Grade 4: units, topics and lessons

1. People Who Build AI
2. Collecting the Examples
3. Choosing What to Teach It
4. Testing Before Sharing
5. Fixing It Later

• UAEExplain how AI uses data to learn
• 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. Clever Is Not Perfect
2. Look-Alikes
3. How Sure Is It?
4. Very Sure and Still Wrong
5. Trust the Right Amount

• UAEIdentify safe and fair ways to use AI
• ADEKExamine dataset gaps/bias, predict wrong outcomes
• ADEKExamine dataset bias, propose fixes
• 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 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).
• 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. Ask a Human
2. Two Sources Are Better
3. Does This Make Sense?
4. Confidently Wrong
5. Be a Good Checker

• UAEWhen to trust AI output, when a human must judge
• ADEKCheck AI output, accept/revise/reject
• ADEKCheck AI output before trusting
• OECD–EC (Europe)Understand why verifying an AI output is necessary (Engage 3, basic).
• OECD–EC (Europe)Verify AI outputs against trusted sources (Engage 3, intermediate).
• SingaporeThink critically about AI's biases, question AI-generated outputs, and discern or identify false or misleading information produced by AI.

1. When AI Invents Facts
2. It Sounds Right But Isn't
3. Fake Pictures
4. Fake Voices
5. Spotting the Fake

• UAEWhen to trust AI output, when a human must judge
• ADEKCheck AI output, accept/revise/reject
• ADEKChatbot NLP, generative AI concepts
• OECD–EC (Europe)Understand why verifying an AI output is necessary (Engage 3, basic).

1. Remember How AI Is Built
2. Remember Sure and Wrong
3. Remember Checking
4. Remember Made-Up Answers
5. All Four Together

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

1. A Machine Made of Questions
2. A Bug in the Tree
3. The Missing Question
4. Test With New Cards
5. Debug the Fruit-Finder

• 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 Choices
2. When to Accept
3. Changing a Suggestion
4. When to Reject
5. You Decide

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

1. Clues as Numbers
2. A Clue Map
3. Which Clues Go In?
4. Words That Live Close Together
5. Where Word Machines Slip

• 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 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. In the Picture, or Known?
2. What Everybody Just Knows
3. Where Machines Get Knowledge
4. Why Byte Misses It
5. Give Byte the Missing Knowledge

• 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 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. Right, but How?
2. Said vs Really
3. Count Each Kind
4. Which Mistake Is Worse?
5. Ready, or Not Yet?

• OECD–EC (Europe)Define criteria for what counts as successfully accomplishing a task with AI (Shape 2, basic).

1. Columns in a Clue Table
2. How a Clue Is Written
3. A Question on Both Sides
4. Facts Into Questions
5. Build, Run, Mend

• CSTA (US)Train a model that can make decisions based on defined criteria (e.g., a dichotomous key to determine which movie to see).
• 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).
• 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]).

1. The Lunch Box Problem
2. Gather the Facts
3. Build the First Tree
4. Test It With New Cards
5. Improve With Feedback

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

1. Only Red Apples
2. Left Out by a Machine
3. Ask Who Is Missing
4. More of the Same Does Not Help
5. Build a Fair Set

• ADEKCareers
• CSTA (US)Explore ways in which some jobs involve the creation and/or use of AI.
• 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).

1. Words Carry Ideas
2. Doctors Are Always...
3. Where Did It Learn That?
4. Changing Unfair Words
5. Fair Words for Everyone

• ADEKCareers
• CSTA (US)Explore ways in which some jobs involve the creation and/or use of AI.
• OECD–EC (Europe)Identify problem types and consider whether AI might help solve them (Manage 3, 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. The Machine Did It
2. The Builder's Job
3. The User's Job
4. Nobody Checked
5. Someone Is Answerable

• 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. Asking for a Reason
2. A Good Explanation
3. I Do Not Know Why
4. Showing Its Working
5. If It Cannot Explain, Be Careful

• 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).
• 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. Remember Who Was Left Out
2. Remember Bias in Words
3. Remember Who Is Responsible
4. Remember Why Did It Say That
5. All Four Together

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

1. Trains Without a Driver
2. Traffic That Talks
3. Water and Electricity
4. Hospitals, Ports and Airports
5. A City That Works Together

