Grade 5: units, topics and lessons

1. What Is Private?
2. My Name and Where I Live
3. My Photos
4. My School and My Friends
5. Once You Share, It Stays

• IBUse digital tools safely, respectfully, and effectively as part of transdisciplinary units of inquiry
• IBUnderstand basic digital-citizenship concepts: online safety, digital etiquette, and awareness of one's own digital footprint
• England + CambridgeUse technology safely, respectfully and responsibly; recognise acceptable/unacceptable behaviour; identify a range of ways to report concerns about content and contact
• CSTA (US)Describe tradeoffs between allowing information to be public and keeping information private and secure.
• ChinaStudents establish basic safety awareness and an understanding of privacy protection when using AI tools
• Australia + NZExplain how digital systems represent data and why personal, sensitive data must be protected, including when interacting online
• 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. A Strong Password
2. Never Share It
3. Different Locks, Different Doors
4. Two Checks Are Safer
5. If Someone Knows It, Change It

• IBUse digital tools safely, respectfully, and effectively as part of transdisciplinary units of inquiry
• IBUnderstand basic digital-citizenship concepts: online safety, digital etiquette, and awareness of one's own digital footprint
• England + CambridgeUse technology safely, respectfully and responsibly; recognise acceptable/unacceptable behaviour; identify a range of ways to report concerns about content and contact
• 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)Explain how physical and digital security measures protect electronic information.
• 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.
• ChinaStudents establish basic safety awareness and an understanding of privacy protection when using AI tools
• 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 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. Footprints Online
2. What a Search Remembers
3. What a Photo Carries
4. Checking What Is Stored
5. Leaving Less Behind

• IBUse digital tools safely, respectfully, and effectively as part of transdisciplinary units of inquiry
• IBUnderstand basic digital-citizenship concepts: online safety, digital etiquette, and awareness of one's own digital footprint
• England + CambridgeUse technology safely, respectfully and responsibly; recognise acceptable/unacceptable behaviour; identify a range of ways to report concerns about content and contact
• 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
• 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.
• Korea, Japan, HKUnderstand laws/regulations on information, the importance of information security, and individual responsibility and information morals in the information society.
• 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

1. Who Is Behind the Screen?
2. Stop Before You Tap
3. Something Scary Appeared
4. Someone Was Mean
5. Telling Is the Fix

• ADEKChatbot NLP, generative AI concepts
• IBUse digital tools safely, respectfully, and effectively as part of transdisciplinary units of inquiry
• IBUnderstand basic digital-citizenship concepts: online safety, digital etiquette, and awareness of one's own digital footprint
• England + CambridgeUse technology safely, respectfully and responsibly; recognise acceptable/unacceptable behaviour; identify a range of ways to report concerns about content and contact
• 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
• CSTA (US)Explore how one's actions may result in the collection of data.
• 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.
• Korea, Japan, HKUnderstand laws/regulations on information, the importance of information security, and individual responsibility and information morals in the information society.
• 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. Remember: My Secrets
2. Remember: Locks and Keys
3. Remember: My Data Trail
4. Remember: When Something Goes Wrong
5. All Four at Once

• CSTA (US)Explain how physical and digital security measures protect electronic information.

1. Three Kinds of Sharing
2. Settings That Share
3. Photos Before Posting
4. My Friends' Privacy
5. My Privacy Check

• 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. Three Kinds of Bug
2. The Missing Step
3. Steps in the Wrong Order
4. The Wrong Condition
5. Test, Fix, Test Again

• 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. Trick Messages
2. Bad Programs
3. Breaking In
4. The Traffic Jam Attack
5. Match the Attack to the Protection

• England + CambridgeDescribe the processes involved in, and aim of, a range of cyber security threats (brute-force, data interception, DDoS, hacking, malware, pharming, phishing, social engineering) and explain solutions to keep data safe (access levels, anti-malware, authentication, firewalls, SSL, etc.)
• CSTA (US)Discuss real-world cybersecurity problems and how personal information can be protected.
• CSTA (US)Explain how physical and digital security measures protect electronic information.
• CSTA (US)Give examples to illustrate how sensitive data can be affected by malware and other attacks.
• CSTA (US)Recommend security measures to address various scenarios based on factors such as efficiency, feasibility, and ethical impacts.
• CSTA (US)Compare various security measures, considering tradeoffs between the usability and security of a computing system.
• CSTA (US)Explain tradeoffs when selecting and implementing cybersecurity recommendations.

