, Japan, HKUnderstand the characteristics of machine learning versus traditional (rule-based) programming, judge which problems are suited to machine-learning-based AI, and collect data appropriate to the problem.
Teach the Teacher20 lessons· meets 16 requirements inADEKCISCE + statesCSTA (US)AI4K12 (US)+3
Lessons
- Training Without Words
- Who Learns Like This?
- The Market Mix-Up
- Train Your Own Fruit Sorter
- Byte's Training Warnings
- The Million-Example Workout
- Training Problems
- What Makes Good Training Data?
- Voices From Everywhere
- Train Your Classmate!
- Build Good Training Data
- Why AI Needs So Many Examples
- Training Data Mixer
- Spot the Training Problem
- How Photo Search Finds Your Beach Pictures
- Training Data Quiz
- Design a Training Set
- New Words
- The Trainer's Final Call
- AI Trainer Master!
Requirements this topic helps meet
- ADEKTrained-on-examples / rule-vs-learned
- ADEKExamine dataset gaps/bias
- CISCE + statesExplain types of data, data identification/acquisition/exploration, binary logic and conditional gates, and deterministic vs probabilistic nature of real-life problems with examples
- CISCE + statesExplain Machine Learning as computers learning to make predictions/decisions from data without explicit programming, with an example such as predicting house prices using a decision tree
- CSTA (US)AI content is integrated (not a separate strand): students learn rule-based vs. data-driven approaches, machine learning basics, and related societal impacts such as bias, within the Algorithms & Design strand.
- CSTA (US)Students build data fluency and examine the societal impacts of large-scale data collection used to train AI systems.
- AI4K12 (US)Progress from describing what an intelligent agent must 'know' to make sense of a spoken question (K-2), to demonstrating how a text-to-speech/speech-recognition system resolves ambiguity using statistical context (3-5), to classifying an image scene type and describing the domain knowledge needed to understand it (6-8), to analyzing an online image dataset (e.g. ImageNet, Coco) to describe how statistical domain knowledge is derived for a computer vision system (9-12).
- AI4K12 (US)Progress from identifying patterns/features in labeled data that predict a label (K-2), to modeling how supervised learning encodes discovered patterns as neural-net weights or decision-tree nodes (3-5), to modeling how unsupervised learning finds clusters in unlabeled data (6-8), to modeling how machine learning constructs a reasoner by adjusting a mathematical model's parameters, e.g. linear/polynomial/logistic regression (9-12).
- AI4K12 (US)Progress from creating a labeled dataset with explicit features to classify things like foods or toys (K-2), to creating a labeled dataset with multiple feature types (discrete, binary, continuous) and training a classifier with a tool such as Machine Learning for Kids (3-5), to creating a decision-tree training dataset and exploring how different feature encodings affect the resulting tree (6-8), to comparing two real-world datasets in terms of their features and encodings and the consequences for learning (9-12).
- AI4K12 (US)Progress from illustrating why training a classifier for a broad concept like 'dog' requires large, diverse image data by browsing datasets such as ImageNet or Coco (3-5), to illustrating how objects in images are segmented and labeled with bounding boxes to build an object-recognition training set (6-8), to evaluating a real AI training dataset's size, acquisition/labeling method, storage cost, and time-to-produce, including where bias can enter (9-12). K-2 is marked N/A.
- AI4K12 (US)Progress from examining a labeled dataset to identify problems that could cause incorrect predictions, e.g. an all-green 'healthy foods' set (K-2), to examining training-data features/labels for potential sources of bias such as Amazon's biased resume sorter (3-5), to explaining how unbalanced training data biases a classifier, e.g. faces or loan applications (6-8), to investigating demographic imbalances in training data using data-visualization tools such as histograms or Pandas (9-12).
- OECD–EC (Europe)Explain how biased data or design choices create unfair outcomes (Engage 6, intermediate).
- OECD–EC (Europe)Evaluate the effectiveness of an AI approach considering context, data, efficiency, transparency, and impact (Manage 2, advanced).
- OECD–EC (Europe)Compare the importance of data selection and collection methods for an AI system (Shape 3, intermediate).
- Korea, Japan, HKUnderstand the characteristics of machine learning versus traditional (rule-based) programming, judge which problems are suited to machine-learning-based AI, and collect data appropriate to the problem.