• UAEEveryday uses of AI across sectors

1. Count the Kinds
2. Gaps in When and Where
3. Predict Who Gets It Wrong
4. Big Is Not Complete
5. The Gap Checklist

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

1. The Fact Box
2. The Rule Cards
3. The Engine Walks Through
4. Build and Test the Plant Doctor
5. Three Kinds of Machine

• England + CambridgeDescribe the main characteristics of AI: the collection of data and rules for using that data, the ability to reason, and (optionally) the ability to learn and adapt
• England + CambridgeExplain the basic operation and components of AI systems that simulate intelligent behaviour, limited to expert systems (knowledge base, rule base, inference engine, interface) and machine learning (a program's ability to automatically adapt its own processes and/or data)
• OECD–EC (Europe)Accurately describe how a specific AI tool operates (Engage 2, intermediate).

1. The Bank Notices Something Odd
2. Shops Guess What We Will Buy
3. Keeping People Safe
4. Films, Songs and Game Characters
5. Match the Sector to the Job

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

1. Face and Voice Clues
2. Word Clues and Sarcasm
3. Two Clues Beat One
4. Find the Names
5. Offer a Break or Carry On

• 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. A Real Giant Dataset
2. Who Labelled It, and the Gaps
3. Voices It Mishears
4. Who It Helps, Who It Lets Down
5. Was It Fair? The 2020 Exam Grades

• CSTA (US)Investigate an example of AI decision making, considering if it is fair, as well as what it means to be fair.
• CSTA (US)Investigate examples of AI, considering differences in experience by different people in different contexts.
• AI4K12 (US)Progress from illustrating why training a classifier for a broad concept like 'dog' requires large, diverse image data by browsing datasets such as ImageNet or Coco (3-5), to illustrating how objects in images are segmented and labeled with bounding boxes to build an object-recognition training set (6-8), to evaluating a real AI training dataset's size, acquisition/labeling method, storage cost, and time-to-produce, including where bias can enter (9-12). K-2 is marked N/A.
• AI4K12 (US)Progress from 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).

1. The Vet's Problem
2. Plan the Fix
3. Build It and See It Fail
4. Test It Again
5. Improve and Share

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

1. Jobs AI Helps With
2. The Human Part of a Job
3. New Jobs AI Made
4. Working Together
5. What Do I Want to Be?

• UAEExplain how AI uses data to learn
• 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.
• 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. Whose Idea Was It?
2. Copying and Creating
3. A Start, Not a Finish
4. Give Credit
5. My Ideas Matter Most

• UAEIdentify safe and fair ways to use AI
• ADEKExamine dataset gaps/bias, predict wrong outcomes
• ADEKHuman-made vs AI-generated art, ethical questions
• 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 discussing why intelligent agents need to understand languages other than English (K-2), to discussing why domain knowledge must be broad enough to cover accents, dialects, and musical genres (3-5), to describing how a vision system can exhibit cultural bias if it lacks knowledge of objects outside its creators' culture, e.g. non-US traffic signs (6-8), to describing technical difficulties (e.g. low-contrast faces, children's speech) in making perception systems work well for diverse groups (9-12).
• AI4K12 (US)Progress from examining a labeled dataset to identify problems that could cause incorrect predictions, e.g. an all-green 'healthy foods' set (K-2), to examining training-data features/labels for potential sources of bias such as Amazon's biased resume sorter (3-5), to explaining how unbalanced training data biases a classifier, e.g. faces or loan applications (6-8), to investigating demographic imbalances in training data using data-visualization tools such as histograms or Pandas (9-12).
• 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. Why Is This App Free?
2. Adverts Pay for It
3. Ads Chosen for You
4. AI Costs Money to Run
5. Using Less

• UAEIdentify safe and fair ways to use AI
• UAEHumans control AI; protect personal information
• 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. Think First, Then Ask
2. Doing the Hard Part Yourself
3. Shortcuts That Cost You
4. Learning Beats Answering
5. Ask It to Quiz Me

• UAECompare AI with humans (Cycle 1 part)
• UAEIdentify safe and fair ways to use AI
• UAEHumans control AI; protect personal information
• 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)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).
• 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).
• 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 Jobs
2. Remember AI and Creativity
3. Remember AI and Money
4. Remember Thinking Well
5. All Four Together

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

1. Where AI Lives
2. Training and Using
3. Keeping Cool
4. Where the Power Comes From
5. Is It Worth It?

• 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.

