Grade 8: units, topics and lessons

1. Arjun's New Year
2. What's an AI Builder?
3. What Changes When You Start Looking Behind The Screen?
4. Your AI Journey
5. User vs Builder
6. Think Like a Builder
7. Why Does Asking How Change Everything?
8. Three Pillars
9. Pillar Check
10. Pillar Sorting
11. AI Systems Breakdown
12. Name the Method
13. AI Builder Lab
14. ChatGPT Shows The Three Pillars In Action
15. Match the AI
16. The Decision Loop
17. Order the Loop
18. Sort the AI Around You
19. Map the AI
20. Builder's Check
21. Explain What Makes Someone Think Like An AI Builder
22. Builder's Words
23. How Your Understanding Is Checked
24. AI Builder Ready!

• CBSEDistinguish key predictive techniques - regression, classification, clustering - and explain key AI domains: Data Science, Computer Vision, Natural Language Processing; explain uses of AI in healthcare, education, transport, communication (Class 7).
• CBSEIdentify and appreciate AI and describe its applications in daily life; relate, apply and reflect on Human-Machine Interaction across the three domains of AI - Data, Computer Vision, and Natural Language Processing; undergo assessment for AI-readiness skills (Unit 1: AI Reflection, Project Cycle and Ethics).
• CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
• England + CambridgeUnderstand what is meant by Artificial Intelligence (AI)
• CSTA (US)Justify the selection of specific control structures when tradeoffs involve implementation, readability, and program performance, and explain the benefits and drawbacks of choices made.
• 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 coding and programming skills as one of the nine core NDLP digital competencies.
• SingaporeUnderstand what AI is.
• SingaporeLearn about AI: understand what AI is and how it works.
• SingaporeDescribe Artificial Intelligence (AI) as the ability of a computer to perform complex tasks without constant human guidance and improve its performance as more data is collected.

1. The Big Day
2. Your Coding Toolkit
3. Why Does One Tiny Program Matter So Much?
4. Two Ways to Run Python
5. Run It: Order the Steps
6. Exact Text or Math?
7. Why Do Coders Care So Much About Their Tools?
8. Predict the Output
9. Coding Setup Lab
10. Google Colab Turns Python Into A Shared Coding Workspace
11. Shell or Script?
12. Example Programs
13. The print() Magic
14. Print Trick Outputs
15. File Name Check
16. Bug Spotter
17. Python Check
18. Explain How A Beginner Turns Code Into A Running Program
19. Python Words
20. First Code Done!

• CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
• England + CambridgeUse two or more programming languages, at least one of which is textual, to solve computational problems; use appropriate data structures (lists, tables, arrays); design and develop modular programs using procedures or functions
• England + CambridgeUse a text-based language (Python recommended) with data types, input/output, variables, arithmetic operators, conditionals, loops and arrays; program physical devices such as micro:bit

1. The Magic Boxes
2. Variable Secrets
3. Why Are Variables So Powerful In Almost Every Program?
4. Name or Value?
5. A Simple Label Can Organize A Whole Program
6. Label Match
7. Why Do Good Variable Names Make Code Feel Smarter?
8. Create Variables
9. The = Sign
10. Student Profile
11. Data Types Intro
12. Strings & Integers
13. Floats & Booleans
14. Type Sorting
15. Variable Design Lab
16. Instagram Profiles Are Built From Named Pieces Of Data
17. Naming Rules
18. Name Check
19. Best Practices
20. Print Methods
21. Print Match
22. Score Tracker
23. Predict the Output
24. App Profile
25. Code Sorting
26. Bug Hunt
27. Variable Quiz
28. Explain Why Variables Make Programs Easier To Manage
29. Variable Vocab
30. F-String Order
31. Variable Master!

• CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions

1. Super Calculator
2. Basic Operators
3. Why Does Coding Math Feel More Powerful Than A Calculator?
4. Variables + Math
5. Simple Operators Can Power Huge Systems
6. Test Scores
7. Operator Match
8. Why Do Tiny Math Mistakes Break Bigger Programs?
9. Integer Division
10. Modulo Operator
11. Exponentiation
12. Special Ops Sort
13. BODMAS Rules
14. Complex BODMAS
15. BODMAS Order
16. Math Formula Lab
17. Uber Fare Estimates Depend On Constant Math
18. Shopping Discount
19. Updating Values
20. Shorthand Ops
21. Shorthand Match
22. Game Score
23. Time Converter
24. Pizza Party
25. Bug Sorting
26. Math Challenge
27. Math Quiz
28. Explain Why Programming Math Needs The Right Operator
29. Math Vocab
30. Code Order
31. Final Match
32. Math Master!

• CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions

1. The Broken Profile Card
2. Understanding Strings
3. String or Number?
4. Why Do Apps Treat Text So Carefully?
5. Joining and Repeating
6. Join & Repeat Match
7. Tiny Symbols Change Big Messages
8. F-Strings Magic
9. Assemble the Welcome Banner
10. String Methods
11. Method Output Match
12. Find, Count & Replace
13. Sort the Methods
14. Multi-Line & Escapes
15. WhatsApp Builds Messages From Strings
16. True or False: String Claims
17. String Master Quiz
18. Explain String Handling
19. Vocabulary Match
20. Text Champion!

• CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions

1. Programs That Listen
2. The input() Function
3. How Does A Program Wait For You?
4. Input to Numbers
5. Type Detective
6. Input Turns Code Into Conversation
7. Build a Calculator
8. Conversion Match
9. Numbers Start As Text
10. Best Practices
11. Input Patterns Sort
12. Spot the Bug!
13. Build the Greeter
14. Interaction Score Lab
15. Duolingo Responds To Every Typed Answer
16. Input Master Quiz
17. Explain User Input
18. Vocab Match
19. Input Champion!

• CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
• England + CambridgeUse two or more programming languages, at least one of which is textual, to solve computational problems; use appropriate data structures (lists, tables, arrays); design and develop modular programs using procedures or functions
• England + CambridgeUse a text-based language (Python recommended) with data types, input/output, variables, arithmetic operators, conditionals, loops and arrays; program physical devices such as micro:bit
• England + CambridgeDescribe the operation of a compiler and an interpreter, including how high-level language is translated and how errors are reported, and explain the role of an IDE and its common functions

1. The Lock Screen Challenge
2. Basic If Statement
3. How Does Code Know Which Path To Take?
4. Comparison Operators
5. Operator Match
6. One Condition Can Change Everything
7. If-Else: Two Paths
8. Which Branch Runs?
9. Indentation Changes Decisions
10. Boolean Logic
11. Logic Sort
12. Indentation & Nesting
13. Inside or Outside?
14. Instagram Login Uses Conditional Checks
15. Assemble the Lock Screen
16. True or False: Bug Hunt
17. Decision Master Quiz
18. Explain Conditional Logic
19. Vocabulary Match
20. Decision Champion!

• CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
• England + CambridgeUnderstand simple Boolean logic (AND, OR, NOT) and its uses in circuits and programming; understand binary representation and carry out simple binary operations
• CSTA (US)Create clearly named variables that represent different data types and perform operations on their values.
• CSTA (US)Design and iteratively develop programs that combine control structures, including nested loops and compound conditionals.
• SingaporeDevelop coding and programming skills as one of the nine core NDLP digital competencies.
• Australia + NZIn authentic contexts, students decompose problems to create simple algorithms using the three building blocks of programming (sequence, selection, iteration), and implement the algorithm as a program using inputs, outputs, sequence, basic selection with comparative operators, and iteration

1. The Ticket Booth Problem
2. The Elif Keyword
3. How Does Code Choose From Many Outcomes?
4. If-Elif-Else Chain
5. Structure Match
6. A Clean Chain Beats Messy Checks
7. Real-World Examples
8. Predict the Branch
9. Complete Examples
10. Order Sort
11. Assemble the Ticket Chain
12. Uber Uses Decision Chains For Ride Options
13. True or False: Code Review
14. Elif Master Quiz
15. Explain Decision Chains
16. Vocabulary Match
17. Elif Champion!

• CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
• CSTA (US)Create clearly named variables that represent different data types and perform operations on their values.
• CSTA (US)Design and iteratively develop programs that combine control structures, including nested loops and compound conditionals.
• SingaporeDevelop coding and programming skills as one of the nine core NDLP digital competencies.
• Australia + NZIn authentic contexts, students decompose problems to create simple algorithms using the three building blocks of programming (sequence, selection, iteration), and implement the algorithm as a program using inputs, outputs, sequence, basic selection with comparative operators, and iteration

1. Loop Magic
2. Why Loops?
3. How Can Two Lines Repeat One Hundred Times?
4. The For Loop
5. range() Decoder
6. Loops Turn Boring Repetition Into Smart Automation
7. Build the Times Table
8. While Loop Basics
9. Password System
10. Pick the Loop Header
11. Choosing the Right Loop Changes Everything
12. Countdown Builder
13. Sum Calculator
14. Pattern Printing
15. Predict the Output
16. Loop Power Lab
17. Spotify Repeats Checks Millions of Times
18. Guess Judge
19. break or continue?
20. String Loops
21. Letter Hunt
22. Vowel Counter Builder
23. Loop Health Check
24. Loop Steps
25. True or False: Loop Claims
26. Loop Quiz
27. Explain Loop Logic
28. Number Analyzer Builder
29. Loop Words
30. Key Takeaways
31. Loop Master!

• CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
• CSTA (US)Create clearly named variables that represent different data types and perform operations on their values.
• CSTA (US)Design and iteratively develop programs that combine control structures, including nested loops and compound conditionals.
• SingaporeDevelop coding and programming skills as one of the nine core NDLP digital competencies.
• Australia + NZIn authentic contexts, students decompose problems to create simple algorithms using the three building blocks of programming (sequence, selection, iteration), and implement the algorithm as a program using inputs, outputs, sequence, basic selection with comparative operators, and iteration

1. Loops Inside Loops
2. Nested Loop Reveal
3. Why Does One Loop Make Another Loop Repeat Too?
4. Nested Loop Basics
5. Grid Cell Match
6. Nested Loops Can Build Whole Worlds
7. Build the Rectangle
8. Right Triangle
9. Triangle Detective
10. Pyramid Pattern
11. Pattern Match
12. Patterns Reveal How Nested Loops Think
13. Seating Chart Builder
14. Tic-Tac-Toe Anatomy
15. Grid Builder Lab
16. Google Sheets Organizes Data In Grids
17. Search Patterns
18. Nested Loop Detective
19. Menu Programs
20. Nested Loop Uses
21. Loop Steps
22. True or False: Nested Loop Claims
23. Pattern Quiz
24. Explain Nested Loop Thinking
25. Pattern Generator Build
26. Pattern Words
27. Key Takeaways
28. Pattern Pro!

• CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
• CSTA (US)Create clearly named variables that represent different data types and perform operations on their values.
• CSTA (US)Design and iteratively develop programs that combine control structures, including nested loops and compound conditionals.
• SingaporeDevelop coding and programming skills as one of the nine core NDLP digital competencies.
• Australia + NZIn authentic contexts, students decompose problems to create simple algorithms using the three building blocks of programming (sequence, selection, iteration), and implement the algorithm as a program using inputs, outputs, sequence, basic selection with comparative operators, and iteration

1. The Storage Problem
2. List Secrets
3. What Are Lists?
4. Creating Lists
5. List Steps
6. How Can One Variable Hold So Much?
7. Why Use Lists?
8. List Benefits
9. One Container Can Organize A Crowd
10. Lists Start Counting At Zero
11. Accessing Elements
12. Zero-Based Secret
13. Index Match
14. Index Order
15. Negative Indices
16. Index Matching
17. List Length
18. Student Marks
19. List Confidence Lab
20. Spotify Organizes Songs Like A List
21. Score Program
22. Real App Data
23. List Types
24. Bug: Index Out of Range
25. List or Not?
26. Bug Hunt
27. Team Manager
28. Quiz Time
29. Explain List Basics
30. Vocabulary
31. Key Takeaways
32. Champion!

• CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions

1. The Dynamic List
2. List Powers
3. How Can A List Change Without Starting Over?
4. Modifying Elements
5. Update Check
6. A List Can Evolve As Fast As An App
7. append() Method
8. insert() Method
9. extend() Method
10. Adding Methods
11. Different Methods Change Lists In Different Ways
12. remove() Method
13. pop() Method
14. clear() and del
15. Removal Results
16. Removing Methods
17. Pick the Remover
18. Attendance Program
19. List Control Lab
20. Trello Boards Change Like Dynamic Lists
21. Leaderboard Check
22. Operation Order
23. Add vs Remove
24. Bug: Multiple Append
25. Crash or Fine?
26. Bug: Loop Modify
27. Bug Hunt
28. List Management
29. Team Manager
30. Quiz Time
31. Explain List Methods
32. Vocabulary
33. Key Takeaways
34. Champion!

• CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions

1. The 100 Names Problem
2. How Can A Program Process 100 Items So Fast?
3. Why Loop?
4. For Loop Basics
5. Trace the Loop
6. One Loop Can Touch Every Item
7. Loop Order
8. Processing Items
9. Total and Average
10. Loop Patterns
11. The Loop Variable Keeps Changing For You
12. Numbered Output & enumerate()
13. Printed or Skipped?
14. Loop Techniques
15. Predict the Grade
16. Once or Every Pass?
17. Processing Power Lab
18. Google Sheets Processes Rows Like A Loop
19. Build a New List
20. Finding Max and Min
21. Built-in Functions
22. Three Loop Traps
23. Bug Hunt
24. Loop Steps
25. Assemble a Counter
26. Score Analyzer
27. Predict the Output
28. Quiz Time
29. Explain List Loops
30. Vocabulary
31. Key Takeaways
32. Champion!

• CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
• 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 + CambridgePlan statistical investigations, design databases/spreadsheets with cell referencing and functions, and know that data is used to solve problems across industries
• CSTA (US)Justify the selection of specific control structures when tradeoffs involve implementation, readability, and program performance, and explain the benefits and drawbacks of choices made.
• CSTA (US)AI content is integrated (not a separate strand): students learn rule-based vs. data-driven approaches, machine learning basics, and related societal impacts such as bias, within the Algorithms & Design strand.
• OECD–EC (Europe)Evaluate the effectiveness of an AI approach considering context, data, efficiency, transparency, and impact (Manage 2, advanced).
• SingaporeDevelop coding and programming skills as one of the nine core NDLP digital competencies.
• SingaporeDefine Machine Learning (ML) as a technique used in AI and explain the difference between ML and traditional programming.
• Korea, Japan, HKUnderstand the characteristics of machine learning versus traditional (rule-based) programming, judge which problems are suited to machine-learning-based AI, and collect data appropriate to the problem.

1. The Sorting Problem
2. How Can One Line Rank 100 Scores?
3. The sort() Method
4. Descending & Alphabetical
5. Sort Method Match
6. Built-In Methods Do Heavy Lifting
7. The reverse() Method
8. Method Sorter
9. Some Methods Change Lists In Place
10. count() and index()
11. Crash or Safe?
12. Method Results Match
13. The copy() Method
14. Copy or Same List?
15. Changes or Reports?
16. Method Mastery Lab
17. Amazon Sorting Uses Ranking And Lookup Rules
18. Leaderboard Predictions
19. Attendance Numbers
20. Find the Median
21. Build a System
22. Three Method Traps
23. Dirty Data, Clean Stats
24. Clean Data, Real Stats
25. Spot the Bug!
26. Method Master Quiz
27. Explain Built-In Methods
28. Method Vocabulary
29. Key Takeaways
30. Chapter Complete!

• CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
• CISCE + statesExplain Machine Learning as computers learning to make predictions/decisions from data without explicit programming, with an example such as predicting house prices using a decision tree
• CSTA (US)Collect data using computational tools and transform the data to make it more useful and reliable.
• CSTA (US)AI content is integrated (not a separate strand): students learn rule-based vs. data-driven approaches, machine learning basics, and related societal impacts such as bias, within the Algorithms & Design strand.
• OECD–EC (Europe)Evaluate the effectiveness of an AI approach considering context, data, efficiency, transparency, and impact (Manage 2, advanced).
• UNESCOAcquire age-appropriate knowledge and skills on datasets — applying tools/languages to acquire, clean and transform data into a form suitable for storing, processing and analysing (e.g. SQL, NoSQL, SparkSQL, Apache Flink) (CG4.2.3.1).
• Korea, Japan, HKUnderstand the characteristics of machine learning versus traditional (rule-based) programming, judge which problems are suited to machine-learning-based AI, and collect data appropriate to the problem.
• Australia + NZUnderstand how mathematical skills and concepts — pattern recognition, spatial reasoning, chance, data and algorithms — are applied to train AI systems

1. The Library Problem
2. How Does Code Find The Right Item So Fast?
3. The 'in' Operator
4. The 'not in' Operator
5. Have It or Buy It?
6. A Tiny Check Can Search A Huge List
7. In or Not In?
8. First Match And All Matches Are Different Jobs
9. The index() Method
10. Safe index() Usage
11. Safe or Unsafe?
12. First Match vs All Matches
13. Linear Search Builder
14. Grade Lookup System
15. Find the Winner
16. Order the Search
17. Search Strategy Lab
18. Spotify Search Relies On Fast Matching
19. Collected or Left Out?
20. Flight Finder Builder
21. Sort It By Hand
22. Binary Search Intro
23. Binary Search Trace
24. Bug 3: Case Sensitivity
25. Spot the Bug!
26. Search Master Quiz
27. Explain List Searching
28. Search Vocabulary
29. Key Takeaways
30. Chapter Complete!

• CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
• CISCE + statesExplain Machine Learning as computers learning to make predictions/decisions from data without explicit programming, with an example such as predicting house prices using a decision tree
• England + CambridgeUnderstand several key algorithms that reflect computational thinking (e.g. sorting and searching); use logical reasoning to compare the utility of alternative algorithms for the same problem
• England + CambridgeWrite algorithms for linear/binary search and insertion/bubble sort; use and implement Abstract Data Types (stack, queue, linked list, binary tree, dictionary); compare algorithms using Big O notation for time and space complexity
• CSTA (US)Use and adapt classic algorithms to solve computational problems.
• CSTA (US)Evaluate algorithms in terms of their efficiency, correctness, and clarity.
• CSTA (US)Illustrate the flow of execution of a recursive algorithm.
• CSTA (US)AI content is integrated (not a separate strand): students learn rule-based vs. data-driven approaches, machine learning basics, and related societal impacts such as bias, within the Algorithms & Design strand.
• OECD–EC (Europe)Evaluate the effectiveness of an AI approach considering context, data, efficiency, transparency, and impact (Manage 2, advanced).
• Korea, Japan, HKUnderstand the characteristics of machine learning versus traditional (rule-based) programming, judge which problems are suited to machine-learning-based AI, and collect data appropriate to the problem.

1. The Phone Book Problem
2. Dictionary Powers
3. How Can A Name Instantly Find A Value?
4. Key or Value?
5. Labels Beat Positions For Lookup
6. Create & Access
7. Dictionary Syntax Match
8. Keys Need To Be Unique To Stay Useful
9. Predict the Lookup
10. Lists vs Dictionaries
11. Lists or Dictionaries?
12. Key Rules, Value Freedom
13. Can It Be a Key?
14. Dictionaries Everywhere
15. Safe Access: in and get()
16. Safe Access Match
17. Lookup Power Lab
18. Contacts Apps Depend On Key-Value Lookup
19. Build the Capitals Quiz
20. Menu Math Quiz
21. Build a Dictionary
22. Bug Corner
23. Spot the Bug!
24. Dictionary Master Quiz
25. Explain Key-Value Lookup
26. Dictionary Vocabulary
27. Key Takeaways
28. Chapter Complete!

• CBSEOrganise and represent data in various forms (text, numbers, images, sounds); recognise simple patterns in data and make decisions based on observations (Class 6); collect/organise structured data and create bar/line/pie charts (Class 7).
• CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
• 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 + CambridgePlan statistical investigations, design databases/spreadsheets with cell referencing and functions, and know that data is used to solve problems across industries
• CSTA (US)AI content is integrated (not a separate strand): students learn rule-based vs. data-driven approaches, machine learning basics, and related societal impacts such as bias, within the Algorithms & Design strand.
• OECD–EC (Europe)Evaluate the effectiveness of an AI approach considering context, data, efficiency, transparency, and impact (Manage 2, advanced).
• Korea, Japan, HKUnderstand the characteristics of machine learning versus traditional (rule-based) programming, judge which problems are suited to machine-learning-based AI, and collect data appropriate to the problem.
• ChinaStudents collect, organize and analyze data, and solve real-world problems through project-based learning

1. The Living Dictionary
2. How Can One Dictionary Keep Changing Safely?
3. Adding & Updating Entries
4. Add or Update?
5. Mutable Dictionaries Feel Like Live Systems
6. Inventory Update Quiz
7. Add vs Modify Match
8. Updating And Overwriting Are Not The Same
9. Three Ways to Remove
10. Removal Methods Sort
11. Dictionary Methods
12. Methods in Action
13. Dictionary Control Lab
14. Shopping Carts Update Like Dictionaries
15. Methods Match Game
16. The update() Method
17. Discount Logic Quiz
18. Build a Grade System
19. Bug Corner
20. Spot the Bug!
21. Operations Master Quiz
22. Explain Dictionary Updates
23. Operations Vocabulary
24. Key Takeaways
25. Chapter Complete!

