Grade 2: units, topics and lessons

1. Last Year and This Year
2. Learns, or Follows Its Buttons
3. The Same Machine, Two Ways
4. People Build Every AI
5. Clever, Not Alive

• UAEAI = human-created tools that follow instructions and use data
• ADEKSort human-made vs natural; machines are tools people build
• ADEKDecide whether an everyday technology is AI or not
• ADEKPoint out AI in daily life and name what it does
• ADEKAI vs non-AI decision
• England + CambridgeKnow that Artificial Intelligence (AI) is a simulation of human intelligence, with example applications such as predictive text and speech-to-text
• CSTA (US)Understand that AI is a tool created by humans to make decisions or to generate something (e.g., an image).
• CSTA (US)Describe the role of humans in the creation of AI.
• CSTA (US)Identify where AI is being used in daily life.
• 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)Understand AI as a nonhuman tool that lacks authentic understanding, addressing common misconceptions (Engage 2, basic).
• OECD–EC (Europe)Explain how AI systems perform tasks without attributing human traits to them (Engage 2, 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).
• SingaporeLearn about AI: understand what AI is and how it works.
• ChinaStudents experience and recognize basic AI technologies such as speech recognition and image classification through hands-on demos
• ChinaLower-primary students build basic AI awareness (example: Haidian Experimental Primary School)
• ChinaMiddle-primary students learn fundamental AI principles (example: Haidian Experimental Primary School)
• Australia + NZDevelop foundational awareness that digital systems, including those using AI, are present in familiar environments (e.g. home, classroom) and are used for particular purposes

1. How You Learned 'Dog'
2. Showing, Not Telling
3. One Picture Is Not Enough
4. Many Pictures, Better Learning
5. Examples Are Called Data

• UAEExplain how AI uses data to learn
• ADEKAI is trained on examples and finds patterns
• ADEKTrained-on-examples / rule-vs-learned distinction
• CISCE + statesExplain Machine Learning basics (role of data/information, importance of programming and algorithms) using an example such as fruit sorting; identify cybersecurity/ethical issues (unauthorized hardware/software use, data theft, hacking, viruses/sabotage)
• IBExplain fundamental machine learning concepts, including how models are trained on data to make predictions or decisions
• CSTA (US)Explore how AI models learn from data.
• CSTA (US)Understand how computers learn from data and patterns.
• CSTA (US)Investigate how AI models learn by using data (including why examples and non-examples are required in training sets) and algorithms to find patterns and generate output.
• 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).
• Korea, Japan, HKThrough hands-on use of a simple AI tool built on machine learning, understand the basic principle of machine learning and explore how AI changes society and jobs ([6실05-05]).

1. Alike Ones Together
2. Every Example Gets a Name
3. Wrong Name, Wrong Learning
4. Who Puts the Names On?
5. Label the Pictures

• UAEExplain how AI uses data to learn
• ADEKAI is trained on examples and finds patterns
• CSTA (US)Explore how AI models learn from data.
• CSTA (US)Understand how computers learn from data and patterns.
• 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).

1. Steps in Order
2. Make Your Own Pattern
3. One Wrong Step
4. Fix It and Try Again
5. Byte Follows, You Decide

• UAECompare AI with humans (Cycle 1 part)
• ADEKCompare human thinking/sensing with how a machine processes info; what "smart" means
• 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).

1. Say It With Me
2. Many Examples, Many Kinds
3. Labels, Checked
4. Steps and Patterns Again
5. Byte's Big Review

• UAEAI = human-created tools that follow instructions and use data

1. A Bug in the Steps
2. Find the Bug
3. Fix the Bug
4. A Missing Step
5. Test It Again

Part of the course sequence; not cited as evidence for a specific requirement.

1. Look Before You Label
2. One Label, Many Looks
3. A Wrong Label Spreads
4. Check the Labels
5. Label It Right

• CSTA (US)Students build data fluency and examine the societal impacts of large-scale data collection used to train AI systems.
• OECD–EC (Europe)Identify examples of the data used to train an AI system (Shape 3, basic).

1. A Long Road
2. Person or Machine?
3. Is the Robot Really Sad?
4. Look After a Robot?
5. What Makes a Machine Smart?

• ADEKCompare human thinking/sensing with how a machine processes info; what "smart" means
• ADEKDecide whether an everyday technology is AI or not
• CSTA (US)Compare and contrast the nature of humans versus the nature of AI (e.g., living versus nonliving).
• 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 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 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).
• SingaporeLearn about AI: understand what AI is and how it works.