- Australia + NZConsider the possible bias or harm to others that may occur when inputting data into AI systems, and explore how AI systems use predictive strategies based on large amounts of data to perform tasks or solve problems
AI Art Project22 lessons· meets 17 requirements inUAEADEKCISCE + statesIB+7
Lessons
- The Robot Art Contest
- Art Project Ideas
- Why the Judge Said 'Gardening'
- Design Your Dream Robot
- Create an AI Concept Poster
- A Museum of Machine-Made Images
- AI Comic Strip
- Match Art to Elements
- Doctors Look for Faint Signals Too
- Spot the Good Art Choices
- Art Creation Steps
- Paint Half, Let a Model Paint Half
- Who Painted This Tile?
- Can AI Create Art?
- Poster Impact Studio
- Human Art vs AI Art
- Canva's AI Design Helpers
- AI Art Quiz
- Pitch Your Art Project
- New Words
- Train the Picture Guesser
- AI Artist Champion!
Requirements this topic helps meet
- UAECompare AI with humans (Cycle 2 part)
- UAEUse generative AI models to create new content
- ADEKHuman/AI-generated art + ethics
- CISCE + statesExplain types of data, data identification/acquisition/exploration, binary logic and conditional gates, and deterministic vs probabilistic nature of real-life problems with examples
- CISCE + statesExplain Machine Learning as computers learning to make predictions/decisions from data without explicit programming, with an example such as predicting house prices using a decision tree
- IBWhere a school offers digital design (rather than or alongside product design), demonstrate digital-tool competencies such as prototyping, graphic/web design, and multimedia editing through the same four-criterion design cycle
- England + CambridgeUndertake creative projects selecting, using and combining multiple applications across devices to achieve challenging goals, including collecting and analysing data and meeting the needs of known users
- England + CambridgeCreate, re-use, revise and re-purpose digital artefacts for a given audience, with attention to trustworthiness, design and usability
- CSTA (US)Create and evaluate different abstract representations (e.g., subway map).
- CSTA (US)Explore the tradeoffs related to human agency (including privacy, safety, creativity, autonomy, and intellectual property) when AI is used.
- CSTA (US)AI content is integrated (not a separate strand): students learn rule-based vs. data-driven approaches, machine learning basics, and related societal impacts such as bias, within the Algorithms & Design strand.
- AI4K12 (US)Progress from constructing a map of a home, school, or neighborhood and understanding that 'the map is not the territory' (K-2), to demonstrating how different map styles (geometric vs. topological) capture different aspects of the world (3-5), to showing how a game board (tic-tac-toe, chess) can be represented as a plain-language description of squares, relationships, and piece positions (6-8), to describing how to represent a concept as a schema.org-style schema with superconcepts and inherited properties (9-12).
- OECD–EC (Europe)Evaluate the effectiveness of an AI approach considering context, data, efficiency, transparency, and impact (Manage 2, advanced).
- UNESCOExplain that AI is human-led and that decisions by AI creators shape how AI systems affect human rights, human-AI interaction, and society; recognise that AI cannot replace human thinking or intellectual development (CG4.1.1.1).
- UNESCOCritically examine the dynamic relationship between human agency and machine agency, including cases where a degree of machine agency may be warranted (e.g. medical pattern detection) versus cases requiring human control (CG4.1.1.3).
- Korea, Japan, HKUnderstand the characteristics of machine learning versus traditional (rule-based) programming, judge which problems are suited to machine-learning-based AI, and collect data appropriate to the problem.
- Australia + NZStudents develop an understanding of how generative AI tools work in broad terms, how they can be used appropriately, and when/how such tools are affecting the content and information they see (transparency about AI-generated or AI-assisted content)
Build a Chatbot20 lessons· meets 6 requirements inCISCE + statesCSTA (US)OECD–EC (Europe)Korea, Japan, HK
Lessons
- The Bakery Gets a Bot
- Types of Chatbots
- The Bakery Bot Mix-Up
- How a Chatbot Answers
- The First Chatbot Ever
- One Answer, Thousands of People
- What Good Replies Do
- Building a Simple Chatbot
- Bots Answer at Midnight Too
- Spot the Good Replies
- Chatbot Design Steps
- Where Chatbots Trip
- Response Tuning Lab
- Which Bot Could You Build?