1. A Letter in Five Switches
2. A Number in Switches
3. Bigger Grids, More Switches
4. Smooth Becomes Counted
5. Four Kinds of Data, One Flip

• 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.
• Korea, Japan, HKCompare digital and analogue data encountered in daily life through play/experiential activities, and identify types/forms of number, text, sound and image data usable for AI ([6실05-04]).
• 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. The World's Rule List
2. Fair to Everyone?
3. Keeps Faces Private?
4. Says It Is a Machine, Explains, Can Be Overruled?
5. The Verdict

• 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).
• 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).

1. Byte Wrote These Steps
2. Every Case, and the Edge Cases
3. Expected and Actual
4. Notes for the Next Builder
5. Debug a Friend's Program

• CSTA (US)Describe choices made during program development using code comments, presentations, and demonstrations.
• CSTA (US)Systematically test and refine programs using a range of test cases.
• CSTA (US)Document programs in order to make them easier to follow, test, and debug.
• CSTA (US)Students read, evaluate, and critique AI-generated code as part of the Programming strand.

1. The Problem
2. Gather: What I Already Know
3. Make: My Safety Plan
4. Test: Try It on Four Stories
5. Improve: Plan Version Two

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

1. Two Machines, One Job
2. Three Ways to Learn
3. It Learns Before You Meet It
4. Teach Byte
5. Older Than You Think

• UAEAI = human-created tools that follow instructions and use data
• UAEAI-manipulated media: safety and security
• ADEKCheck AI output, accept/revise/reject
• ADEKPoint out AI in daily life
• 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 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 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)Understand why verifying an AI output is necessary (Engage 3, 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).

1. Kind Words to Everyone
2. Do Not Teach It Mean Things
3. Standing Up for Others
4. Machines Copy Us
5. Be the Example

• UAEAI = human-created tools that follow instructions and use data
• UAEGlobal challenges: sustainability, humanitarian aid, social impact
• ADEKFootprint / resource cost of AI
• 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)Evaluate when AI is or is not a helpful resource to carry out a task.
• 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.
• OECD–EC (Europe)Describe environmental impacts of AI such as hardware, data centres, water and energy use (Engage 5, intermediate).
• OECD–EC (Europe)Identify AI systems and the tasks they support (Manage 1, basic).
• OECD–EC (Europe)Identify what different AI systems are designed to do (Shape 1, basic).
• 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. Not Everything Online Is True
2. Deepfakes
3. Who Made This, and Why?
4. Checking Before Sharing
5. Stopping a Rumour

• UAEHow AI is governed through national policies
• 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.

1. Both, or Either
2. Not
3. Rules in a Row
4. On or Off
5. Build a Rule Chain

• ADEKCheck AI output, accept/revise/reject
• IBTake individual or collective action informed by learning, including reflecting on the responsibilities of using technology, as part of the PYP's agency/action outcome
• OECD–EC (Europe)Understand why verifying an AI output is necessary (Engage 3, basic).
• 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. Remember: How Machines Learn
2. Remember: Being Kind
3. Remember: AI and the Truth
4. Remember: And, Or, Not
5. All Four Together

• 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 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.
• 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).

1. The Wrong Word
2. Too Often or Too Rarely
3. The Missing NOT
4. Test With Every Case
5. Fix the School Gate Rule

• UAEEveryday uses of AI across sectors
• ADEKRobotics / physical device
• 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. Sense, Decide, Act
2. Sensors Everywhere
3. Actuators Make It Happen
4. Follow One Device
5. Design a Device

• UAEEveryday uses of AI across sectors
• ADEKRobotics / physical device
• ADEKRobotics / physical device
• 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 + 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)

1. Can Machines Think? 1950
2. A Summer That Named AI: 1956
3. AI Winters
4. Big Data and Deep Learning
5. The 2020s and My Timeline

• CSTA (US)Students study the history of computing and AI, emerging technologies, human-AI relationships, and responsible decision-making about technology use.

