Grade 3: units, topics and lessons

1. What Data Is
2. A Picture Is Dots and Shapes
3. A Sound Is Loud or Quiet, High or Low
4. One Thing, Many Clues
5. Too Little Data

• UAEExplain how AI uses data to learn
• CSTA (US)Explore the relationship between the properties of training data (e.g., size, features, biases) and an AI model's output.
• 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 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).
• OECD–EC (Europe)Identify examples of the data used to train an AI system (Shape 3, basic).

1. What Training Means
2. Round After Round
3. Testing What It Learned
4. Learn, Test, Fix
5. Mistakes Are Data Too

• UAEExplain how AI uses data to learn
• ADEKTrained-on-examples / rule-vs-learned distinction
• IBExplain fundamental machine learning concepts, including how models are trained on data to make predictions or decisions
• 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).
• 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.
• 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. A Pattern Comes Back
2. Two Rules, and Rows That Grow
3. Exact Steps
4. One Wrong Step
5. Find the Broken Step

• UAEExplain how AI uses data to learn
• ADEKTrained-on-examples / rule-vs-learned distinction
• ADEKPoint out AI in daily life
• CBSEIdentify and, by Grade 5, extend progressive patterns involving one or more changes, using numbers, shapes/images, letters, or a mix.
• 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).
• 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)Identify AI's presence in everyday life (Engage 1, basic level).
• 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]).
• ChinaStudents experience and recognize basic AI technologies such as speech recognition and image classification through hands-on demos

1. Guessing the Weather
2. What Comes Next in a Story
3. Suggesting the Next Video
4. Finishing Your Sentence
5. Prediction Isn't Certainty

• UAEWhen to trust AI output, when a human must judge
• UAEHumans control AI; protect personal information
• 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
• OECD–EC (Europe)Identify what different AI systems are designed to do (Shape 1, basic).
• 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).
• SingaporeUnderstand AI's uses, risks and limitations, and apply ethical considerations (e.g. academic integrity, proper data handling) when using AI to support learning.

1. Remember Data
2. Remember Training
3. Remember Patterns and Steps
4. Remember Exact Steps
5. Remember Predicting

• CBSEIdentify and, by Grade 5, extend progressive patterns involving one or more changes, using numbers, shapes/images, letters, or a mix.

1. Steps Have a Name
2. A Bug Is a Wrong Step
3. A Missing Step
4. Fix It, Then Test Again
5. Debug the Seed

• ADEKDebug an algorithm
• England + CambridgeUse logical reasoning to explain how some simple algorithms work and to detect and correct errors in algorithms and programs
• CSTA (US)Test and debug (identify and fix errors) a program or algorithm to ensure it runs as intended.
• Australia + NZWithin authentic contexts and taking account of end-users, students give, follow, and debug simple algorithms in computerised and non-computerised contexts, using these algorithms to create simple programs involving outputs and sequencing in age-appropriate programming environments (e.g. ScratchJr)

1. Look at the Data First
2. Find the Pattern, Then Predict
3. More Data, Better Guess
4. How Sure Can You Be?
5. When the Guess Is Wrong

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

1. A Colour Is a Number
2. On and Off Pictures
3. A Sound Becomes Numbers
4. Code to Picture, Picture to Code
5. Change One Number

• 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.

1. You Learn Fast, Machines Need Lots
2. Pick the Examples
3. One Change, New Guess
4. Find My Own Miss
5. Teach It Better

• ADEKTrain a no-code classifier, vary the training set
• CSTA (US)Using a dataset, develop an AI model to classify inputs.
• 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 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 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 describing and using AI extensions/plugins in a familiar programming framework such as Scratch, e.g. speech-to-text, face recognition (3-5), to creating a novel application using AI extensions/plugins in a chosen framework such as Scratch, MIT App Inventor, or Calypso (6-8), to creating a novel application using AI tools of the student's choice, now commonplace and freely available without advanced degrees or expensive equipment (9-12). K-2 is marked N/A.
• SingaporeTrain a robot or AI-enabled prototype to respond to external signals through hands-on exploration and tinkering.
• SingaporeDesign and build a prototype that incorporates artificial intelligence to address a problem.

1. More of the Same
2. Narrower or Wider?
3. Words In, Pages Out
4. Paid Spot or Earned Spot
5. The Answer Box on Top

• England + CambridgeUse search technologies effectively, appreciate how results are selected and ranked, and be discerning in evaluating digital content
• OECD–EC (Europe)Know that AI uses personal/user data to generate recommendations (Engage 4, basic).
• 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).