- Duolingo's Role-Play Chats
- Chatbot Quiz
- Write the Bot Reply
- New Words
- What You Learned
- Chatbot Builder Champion!
Requirements this topic helps meet
- CISCE + statesExplain types of data, data identification/acquisition/exploration, binary logic and conditional gates, and deterministic vs probabilistic nature of real-life problems with examples
- CISCE + statesExplain Machine Learning as computers learning to make predictions/decisions from data without explicit programming, with an example such as predicting house prices using a decision tree
- CSTA (US)AI content is integrated (not a separate strand): students learn rule-based vs. data-driven approaches, machine learning basics, and related societal impacts such as bias, within the Algorithms & Design strand.
- OECD–EC (Europe)Evaluate the effectiveness of an AI approach considering context, data, efficiency, transparency, and impact (Manage 2, advanced).
- OECD–EC (Europe)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).
- Korea, Japan, HKUnderstand the characteristics of machine learning versus traditional (rule-based) programming, judge which problems are suited to machine-learning-based AI, and collect data appropriate to the problem.
AI Debate Club20 lessons· meets 12 requirements inADEKCISCE + statesIBCSTA (US)+4
Lessons
- The Classroom Debate
- Hot AI Debate Topics
- Same News, Different Conclusions
- What is a Debate?
- Match Benefits and Concerns
- One Question, Thousands of Viewpoints
- Building Strong Arguments
- Build an Argument
- Whole Countries Debate AI Too
- Spot Strong Arguments
- Argument Strength Meter
- ChatGPT in the Classroom Debate
- Draft a Class AI Policy
- Debate Quiz
- AI Ethics Challenge
- Make the Best Case
- Sort by Argument Strength
- New Words
- Critical Thinking Match
- Debate Champion!
Requirements this topic helps meet
- ADEKTake part in shaping AI policy
- ADEKStakeholder debate / ethical matrix
- CISCE + statesExplain types of data, data identification/acquisition/exploration, binary logic and conditional gates, and deterministic vs probabilistic nature of real-life problems with examples
- CISCE + statesExplain Machine Learning as computers learning to make predictions/decisions from data without explicit programming, with an example such as predicting house prices using a decision tree
- IBAct with integrity and honesty and take responsibility for one's own actions when making design decisions
- CSTA (US)AI content is integrated (not a separate strand): students learn rule-based vs. data-driven approaches, machine learning basics, and related societal impacts such as bias, within the Algorithms & Design strand.
- AI4K12 (US)Progress from demonstrating ways intelligent agents don't understand things the way people do, e.g. no frustration at repeated questions (K-2), to demonstrating what a 'broad AI'/general-AI system could do that current narrow AI cannot (3-5), to defining criteria for consciousness (self-awareness, personal history) and evaluating AI systems or fictional AI characters against them (6-8), to debating alternative perspectives on human vs. artificial intelligence, including the Turing test and artificial superintelligence (9-12).
- AI4K12 (US)Progress from analyzing deepfake images/videos to identify the flaws that currently reveal them, e.g. thispersondoesnotexist.com (3-5), to critiquing uses of AI technology for surveillance or privacy violation, e.g. face recognition, license-plate trackers (6-8), to identifying appropriate areas for AI regulation and evaluating proposed legislation, e.g. autonomous-vehicle laws (9-12). K-2 is marked N/A.
- OECD–EC (Europe)Evaluate the effectiveness of an AI approach considering context, data, efficiency, transparency, and impact (Manage 2, advanced).
- UNESCOIllustrate dilemmas around AI and identify the main reasons behind ethical conflicts that AI creators and companies face (e.g. data collection vs. ownership, profit vs. privacy, safety vs. speed of iteration) (CG4.1.2.1).
- UNESCOEvaluate the consequences of institutional use of AI to make high-stakes decisions about people (e.g. profiling students, hiring, loan/assessment scoring) and understand why human accountability must not be ceded to AI in such decisions (CG4.2.1.2).
- Korea, Japan, HKUnderstand the characteristics of machine learning versus traditional (rule-based) programming, judge which problems are suited to machine-learning-based AI, and collect data appropriate to the problem.