1. Eyes in the Forest
2. Ears That Listen
3. Pictures From Space
4. Air and Water
5. What It Cannot Do

• UAEEveryday uses of AI across sectors

1. Before and After
2. Four Big Changes
3. One Sector Through Time
4. Jobs That Change, Start or Fade
5. My Forecast

• ADEKCareers
• IBDevelop an appreciation of the impact of design and technology innovations on life, global society, and environments, including their historical, political, and social contexts
• CSTA (US)Discuss computing technologies that have changed the world, and express how those technologies influence, and are influenced by, cultural practices.
• AI4K12 (US)Progress from identifying current uses of AI and how they have impacted society (K-2), to identifying how sectors of society such as manufacturing, retail, and healthcare are changing due to AI (3-5), to comparing AI-driven societal change to previous industrial revolutions (6-8), to predicting how a specific sector of society is likely to change in the short and intermediate term as a result of AI, referencing Amara's Law (9-12).
• AI4K12 (US)Progress from describing jobs that no longer exist due to past technology advances, e.g. farriers replaced by auto mechanics (K-2), to describing how a specific job will change due to AI/robotic technology, e.g. warehouse workers alongside robots (3-5), to predicting a new job type that might arise or an existing job that might change/disappear due to AI adoption (6-8), to investigating the specific skills needed for AI-enabled careers across the workforce, not just for programmers (9-12).

1. My Ideas First
2. Keep, Change or Drop
3. Mine Plus Machine's
4. Ask the Picture Tool
5. My Poster

• IBWhere a school offers digital design (rather than or alongside product design), demonstrate digital-tool competencies such as prototyping, graphic/web design, and multimedia editing through the same four-criterion design cycle
• England + CambridgeUndertake creative projects selecting, using and combining multiple applications across devices to achieve challenging goals, including collecting and analysing data and meeting the needs of known users
• England + CambridgeCreate, re-use, revise and re-purpose digital artefacts for a given audience, with attention to trustworthiness, design and usability
• OECD–EC (Europe)Explore how AI tools can expand thinking and spark ideas (Create 1, basic).
• OECD–EC (Europe)Compare AI-generated suggestions to one's own ideas during brainstorming (Create 1, intermediate).
• OECD–EC (Europe)Use AI to generate images, stories, or models representing an idea (Create 2, 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 automatically create digital content (text, news, music, images) from existing content as its source, which can be difficult to distinguish from human creations, including in news media/journalism (AI examples 5 and 6, competence 3.1).
• 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).
• 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. Four Tools, Real Names
2. Break It Up
3. Keep What Matters
4. Spot the Pattern, Write the Steps
5. Which Tool Fits?

• 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.
• IBDevelop early algorithmic thinking through inquiry-based problem-solving activities embedded in units of inquiry
• CSTA (US)Decompose (break down) problems into smaller, manageable subproblems to facilitate the program development process.
• CSTA (US)Use data to highlight or propose cause-and-effect relationships, predict outcomes, or communicate an idea.
• SingaporeDevelop computational thinking as one of the nine core NDLP digital competencies.
• ChinaStudents build foundational logical/computational thinking through age-appropriate AI activities