1. A Network of Small Units
2. A Unit Adds Weighted Clues
3. Layers
4. Turn the Dials
5. What Changes Inside?

• 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.
• 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.

1. Dots to Edges to Outlines
2. Thing or Background?
3. Half-Hidden Things
4. Wave, Sounds, Words
5. The Sentence Picks the Word

• 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 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 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. Input, Condition, Output
2. Read the Number, Pick the Line
3. Test Three Cases
4. A Gadget That Learns Its Line
5. Rule or Learned? My Gadget

• ADEKTrain a no-code classifier, vary the training set
• ADEKRobotics / physical device
• 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)Using a dataset, develop an AI model to classify inputs.
• SingaporeUse basic coding concepts (variables, loops, functions) in a block-based coding platform to build simple programs.
• 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.
• Korea, Japan, HKExplore examples of information technology fused into robots, know about products developed by combining robots with such technology, and control a simple AI-type robot's sensors and motions using block-based programming ([6실04-06]).
• 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. The Problem
2. Gather the Examples
3. Train and Test
4. Measure the Misses
5. Improve and Test Again

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

1. Homework Is Practice
2. Getting the Answer vs Learning
3. Using AI to Understand
4. Copying Is Not Yours
5. Saying How I Used It

• UAEPrivacy, accountability and personal rights
• 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).
• CSTA (US)Describe tradeoffs between allowing information to be public and keeping information private and secure.
• CSTA (US)Explore how one's actions may result in the collection of data.
• Australia + NZExplain how digital systems represent data and why personal, sensitive data must be protected, including when interacting online

1. Somebody Paid for This
2. Aimed at You
3. Built Not to Stop
4. The Honest Report
5. I Decide When It Ends

• UAEPrivacy, accountability and personal rights
• England + CambridgeUnderstand data security during transmission and different user authentication methods (password, fingerprint, facial recognition)
• Australia + NZExplain how digital systems represent data and why personal, sensitive data must be protected, including when interacting online

1. A Machine That Chats
2. It Is Not a Friend
3. It Agrees Too Much
4. Real People Are Different
5. Talk to People Too

• ADEKChatbot NLP, generative AI concepts
• OECD–EC (Europe)Identify AI's presence in everyday life (Engage 1, basic level).
• OECD–EC (Europe)Share examples of how AI shapes personal choices (Engage 1, intermediate level).

1. AI Uses Electricity
2. Big Machines in Big Rooms
3. AI That Helps Nature
4. Using Only What We Need
5. Small Choices Add Up

• OECD–EC (Europe)Identify AI's presence in everyday life (Engage 1, basic level).
• OECD–EC (Europe)Share examples of how AI shapes personal choices (Engage 1, intermediate level).
• 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).

1. Remember: AI and School Work
2. Remember: Built to Keep You Watching
3. Remember: AI and Friends
4. Remember: AI and the Planet
5. One Evening, Four Themes

• ADEKExamine a dataset for gaps/bias

1. What Is a Footprint?
2. Training Uses a Lot
3. Using Adds Up
4. Water to Keep Cool
5. Choices People and Companies Make

• ADEKRobotics / physical device
• ADEKFootprint / resource cost
• CSTA (US)Explore the impact of AI on the environment.
• CSTA (US)Students examine security implications and environmental impacts of AI systems within Systems & Security.
• 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.
• OECD–EC (Europe)Recognise that computers and AI require physical resources to run (Engage 5, basic).

1. Pick a Goal
2. Gather the Photos
3. Build and Test the Helper
4. Who It Helps, Who It Might Miss
5. Improve and Report

• UAEEveryday uses of AI across sectors
• OECD–EC (Europe)Propose specific design changes to improve an AI system for human well-being (Shape 4, intermediate).