Group AI Project19 lessons· meets 17 requirements inCISCE + statesIBEngland + CambridgeCSTA (US)+3
Lessons
- The AI Team Challenge
- Team Roles in AI Projects
- The Team With Too Many Builders
- Why Teams Build Better AI
- Match Skills to Roles
- No Lone Genius
- Steps for Success
- Project Steps in Order
- Real AI Teams Have Translators
- Spot Good Teamwork
- Team Balance Lab
- One Map, Many Teams
- Teamwork Quiz
- Team Leadership Challenge
- Pick the Right Role
- Great Teams vs Struggling Teams
- New Words
- Teamwork Concepts Match
- Team Leader Champion!
Requirements this topic helps meet
- CISCE + statesExplain types of data, data identification/acquisition/exploration, binary logic and conditional gates, and deterministic vs probabilistic nature of real-life problems with examples
- CISCE + statesExplain Machine Learning as computers learning to make predictions/decisions from data without explicit programming, with an example such as predicting house prices using a decision tree
- IBWhere a school offers digital design (rather than or alongside product design), demonstrate digital-tool competencies such as prototyping, graphic/web design, and multimedia editing through the same four-criterion design cycle
- England + CambridgeUndertake creative projects selecting, using and combining multiple applications across devices to achieve challenging goals, including collecting and analysing data and meeting the needs of known users
- England + CambridgeCreate, re-use, revise and re-purpose digital artefacts for a given audience, with attention to trustworthiness, design and usability
- CSTA (US)Distribute tasks and maintain a project timeline when collaboratively developing computational artifacts.
- CSTA (US)Discuss issues of bias and accessibility in the design of existing technologies.
- CSTA (US)Collaborate with many contributors through strategies such as crowdsourcing or surveys when creating a computational artifact.
- CSTA (US)Describe the roles that humans play (including in data curation and labeling) in creating and refining AI models.
- CSTA (US)AI content is integrated (not a separate strand): students learn rule-based vs. data-driven approaches, machine learning basics, and related societal impacts such as bias, within the Algorithms & Design strand.
- OECD–EC (Europe)Experiment with multiple AI tools to develop diverse ideas (Create 2, intermediate).
- OECD–EC (Europe)Evaluate the effectiveness of an AI approach considering context, data, efficiency, transparency, and impact (Manage 2, advanced).
- OECD–EC (Europe)Compare AI outputs to desired results and redirect the approach as needed (Manage 4, intermediate).
- OECD–EC (Europe)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).
- UNESCOUnderstand the necessity of exercising sufficient human control over AI, including the consequences when human oversight fails or is absent (CG4.1.1.2).
- UNESCOUnderstand human roles across the AI life cycle (researchers, architecture/data engineers, beta testers, ethics/safety regulators, human-AI interface specialists, compliance auditors) and the main ethical issues in using data to train AI systems (CG4.1.3.4).
- Korea, Japan, HKUnderstand the characteristics of machine learning versus traditional (rule-based) programming, judge which problems are suited to machine-learning-based AI, and collect data appropriate to the problem.
Unit Review Games18 lessons· meets 5 requirements inCISCE + statesCSTA (US)OECD–EC (Europe)Korea, Japan, HK
Lessons
- Your AI Adventures Journey
- Key Concepts Review
- The Mixed-Up Review Wall
- Your AI Skills Checklist
- Unit 5 Concept Match
- Your Brain Learned a Toolkit
- Chapter to Skill Match
- AI Learning Process
- Review Changes Memory
- Rapid Review Challenge
- Memory Boost Mixer
- Khan Academy's Practice Loops
- Unit 5 Final Quiz - Part 1
- Unit 5 Final Quiz - Part 2
- Your Best Unit 5 Skill
- Before vs After Unit 5
- Unit 5 Vocabulary Review
- Unit 5 Complete! AI Adventurer!
Requirements this topic helps meet
- CISCE + statesExplain types of data, data identification/acquisition/exploration, binary logic and conditional gates, and deterministic vs probabilistic nature of real-life problems with examples
- CISCE + statesExplain Machine Learning as computers learning to make predictions/decisions from data without explicit programming, with an example such as predicting house prices using a decision tree
- CSTA (US)AI content is integrated (not a separate strand): students learn rule-based vs. data-driven approaches, machine learning basics, and related societal impacts such as bias, within the Algorithms & Design strand.