• CBSEOrganise and represent data in various forms (text, numbers, images, sounds); recognise simple patterns in data and make decisions based on observations (Class 6); collect/organise structured data and create bar/line/pie charts (Class 7).
• CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
• CISCE + statesExplain Machine Learning as computers learning to make predictions/decisions from data without explicit programming, with an example such as predicting house prices using a decision tree
• CSTA (US)AI content is integrated (not a separate strand): students learn rule-based vs. data-driven approaches, machine learning basics, and related societal impacts such as bias, within the Algorithms & Design strand.
• OECD–EC (Europe)Evaluate the effectiveness of an AI approach considering context, data, efficiency, transparency, and impact (Manage 2, advanced).
• Korea, Japan, HKUnderstand the characteristics of machine learning versus traditional (rule-based) programming, judge which problems are suited to machine-learning-based AI, and collect data appropriate to the problem.
• ChinaStudents collect, organize and analyze data, and solve real-world problems through project-based learning

1. Folders Inside Folders
2. Four Nesting Types
3. How Can One Structure Hold So Many Layers?
4. Lists Inside Dictionaries
5. Pick the Structure
6. Simple Blocks Can Build Complex Worlds
7. Dictionaries Inside Lists
8. Predict the Access
9. Nested Access Match
10. Access Order Changes The Meaning
11. Lists Inside Lists
12. Dictionaries in Dictionaries
13. Nested Access Quiz
14. Access Type Sort
15. Structures in the Wild
16. Menu Lookup Challenge
17. Nested Navigation Lab
18. Instagram Stores Profiles As Nested Data
19. Order the Access
20. Modify Nested Quiz
21. Bug Corner
22. Spot the Bug!
23. Nested Master Quiz
24. Explain Nested Data
25. Nested Vocabulary
26. Key Takeaways
27. Chapter Complete!

• CBSEOrganise and represent data in various forms (text, numbers, images, sounds); recognise simple patterns in data and make decisions based on observations (Class 6); collect/organise structured data and create bar/line/pie charts (Class 7).
• CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
• 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 + CambridgeDesign, use and evaluate computational abstractions that model the state and behaviour of real-world problems and physical systems
• CSTA (US)Understand that representation includes modalities (text, speech, audio, image, video) and symbolic mappings (text, graphs).
• CSTA (US)AI content is integrated (not a separate strand): students learn rule-based vs. data-driven approaches, machine learning basics, and related societal impacts such as bias, within the Algorithms & Design strand.
• AI4K12 (US)Progress from giving examples of symbols encountered in daily life such as restroom icons and card suits (K-2), to explaining hierarchical tree structures such as ancestry trees and taxonomies (3-5), to translating a structure like a game board, road map, or mind map into a labeled graph of nodes and links (6-8), to translating the premises of a syllogism from English into predicate-logic notation and completing the inference (9-12).
• OECD–EC (Europe)Evaluate the effectiveness of an AI approach considering context, data, efficiency, transparency, and impact (Manage 2, advanced).
• 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.
• ChinaStudents collect, organize and analyze data, and solve real-world problems through project-based learning

1. What Makes AI Intelligent?
2. Maya's Big Question
3. Why Can Two Smart Systems Solve The Same Problem Differently?
4. Two Types of Intelligence
5. Two Types: Quick Match!
6. Different Kinds Of Intelligence Can Still Reach Useful Answers
7. Rule-Based AI
8. Traffic Light: Predict!
9. Order the Steps: Rule-Based AI
10. Learning-Based AI
11. Smart Traffic Light
12. Order the Steps: Learning-Based AI
13. Match the Features!
14. The Same Input Can Trigger Very Different Strategies
15. Calculator: True or False?
16. Movie Recommender: Predict the Pick!
17. Spam Filters: True or False?
18. Sort the Examples!
19. When Rules Win, When Learning Wins
20. AI Strategy Lab
21. YouTube Mixes Learning With Fixed Rules
22. Choose the Type!
23. Real AI or Not? True or False
24. Predict the Crash!
25. Bug Corner: Mistake 3
26. Spot the AI Type!
27. Knowledge Check!
28. Explain Two AI Styles
29. New Words!
30. Key Takeaways
31. Chapter Complete!

• ADEKAI vs non-AI decision, AI in daily life
• ADEKTrained-on-examples / rule-vs-learned
• CBSESummarise basic ideas and concepts of AI and its applications; describe key differences between machine intelligence and human intelligence; explain the difference between automation and AI using real-world cases; differentiate supervised, unsupervised, and reinforcement learning (Class 6).
• CISCE + statesExplain the evolution of computing from deterministic (binary logic, conditional gates) to probabilistic approaches and why AI is needed for probabilistic, real-life problems
• CISCE + statesCompare automated vs autonomous systems and deterministic vs probabilistic decision making; contrast human decision making (subjective) with machine decision making (objective) via object-classification examples
• CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
• 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 + CambridgeExplore Artificial Intelligence (computers taking information from surroundings to produce outputs), machine learning, autonomous robotics, and augmented reality applications
• 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)Accurately describe how a specific AI tool operates (Engage 2, intermediate).
• OECD–EC (Europe)Identify the benefits and drawbacks of each AI approach for a task (Manage 2, intermediate).
• 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).
• SingaporeUnderstand what AI is.
• SingaporeLearn about AI: understand what AI is and how it works.
• Korea, Japan, HKExplain the concept and characteristics of artificial intelligence and distinguish AI software from non-AI software ([9정04-01]).
• Korea, Japan, 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.

1. Simple Decision Systems
2. Build Your Morning Decision!
3. How Does A Computer Choose Without Guessing?
4. What is a Decision System?
5. Simple Rule Chains Can Feel Surprisingly Smart
6. Study Advisor AI
7. Study Advisor: Predict the Advice!
8. Decision Trees
9. Build the Decision!
10. Order Matters More Than It Looks
11. Traffic Signal AI
12. Traffic Decision Logic
13. Traffic Scenarios!
14. Order the Decision Levels!
15. Sports Team Selector AI
16. Match the Situation to the Pick!
17. Helper Functions
18. Naming Your Inputs: Keyword Arguments
19. Predict the Call: Keyword Arguments
20. Decision Tree Lab
21. Google Maps Makes Layered Route Decisions
22. Restaurant Chooser AI
23. Restaurant Chooser: Predict the Verdict!
24. Sort the Factors!
25. Weird Inputs: True or False?
26. Weather Advisor AI
27. Weather Table Match!
28. Bug 1: Forgetting Else
29. Predict the Output!
30. Clean Code: True or False?
31. Spot Good Decisions!
32. Knowledge Check!
33. Explain Decision Systems
34. New Words!
35. Key Takeaways
36. Chapter Complete!

• CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
• CISCE + statesExplain Machine Learning as computers learning to make predictions/decisions from data without explicit programming, with an example such as predicting house prices using a decision tree
• CSTA (US)Create clearly named variables that represent different data types and perform operations on their values.
• CSTA (US)Design and iteratively develop programs that combine control structures, including nested loops and compound conditionals.
• CSTA (US)Create procedures with parameters to organize code and make it easier to reuse.
• CSTA (US)Decompose problems into smaller components through systematic analysis, using constructs such as procedures, modules, and/or objects.
• CSTA (US)Create artifacts by using procedures within a program, combinations of data and procedures, or independent but interrelated programs.
• CSTA (US)AI content is integrated (not a separate strand): students learn rule-based vs. data-driven approaches, machine learning basics, and related societal impacts such as bias, within the Algorithms & Design strand.
• OECD–EC (Europe)Evaluate the effectiveness of an AI approach considering context, data, efficiency, transparency, and impact (Manage 2, advanced).
• SingaporeDevelop coding and programming skills as one of the nine core NDLP digital competencies.
• Korea, Japan, HKUnderstand the characteristics of machine learning versus traditional (rule-based) programming, judge which problems are suited to machine-learning-based AI, and collect data appropriate to the problem.
• Korea, Japan, HKUnderstand means of expressing algorithms and be able to write programs (including defining/using functions) to make use of computers and networks, and evaluate/improve the resulting program.
• Australia + NZIn authentic contexts, students decompose problems to create simple algorithms using the three building blocks of programming (sequence, selection, iteration), and implement the algorithm as a program using inputs, outputs, sequence, basic selection with comparative operators, and iteration

1. Building a Quiz Bot
2. Quiz Bot Jobs: True or False?
3. How Can A Quiz Bot Judge So Many Answers So Fast?
4. Data Structures
5. A Few Simple Structures Can Power A Full Learning Loop
6. Quiz Bot Match
7. Feedback Makes A Quiz Bot Feel Smarter
8. Smart Feedback
9. Sort the Steps
10. Code Logic Flow
11. The Quiz Bot Program
12. Build It!
13. Quiz Bot Tuning Lab
14. Duolingo Relies On Fast Checking And Feedback
15. Quiz Bots Everywhere: Match!
16. String Cleanup: Predict!
17. Bug: Case Sensitivity
18. Messy Typing: Survives or Breaks?
19. The Zero-Score Mystery!
20. Spot the Bot!
21. Knowledge Check
22. Explain Quiz Bots
23. New Words
24. Key Takeaways
25. How Your Quiz Bot Plan Is Judged
26. Champion!

• ADEKDebug an algorithm/program
• CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
• CISCE + statesExplain Machine Learning as computers learning to make predictions/decisions from data without explicit programming, with an example such as predicting house prices using a decision tree
• CSTA (US)AI content is integrated (not a separate strand): students learn rule-based vs. data-driven approaches, machine learning basics, and related societal impacts such as bias, within the Algorithms & Design strand.
• OECD–EC (Europe)Evaluate the effectiveness of an AI approach considering context, data, efficiency, transparency, and impact (Manage 2, advanced).
• Korea, Japan, HKUnderstand the characteristics of machine learning versus traditional (rule-based) programming, judge which problems are suited to machine-learning-based AI, and collect data appropriate to the problem.

1. Pattern Matching Basics
2. Pattern Discovery
3. How Can One Tiny Rule Search Through Huge Text?
4. The 'in' Keyword
5. Method Match
6. Small Text Checks Can Drive Giant Search Systems
7. Predict the Output
8. One Matching Rule Can Change Every Result
9. Sort Techniques
10. Topic Detector
11. Pattern Pipeline
12. Pattern Matching Lab
13. Gmail Filters Messages With Pattern Checks
14. Chatbot Patterns
15. Voice Command Match
16. Intent Sort
17. Bug: Messy Text
18. Will It Match?
19. Spot the Pattern!
20. Knowledge Check
21. Explain Pattern Matching
22. New Words
23. Key Takeaways
24. Bug Fix Match
25. Champion!

• CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
• CISCE + statesExplain Machine Learning as computers learning to make predictions/decisions from data without explicit programming, with an example such as predicting house prices using a decision tree
• CSTA (US)AI content is integrated (not a separate strand): students learn rule-based vs. data-driven approaches, machine learning basics, and related societal impacts such as bias, within the Algorithms & Design strand.
• OECD–EC (Europe)Evaluate the effectiveness of an AI approach considering context, data, efficiency, transparency, and impact (Manage 2, advanced).
• Korea, Japan, HKUnderstand the characteristics of machine learning versus traditional (rule-based) programming, judge which problems are suited to machine-learning-based AI, and collect data appropriate to the problem.

1. Simple Text Classifier
2. Sorting Secrets
3. How Can One Message Belong To Just The Right Category?
4. Keyword Magic
5. Category Match
6. Simple Keyword Checks Can Organize Huge Streams Of Information
7. Weighted Keywords
8. A Tiny Weight Change Can Shift The Winning Category
9. Multi-Label Magic
10. Classifier Types
11. Build Pipeline
12. Classification Tuning Lab
13. Gmail Sorts Messages With Similar Scoring Ideas
14. Email Keyword Match
15. Spam or Work?
16. Classifier Traps
17. Clear or Unclear?
18. Specific or Ambiguous?
19. Spot the Classifier!
20. Knowledge Check
21. Explain Text Classification
22. New Words
23. Key Takeaways
24. Fix the Classifier
25. Champion!

• CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
• CISCE + statesExplain Machine Learning as computers learning to make predictions/decisions from data without explicit programming, with an example such as predicting house prices using a decision tree
• CSTA (US)AI content is integrated (not a separate strand): students learn rule-based vs. data-driven approaches, machine learning basics, and related societal impacts such as bias, within the Algorithms & Design strand.
• AI4K12 (US)Progress from 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).
• OECD–EC (Europe)Evaluate the effectiveness of an AI approach considering context, data, efficiency, transparency, and impact (Manage 2, advanced).
• Korea, Japan, HKUnderstand the characteristics of machine learning versus traditional (rule-based) programming, judge which problems are suited to machine-learning-based AI, and collect data appropriate to the problem.
• Korea, Japan, HKUnderstand speech recognition and natural-language-processing techniques and try a word-association-style generative-AI tool to see how language models generate text.

1. Sentiment Detection
2. Emotion Basics
3. How Can A Computer Sense Feelings In Plain Text?
4. Word Lists
5. Word Match
6. Emotion Can Be Estimated From Tiny Language Clues
7. One Word Can Change The Entire Emotional Score
8. Word Weights
9. Strength Sort
10. Negation Magic
11. Negation Game
12. Sentiment Tuning Lab
13. Amazon Summaries Depend On Large-Scale Review Sentiment
14. Review Analyzer
15. Aspect Match
16. Social Sentiment
17. How Sure?
18. Detection Pipeline
19. Fooled or Fine?
20. Bug Fix Match
21. Spot Sentiment!
22. Knowledge Check
23. Explain Sentiment Detection
24. New Words
25. Key Takeaways
26. AI Journey Recap
27. Champion!

• CISCE + statesRepresent data and write Python programs using datatypes, variables, operators, conditional statements, control statements and functions
• CISCE + statesExplain Machine Learning as computers learning to make predictions/decisions from data without explicit programming, with an example such as predicting house prices using a decision tree
• CSTA (US)AI content is integrated (not a separate strand): students learn rule-based vs. data-driven approaches, machine learning basics, and related societal impacts such as bias, within the Algorithms & Design strand.
• AI4K12 (US)Progress from 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).
• OECD–EC (Europe)Evaluate the effectiveness of an AI approach considering context, data, efficiency, transparency, and impact (Manage 2, advanced).
• Korea, Japan, HKUnderstand the characteristics of machine learning versus traditional (rule-based) programming, judge which problems are suited to machine-learning-based AI, and collect data appropriate to the problem.

1. Maya's Discovery
2. Meet Teachable Machine
3. How Can A Machine Learn From Examples Without Code?
4. Three Model Types
5. Model Match-Up
6. A Browser Tool Can Turn Everyday Examples Into Working AI
7. A Better Set Of Examples Can Completely Change The Model
8. Classes & Training Data
9. Class or Example?
10. Programming vs ML
11. Sort the Methods
12. Training Steps
13. Training Quality Lab
14. Google Teachable Machine Makes Prototyping Fast
15. Sort the Projects
16. Fix the Model
17. Does It Work for Everyone?
18. Fairness Check
19. Spot the ML!
20. Knowledge Quiz
21. Explain Teachable Machine
22. Vocabulary Match-Up
23. Key Takeaways
24. Chapter Complete!

• ADEKTrained-on-examples / rule-vs-learned
• ADEKClassification vs generation, how generative AI is trained
• ADEKHuman/AI-generated art, ethics of generative tools
• CBSEDefine Generative AI and classify different kinds; explain how Generative AI works and how it learns, differentiate from conventional AI; apply Generative AI tools to create content; understand ethical considerations of using Generative AI (Unit 4: Introduction to Generative AI).
• CBSEDefine Statistical Data and its applications; define No-Code and Low-Code AI and identify differences from coded AI; use a no-code tool (Orange Data Mining) to perform data exploration, modeling, and evaluation, relating stages to the AI project cycle (Unit 4: Statistical Data, assessed via practicals only).
• CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
• CISCE + statesBuild a program (using block-based tools such as PictoBlox) that recognizes facial expressions using a pre-trained model, and explore gesture-based software applications for accessibility
• CISCE + statesInteract with Large Language Models (LLMs) and apply prompt engineering to perform text generation and image creation, and understand how responsive AI agents function in educational contexts
• IBUnderstand the bias inherent in content produced by an AI tool and be able to critically review AI-generated output
• CSTA (US)Analyze the risks, benefits, and effectiveness of using AI for specific tasks (e.g., coding, brainstorming), including when AI is used to fully automate a process or is used with a human-in-the-loop approach.
• CSTA (US)Using sensor data (e.g., from autonomous vehicles), train an AI model.
• CSTA (US)Evaluate how AI use impacts an individual's decision making and other behavior.
• AI4K12 (US)Progress from locating and identifying computer sensors such as cameras and microphones on everyday devices (K-2), to illustrating how computer sensing differs from human sensing (3-5), to giving examples of intelligent agents (e.g. self-driving cars) combining data from multiple sensors (6-8), to describing the limitations and advantages of specific sensor types such as cameras, microphones, radar, lidar, GPS, and accelerometers (9-12).
• AI4K12 (US)Progress from giving examples of intelligent vs. non-intelligent machines and discussing what makes a machine 'intelligent' (K-2), to demonstrating a machine perception tool (e.g. speech transcription, object recognition) and explaining why it is perception rather than mere sensing (3-5), to giving examples of specialized perception algorithms (face detection, obstacle detection, speech recognition, etc.) (6-8), to explaining how perception algorithms power real-world applications like license plate readers and Google Lens (9-12).
• AI4K12 (US)Progress from discussing why intelligent agents need to understand languages other than English (K-2), to discussing why domain knowledge must be broad enough to cover accents, dialects, and musical genres (3-5), to describing how a vision system can exhibit cultural bias if it lacks knowledge of objects outside its creators' culture, e.g. non-US traffic signs (6-8), to describing technical difficulties (e.g. low-contrast faces, children's speech) in making perception systems work well for diverse groups (9-12).
• AI4K12 (US)Progress from identifying 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.
• 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)Evaluate whether an AI output should be accepted, revised, or rejected, and justify the decision (Engage 3, advanced).
• OECD–EC (Europe)Critically evaluate how AI feedback shapes one's ideas and reflect on the creative process (Create 3, advanced).
• OECD–EC (Europe)Compare task complexity, the need for human judgment, and ethical implications when deciding whether to use AI (Manage 1, advanced).
• OECD–EC (Europe)Know how to formulate unambiguous search queries so a conversational agent or smart speaker can respond as required (AI example 29, competence 1.1).
• OECD–EC (Europe)Be aware that AI systems are typically developed in English-speaking contexts and can work less accurately in non-English contexts, e.g. machine translation between less-used language pairs (AI example 55, competence 2.5).
• OECD–EC (Europe)Be interested in experimenting with various types of AI systems depending on personal needs (e.g. virtual assistants, image analysis, speech/face recognition, robots) (AI example 71, competence 5.2).
• OECD–EC (Europe)Use AI tools within a guided national learning app designed to build critical thinking by prompting students to find answers and form their own conclusions rather than supplying direct answers.
• UNESCOExplain that AI is human-led and that decisions by AI creators shape how AI systems affect human rights, human-AI interaction, and society; recognise that AI cannot replace human thinking or intellectual development (CG4.1.1.1).
• UNESCOCritically examine the dynamic relationship between human agency and machine agency, including cases where a degree of machine agency may be warranted (e.g. medical pattern detection) versus cases requiring human control (CG4.1.1.3).
• UNESCOCritically examine, from legal, ethical and logical perspectives, whether AI should or should not be used for a given real-world challenge, and define the boundaries, goals and constraints of a problem before attempting to train an AI model (CG4.1.4.1).
• UNESCOCritically evaluate and leverage free/open-source AI datasets (e.g. MNIST, CIFAR, ImageNet) and AI/algorithm libraries (e.g. Teachable Machine, PyTorch, Keras) to address authentic, task-based problems (CG4.2.3.3).
• UNESCOTransfer values, knowledge and skills to craft a task-based AI tool by customizing an existing AI model or toolkit, including modifying/creating a dataset for real-world contexts (e.g. scraping data with tools like BeautifulSoup, cleaning/encoding/preprocessing it) (CG4.3.3.1).
• UNESCOEnhance creativity in customizing AI toolkits and coding — leveraging AI development platforms, enhancing datasets, and modifying programming code (including open-source options) to design and test creative task-based AI tools (CG4.3.3.2).
• SingaporeLeverage AI tools effectively for learning and for daily life.
• SingaporeLearn with AI: apply AI to support and enhance one's own learning process.
• SingaporeThink critically about AI's biases, question AI-generated outputs, and discern or identify false or misleading information produced by AI.
• Korea, Japan, HKJudge, based on scientific reasoning, the appropriate and effective use of information and information technology suited to purpose and situation, and consider what constitutes a desirable information society.
• Korea, Japan, HKFindings from FY2023 pilot schools showed some students uncritically accepted AI answers as fact or could not judge appropriate situations for use on their own — confirming an explicit target competency: judge the appropriateness/veracity of generative-AI output rather than accept it uncritically.
• ChinaStudents understand the technical characteristics of generative AI and its significance to national strategy
• Australia + NZStudents and educators use generative AI tools in ways designed to enhance teaching and learning outcomes rather than replace core learning processes

1. Arjun's Smart Game
2. Scratch Gets Superpowers
3. How Can A Scratch Project Suddenly See And React?
4. AI or Not?
5. A Few New Blocks Can Transform How A Game Feels
6. Extension Match-Up
7. The Same Project Idea Can Feel Totally Different With AI Input
8. Core or Extension?
9. Video Sensing Magic
10. Game Building Steps
11. Scratch AI Design Lab
12. Scratch Projects Can Prototype Assistive Tools Fast
13. Inside Arjun's Game
14. Project Match-Up
15. Spot the AI!
16. Knowledge Quiz
17. Explain Scratch AI Extensions
18. New Vocabulary
19. Key Takeaways
20. Chapter Complete!

• CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
• 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
• IBUnderstand the bias inherent in content produced by an AI tool and be able to critically review AI-generated output
• CSTA (US)Discuss issues of bias and accessibility in the design of existing technologies.
• CSTA (US)Analyze the risks, benefits, and effectiveness of using AI for specific tasks (e.g., coding, brainstorming), including when AI is used to fully automate a process or is used with a human-in-the-loop approach.
• CSTA (US)Using sensor data (e.g., from autonomous vehicles), train an AI model.
• CSTA (US)Create a program using available AI tools, AI plugins, APIs, and/or AI models, with ethical considerations for the model's end users and others impacted: fairness, bias, and accuracy, and then create a model card.
• 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)Evaluate whether an AI output should be accepted, revised, or rejected, and justify the decision (Engage 3, advanced).
• OECD–EC (Europe)Compare task complexity, the need for human judgment, and ethical implications when deciding whether to use AI (Manage 1, advanced).
• UNESCOCritically evaluate and leverage free/open-source AI datasets (e.g. MNIST, CIFAR, ImageNet) and AI/algorithm libraries (e.g. Teachable Machine, PyTorch, Keras) to address authentic, task-based problems (CG4.2.3.3).
• UNESCOTransfer values, knowledge and skills to craft a task-based AI tool by customizing an existing AI model or toolkit, including modifying/creating a dataset for real-world contexts (e.g. scraping data with tools like BeautifulSoup, cleaning/encoding/preprocessing it) (CG4.3.3.1).
• UNESCOEnhance creativity in customizing AI toolkits and coding — leveraging AI development platforms, enhancing datasets, and modifying programming code (including open-source options) to design and test creative task-based AI tools (CG4.3.3.2).
• SingaporeUse basic coding concepts (variables, loops, functions) in a block-based coding platform to build simple programs.
• 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. Emma's Design Challenge
2. Types of AI Tools
3. How Can A Picture Be Created From Just Words?
4. Popular AI Tools
5. Tool Match-Up
6. A Good Prompt Can Unlock Entire Imaginary Worlds
7. Tiny Prompt Changes Can Completely Reshape The Image
8. Sort the Methods
9. How AI Creates Images
10. Early or Late?
11. Order the Steps
12. Prompt Design Lab
13. Canva Uses AI To Speed Up Visual Creation
14. Pick the Tool for the Job
15. Spot the AI!
16. Knowledge Quiz
17. Explain AI Image Tools
18. Vocabulary Match-Up
19. Key Takeaways
20. Chapter Complete!

• UAEAI-manipulated media: safety and security
• CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
• IBUnderstand the bias inherent in content produced by an AI tool and be able to critically review AI-generated output
• CSTA (US)Analyze the risks, benefits, and effectiveness of using AI for specific tasks (e.g., coding, brainstorming), including when AI is used to fully automate a process or is used with a human-in-the-loop approach.
• CSTA (US)Using sensor data (e.g., from autonomous vehicles), train an AI model.
• 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)Evaluate whether an AI output should be accepted, revised, or rejected, and justify the decision (Engage 3, advanced).
• OECD–EC (Europe)Compare task complexity, the need for human judgment, and ethical implications when deciding whether to use AI (Manage 1, advanced).
• UNESCOCritically evaluate and leverage free/open-source AI datasets (e.g. MNIST, CIFAR, ImageNet) and AI/algorithm libraries (e.g. Teachable Machine, PyTorch, Keras) to address authentic, task-based problems (CG4.2.3.3).
• UNESCOTransfer values, knowledge and skills to craft a task-based AI tool by customizing an existing AI model or toolkit, including modifying/creating a dataset for real-world contexts (e.g. scraping data with tools like BeautifulSoup, cleaning/encoding/preprocessing it) (CG4.3.3.1).
• UNESCOEnhance creativity in customizing AI toolkits and coding — leveraging AI development platforms, enhancing datasets, and modifying programming code (including open-source options) to design and test creative task-based AI tools (CG4.3.3.2).
• 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
• 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. Carlos's Writing Helper
2. What is NLP?
3. How Can A Machine Respond Like It Understands Writing?
4. AI Text Capabilities
5. Tool Match-Up
6. Responsible Use
7. Sort the Uses
8. How AI Writes
9. Order the Steps
10. Same Prompt, Different Answers
11. Compare the Runs
12. Sampling Quiz
13. Prompt Quality Lab
14. AI's Job or Your Job?
15. Grammarly Helps People Revise Instead Of Starting From Zero
16. AI Limitations
17. Trust Check
18. Spot the AI Use!
19. Knowledge Quiz
20. New Vocabulary
21. Vocab Match
22. Key Takeaways
23. Chapter Complete!

• UAEUse generative AI models to create new content
• ADEKExplain why generative AI can fabricate/hallucinate
• ADEKCheck AI output before trusting
• CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
• IBAct with integrity and honesty and take responsibility for one's own actions when making design decisions
• IBUnderstand the bias inherent in content produced by an AI tool and be able to critically review AI-generated output
• CSTA (US)Analyze the risks, benefits, and effectiveness of using AI for specific tasks (e.g., coding, brainstorming), including when AI is used to fully automate a process or is used with a human-in-the-loop approach.
• CSTA (US)Using sensor data (e.g., from autonomous vehicles), train an AI model.
• 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)Verify AI outputs against trusted sources (Engage 3, intermediate).
• OECD–EC (Europe)Evaluate whether an AI output should be accepted, revised, or rejected, and justify the decision (Engage 3, advanced).
• OECD–EC (Europe)Compare AI-generated suggestions to one's own ideas during brainstorming (Create 1, intermediate).
• OECD–EC (Europe)Refine work through iterative exchanges with an AI system (Create 3, intermediate).
• OECD–EC (Europe)Compare task complexity, the need for human judgment, and ethical implications when deciding whether to use AI (Manage 1, advanced).
• OECD–EC (Europe)Break a problem into parts and consider AI support for specific steps (Manage 3, intermediate).
• OECD–EC (Europe)Know how to formulate unambiguous search queries so a conversational agent or smart speaker can respond as required (AI example 29, competence 1.1).
• UNESCOCritically evaluate and leverage free/open-source AI datasets (e.g. MNIST, CIFAR, ImageNet) and AI/algorithm libraries (e.g. Teachable Machine, PyTorch, Keras) to address authentic, task-based problems (CG4.2.3.3).
• UNESCOTransfer values, knowledge and skills to craft a task-based AI tool by customizing an existing AI model or toolkit, including modifying/creating a dataset for real-world contexts (e.g. scraping data with tools like BeautifulSoup, cleaning/encoding/preprocessing it) (CG4.3.3.1).
• UNESCOEnhance creativity in customizing AI toolkits and coding — leveraging AI development platforms, enhancing datasets, and modifying programming code (including open-source options) to design and test creative task-based AI tools (CG4.3.3.2).
• SingaporeLeverage AI tools effectively for learning and for daily life.
• SingaporeLearn with AI: apply AI to support and enhance one's own learning process.
• SingaporeThink critically about AI's biases, question AI-generated outputs, and discern or identify false or misleading information produced by AI.
• ChinaStudents deepen ethical understanding and critically identify misinformation/hallucination risks in generative-AI outputs
• Australia + NZUnderstand how mathematical skills and concepts — pattern recognition, spatial reasoning, chance, data and algorithms — are applied to train AI systems
• Australia + NZStudents and educators use generative AI tools in ways designed to enhance teaching and learning outcomes rather than replace core learning processes

1. Aisha's Helpful Project
2. Voice AI Technologies
3. How Can A Device Turn Messy Sound Into Meaningful Words?
4. Tech Comparison
5. Tech Match-Up
6. Typing vs Voice
7. Sort the Features
8. How Voice AI Works
9. Order the Steps
10. Hearing or Understanding?
11. Voice Accuracy Lab
12. Alexa Relies On Constant Listening And Careful Speech Processing
13. Pipeline Check
14. Privacy Concerns
15. Spot the Voice AI!
16. Knowledge Quiz
17. New Vocabulary
18. Vocab Match
19. Key Takeaways
20. How Your Voice AI Explanation Is Judged
21. Chapter Complete!

• UAEHow AI generates outputs from learned patterns
• CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
• IBUnderstand the bias inherent in content produced by an AI tool and be able to critically review AI-generated output
• CSTA (US)Analyze the risks, benefits, and effectiveness of using AI for specific tasks (e.g., coding, brainstorming), including when AI is used to fully automate a process or is used with a human-in-the-loop approach.
• CSTA (US)Using sensor data (e.g., from autonomous vehicles), train an AI model.
• AI4K12 (US)Progress from giving examples of intelligent vs. non-intelligent machines and discussing what makes a machine 'intelligent' (K-2), to demonstrating a machine perception tool (e.g. speech transcription, object recognition) and explaining why it is perception rather than mere sensing (3-5), to giving examples of specialized perception algorithms (face detection, obstacle detection, speech recognition, etc.) (6-8), to explaining how perception algorithms power real-world applications like license plate readers and Google Lens (9-12).
• AI4K12 (US)Progress from describing the sounds that make up spoken language and giving example words for each vowel sound (K-2), to illustrating how sequences of sounds are recognized as candidate words despite unclear signals (3-5), to illustrating how sequences of words are recognized as phrases by resolving ambiguity with higher-level information (6-8), to illustrating the full abstraction hierarchy from waveform to sentence in speech understanding (9-12).
• AI4K12 (US)Progress from 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)Evaluate whether an AI output should be accepted, revised, or rejected, and justify the decision (Engage 3, advanced).
• OECD–EC (Europe)Compare task complexity, the need for human judgment, and ethical implications when deciding whether to use AI (Manage 1, advanced).
• UNESCOCritically evaluate and leverage free/open-source AI datasets (e.g. MNIST, CIFAR, ImageNet) and AI/algorithm libraries (e.g. Teachable Machine, PyTorch, Keras) to address authentic, task-based problems (CG4.2.3.3).
• UNESCOTransfer values, knowledge and skills to craft a task-based AI tool by customizing an existing AI model or toolkit, including modifying/creating a dataset for real-world contexts (e.g. scraping data with tools like BeautifulSoup, cleaning/encoding/preprocessing it) (CG4.3.3.1).
• UNESCOEnhance creativity in customizing AI toolkits and coding — leveraging AI development platforms, enhancing datasets, and modifying programming code (including open-source options) to design and test creative task-based AI tools (CG4.3.3.2).
• 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
• 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. Liam's Art Dilemma
2. What is AI Art?
3. How Can A Machine Make Art That Feels Original?
4. Popular AI Art Tools
5. Tool Match-Up
6. Human vs AI Art
7. Sort the Differences
8. Hear the Picture
9. Person or Pattern Model?
10. Prompt Engineering
11. Build the Prompt
12. AI Art Prompt Lab
13. Vivid or Vague?
14. Midjourney Shows How Fast AI Art Iteration Can Be
15. AI Art Ethics
16. Name the Ethical Issue
17. Who Is the Author?
18. Discussion: Who Gets the Credit?
19. My Credit Label
20. Spot the AI Art!
21. When the Picture Is a Person
22. Honest Use or Harm?
23. Critique Like an Investigator
24. Investigate the Viral Image
25. Knowledge Quiz
26. New Vocabulary
27. Vocab Match
28. Key Takeaways
29. The Contest Label
30. Chapter Complete!