1. A Big Job Is Many Jobs
2. Which Part First?
3. Split It Yourself
4. Clues One at a Time
5. One Job Each

• CBSEBreak down problems into 2-3 or more clues (numbers, 3-D object parts, step-by-step exchanges/transfers, tables/charts with multiple data points), progressing to conditions for counting/grouping/sorting by Grade 5.
• CBSEFollow clear, then multi-layered, step-by-step rules to solve problems involving number sequences, grid movements/paths, before/after/in-between event ordering, and multi-step instructions (moves, changes, transfers, swaps).
• CSTA (US)Decompose (break down) problems into smaller, manageable subproblems to facilitate the program development process.

1. Byte's New Job
2. Plan the Set
3. Build the Set
4. Test on New Pictures
5. Improve and Test Again

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

1. Real Post or Junk
2. Ripe or Not Ripe
3. Safe or Unsafe
4. Yes, No, or Not Sure
5. Where to Draw the Line

• ADEKPoint out AI in daily life
• ADEKCareers
• 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)Identify problem types and consider whether AI might help solve them (Manage 3, basic).
• ChinaStudents experience and recognize basic AI technologies such as speech recognition and image classification through hands-on demos

1. A Machine With Rules
2. A Machine That Learns
3. Which One Is This?
4. Rules Are Easy to Explain
5. Learning Is Harder to Explain

• UAEAI = human-created tools that follow instructions and use data
• ADEKPoint out AI in daily life

1. AI That Helps Doctors
2. AI That Helps Farmers
3. AI That Helps Drivers
4. AI That Helps Teachers
5. AI That Helps Animals

• ADEKCompare human vs machine "smart"
• ADEKAI vs non-AI decision
• ADEKTrained-on-examples / rule-vs-learned distinction
• 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)Compare and contrast the ability of humans and of AI to perform various tasks and serve in various roles.
• CSTA (US)Describe the role of humans in the creation of AI.
• 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)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).
• OECD–EC (Europe)Recognise the difference between rules-based and pattern-learning AI systems (Manage 2, basic).
• 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.
• ChinaMiddle-primary students learn fundamental AI principles (example: Haidian Experimental Primary School)

1. Somebody Collected It
2. Labels Make It Useful
3. Sensors Measure It
4. What You Type Is Kept
5. Your Data, Your Say

• 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.

1. Remember Sorting and Deciding
2. Remember Rules or Learning
3. Remember AI at Work
4. Remember Where Data Comes From
5. Remember Your Say

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

1. A Tablet Full of Sensors
2. Touch and Sound
3. Camera and Motion
4. Sense, Think, Act
5. Which Sensor Did It?

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

1. Long and Short
2. Find the Important Parts
3. A Machine Can Summarise
4. Something Important Was Lost
5. Check the Summary

• UAEEveryday uses of AI across sectors

1. Five Senses, Five Organs
2. Animals Sense Differently
3. One Sense Can Be Fooled
4. Two Senses Settle It
5. Find the Sensors on Your Tablet

• ADEKSensors, human vs computer sensing
• AI4K12 (US)Progress from identifying the five human senses and sensory organs (K-2), to comparing how different animals perceive the world differently than humans, e.g. sonar, ultraviolet vision, colorblindness (3-5), to giving examples of how humans combine information from multiple sensory modalities to resolve ambiguous signals (6-8); AI4K12 marks 9-12 N/A for this topic since it is considered adequately covered by the lower bands (leaving deeper biology/psychology to other courses).
• 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. Sensing, Then Perceiving
2. An Edge Is Where Colour Changes
3. Be the Detector
4. Texture and Sound Fingerprints
5. Muffin or Puppy?

• 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 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).

1. Four Ways to Ask
2. Which Way Suits This Person?
3. Pick the Right Helper
4. Try It: Did It Meet the Need?
5. My Way to Ask

• CSTA (US)Describe various ways that a human might interact with an AI system (e.g., through voice, text, or gestures).