- OECD–EC (Europe)Evaluate the effectiveness of an AI approach considering context, data, efficiency, transparency, and impact (Manage 2, advanced).
- Korea, Japan, HKUnderstand the characteristics of machine learning versus traditional (rule-based) programming, judge which problems are suited to machine-learning-based AI, and collect data appropriate to the problem.
Unit 6: My AI Future 10 topics
AI Jobs of Tomorrow19 lessons· meets 16 requirements inADEKCBSEEngland + CambridgeCSTA (US)+4
Lessons
- Career Day Surprise
- AI Careers Today
- The Job With No Name Yet
- Jobs Being Created Now
- Match Career to Task
- Tomorrow's Career Menu
- Skills for AI Careers
- How an AI Gets Built
- Which Step Went Wrong?
- AI Needs Many Kinds of Talent
- Right Person for the Task?
- What Draws You In?
- One App, Many Jobs
- AI Careers Quiz
- Future Careers Challenge
- Invent a Future Job
- Jobs Then vs Now
- New Words
- Future Career Explorer!
Requirements this topic helps meet
- ADEKCareers
- ADEKCareers, future of work
- CBSEDescribe the stages of the AI project cycle (Define Problem, Collect Data, Test AI Tools, Reflect and Improve); apply no-code tools to tackle real-world problems and reflect on their utility/effectiveness; analyse contributions of AI to fields like healthcare, automation, and education, understanding both benefits and risks; describe AI ethics as values/guidelines for responsible AI (Class 8).
- England + CambridgeDevelop and apply analytic, problem-solving, design and computational thinking skills
- England + CambridgeMedia literacy (taught mainly via Citizenship) should cover critically engaging with messages conveyed through different media channels, including AI, and identifying/protecting against misinformation and disinformation
- CSTA (US)Compare tradeoffs associated with computing technologies that affect people's everyday activities and career options.
- CSTA (US)Describe the roles that humans play (including in data curation and labeling) in creating and refining AI models.
- CSTA (US)Evaluate and analyze the roles of humans and human decision-making in the creation of AI.
- AI4K12 (US)Progress from describing jobs that no longer exist due to past technology advances, e.g. farriers replaced by auto mechanics (K-2), to describing how a specific job will change due to AI/robotic technology, e.g. warehouse workers alongside robots (3-5), to predicting a new job type that might arise or an existing job that might change/disappear due to AI adoption (6-8), to investigating the specific skills needed for AI-enabled careers across the workforce, not just for programmers (9-12).
- UNESCOUnderstand human roles across the AI life cycle (researchers, architecture/data engineers, beta testers, ethics/safety regulators, human-AI interface specialists, compliance auditors) and the main ethical issues in using data to train AI systems (CG4.1.3.4).
- UNESCOPractise skills to identify and scope a problem that could be solved by building or training a new AI model, based on a simulation project (CG4.1.4.2).
- UNESCODevelop skills to assess an AI system's needs for data, algorithms, programming languages, software, and computing hardware, including feasibility given regulatory/ethical restrictions and cost (CG4.1.4.3).
- UNESCOEvaluate the consequences of institutional use of AI to make high-stakes decisions about people (e.g. profiling students, hiring, loan/assessment scoring) and understand why human accountability must not be ceded to AI in such decisions (CG4.2.1.2).
- SingaporeDevelop computational thinking as one of the nine core NDLP digital competencies.
- Korea, Japan, HKScientifically understand the characteristics of media and communication methods, including how they have changed over time, and the role information design plays for people and society.
- Korea, Japan, HKAnalyse how AI adoption is changing the nature of jobs and the skills workers need, and reflect on implications for the student's own future career choices.
AI Scientists to Know19 lessons· meets 2 requirements inCSTA (US)AI4K12 (US)
Lessons
- The Research Project
- Seven AI Scientists
- The Scientist Behind the Images
- Geoffrey Hinton - Godfather of AI
- Match Scientist to Contribution
- One Dataset Changed AI
- What Makes Great AI Scientists?