1. In, Work It Out, Out
2. Ask the Plant Doctor
3. Three Ways to Learn
4. Rules or Examples for This Job?
5. Name It, Guess Next, or Find the Way?

• UAEAI = human-created tools that follow instructions and use data
• ADEKCompare human thinking/sensing with how a machine processes info; what "smart" means
• ADEKDecide whether an everyday technology is AI or not
• CSTA (US)Compare and contrast the nature of humans versus the nature of AI (e.g., living versus nonliving).
• 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 identifying a problem as a classification problem or a search problem (K-2), to categorizing problems as classification vs. search using examples like Teachable Machine (3-5), to categorizing problems as classification, prediction, combinatorial search, or sequential decision problems (6-8), to categorizing real-world problems across eight reasoning-problem types including heuristic search, adversarial search, logical deduction, and statistical inference (9-12).
• AI4K12 (US)Progress from modeling the use of a classification or search algorithm to solve a simple problem (K-2), to describing differences between decision-tree and neural-network classification algorithms (3-5), to comparing several algorithms that could solve a specific reasoning problem based on input data and decision complexity (6-8), to listing, for each of eight reasoning-problem types, an algorithm that could solve it (9-12).
• 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 explaining how reinforcement learning lets a computer learn from trial-and-error experience with a reward signal, e.g. a grid-world navigation demo (3-5), to explaining the difference between supervised learning (told the correct output) and reinforcement learning (told only how well it's doing) (6-8), to selecting the appropriate learning-algorithm family (supervised, unsupervised, or reinforcement) for a given reasoning problem (9-12). K-2 is marked N/A.
• OECD–EC (Europe)Recognise the difference between rules-based and pattern-learning AI systems (Manage 2, basic).
• UNESCODevelop an age-appropriate understanding of the three types of AI algorithms — supervised learning, unsupervised learning, and reinforcement learning — and how the data behind them is acquired and labelled (CG4.1.3.2).
• SingaporeLearn about AI: understand what AI is and how it works.

1. Spot the Pattern
2. Break It Into Parts
3. Keep What Matters
4. Steps a Machine Can Follow
5. Which Tool Did I Use?

• ChinaStudents build foundational logical/computational thinking through age-appropriate AI activities

1. Clues Come in Kinds
2. Fill the Clue Card
3. Too Few Clues
4. The Wrong Clue
5. Alike Things, Same Clues

• AI4K12 (US)Progress from identifying the features that make objects in a collection unique and organizing them into a feature table (K-2), to constructing a feature-vector representation for a set of objects and showing that similar objects lie close together in feature space (3-5), to explaining how word embeddings represent words as numeric vectors for tasks like machine translation (6-8), to describing how a transformer network maps sequences of word-embedding vectors to output sequences (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).

1. Byte's Fruit Problem
2. Plan and Gather
3. Teach Byte
4. Test Byte
5. Make Byte Better

Part of the course sequence; not cited as evidence for a specific requirement.

1. A Camera Is a Machine Eye
2. Finding Faces in Photos
3. Sorting Photos by What Is In Them
4. Reading Words From a Picture
5. Seeing Is Guessing

• UAECompare AI with humans (Cycle 1 part)
• ADEKCompare human thinking/sensing with how a machine processes info; what "smart" means
• 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).

1. A Microphone Is a Machine Ear
2. Hey, Speaker!
3. Turning Sound Into Words
4. Noisy Rooms Are Hard
5. It Hears Grown-Ups Best

• UAECompare AI with humans (Cycle 1 part)
• UAEIdentify safe and fair ways to use AI
• ADEKCompare human thinking/sensing with how a machine processes info; what "smart" means
• ADEKExamine a dataset for gaps/bias
• CSTA (US)Understand that AI is a tool created by humans to make decisions or to generate something (e.g., an image).
• 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)Understand AI as a nonhuman tool that lacks authentic understanding, addressing common misconceptions (Engage 2, basic).
• OECD–EC (Europe)Understand that biases in training data can be perpetuated by AI systems (Engage 6, basic).