1. A Maker Owns Their Work
2. Licence Badges
3. My Sources List
4. My AI Rules
5. A Poster, Made Honestly

• CSTA (US)Observe intellectual property rights and give appropriate attribution when creating or remixing programs.
• CSTA (US)Use public domain or creative commons media, and refrain from copying or using material created by others without permission.
• OECD–EC (Europe)Know that AI-generated content may reuse or copy protected work (Create 4, basic).
• OECD–EC (Europe)Consider when attribution, permission, or avoiding AI use is the appropriate choice (Create 4, intermediate).
• 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).
• 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).
• SingaporeLearn to use AI: use AI tools safely and responsibly.
• 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)

1. A Question for Our Class
2. Collect the Answers
3. Build the Chart
4. What Can the Chart Tell Us?
5. Feedback and a Better Chart

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

1. More, Less, Most, Least
2. Draw It as Bars
3. Keeping Score
4. When the Score Stops Moving
5. Numbers Beat Boasts

• UAEHumans control AI; protect personal information
• CSTA (US)Select data collection tools and techniques to generate data sets that support a claim or communicate information.
• CSTA (US)Explain the privacy concerns related to the collection and generation of data through automated processes that may not be evident to users.
• CSTA (US)Explore the relationship between the properties of training data (e.g., size, features, biases) and an AI model's output.
• 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).
• 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).

1. Machines That Draw
2. Machines That Write
3. Machines That Make Music
4. Is It Real or Made?
5. Made by a Machine — Say So

• UAEWhen to trust AI output, when a human must judge

1. Many Languages
2. A Machine Translates
3. Some Words Do Not Translate
4. Same Word, Two Meanings
5. Translation Can Be Wrong

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

1. A Robot Is a Body
2. Robots With AI, Robots Without
3. Robots in Factories
4. Robots in Space
5. Robots Cannot Feel

• ADEKAI vs non-AI decision
• ADEKTrained-on-examples / rule-vs-learned distinction
• CISCE + statesExplain what a robot is, its characteristics, and the benefits of robots over humans (quality, productivity, efficiency, working span, hazardous environments)
• CISCE + statesState a brief history of robotics evolution since 1900 and recite Isaac Asimov's Three Laws of Robotics
• CISCE + statesClassify robots by terrain/field (aerial, ground, underwater), by control (manual, automatic), and identify bio-inspired robots (humanoid, bird, snake, insect)
• CISCE + statesDifferentiate a machine from a robot, and a robot from a cobot (human-in-the-loop, multi-robot systems), tracing the progress from robots to cobots
• 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 + CambridgeKnow that Artificial Intelligence (AI) is a simulation of human intelligence, with example applications such as predictive text and speech-to-text
• England + CambridgeUnderstand what is meant by robotics, describe the characteristics of a robot (mechanical structure, electrical components/sensors/actuators/microprocessors, programmability), and the roles robots perform (industry, transport, agriculture, medicine, domestic, entertainment) with advantages/disadvantages
• 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)Recognise the difference between rules-based and pattern-learning AI systems (Manage 2, basic).
• OECD–EC (Europe)Evaluate the effectiveness of an AI approach considering context, data, efficiency, transparency, and impact (Manage 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.
• 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.
• ChinaMiddle-primary students learn fundamental AI principles (example: Haidian Experimental Primary School)

1. Remember Counting, Charts and Scores
2. Remember AI That Makes Things
3. Remember AI and Language
4. Remember Robots
5. Which Idea Helps?

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

1. Machines Need Power
2. A Building Full of Computers
3. Hot Machines Need Cooling
4. Training Uses a Lot
5. Small Choices Add Up

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

1. Camera Traps Count Animals
2. Sensors Watch the Air
3. Sensors Watch the Water
4. Spotting Fires Early
5. The Machine Watches, People Act

• UAEEveryday uses of AI across sectors

1. Bars or a Line?
2. Who Is It For?
3. Make the Poster
4. Trust Check
5. Version Two and Show It

• ADEKBuild a chart / dashboard
• CBSEDevelop familiarity with parts of a computer, input/output devices, file management, internet safety, educational software, and block-based coding (e.g. Scratch) (CG-3, Classes 3-5).
• 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 + CambridgeSelect, use and combine a variety of software (including internet services) on a range of digital devices to design and create programs, systems and content, including collecting, analysing, evaluating and presenting data and information
• 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)Organize and present collected data visually to highlight relationships and support a claim.
• SingaporeDevelop digital literacy and technological skills needed to thrive in an increasingly technology-driven world, as a central goal of the Masterplan.
• 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
• Australia + NZStudents plan and create simple digital content/outcomes for an authentic purpose and audience (e.g. planning and writing a script to film, edit and export a short film; creating and using QR codes)

1. Made by Nadia, Made by a Machine
2. Pick the Settings
3. Change One Thing
4. Is It Nadia's?
5. Artists and Machines

• ADEKHuman-made vs AI-generated art, ethical questions

1. Sounds to Sentences
2. Who Does What
3. Fits the Pattern, Makes No Sense
4. Two Readings
5. When the Machine Picks Wrong

• 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).
• 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).