- Fei-Fei Li's Journey
- Science Crosses Borders
- Where They Started
- Right Match or Mixed Up?
- Breakthrough Builder
- Stanford's Vision Lab Legacy
- AI Scientists Quiz
- Inspiration Challenge
- Which Scientist Inspires You?
- Young Innovators
- New Words
- AI History Champion!
Requirements this topic helps meet
- CSTA (US)Evaluate and analyze the roles of humans and human decision-making in the creation of AI.
- AI4K12 (US)Progress from illustrating why training a classifier for a broad concept like 'dog' requires large, diverse image data by browsing datasets such as ImageNet or Coco (3-5), to illustrating how objects in images are segmented and labeled with bounding boxes to build an object-recognition training set (6-8), to evaluating a real AI training dataset's size, acquisition/labeling method, storage cost, and time-to-produce, including where bias can enter (9-12). K-2 is marked N/A.
Kids Who Made AI18 lessons· meets 4 requirements inCSTA (US)UNESCOKorea, Japan, HKAustralia + NZ
Lessons
- The Voice It Could Not Hear
- Meet Young Inventors
- The Idea That Stayed In The Notebook
- Tanmay's Ladder
- Inventor Match-Up
- Every One Of Them Started Rough
- Teach It Like Samaira
- Inventor Mindset vs Stuck Mindset
- Small Start, Long Road
- What Kids Are Actually Building
- Steps to Start
- Idea Starter Lab
- Board Games In Real Classrooms
- Smart Move or Not?
- Young Inventor Quiz
- My First Invention Idea
- Inventor Vocabulary
- Future Inventor!
Requirements this topic helps meet
- CSTA (US)Evaluate and analyze the roles of humans and human decision-making in the creation of AI.
- UNESCOCategorise general safety risks of AI and typical AI incidents, deepen knowledge of human rights to data protection and privacy and creators' legal responsibility to collect data only with informed consent, and practise strategies to ensure personal data is collected, used, shared, archived and deleted only with consent (CG4.2.2.3).
- Korea, Japan, HKExamine problems that can arise in the collection and use of data needed for AI learning (bias, privacy, consent) and propose legally, socially and ethically valid solutions ([9정04-05]).
- Australia + NZStudents understand that their privacy and personal data must be protected when using generative AI tools, and are taught not to input sensitive personal or others' data into such tools without appropriate safeguards
AI in 203018 lessons· meets 12 requirements inUAEADEKIBCSTA (US)+3
Lessons
- A Day in 2030
- 2030 Predictions
- The Future That Helps Some More Than Others
- Hospital 2030: Who Does What
- Today vs 2030
- 2030 Is Closer Than It Feels
- How an AI Tutor Finds Your Gap
- Amazing vs Careful
- Predictions Need Evidence Too
- AI and the Planet: Match the Fix
- The Readiness Ladder
- Future Readiness Meter
- Google Translate's 2030 Path
- 2030 Possibilities
- 2030 Vision Quiz
- My 2030 Prediction
- Future Vocabulary
- Future Visionary!
Requirements this topic helps meet
- UAEGlobal challenges: sustainability, humanitarian aid, social impact
- ADEKFootprint / resource cost of AI
- IBDevelop an appreciation of the impact of design and technology innovations on life, global society, and environments, including their historical, political, and social contexts
- CSTA (US)Evaluate and analyze the roles of humans and human decision-making in the creation of AI.
- CSTA (US)Identify the intended and unintended impacts of AI on society, including government, education, entertainment, culture, careers, and national security, while considering how these impacts may differ among diverse communities.
- CSTA (US)Investigate the positive and negative environmental impacts of AI (e.g., minimizing deforestation via application of AI, energy use by AI).
- CSTA (US)Students examine security implications and environmental impacts of AI systems within Systems & Security.
- AI4K12 (US)Progress from evaluating the ways a decision impacts different people differently (K-2), to evaluating how an AI technology, e.g. speech or facial recognition, has different effects on different demographic groups (3-5), to evaluating how various stakeholders' conflicting goals and values shape AI system design decisions (6-8), to explaining how the use of AI systems (e.g. health-risk algorithms) has led to documented disparate impacts on different groups (9-12).