1. A Machine Reading Aloud
2. Robot Voices and Real Voices
3. Machines in Many Languages
4. Talking Isn't Understanding
5. Where Its Feeling Words Came From

• ADEKPoint out AI in daily life and name what it does
• ADEKPoint out AI in daily life
• CSTA (US)Identify where AI is being used in daily life.
• 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).
• ChinaStudents experience and recognize basic AI technologies such as speech recognition and image classification through hands-on demos
• ChinaLower-primary students build basic AI awareness (example: Haidian Experimental Primary School)
• Australia + NZDevelop foundational awareness that digital systems, including those using AI, are present in familiar environments (e.g. home, classroom) and are used for particular purposes

1. The Taking-In Part
2. The Working-Out Part
3. The Putting-Out Part
4. It Runs on Power
5. Some Machines Have No Working-Out Part

• UAEWhen to trust AI output, when a human must judge
• England + CambridgeRecognise common uses of information technology beyond school
• England + CambridgeIdentify that everyday devices contain computers and recognise basic input/output methods
• Australia + NZRecognise and use digital systems (e.g. computers, robotic devices) in familiar environments, and identify how data is represented on digital systems and displayed for a purpose
• Australia + NZStudents discuss the differences between digital and non-digital devices/outcomes, and identify and understand the inputs and outputs of a simple digital system

1. Machine Eyes, Again
2. Machine Ears, Again
3. Machine Voices, Again
4. Inside, Again
5. See, Hear, Talk, Inside

Part of the course sequence; not cited as evidence for a specific requirement.

1. Your Tablet Notices
2. It Notices a Touch
3. It Notices Sounds and Pictures
4. It Notices Tipping
5. Which Part Notices?

• ADEKRobotics / physical device

1. Be the Robot
2. Code to the Ball
3. A Bug in the Code
4. A Missing Card
5. Test Your Code

• ADEKBuild/debug a simple sequence
• ADEKRobotics / physical device

1. Plan, Then Code
2. Start When Tapped
3. Repeat and If
4. Test From Three Starts
5. What Kind of Bug?

• ADEKBuild/debug a simple sequence
• 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
• England + CambridgeUnderstand what algorithms are, how they are implemented as programs on digital devices, and that programs execute by following precise and unambiguous instructions
• England + CambridgeCreate and debug simple programs
• England + CambridgeUse logical reasoning to predict the behaviour of simple programs
• England + CambridgeFollow, and identify single errors in, everyday task algorithms; use directional language and create ordered instructions for simple tasks
• CSTA (US)Model daily processes by creating and following algorithms (step-by-step instructions) to complete tasks.
• CSTA (US)Develop programs with sequences and simple loops to express ideas or address a problem.
• CSTA (US)Develop plans that describe a program's sequence of events, goals, and expected outcomes.
• CSTA (US)Debug (identify and fix) errors in an algorithm or program that includes sequences and simple loops.
• CSTA (US)Using correct terminology, describe steps taken and choices made during the iterative process of program development.
• SingaporeUse basic coding concepts (variables, loops, functions) in a block-based coding platform to build simple programs.
• Korea, Japan, HKIntroduce Scratch block-based programming environment to build foundational computational-thinking concepts of abstraction, algorithm and automation.
• Korea, Japan, HKUse Scratch programming (sequencing, loops, events, sprites) to build interactive stories/games as a vehicle for logical thinking and problem-solving.
• ChinaStudents operate simple AI-related tools and complete visual/block-based programming tasks
• Australia + NZFollow, describe and represent a sequence of steps and decisions (algorithms) needed to solve simple problems, building basic connections to understand why data and algorithms are necessary for digital solutions

1. Notice, Decide, Do
2. When Tipped, Go
3. When Shaken, Light
4. Oops, the Wrong Rule
5. My Notice-Then-Do Helper

• ADEKLocate sensors, compare computer vs human sensing
• ADEKPrepare data, train, test, report performance
• ADEKRobotics / physical device
• England + CambridgeCreate and debug simple programs
• CSTA (US)Compare and contrast human sensing with computer sensors.
• 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 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, with instructor help, how to train a computer to recognize something using a tool like Teachable Machine (K-2), to training a classification model and examining its accuracy on new inputs (3-5), to training and evaluating a classification or prediction model on a tabular dataset (6-8), to using supervised or unsupervised learning on real-world data and quantitatively evaluating the results on a nontrivial test set (9-12).
• Korea, Japan, HKIntroduce Scratch block-based programming environment to build foundational computational-thinking concepts of abstraction, algorithm and automation.
• Korea, Japan, HKUse Scratch programming (sequencing, loops, events, sprites) to build interactive stories/games as a vehicle for logical thinking and problem-solving.
• Korea, Japan, HKConnect coding to a physical micro:bit device, using its sensors and actuators to interact with the physical environment as an extension of a Scratch-built maze game.
• ChinaStudents operate simple AI-related tools and complete visual/block-based programming tasks