• UAEAI-manipulated media: safety and security
• UAEHow AI is governed through national policies
• UAEUse generative AI models to create new content
• ADEKHuman/AI-generated art + ethics
• ADEKGenerative image tools (GAN/diffusion), deepfakes
• ADEKHuman/AI-generated art, ethics of generative tools
• CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
• IBUnderstand the bias inherent in content produced by an AI tool and be able to critically review AI-generated output
• CSTA (US)Incorporate existing code, media, and libraries into original programs, and give attribution.
• CSTA (US)Analyze the risks, benefits, and effectiveness of using AI for specific tasks (e.g., coding, brainstorming), including when AI is used to fully automate a process or is used with a human-in-the-loop approach.
• CSTA (US)Using sensor data (e.g., from autonomous vehicles), train an AI model.
• CSTA (US)Explore the tradeoffs related to human agency (including privacy, safety, creativity, autonomy, and intellectual property) when AI is used.
• AI4K12 (US)Progress from 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 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)Evaluate whether an AI output should be accepted, revised, or rejected, and justify the decision (Engage 3, advanced).
• OECD–EC (Europe)Identify unintended impacts of an AI use that differ from its immediate goals (Engage 7, intermediate).
• OECD–EC (Europe)Experiment with multiple AI tools to develop diverse ideas (Create 2, intermediate).
• OECD–EC (Europe)Refine work through iterative exchanges with an AI system (Create 3, intermediate).
• OECD–EC (Europe)Consider when attribution, permission, or avoiding AI use is the appropriate choice (Create 4, intermediate).
• OECD–EC (Europe)Compare task complexity, the need for human judgment, and ethical implications when deciding whether to use AI (Manage 1, advanced).
• OECD–EC (Europe)Know how to formulate unambiguous search queries so a conversational agent or smart speaker can respond as required (AI example 29, competence 1.1).
• 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).
• OECD–EC (Europe)Be interested in experimenting with various types of AI systems depending on personal needs (e.g. virtual assistants, image analysis, speech/face recognition, robots) (AI example 71, competence 5.2).
• 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).
• 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).
• 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).
• UNESCOCritically evaluate and leverage free/open-source AI datasets (e.g. MNIST, CIFAR, ImageNet) and AI/algorithm libraries (e.g. Teachable Machine, PyTorch, Keras) to address authentic, task-based problems (CG4.2.3.3).
• UNESCOTransfer values, knowledge and skills to craft a task-based AI tool by customizing an existing AI model or toolkit, including modifying/creating a dataset for real-world contexts (e.g. scraping data with tools like BeautifulSoup, cleaning/encoding/preprocessing it) (CG4.3.3.1).
• UNESCOEnhance creativity in customizing AI toolkits and coding — leveraging AI development platforms, enhancing datasets, and modifying programming code (including open-source options) to design and test creative task-based AI tools (CG4.3.3.2).
• SingaporeLearn to use AI: use AI tools safely and responsibly.
• SingaporeLearn with AI: apply AI to support and enhance one's own learning process.
• Korea, Japan, HKIdentify AI ethical principles and analyse ethical issues raised by real AI applications (bias, fairness, accountability), as a dedicated unit distinct from general 'societal impact'.
• Australia + NZStudents and educators use generative AI tools in ways designed to enhance teaching and learning outcomes rather than replace core learning processes
• Australia + NZStudents 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. Emma's Helper Bot
2. Meet the Chatbot
3. The Mixed-Up Reply
4. Chatbot Types and Jobs
5. Match Bot Types and Jobs
6. How Bots Understand
7. Which Superpower?
8. Sort the Bots
9. Build a Chatbot
10. Tune the Chatbot
11. Chatbots as Study Coaches
12. Knowing Limits
13. Spot the Chatbot
14. Chatbot Quiz
15. Vocabulary Match
16. Key Takeaways
17. Chatbot Champion!

• CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
• IBUnderstand the bias inherent in content produced by an AI tool and be able to critically review AI-generated output
• CSTA (US)Analyze the risks, benefits, and effectiveness of using AI for specific tasks (e.g., coding, brainstorming), including when AI is used to fully automate a process or is used with a human-in-the-loop approach.
• CSTA (US)Using sensor data (e.g., from autonomous vehicles), train an AI model.
• 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)Evaluate whether an AI output should be accepted, revised, or rejected, and justify the decision (Engage 3, advanced).
• OECD–EC (Europe)Compare task complexity, the need for human judgment, and ethical implications when deciding whether to use AI (Manage 1, advanced).
• UNESCOCritically evaluate and leverage free/open-source AI datasets (e.g. MNIST, CIFAR, ImageNet) and AI/algorithm libraries (e.g. Teachable Machine, PyTorch, Keras) to address authentic, task-based problems (CG4.2.3.3).
• UNESCOTransfer values, knowledge and skills to craft a task-based AI tool by customizing an existing AI model or toolkit, including modifying/creating a dataset for real-world contexts (e.g. scraping data with tools like BeautifulSoup, cleaning/encoding/preprocessing it) (CG4.3.3.1).
• UNESCOEnhance creativity in customizing AI toolkits and coding — leveraging AI development platforms, enhancing datasets, and modifying programming code (including open-source options) to design and test creative task-based AI tools (CG4.3.3.2).
• 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. Arjun's Smart Home
2. Inside a Smart Home
3. The 6 PM Mystery
4. Anatomy of a Routine
5. Match the Parts
6. The Home's Senses
7. Trigger, Condition, or Action?
8. Pick the Sensor
9. Run the Routine
10. Tune the Automations
11. Alexa Routines in Smart Homes
12. Should You Automate It?
13. Smart Call or Bad Call?
14. Smart Home Quiz
15. Vocabulary Match
16. Key Takeaways
17. Smart Home Architect!

• ADEKSensors / perception
• 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)
• CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
• IBUnderstand the bias inherent in content produced by an AI tool and be able to critically review AI-generated output
• 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)
• CSTA (US)Debate when humans should or should not use AI to perform a specific task.
• CSTA (US)Analyze the risks, benefits, and effectiveness of using AI for specific tasks (e.g., coding, brainstorming), including when AI is used to fully automate a process or is used with a human-in-the-loop approach.
• CSTA (US)Use sensors to collect data, and then train an AI model using the sensor data.
• CSTA (US)Using sensor data (e.g., from autonomous vehicles), train an AI model.
• AI4K12 (US)Progress from locating and identifying computer sensors such as cameras and microphones on everyday devices (K-2), to illustrating how computer sensing differs from human sensing (3-5), to giving examples of intelligent agents (e.g. self-driving cars) combining data from multiple sensors (6-8), to describing the limitations and advantages of specific sensor types such as cameras, microphones, radar, lidar, GPS, and accelerometers (9-12).
• AI4K12 (US)Progress from 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)Evaluate whether an AI output should be accepted, revised, or rejected, and justify the decision (Engage 3, advanced).
• OECD–EC (Europe)Determine whether AI is appropriate for a specific task (Manage 1, intermediate).
• OECD–EC (Europe)Compare task complexity, the need for human judgment, and ethical implications when deciding whether to use AI (Manage 1, advanced).
• OECD–EC (Europe)Compare AI outputs to desired results and redirect the approach as needed (Manage 4, intermediate).
• UNESCOUnderstand the necessity of exercising sufficient human control over AI, including the consequences when human oversight fails or is absent (CG4.1.1.2).
• UNESCOCritically examine, from legal, ethical and logical perspectives, whether AI should or should not be used for a given real-world challenge, and define the boundaries, goals and constraints of a problem before attempting to train an AI model (CG4.1.4.1).
• UNESCOCritically evaluate and leverage free/open-source AI datasets (e.g. MNIST, CIFAR, ImageNet) and AI/algorithm libraries (e.g. Teachable Machine, PyTorch, Keras) to address authentic, task-based problems (CG4.2.3.3).
• UNESCOTransfer values, knowledge and skills to craft a task-based AI tool by customizing an existing AI model or toolkit, including modifying/creating a dataset for real-world contexts (e.g. scraping data with tools like BeautifulSoup, cleaning/encoding/preprocessing it) (CG4.3.3.1).
• UNESCOEnhance creativity in customizing AI toolkits and coding — leveraging AI development platforms, enhancing datasets, and modifying programming code (including open-source options) to design and test creative task-based AI tools (CG4.3.3.2).
• SingaporeTrain a robot or AI-enabled prototype to respond to external signals through hands-on exploration and tinkering.
• SingaporeLearn with AI: apply AI to support and enhance one's own learning process.
• Korea, Japan, HKIdentify problems that can be solved with an AI system and apply an AI system suited to that problem ([9정04-04]).
• Australia + NZStudents and educators use generative AI tools in ways designed to enhance teaching and learning outcomes rather than replace core learning processes

1. The AI Toolkit
2. Your Toolkit So Far
3. The Tool Mix-Up
4. Text & Language AI
5. Match the Tools
6. Voice, Art & Builder Tools
7. Ready-Made or You Build It?
8. Sort the Categories
9. Train It Right
10. Design the AI Toolkit
11. Gemini and Mixed Media
12. The Builder's Question
13. AI Tools Challenge
14. Checkpoint Quiz
15. Master Vocabulary Match
16. Checkpoint Takeaways
17. Checkpoint Champion!