- AI4K12 (US)Progress from identifying current uses of AI and how they have impacted society (K-2), to identifying how sectors of society such as manufacturing, retail, and healthcare are changing due to AI (3-5), to comparing AI-driven societal change to previous industrial revolutions (6-8), to predicting how a specific sector of society is likely to change in the short and intermediate term as a result of AI, referencing Amara's Law (9-12).
- OECD–EC (Europe)Describe environmental impacts of AI such as hardware, data centres, water and energy use (Engage 5, intermediate).
- OECD–EC (Europe)Break a problem into parts and consider AI support for specific steps (Manage 3, intermediate).
- Korea, Japan, HKEvaluate the societal impact of AI, including both opportunities and risks it poses to communities, drawing on real cases (e.g. AlphaFold's protein-folding breakthrough, AI-assisted astronomy, AI-generated music).
How to Learn More18 lessons· meets 19 requirements inADEKCISCE + statesIBEngland + Cambridge+4
Lessons
- The Scoreboard That Counted Wrong
- Six Free Places to Learn
- Same Class, Very Different Six Months
- Start Now: Train One Yourself
- Match the Job to the Tool
- Millions Can Learn From One Good Platform
- Five Habits That Actually Work
- Alone or Together
- Learning Communities Count Too
- Share It, or Keep It In?
- Build Your Learning Plan
- Learning Momentum Meter
- The Feed That Picks Your Next Lesson
- Good Learning Habits
- Learning Resources Quiz
- One More Step Forward
- New Words
- Lifelong Learner!
Requirements this topic helps meet
- ADEKDebug an algorithm/program
- CISCE + statesDevelop computational-thinking and coding skills using block-based tools such as Scratch/Blockly through hands-on, project-based learning applied to real-world problems
- IBUse and apply technology effectively to access, process, and communicate information, and to model and create solutions to problems
- England + CambridgeUnderstand a range of ways to use technology safely, respectfully, responsibly and securely, including protecting online identity and privacy; recognise inappropriate content, contact and conduct and know how to report concerns
- CSTA (US)Describe tradeoffs between allowing information to be public and keeping information private and secure.
- CSTA (US)Evaluate and analyze the roles of humans and human decision-making in the creation of AI.
- OECD–EC (Europe)Consider whether an AI recommendation is helpful or narrows perspective (Engage 4, intermediate).
- OECD–EC (Europe)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).
- OECD–EC (Europe)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).
- SingaporeUse basic coding concepts (variables, loops, functions) in a block-based coding platform to build simple programs.
- SingaporeDevelop digital safety and security competencies as one of the nine core NDLP digital competencies.
- SingaporeDevelop digital responsibility as one of the nine core NDLP digital competencies.
- SingaporeKeep digital knowledge current, including awareness of emerging technologies such as AI, as one of the nine core NDLP digital competencies.
- SingaporeDevelop digital information management skills as one of the nine core NDLP digital competencies.
- SingaporeDevelop digital communication, collaboration and participation skills as one of the nine core NDLP digital competencies.
- SingaporeApply the Find-Think-Apply-Create framework when locating, evaluating, using and creating with digital information (exact breakdown of the four components not published on the primary MOE page reviewed).
- SingaporeDevelop digital literacy and technological skills needed to thrive in an increasingly technology-driven world, as a central goal of the Masterplan.
- Australia + NZPractise online safety skills and digital literacy behaviours (the Version 9.0 sub-strand that replaced the old ICT Capability general capability), including safe, respectful interaction with digital systems and other users
- Australia + NZApply digital literacy skills (investigating with, creating with, communicating and collaborating with, and managing/operating digital tools) across all learning areas, including recognising the role of AI-enabled tools in everyday digital systems
Your AI Superpowers18 lessons· meets 6 requirements inADEKIBCSTA (US)Singapore
Lessons
- Raj's Hidden Powers
- Spotted It or Told It?
- The Hidden Skill AI Teams Need
- Curiosity and the AI Team
- Match to Careers
- Human Skills Power AI Too
- Breaking Down Problems
- Sort Your Skills
- Small Clues, Big Breakthroughs
- Teach It, Correct It, Repeat
- Power-Up Timeline
- Superpower Mix Meter
- Google Photos Uses Pattern Power
- Spot Your Superpowers
- Superpower Quiz
- My Strongest AI Superpower
- New Words
- Superpower Unlocked!