1. Play the Imitation Game
2. One Job, Any Job, Smarter Than Us?
3. Does the Chatbot Understand?
4. Should a Robot Have Rights?
5. My Argument Card

• 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)
• CSTA (US)Debate what differences do or should exist between human and artificial intelligence, sentience, consciousness, rights, and responsibilities.
• 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)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).

1. Two Ways to Reply
2. What Does the Message Want?
3. Build the Rule Helper
4. New Messages, New Problems
5. Steer the Conversation

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

1. Research the Problem
2. Gather and Train
3. Impact Beyond the Score
4. When Do We Pause It?
5. Keep, Change or Stop

• ADEKResearch an SDG, build/evaluate an AI solution
• CBSEComplete a Part D project/field visit/student portfolio related to Sustainable Development Goals, e.g. building an AI model with Teachable Machine or Machine Learning for Kids, or maintaining a portfolio of AI activities (4Ws canvas, system map, research on AI for SDGs).
• CBSEEmpower students to create positive change through AI-driven social impact projects, and equip students with skills to develop AI solutions addressing societal challenges (course objectives).
• CSTA (US)Use data to highlight or propose cause-and-effect relationships, predict outcomes, or communicate an idea.
• 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)Recognise the need for decisions that support accountability and fairness when using AI (Manage 4, basic).
• OECD–EC (Europe)Propose specific design changes to improve an AI system for human well-being (Shape 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).

1. What a Report Card Says
2. What It Learned From, and Its Score
3. How It Decides, and Its Limits
4. People in the Photos
5. Audit a Classmate's Card

• CSTA (US)Describe an AI design process that considers the impact on end users and others who are impacted by the AI system.
• AI4K12 (US)Progress from creating a model card for a classification or prediction model documenting its intended use, training data, accuracy, and limitations (3-5), to constructing an ethical matrix of stakeholders and their values for an AI system and evaluating alignment (6-8), to designing an AI system using a full ethical design process that considers all stakeholders' values across fairness, transparency, explainability, accountability, privacy, and societal values (9-12). K-2 is marked N/A.
• OECD–EC (Europe)Recognise that AI can be designed to better support individuals and communities (Shape 4, basic).

1. The Problem: Showing What Happened
2. Gather: Count the Results
3. Make: Chart Right and Wrong
4. Test the Conclusion
5. Improve: Chart the Misses

• ADEKExamine a dataset for gaps/bias

1. Different Countries, Different Rules
2. Not Everyone Has the Same Access
3. Languages With Little Data
4. AI Built Near Home
5. Fair for the Whole World

• IBTake individual or collective action informed by learning, including reflecting on the responsibilities of using technology, as part of the PYP's agency/action outcome
• CSTA (US)Evaluate when AI is or is not a helpful resource to carry out a task.
• OECD–EC (Europe)Identify AI systems and the tasks they support (Manage 1, basic).
• SingaporeLeverage AI tools effectively for learning and for daily life.
• 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

1. Why AI Needs Rules
2. Rules at Home
3. Rules at School
4. Rules for Countries
5. What Rule Would I Make?

• UAECompare AI with humans (Cycle 2 part)
• CSTA (US)Describe the role of humans in the creation of AI.
• OECD–EC (Europe)Understand AI as a nonhuman tool that lacks authentic understanding, addressing common misconceptions (Engage 2, basic).
• 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).
• SingaporeLearn beyond AI: develop distinctly human capabilities and dispositions that AI cannot replicate.