1. Robot or Ordinary Machine?
2. Where Robots Work
3. Who Controls It?
4. Cobots Work Beside Us
5. Which Robot for the Job?

• CISCE + statesExplain what a robot is, its characteristics, and the benefits of robots over humans (quality, productivity, efficiency, working span, hazardous environments)
• CISCE + statesState a brief history of robotics evolution since 1900 and recite Isaac Asimov's Three Laws of Robotics
• CISCE + statesClassify robots by terrain/field (aerial, ground, underwater), by control (manual, automatic), and identify bio-inspired robots (humanoid, bird, snake, insect)
• CISCE + statesDifferentiate a machine from a robot, and a robot from a cobot (human-in-the-loop, multi-robot systems), tracing the progress from robots to cobots
• England + CambridgeUnderstand what is meant by robotics, describe the characteristics of a robot (mechanical structure, electrical components/sensors/actuators/microprocessors, programmability), and the roles robots perform (industry, transport, agriculture, medicine, domestic, entertainment) with advantages/disadvantages
• SingaporeUnderstand what a smart/intelligent robot is and how AI enables it to respond to inputs.

1. The Problem at the Bins
2. Plan the Helper
3. Collect the Photos
4. Test It and Count the Misses
5. Improve and Test Again

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

1. Parts and Jobs
2. In, Work, Store, Out
3. Memory: Kept for Later
4. Fast, Not Clever
5. Which Part Broke?

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

1. One Machine, Many Machines
2. Sending a Message
3. The Internet Is a Big Web
4. When One Breaks
5. Big Machines in Big Rooms

• England + CambridgeExplain IP addresses, URLs and DNS; distinguish PAN/LAN/WAN and topologies; understand encryption, firewalls, error detection and protocols (TCP/IP, HTTP)
• CSTA (US)Model the role of protocols in transmitting data across networks and the Internet.
• 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. Playing Against a Machine
2. Easy Mode, Hard Mode
3. The Machine Learns Your Moves
4. A Machine That Never Loses
5. Games Made AI Better

• ADEKHuman/AI-generated art, ethics of generative tools
• 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).
• 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. Remember Inside a Computer
2. Remember Machines Joined Up
3. Remember AI in Games
4. A Game Machine, Inside and Out
5. Fix the Game

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

1. A Robot Senses, Thinks and Acts
2. A Robot Follows Its Rules
3. Play Robot Friend
4. Program Your Robot Friend
5. Robots Around Us

• ADEKRobotics / physical device
• CISCE + statesExplain what a robot is, its characteristics, and the benefits of robots over humans (quality, productivity, efficiency, working span, hazardous environments)
• CISCE + statesState a brief history of robotics evolution since 1900 and recite Isaac Asimov's Three Laws of Robotics
• CISCE + statesClassify robots by terrain/field (aerial, ground, underwater), by control (manual, automatic), and identify bio-inspired robots (humanoid, bird, snake, insect)
• CISCE + statesDescribe the general block diagram and building blocks of a robot (mechanical, electronic, computational) and identify robots by demonstration/video (industrial arm, humanoid, mobile)
• CISCE + statesDifferentiate a machine from a robot, and a robot from a cobot (human-in-the-loop, multi-robot systems), tracing the progress from robots to cobots
• CISCE + statesExplain gears (role, principles, gear ratio) and classify sensors (internal: position/velocity/acceleration/force; external: contact/non-contact) and their functions in a robotic system
• CISCE + statesDescribe actuators (linear, rotary), control systems (manual vs automatic) and integrate sensors, actuators and controller in a robotic system using a block diagram (input-controller-robot-feedback)
• England + CambridgeDescribe how sensors, microprocessors and actuators combine to create automated systems, and describe advantages/disadvantages for given scenarios (industry, transport, agriculture, weather, gaming, lighting, science)
• England + CambridgeUnderstand what is meant by robotics, describe the characteristics of a robot (mechanical structure, electrical components/sensors/actuators/microprocessors, programmability), and the roles robots perform (industry, transport, agriculture, medicine, domestic, entertainment) with advantages/disadvantages