1. Tiny Squares
2. On or Off Squares
3. Where Colour Changes
4. Thing or Background?
5. Hidden Behind

• CSTA (US)Model the way programs store and manipulate data by using numbers or other symbols to represent information.
• 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).
• 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).

1. Catch It, Work It Out
2. A Sound Is a Wave
3. Sound Pieces Make Words
4. Words Build a Sentence
5. The Sentence Helps

• CSTA (US)Model the way programs store and manipulate data by using numbers or other symbols to represent information.
• 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 describing the sounds that make up spoken language and giving example words for each vowel sound (K-2), to illustrating how sequences of sounds are recognized as candidate words despite unclear signals (3-5), to illustrating how sequences of words are recognized as phrases by resolving ambiguity with higher-level information (6-8), to illustrating the full abstraction hierarchy from waveform to sentence in speech understanding (9-12).
• AI4K12 (US)Progress from demonstrating knowledge of language structure by generating plausible/implausible novel words or unscrambling sentences (K-2), to demonstrating that human language is infinite via sentence extension (3-5), to using a parser to display a sentence's syntactic (parse-tree) structure and explaining the node categories (6-8), to demonstrating how a small context-free grammar can parse or generate simple sentences, and its limits with agreement constraints (9-12).
• AI4K12 (US)Progress from illustrating ambiguity via homophones/homonyms resolved by context (K-2), to illustrating pronoun reference ambiguity resolved by world knowledge (3-5), to illustrating prepositional-phrase attachment ambiguity resolved by meaning (6-8), to describing polysemy (multiple word senses) and how statistical NLP systems resolve it less reliably than human common sense (9-12).

1. Our Pet Question
2. Ask and Count
3. Make the Chart
4. Read the Chart
5. A Pet Was Missed

Part of the course sequence; not cited as evidence for a specific requirement.

1. It Watched What You Liked
2. Other Shoppers Like You
3. The Map Picks the Way
4. The Game Chooses Too
5. You Can Say No Thanks

• UAEExplain how AI uses data to learn
• ADEKAI is trained on examples and finds patterns
• CSTA (US)Explore how AI models learn from data.
• CSTA (US)Understand how computers learn from data and patterns.
• 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).

1. A Clear Picture Helps
2. A Muddling Picture Confuses
3. Wobbly Writing Is Hard
4. A Picture With Two Things
5. Good Examples Make Good AI

• UAEExplain how AI uses data to learn

1. Count Your Examples
2. Two Is Not Many
3. Bars Tell You Fast
4. Make Your Own Chart
5. What the Chart Tells Byte

• UAECompare AI with humans (Cycle 1 part)
• ADEKCompare human thinking/sensing with how a machine processes info; what "smart" means
• ADEKLocate sensors, compare computer vs human sensing
• ADEKCompare human vs machine "smart"
• CSTA (US)Compare and contrast the ability of humans and of AI to perform various tasks and serve in various roles.
• CSTA (US)Understand that AI is a tool created by humans to make decisions or to generate something (e.g., an image).
• CSTA (US)Students build data fluency and examine the societal impacts of large-scale data collection used to train AI systems.
• OECD–EC (Europe)Understand AI as a nonhuman tool that lacks authentic understanding, addressing common misconceptions (Engage 2, basic).
• OECD–EC (Europe)Identify examples of the data used to train an AI system (Shape 3, basic).

1. Best Guess, Not Sure Thing
2. Sure and Not Sure
3. Two Answers, One Better
4. Some Things Fit Two Boxes
5. When It Says Maybe, Check

• 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)

1. Just Red Apples
2. Find the Missing Kind
3. Missing Voices, Missing Faces
4. Count to Find the Gap
5. Fill the Gap

• ADEKExamine a dataset for gaps/bias
• 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 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)Understand that biases in training data can be perpetuated by AI systems (Engage 6, basic).
• OECD–EC (Europe)Identify examples of the data used to train an AI system (Shape 3, basic).