1. What the Robot's Map Keeps
2. Design Your Own Map
3. States and Changes
4. Model the School Bell
5. Reuse the Design

• CBSESolve problems with hidden, unseen, or partially visible ideas using different viewpoints of 3-D objects, shape transformations (flips/turns/folds/rotations), and identification of missing/hidden parts in incomplete shapes or patterns, progressing to symmetry and mirror-image reasoning by Grade 5.
• England + CambridgeDesign, use and evaluate computational abstractions that model the state and behaviour of real-world problems and physical systems
• CSTA (US)Understand how a representation is an abstraction that focuses on some features and leaves others out.
• CSTA (US)Create an abstract representation of a physical system that can be used to solve a problem (e.g., a map).
• AI4K12 (US)Progress from constructing a map of a home, school, or neighborhood and understanding that 'the map is not the territory' (K-2), to demonstrating how different map styles (geometric vs. topological) capture different aspects of the world (3-5), to showing how a game board (tic-tac-toe, chess) can be represented as a plain-language description of squares, relationships, and piece positions (6-8), to describing how to represent a concept as a schema.org-style schema with superconcepts and inherited properties (9-12).
• AI4K12 (US)Progress from giving examples of symbols encountered in daily life such as restroom icons and card suits (K-2), to explaining hierarchical tree structures such as ancestry trees and taxonomies (3-5), to translating a structure like a game board, road map, or mind map into a labeled graph of nodes and links (6-8), to translating the premises of a syllogism from English into predicate-logic notation and completing the inference (9-12).
• 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).

1. A Question to Answer
2. Plan the Survey
3. Make the Chart
4. Check the Conclusion
5. Improve and Share

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

1. Vague and Clear
2. Give It More to Go On
3. Say What Kind of Answer You Want
4. Try Again, Differently
5. The Question Shapes the Answer

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

1. Make a Guess First
2. Try It
3. Write Down What Happened
4. Change One Thing
5. Try, Check, Improve

• UAEHumans control AI; protect personal information

1. Exactly What I Said
2. If This, Then That
3. Two Ways to Do It
4. Break It Into Parts
5. Write Your Own Steps

• UAEHumans control AI; protect personal information
• CSTA (US)Explore how one's actions may result in the collection of data.

1. Remember Better Questions
2. Remember Testing Like a Scientist
3. Remember Steps and Rules
4. Put Them Together
5. Ready for the Project

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

1. Choose and Plan
2. Build Version 1
3. Test and Count
4. Get Feedback
5. Version 2

• UAEDesign AI projects that support humans

1. What a Talk Needs
2. Show Your Evidence
3. Say It Clearly and Honestly
4. Answer a Question
5. Give the Talk

• UAEDesign AI projects that support humans
• ADEKBuild AI solution to a real problem

1. Build Your Ask
2. Look at the Reply
3. Change One Thing
4. Round Three
5. My Poster Caption

• OECD–EC (Europe)Know how to prompt an AI system to get accurate feedback and helpful responses (Create 3, basic).

1. Spot What Repeats
2. Repeat Until
3. The Score Box
4. A Repeat Inside a Repeat
5. Pick the Right Structure

• England + CambridgeUse sequence, selection and repetition in programs; work with variables and various forms of input and output
• England + CambridgeDevelop multi-object programs with repetition/iteration (count-controlled loops) and debug systematically, including programming physical devices
• England + CambridgeUse selection (IF/THEN/ELSE), variables and comparison/arithmetic operators when developing algorithms
• CSTA (US)Create programs that use variables to store and modify data.
• CSTA (US)Create programs that include sequences, events, loops, and conditionals.
• Australia + NZDesign, modify and follow algorithms involving sequencing, branching (decisions) and iteration (repetition), implementing them as visual programs

1. Two Screens
2. Four Screen Rules
3. Lay Out My Screen
4. Check It Through Three Users
5. Version Two

• CSTA (US)Brainstorm ways to improve the accessibility and usability of technology products for the diverse needs and wants of users.

1. Team Roles
2. Split the Tasks
3. A Timeline
4. Design Note and Version Log
5. Finish and Review

• CSTA (US)Compare and refine multiple algorithms for the same task and determine which is the most appropriate.
• CSTA (US)Use an iterative process to plan the development of a program by including others' perspectives and considering user preferences.
• CSTA (US)Take on varying roles, with teacher guidance, when collaborating with peers during the design, implementation, and review stages of program development.
• CSTA (US)Describe choices made during program development using code comments, presentations, and demonstrations.
• CSTA (US)Train a model that can make decisions based on defined criteria (e.g., a dichotomous key to determine which movie to see).

1. If-Then Practice
2. Which Question Catches It?
3. Spot the Bug Fast
4. Two Machines, One Job
5. Practice Build: Four-Sided Shapes

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

1. Twenty Questions
2. A Question Tree
3. Rules From Examples
4. Build Your Rule Machine
5. Rules or Examples?

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