1. Five Belts, Five Ideas
2. Questions Worth Asking
3. Four Things I Can Do Now
4. Kind, Careful, Curious
5. Put It All Together

• UAEEthics, cultural values and inclusion guide AI decisions
• UAEHow AI is governed through national policies
• 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. Building and Deciding
2. Rules at Every Size
3. When the Builder Makes the Rules
4. Why Both Matter
5. Your Voice in the Rules

• ADEKExplain governance as distinct from building AI

1. Choose the Problem
2. Gather the Examples
3. Build and Test
4. Listen to Feedback
5. Improve and Test Again

• UAEDesign AI projects that support humans
• ADEKRobotics / physical device
• ADEKBuild AI solution to a real problem

1. Remember: AI Around the World
2. Remember: Rules for AI
3. Remember: What I Now Know
4. Remember: My Moves
5. The Year on One Page

• UAEAI = human-created tools that follow instructions and use data
• UAEDesign AI projects that support humans
• ADEKAI governance / civic participation
• ADEKBuild AI solution to a real problem

1. A Real Rule
2. Check It Against the Checklist
3. Watching and Profiling
4. Wants and Worries
5. Argue Both Sides, Then Improve the Rule

• ADEKTake part in shaping AI policy — draft class AI rules
• ADEKStakeholder debate / ethical matrix
• ADEKAI governance/civic participation
• CSTA (US)Seek diverse perspectives for the purpose of improving computational artifacts.
• 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)Recognise that ethical AI use depends on design, development and user intentions (Engage 7, basic).
• 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).
• 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).
• UNESCOCultivate social responsibilities to uphold 'ethics by design' in regulations on AI — evaluating how selected local AI regulations align with ethics-by-design, and drafting proposals to modify or create new regulations governing ethics by design (CG4.3.2.3).
• UNESCONurture a self-identity as a co-creator and 'driver' of the next generation of AI technology, critically analyse the long-term impacts of AI systems on social relations and behaviours, and discuss how regulations/policies should adapt to enhance AI governance (e.g. by engaging with local/online communities of AI co-creators) (CG4.3.4.3).
• 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

1. Five Kinds of Machine Job
2. Pick a Method
3. Say Why: Data and Decisions
4. Same Problem, Two Methods
5. Tool Check

• 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 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).

1. A Real Person's Problem
2. Gather the Set
3. Train and Test
4. Improve Once
5. Demo Day

• ADEKTrain a no-code classifier, vary the training set
• ADEKBuild AI solution to a real problem
• CSTA (US)Use an iterative process to plan the development of a program by including others' perspectives and considering user preferences.
• CSTA (US)Describe an AI design process that considers the impact on end users and others who are impacted by the AI system.
• 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).
• 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 creating a model card for a classification or prediction model documenting its intended use, training data, accuracy, and limitations (3-5), to constructing an ethical matrix of stakeholders and their values for an AI system and evaluating alignment (6-8), to designing an AI system using a full ethical design process that considers all stakeholders' values across fairness, transparency, explainability, accountability, privacy, and societal values (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.
• OECD–EC (Europe)Define criteria for what counts as successfully accomplishing a task with AI (Shape 2, basic).
• OECD–EC (Europe)Recognise that AI can be designed to better support individuals and communities (Shape 4, basic).

1. Find One Fact
2. Save It So You Can Find It
3. Repurpose It
4. Usable and Trustworthy
5. Present It, and Keep Learning

• ADEKAI governance / civic participation
• ADEKTrain a no-code classifier, vary the training set
• 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 + CambridgeUse search technologies effectively, appreciate how results are selected and ranked, and be discerning in evaluating digital content
• 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
• 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)Know that AI-generated content may reuse or copy protected work (Create 4, basic).
• 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).
• 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. Problem First, Tool Second
2. One Real Person
3. Examples Somebody Collects
4. What Could Go Wrong, and Who to Ask
5. A Practice Plan

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

1. A Problem Worth Solving
2. One Real Person
3. What Would It Learn From?
4. What Could Go Wrong, and Who I Would Ask
5. My Plan, and Our Rules

• UAEAI = human-created tools that follow instructions and use data
• ADEKTake part in shaping AI policy — draft class AI rules
• ADEKBuild AI solution to a real problem
• ADEKStakeholder debate / ethical matrix
• ADEKCareers
• IBTake individual or collective action informed by learning, including reflecting on the responsibilities of using technology, as part of the PYP's agency/action outcome