1. Machines Suggest
2. Check, Then Say Yes
3. Change It
4. Say No
5. Yes, Change It, or No

• ADEKCheck an AI output before trusting it

1. Choosers, Again
2. Good Examples, Again
3. Charts, Again
4. Guesses, Again
5. Examples and Guesses

Part of the course sequence; not cited as evidence for a specific requirement.

1. What It Noticed Is Data
2. More of the Same
3. Something New
4. Sam's Evening
5. My Telly Card

• OECD–EC (Europe)Identify AI's presence in everyday life (Engage 1, basic level).
• OECD–EC (Europe)Know that AI uses personal/user data to generate recommendations (Engage 4, basic).

1. Machines Do Word Jobs
2. From Gran, or Junk?
3. Make It Short
4. Happy Clue, Sad Clue
5. A Kind App Changes

• AI4K12 (US)Progress from demonstrating what an intelligent assistant like Siri or Alexa can and cannot do (K-2), to demonstrating question types a search engine or assistant can and cannot answer (3-5), to describing NLP tasks such as summarization, sentiment analysis, question answering, and machine translation (6-8), to describing NLP approaches ranging from keyword/template matching to deep neural networks, and when each is appropriate (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).

1. Test on New Pictures
2. What a Score Means
3. Two Kinds of Mistake
4. Watch Byte Each Round
5. Check It, and Say Why

• ADEKPrepare data, train, test, report performance
• ADEKBuild/debug a simple sequence
• ADEKCheck an AI output before trusting it
• England + CambridgeUse logical reasoning to predict the behaviour of simple programs
• CSTA (US)Debug (identify and fix) errors in an algorithm or program that includes sequences and simple loops.
• CSTA (US)Students read, evaluate, and critique AI-generated code as part of the Programming strand.
• AI4K12 (US)Progress from demonstrating, with instructor help, how to train a computer to recognize something using a tool like Teachable Machine (K-2), to training a classification model and examining its accuracy on new inputs (3-5), to training and evaluating a classification or prediction model on a tabular dataset (6-8), to using supervised or unsupervised learning on real-world data and quantitatively evaluating the results on a nontrivial test set (9-12).
• OECD–EC (Europe)Understand why verifying an AI output is necessary (Engage 3, basic).
• OECD–EC (Europe)Recognise the need for decisions that support accountability and fairness when using AI (Manage 4, basic).
• OECD–EC (Europe)Define criteria for what counts as successfully accomplishing a task with AI (Shape 2, basic).

1. Byte Learned Doctor
2. Gran's Language
3. Helps Some, Lets Others Down
4. Is Talky a Good Robot?
5. Helps or Could Hurt?

• 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
• CSTA (US)Explore how an AI system can help and harm different groups at the same time.
• 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 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 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).
• OECD–EC (Europe)Recognise that ethical AI use depends on design, development and user intentions (Engage 7, basic).
• OECD–EC (Europe)Identify what different AI systems are designed to do (Shape 1, basic).
• 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

1. Byte at the Pond
2. Count the Kinds
3. Teach Byte Fairly
4. Test Byte Fairly
5. Make It Fairer

• ADEKExamine a dataset for gaps/bias
• 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)Understand that biases in training data can be perpetuated by AI systems (Engage 6, basic).

1. You Learned It Once
2. A Machine Remembers Too
3. It Remembers What You Taught It
4. Forgetting
5. Remembering Too Much

• UAEHumans control AI; protect personal information
• ChinaStudents establish basic safety awareness and an understanding of privacy protection when using AI tools
• 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

1. Name the Thing
2. Say What to Do
3. Build Your Ask
4. If It Comes Out Wrong
5. Say a Machine Drew It

Part of the course sequence; not cited as evidence for a specific requirement.

1. I Do Not Know
2. Some Things It Cannot Do
3. Some Things It Should Not Do
4. Asking a Grown-Up Instead
5. Saying No Is Good

• CISCE + statesInteract with Large Language Models (LLMs) and apply prompt engineering to perform text generation and image creation, and understand how responsive AI agents function in educational contexts
• OECD–EC (Europe)Critically evaluate how AI feedback shapes one's ideas and reflect on the creative process (Create 3, advanced).
• OECD–EC (Europe)Know how to formulate unambiguous search queries so a conversational agent or smart speaker can respond as required (AI example 29, competence 1.1).