• CBSEDistinguish key predictive techniques - regression, classification, clustering - and explain key AI domains: Data Science, Computer Vision, Natural Language Processing; explain uses of AI in healthcare, education, transport, communication (Class 7).
• CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
• IBUnderstand the bias inherent in content produced by an AI tool and be able to critically review AI-generated output
• CSTA (US)Analyze the risks, benefits, and effectiveness of using AI for specific tasks (e.g., coding, brainstorming), including when AI is used to fully automate a process or is used with a human-in-the-loop approach.
• CSTA (US)Using sensor data (e.g., from autonomous vehicles), train an AI model.
• AI4K12 (US)Progress from demonstrating ways intelligent agents don't understand things the way people do, e.g. no frustration at repeated questions (K-2), to demonstrating what a 'broad AI'/general-AI system could do that current narrow AI cannot (3-5), to defining criteria for consciousness (self-awareness, personal history) and evaluating AI systems or fictional AI characters against them (6-8), to debating alternative perspectives on human vs. artificial intelligence, including the Turing test and artificial superintelligence (9-12).
• AI4K12 (US)Progress from 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)Evaluate whether an AI output should be accepted, revised, or rejected, and justify the decision (Engage 3, advanced).
• OECD–EC (Europe)Compare task complexity, the need for human judgment, and ethical implications when deciding whether to use AI (Manage 1, advanced).
• UNESCOCritically evaluate and leverage free/open-source AI datasets (e.g. MNIST, CIFAR, ImageNet) and AI/algorithm libraries (e.g. Teachable Machine, PyTorch, Keras) to address authentic, task-based problems (CG4.2.3.3).
• UNESCOTransfer values, knowledge and skills to craft a task-based AI tool by customizing an existing AI model or toolkit, including modifying/creating a dataset for real-world contexts (e.g. scraping data with tools like BeautifulSoup, cleaning/encoding/preprocessing it) (CG4.3.3.1).
• UNESCOEnhance creativity in customizing AI toolkits and coding — leveraging AI development platforms, enhancing datasets, and modifying programming code (including open-source options) to design and test creative task-based AI tools (CG4.3.3.2).
• 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. The AI Challenge
2. Project Ideas
3. Right-Size the Idea
4. Planning Your Project
5. Small Starts, Big Results
6. Match the Tools
7. Prototype Thinking
8. Project Lifecycle
9. Match Project to Tool Combo
10. Sort the Phases
11. Project Steps
12. Scope the First Version
13. Canva's Fast Prototypes
14. Teamwork Power
15. Project Challenge
16. Challenge Quiz
17. Explain Prototype Planning
18. Vocabulary Match
19. Key Takeaways
20. Byte's Message
21. Challenge Champion!

• CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
• 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
• IBUnderstand the bias inherent in content produced by an AI tool and be able to critically review AI-generated output
• 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)Seek and incorporate feedback from team members and users to refine a solution that meets user needs.
• CSTA (US)Analyze the risks, benefits, and effectiveness of using AI for specific tasks (e.g., coding, brainstorming), including when AI is used to fully automate a process or is used with a human-in-the-loop approach.
• CSTA (US)Using sensor data (e.g., from autonomous vehicles), train an AI model.
• 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)Evaluate whether an AI output should be accepted, revised, or rejected, and justify the decision (Engage 3, advanced).
• OECD–EC (Europe)Experiment with multiple AI tools to develop diverse ideas (Create 2, intermediate).
• OECD–EC (Europe)Compare task complexity, the need for human judgment, and ethical implications when deciding whether to use AI (Manage 1, advanced).
• 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).
• UNESCOCritically evaluate and leverage free/open-source AI datasets (e.g. MNIST, CIFAR, ImageNet) and AI/algorithm libraries (e.g. Teachable Machine, PyTorch, Keras) to address authentic, task-based problems (CG4.2.3.3).
• UNESCOTransfer values, knowledge and skills to craft a task-based AI tool by customizing an existing AI model or toolkit, including modifying/creating a dataset for real-world contexts (e.g. scraping data with tools like BeautifulSoup, cleaning/encoding/preprocessing it) (CG4.3.3.1).
• UNESCOEnhance creativity in customizing AI toolkits and coding — leveraging AI development platforms, enhancing datasets, and modifying programming code (including open-source options) to design and test creative task-based AI tools (CG4.3.3.2).
• 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. The Wrong Solution
2. Problem Examples
3. The Missing Step
4. Why Definition Matters
5. Clear Problems Save Time
6. Match the Clarity
7. Spot the Problem Signal
8. Problem Statement
9. Match Research Methods
10. Sort Statements
11. Definition Steps
12. Strengthen the Problem Statement
13. Schools Use Surveys First
14. Finding Root Cause
15. Problem Challenge
16. Definition Quiz
17. Explain Root Cause
18. Vocabulary Match
19. Key Takeaways
20. Byte's Message
21. Problem Solver!

• CBSEDescribe the stages of the AI project cycle (Define Problem, Collect Data, Test AI Tools, Reflect and Improve); apply no-code tools to tackle real-world problems and reflect on their utility/effectiveness; analyse contributions of AI to fields like healthcare, automation, and education, understanding both benefits and risks; describe AI ethics as values/guidelines for responsible AI (Class 8).
• CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
• IBGiven a design situation, identify a problem that needs to be solved and analyse the need for a solution
• IBConduct an inquiry into the nature of a design problem to inform the design brief
• IBWrite a detailed design specification that drives the development of a solution
• IBDevelop and present a proposed design solution derived from the specification
• IBUnderstand the bias inherent in content produced by an AI tool and be able to critically review AI-generated output
• CSTA (US)Analyze the risks, benefits, and effectiveness of using AI for specific tasks (e.g., coding, brainstorming), including when AI is used to fully automate a process or is used with a human-in-the-loop approach.
• CSTA (US)Using sensor data (e.g., from autonomous vehicles), train an AI model.
• 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)Evaluate whether an AI output should be accepted, revised, or rejected, and justify the decision (Engage 3, advanced).
• OECD–EC (Europe)Compare task complexity, the need for human judgment, and ethical implications when deciding whether to use AI (Manage 1, advanced).
• UNESCOPractise skills to identify and scope a problem that could be solved by building or training a new AI model, based on a simulation project (CG4.1.4.2).
• UNESCODevelop skills to assess an AI system's needs for data, algorithms, programming languages, software, and computing hardware, including feasibility given regulatory/ethical restrictions and cost (CG4.1.4.3).
• UNESCOCritically evaluate and leverage free/open-source AI datasets (e.g. MNIST, CIFAR, ImageNet) and AI/algorithm libraries (e.g. Teachable Machine, PyTorch, Keras) to address authentic, task-based problems (CG4.2.3.3).
• UNESCOTransfer values, knowledge and skills to craft a task-based AI tool by customizing an existing AI model or toolkit, including modifying/creating a dataset for real-world contexts (e.g. scraping data with tools like BeautifulSoup, cleaning/encoding/preprocessing it) (CG4.3.3.1).
• UNESCOEnhance creativity in customizing AI toolkits and coding — leveraging AI development platforms, enhancing datasets, and modifying programming code (including open-source options) to design and test creative task-based AI tools (CG4.3.3.2).
• SingaporeApply computational thinking and design-thinking concepts to create a solution for a real-world problem (secondary school students).
• SingaporeLearn with AI: apply AI to support and enhance one's own learning process.
• ChinaStudents apply a 'needs analysis → technology matching → outcome evaluation' engineering-thinking framework to AI projects
• Australia + NZStudents and educators use generative AI tools in ways designed to enhance teaching and learning outcomes rather than replace core learning processes

1. The AI Hammer
2. When AI Shines
3. Timer or AI?
4. AI Suitability Check
5. Simple Tools Can Win
6. Match Solutions
7. Overkill Happens a Lot
8. When NOT to Use AI
9. Match Simple Tools
10. Sort Solutions
11. Decision Process
12. Green Light or Red Flag?
13. Rules Before AI
14. Data Requirements
15. AI Decision Challenge
16. AI Suitability Quiz
17. Explain AI Suitability
18. Vocabulary Match
19. Key Takeaways
20. Byte's Message
21. Decision Master!

• ADEKTrained-on-examples / rule-vs-learned
• CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
• IBUnderstand the bias inherent in content produced by an AI tool and be able to critically review AI-generated output
• England + CambridgeYoung people should understand how AI works, its capabilities and limitations, and learn to use it effectively without becoming dependent on it; this should be delivered primarily through Computing rather than assumed to develop automatically
• England + CambridgeEvaluate models and simulators for real-life scenarios; create relational databases with linked tables; understand Big Data and describe its applications
• CSTA (US)Debate when humans should or should not use AI to perform a specific task.
• CSTA (US)Analyze the risks, benefits, and effectiveness of using AI for specific tasks (e.g., coding, brainstorming), including when AI is used to fully automate a process or is used with a human-in-the-loop approach.
• CSTA (US)Using sensor data (e.g., from autonomous vehicles), train an AI model.
• CSTA (US)AI content is integrated (not a separate strand): students learn rule-based vs. data-driven approaches, machine learning basics, and related societal impacts such as bias, within the Algorithms & Design strand.
• AI4K12 (US)Progress from modeling the use of a classification or search algorithm to solve a simple problem (K-2), to describing differences between decision-tree and neural-network classification algorithms (3-5), to comparing several algorithms that could solve a specific reasoning problem based on input data and decision complexity (6-8), to listing, for each of eight reasoning-problem types, an algorithm that could solve it (9-12).
• AI4K12 (US)Progress from describing and 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)Evaluate whether an AI output should be accepted, revised, or rejected, and justify the decision (Engage 3, advanced).
• OECD–EC (Europe)Determine whether AI is appropriate for a specific task (Manage 1, intermediate).
• OECD–EC (Europe)Compare task complexity, the need for human judgment, and ethical implications when deciding whether to use AI (Manage 1, advanced).
• OECD–EC (Europe)Identify the benefits and drawbacks of each AI approach for a task (Manage 2, intermediate).
• OECD–EC (Europe)Describe an AI system's purpose, intended users, and basic constraints (Shape 1, intermediate).
• OECD–EC (Europe)States that a foundational understanding of AI's technical functioning and the ability to recognise chances, limits and risks are indispensable for students to use AI responsibly and reflectively ('digital mündig'); calls on the Länder to integrate AI-related informatics competences and 'Bildung in der digitalen Welt' competences into curricula for all subjects and school types, without itself specifying discrete grade-level student outcomes.
• UNESCOCritically examine, from legal, ethical and logical perspectives, whether AI should or should not be used for a given real-world challenge, and define the boundaries, goals and constraints of a problem before attempting to train an AI model (CG4.1.4.1).
• UNESCOCritically evaluate and leverage free/open-source AI datasets (e.g. MNIST, CIFAR, ImageNet) and AI/algorithm libraries (e.g. Teachable Machine, PyTorch, Keras) to address authentic, task-based problems (CG4.2.3.3).
• UNESCOTransfer values, knowledge and skills to craft a task-based AI tool by customizing an existing AI model or toolkit, including modifying/creating a dataset for real-world contexts (e.g. scraping data with tools like BeautifulSoup, cleaning/encoding/preprocessing it) (CG4.3.3.1).
• UNESCOEnhance creativity in customizing AI toolkits and coding — leveraging AI development platforms, enhancing datasets, and modifying programming code (including open-source options) to design and test creative task-based AI tools (CG4.3.3.2).
• SingaporeUnderstand AI's uses, risks and limitations, and apply ethical considerations (e.g. academic integrity, proper data handling) when using AI to support learning.
• SingaporeIdentify and explain AI's potential benefits, limitations and risks.
• SingaporeLearn with AI: apply AI to support and enhance one's own learning process.
• Korea, Japan, HKIdentify problems that can be solved with an AI system and apply an AI system suited to that problem ([9정04-04]).
• Australia + NZBuild knowledge about the function and purpose of AI systems (Years 5-6), progressing to evaluating the advantages and disadvantages of AI and other emerging technologies (Years 7-8)
• Australia + NZStudents and educators use generative AI tools in ways designed to enhance teaching and learning outcomes rather than replace core learning processes