Requirements this topic helps meet
- ADEKBuild a chart/dashboard
- IBApply computational thinking strategies (decomposition, abstraction, pattern recognition, algorithm design) to solve problems
- CSTA (US)Decompose problems and subproblems into parts to facilitate the design, implementation, and review of programs.
- CSTA (US)Evaluate and analyze the roles of humans and human decision-making in the creation of AI.
- SingaporeDevelop computational thinking as one of the nine core NDLP digital competencies.
- SingaporeLearn beyond AI: develop distinctly human capabilities and dispositions that AI cannot replicate.
Final Project Prep20 lessons· meets 11 requirements inUAEADEKIBEngland + Cambridge+2
Lessons
- The Amazing Exhibition
- Design an AI System
- The Project That Judges Remember
- Design It or Take It Apart
- Match Project to Interest
- Big Impact Can Start With a Small Idea
- The Five Checks
- Do's and Don'ts
- Clarity Beats Clutter
- Project Timeline
- Timeline Steps
- Project Readiness Meter
- Canva Turns Ideas Into Clear Visuals
- Brainstorm Ideas
- Choose Your Own Problem
- Check Raj's Own Problem
- Project Planning Quiz
- My Final Project Plan
- New Words
- Ready to Plan!
Requirements this topic helps meet
- UAEDesign AI projects for real-world problems
- ADEKBuild an AI solution to a real problem
- IBWhere a school offers digital design (rather than or alongside product design), demonstrate digital-tool competencies such as prototyping, graphic/web design, and multimedia editing through the same four-criterion design cycle
- England + CambridgeUndertake creative projects selecting, using and combining multiple applications across devices to achieve challenging goals, including collecting and analysing data and meeting the needs of known users
- England + CambridgeCreate, re-use, revise and re-purpose digital artefacts for a given audience, with attention to trustworthiness, design and usability
- England + CambridgeFollow and create algorithms presented as flowcharts using standard symbols (start, stop, process, decision, connector), including multiple variables
- England + CambridgeFollow, edit and create algorithms using flowcharts and pseudocode; use selection statements and Boolean logic; progress from linear to binary search
- CSTA (US)Use flowcharts and/or pseudocode to address complex problems as algorithms.
- CSTA (US)Evaluate and analyze the roles of humans and human decision-making in the creation of AI.
- OECD–EC (Europe)Propose specific design changes to improve an AI system for human well-being (Shape 4, intermediate).
- OECD–EC (Europe)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).
Year in Review18 lessons· meets 1 requirement inCSTA (US)
Lessons
- The Memory Walk
- Units 1 & 2: The Story and the Machine
- The Timer That Stopped Being AI
- Unit 3: Run the Five-Clue Test
- Match Units to Ideas
- One Year Changes What You Notice
- Units 4, 5 & 6: Tools, Builds and What's Next
- Day-One Myths vs What's True
- Pulling It Out Beats Reading It Again
- Raise One Last Model
- Train It In Order
- Growth Tracker
- Duolingo Shows You the Steps Adding Up
- Prove It
- Year Review Quiz
- The Thing I Was Wrong About
- Four Words for Grade 7
- Victory Lap Complete!
Requirements this topic helps meet
AI Certificate18 lessons· meets 2 requirements inIBCSTA (US)
Lessons
- Certificate Day
- Six Steps, From Memory
- Two Identical Certificates
- Six Badges, Six Tests
- Match the Badge to the Test
- The Label Changes Last
- Build One While He Watches
- What It Proves - And What It Doesn't
- It Means 'Made Certain'
- The AI Explorer Pledge
- Pledge to Action
- The Certificate Meter
- A Certificate Is a Signal, Not the Skill
- The Uncle Test
- Certificate Quiz
- Answer the Uncle
- Four Words That Travel With You
- Congratulations, AI Explorer!
Welcome to Grade 76 lessons· meets 1 requirement inCSTA (US)
Lessons
- Bridge to Tomorrow
- What's Coming in Grade 7
- Match the Unit
- Grade 7 Preview Quiz
- Grade 7 or Grade 8?
- Ready for Grade 7!
Requirements this topic helps meet
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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.