1. Exact Steps, Again
2. Rules a Machine Can Follow
3. Debug Practice
4. Rule or Learned?
5. Steps for Collecting Data

• ADEKDebug an algorithm
• England + CambridgeUse logical reasoning to explain how some simple algorithms work and to detect and correct errors in algorithms and programs
• CSTA (US)Test and debug (identify and fix errors) a program or algorithm to ensure it runs as intended.
• Australia + NZWithin authentic contexts and taking account of end-users, students give, follow, and debug simple algorithms in computerised and non-computerised contexts, using these algorithms to create simple programs involving outputs and sequencing in age-appropriate programming environments (e.g. ScratchJr)

1. Cut Into Pieces
2. Addresses on the Network
3. The Web's Phone Book
4. Agreed Rules
5. Many Paths, Locked Messages

• England + CambridgeUnderstand computer networks including the internet, how they provide multiple services such as the world wide web, and the opportunities they offer for communication and collaboration
• England + CambridgeIdentify networked hardware at school/home, understand advantages/disadvantages of networks, and that ciphers/simple codes can protect information
• England + CambridgeExplain IP addresses, URLs and DNS; distinguish PAN/LAN/WAN and topologies; understand encryption, firewalls, error detection and protocols (TCP/IP, HTTP)
• CSTA (US)Model how information is broken down into smaller pieces, transmitted as packets through multiple devices over networks and the Internet, and reassembled at the destination.
• CSTA (US)Model the role of protocols in transmitting data across networks and the Internet.
• Australia + NZExamine the main components of common digital systems and how they may connect together to form networks, and describe artificial intelligence (AI) systems and how they require specific algorithms to achieve a goal

1. A Map Made of Dots
2. Parents, Children and Leaves
3. A Web of Facts
4. Positions and Moves
5. Two Ways to Search a Maze

• CSTA (US)Compare and refine multiple algorithms for the same task and determine which is the most appropriate.
• 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).
• AI4K12 (US)Progress from drawing a tree by repeatedly splitting branches and placing data at nodes (K-2), to describing the parts of a tree (nodes, parent, leaves) and how they relate (3-5), to describing the parts of a labeled graph (nodes, links, source/target) and how trees are a special case of graphs (6-8), to describing how schemas structure information about people, places, or things within knowledge graphs used by search engines (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 drawing a simple 3-node search tree showing two possible tic-tac-toe moves (K-2), to drawing a full search tree of next moves for tic-tac-toe or nim and picking the best move (3-5), to modeling breadth-first search on a graph problem such as common-friend detection (6-8), to illustrating and comparing breadth-first, depth-first, and best-first search algorithms for growing a search tree (9-12).

1. A Sequence Reaches the Flower
2. When the Bell Rings
3. Repeat 4 Times
4. Run It, Watch It, Fix It
5. Two Programs, One Job

• CBSEFollow clear, then multi-layered, step-by-step rules to solve problems involving number sequences, grid movements/paths, before/after/in-between event ordering, and multi-step instructions (moves, changes, transfers, swaps).
• 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 + CambridgeDesign, write and debug programs that accomplish specific goals, including controlling or simulating physical systems; solve problems by decomposing them into smaller parts
• CSTA (US)Compare and refine multiple algorithms for the same task and determine which is the most appropriate.
• SingaporeUse basic coding concepts (variables, loops, functions) in a block-based coding platform to build simple programs.
• ChinaStudents operate simple AI-related tools and complete visual/block-based programming tasks
• Australia + NZStudents learn computational thinking in authentic contexts using block-based coding environments (e.g. making a character move/dance in Scratch), extending sequencing to include simple selection and repetition

1. A Helper Someone Else Made
2. What Was It Never Taught?
3. Add a New Label
4. What Else Changed?
5. Credit the Makers

• CSTA (US)Modify, remix, or incorporate portions of an existing program into one's own work, to develop something new or add more advanced features.
• CSTA (US)Incorporate existing code, media, and libraries into original programs, and give attribution.

1. Plan the Project
2. Build Version One
3. A Friend Tests It
4. Give Good Feedback
5. Build Version Two

• UAEDesign AI projects that support humans

1. What I Know Now
2. A Question Worth Counting
3. Collect and Label
4. Make Your Picture Set
5. What My Set Cannot Tell

• UAEIdentify safe and fair ways to use AI