1. Machines That Make Pictures
2. Machines That Make Stories
3. A Person Asks
4. A Person Checks
5. Ask, Make, Check

Part of the course sequence; not cited as evidence for a specific requirement.

1. Photo or Made?
2. Clue: A Strange Join
3. Clue: Mixed-Up Parts
4. Clues Help, Mostly
5. Spot the Made Picture

Part of the course sequence; not cited as evidence for a specific requirement.

1. Why Rules?
2. Rule: A Grown-Up Helps
3. Rule: Keep Secrets Private
4. Rule: Check It, Say Who Made It
5. Our Class AI Rules

• UAEAI = human-created tools that follow instructions and use data
• ADEKAI governance / civic participation
• SingaporeLearn to use AI: use AI tools safely and responsibly.

1. Clap the Word
2. Doing Words, Naming Words
3. Order Tells Who Does What
4. One Word, Two Meanings
5. Words That Go Together

• AI4K12 (US)Progress from demonstrating knowledge of language structure by generating plausible/implausible novel words or unscrambling sentences (K-2), to demonstrating that human language is infinite via sentence extension (3-5), to using a parser to display a sentence's syntactic (parse-tree) structure and explaining the node categories (6-8), to demonstrating how a small context-free grammar can parse or generate simple sentences, and its limits with agreement constraints (9-12).
• AI4K12 (US)Progress from illustrating ambiguity via homophones/homonyms resolved by context (K-2), to illustrating pronoun reference ambiguity resolved by world knowledge (3-5), to illustrating prepositional-phrase attachment ambiguity resolved by meaning (6-8), to describing polysemy (multiple word senses) and how statistical NLP systems resolve it less reliably than human common sense (9-12).
• AI4K12 (US)Progress from demonstrating how a computer produces different verb tense/agreement forms (K-2), to experimenting with a speech-to-text system's ability to resolve ambiguous words via context (3-5), to illustrating how word embeddings support reasoning about word meaning, e.g. outlier detection and analogy completion (6-8), to demonstrating how a small context-free grammar parses or generates simple sentences and where it falls short, e.g. subject-verb agreement (9-12).

1. My Ask, My Picture
2. Does It Match My Ask?
3. Ask Again
4. A Talking Machine Guesses
5. My Picture-Book Page

• ADEKGenerative AI / chatbots / deepfakes
• OECD–EC (Europe)Use AI to generate images, stories, or models representing an idea (Create 2, basic).
• OECD–EC (Europe)Know how to prompt an AI system to get accurate feedback and helpful responses (Create 3, basic).
• OECD–EC (Europe)Know that AI-generated content may reuse or copy protected work (Create 4, basic).

1. The Place Is a Clue
2. Where Byte Learned It
3. What Is That Sound?
4. Things Everyone Just Knows
5. A Machine Never Lived It

• 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 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 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).

1. A Real Problem
2. Plan for Everyone
3. Build It: Boxes and Byte
4. Test It Fairly. Did It Help?
5. Listen, Fix, Show and Tell

• ADEKBuild an AI solution to a real, student-chosen problem
• ADEKResearch an SDG and design/evaluate an AI solution
• 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 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)Identify examples of the data used to train an AI system (Shape 3, basic).
• OECD–EC (Europe)Recognise that AI can be designed to better support individuals and communities (Shape 4, basic).
• Korea, Japan, HKApply computational thinking and creativity in an 8-hour project-based unit, using programming/innovative technology to formulate a solution to an everyday problem for the benefit of society.

1. Find a Problem
2. Plan It
3. Make It
4. Test It
5. Fix It

• UAEDesign AI projects that support humans
• ADEKBuild an AI solution to a real, student-chosen problem

1. What Byte Needs From You
2. Pick Good Ones
3. Put the Right Name On
4. Teach Byte
5. Ask Byte and Check

• UAEHumans control AI; protect personal information
• CSTA (US)Understand that AI is a tool created by humans to make decisions or to generate something (e.g., an image).
• OECD–EC (Europe)Understand AI as a nonhuman tool that lacks authentic understanding, addressing common misconceptions (Engage 2, basic).
• OECD–EC (Europe)Explain how AI systems perform tasks without attributing human traits to them (Engage 2, advanced).
• ChinaStudents establish basic safety awareness and an understanding of privacy protection when using AI tools
• 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