1. The Big Problem
2. Design Thinking Steps
3. Start With Empathy
4. Design Steps Match
5. Define Your Problem
6. Strong or Vague?
7. Brainstorming Ideas
8. Sort Solution Ideas
9. Build-Test-Learn
10. Balance the MVP
11. Figma Before Code
12. MVP Explained
13. Build the MVP
14. Spot Good Design
15. Design Quiz
16. Design Vocabulary
17. Key Takeaways
18. Maya's Project, Step by Step
19. Design Champion!

• ADEKCareers, future of work
• CBSEDecompose any problem using the 5W1H method; apply Design Thinking methodology; create empathy maps; align problems to Sustainable Development Goals; express a solution to a problem in non-technical words (Unit 4: Introduction to Capstone Project).
• IBGiven a design situation, identify a problem that needs to be solved and analyse the need for a solution
• IBConduct an inquiry into the nature of a design problem to inform the design brief
• IBWrite a detailed design specification that drives the development of a solution
• IBDevelop and present a proposed design solution derived from the specification
• CSTA (US)Evaluate and refine computational artifacts to make them more usable and accessible.
• OECD–EC (Europe)Compare AI-generated suggestions to one's own ideas during brainstorming (Create 1, intermediate).
• SingaporeApply computational thinking and design-thinking concepts to create a solution for a real-world problem (secondary school students).
• Korea, Japan, HKUnderstand and apply the concepts and methods of information design (including universal design, usability and accessibility) needed for effective communication with a given audience, and evaluate/improve created content.
• ChinaStudents apply a 'needs analysis → technology matching → outcome evaluation' engineering-thinking framework to AI projects
• Australia + NZDesign the user experience of a digital system generating or processing data, and evaluate the suitability of user interfaces for defined purposes and end-users, applying accessibility standards for diverse audiences

1. The Data Question
2. Types of Data
3. Which Data Type?
4. Quality Over Quantity
5. Data Match Game
6. How Much Data?
7. Data Sources
8. Where Would You Get It?
9. Good vs Bad Data
10. Data Labeling Steps
11. Tune the Dataset
12. Photos Need Labels
13. Watch for Bias!
14. Spot Data Issues
15. Data Quiz
16. Data Vocabulary
17. Key Takeaways
18. Fix the Dataset
19. Data Expert!

• CBSEDecompose any problem using the 5W1H method; apply Design Thinking methodology; create empathy maps; align problems to Sustainable Development Goals; express a solution to a problem in non-technical words (Unit 4: Introduction to Capstone Project).
• CSTA (US)Students build data fluency and examine the societal impacts of large-scale data collection used to train AI systems.
• OECD–EC (Europe)Compare the importance of data selection and collection methods for an AI system (Shape 3, intermediate).
• UNESCOCategorise general safety risks of AI and typical AI incidents, deepen knowledge of human rights to data protection and privacy and creators' legal responsibility to collect data only with informed consent, and practise strategies to ensure personal data is collected, used, shared, archived and deleted only with consent (CG4.2.2.3).
• Korea, Japan, HKExamine problems that can arise in the collection and use of data needed for AI learning (bias, privacy, consent) and propose legally, socially and ethically valid solutions ([9정04-05]).

1. Time to Build!
2. Parts of an AI
3. How Training Works
4. The Training Loop
5. AI Building Match
6. No-Code AI Tools
7. Building Step by Step
8. Types of Simple AI
9. AI Build Order
10. Training Pile or Testing Pile?
11. Find the Training Sweet Spot
12. Teachable Machine in Action
13. Avoid These Mistakes
14. Spot Good Building
15. Building AI Quiz
16. AI Builder Vocabulary
17. Key Takeaways
18. The Builder's Road
19. How Your Training Calls Are Judged
20. AI Builder!

• CBSEDecompose any problem using the 5W1H method; apply Design Thinking methodology; create empathy maps; align problems to Sustainable Development Goals; express a solution to a problem in non-technical words (Unit 4: Introduction to Capstone Project).
• IBPlan the creation of the chosen solution and follow that plan to build a prototype sufficient for testing and evaluation
• 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.
• SingaporeDesign and build a prototype that incorporates artificial intelligence to address a problem.

1. Did It Work?
2. Why Test AI?
3. The Misleading Score
4. Understanding Accuracy
5. Testing Terms Match
6. Edge Cases Matter
7. Types of Errors
8. Normal or Edge Case?
9. Error Severity Sort
10. Testing Process Order
11. The Threshold Tradeoff
12. Spam Filters Need Careful Testing
13. Pro Technique Match
14. Spot Good Testing
15. Who Answers for the Decision?
16. Opaque Decision Check
17. Testing Quiz
18. Explain Testing Risk
19. Testing Vocabulary
20. Key Takeaways
21. Byte's Final Check
22. Testing Expert!

• UAEEvaluate model performance
• UAEPrivacy, accountability and personal rights
• CBSEDecompose any problem using the 5W1H method; apply Design Thinking methodology; create empathy maps; align problems to Sustainable Development Goals; express a solution to a problem in non-technical words (Unit 4: Introduction to Capstone Project).
• IBDesign tests to evaluate a solution, carry out those tests, and objectively evaluate the solution's success
• IBIdentify areas where a design solution could be improved and explain the solution's impact on the client or target audience
• 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)Refine computational models based on the data they have generated.
• 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)Assess an AI system's performance using defined criteria and benchmark tests (Shape 2, intermediate).
• UNESCOUnderstand that human accountability is a legal obligation of AI creators and AI service providers, who are answerable for safety incidents, ethical risks in design/training, and misuses of their AI service (CG4.2.1.1).
• Australia + NZEvaluate existing and student-developed digital solutions and information systems, and their suitability for personal, environmental and social criteria, against needs and requirements

1. AI Isn't Perfect
2. Four Power-Ups
3. The 99% Trap
4. Power of More Data
5. Strategy Match
6. Augmentation Is a Shortcut
7. Keep or Fix?
8. The Overfitting Trap
9. Quick vs Deep Fixes
10. The Improvement Loop
11. The Overfitting Curve
12. Photo Apps Improve Over Time
13. Borrow a Brain, Ask a Team
14. Pro Technique Match
15. Read the Errors
16. Spot Good Improvement
17. Improvement Quiz
18. Explain Overfitting
19. Improvement Vocabulary
20. Key Takeaways
21. Byte's Final Check
22. Improvement Master!

• CBSEDecompose any problem using the 5W1H method; apply Design Thinking methodology; create empathy maps; align problems to Sustainable Development Goals; express a solution to a problem in non-technical words (Unit 4: Introduction to Capstone Project).
• England + CambridgeEvaluate models and simulators for real-life scenarios; create relational databases with linked tables; understand Big Data and describe its applications

1. AI Everywhere!
2. AI in Healthcare
3. The Trust Question
4. AI for the Planet
5. AI Project Match
6. Accessibility Is Innovation
7. AI in Education
8. Creative Tool Match
9. AI Impact Sort
10. Real Project Journey
11. Measure Real-World Impact
12. Be My Eyes
13. Who Does It Help?
14. Case File #6: Whose Values?
15. Whose Values? Check
16. Spot Real AI
17. Real-World Quiz
18. Explain AI Impact
19. Project Vocabulary
20. Key Takeaways
21. The Builder's Six Questions
22. Real-World AI Explorer!

• UAEEthics, cultural values and inclusion guide AI decisions
• CBSEDecompose any problem using the 5W1H method; apply Design Thinking methodology; create empathy maps; align problems to Sustainable Development Goals; express a solution to a problem in non-technical words (Unit 4: Introduction to Capstone Project).
• England + CambridgeUnderstand that robots can work autonomously and describe robotics benefits in manufacturing/food production
• CSTA (US)Identify the intended and unintended impacts of AI on society, including government, education, entertainment, culture, careers, and national security, while considering how these impacts may differ among diverse communities.
• Korea, Japan, HKEvaluate the societal impact of AI, including both opportunities and risks it poses to communities, drawing on real cases (e.g. AlphaFold's protein-folding breakthrough, AI-assisted astronomy, AI-generated music).

1. The Final Review
2. Six Steps, One Loop
3. Which Skill Comes First?
4. Data Essentials
5. One Cycle, Many Skills
6. Concept Match
7. Review Builds Mastery
8. Building AI
9. Testing Term Match
10. Sort the Steps
11. Order the Process
12. Balance the Builder Skills
13. Duolingo's Learning Loop
14. Diagnose the Failure
15. Spot the Concept
16. Unit Review Quiz
17. Explain the Full Cycle
18. Vocabulary Review
19. Key Takeaways
20. Maya's Unit Journey
21. Unit Review Complete!

• CBSEDecompose any problem using the 5W1H method; apply Design Thinking methodology; create empathy maps; align problems to Sustainable Development Goals; express a solution to a problem in non-technical words (Unit 4: Introduction to Capstone Project).

1. The Big Challenge
2. Step 1: Empathize
3. The Missing Reader
4. Step 2: Define
5. One Problem, Full System
6. Feature Match
7. Bring Your Own Problem
8. Scope a Classmate's Problem
9. Why Capstones Matter
10. Step 3: Ideate
11. Data Planning
12. Data Priorities
13. Step 4: Prototype
14. Balance the Project
15. Spotify Tests the Whole Flow
16. Step 5: Test
17. Spot the Issues
18. Challenge Quiz
19. Explain the Capstone
20. Capstone Term Match
21. Key Takeaways
22. Maya's Capstone Moves
23. How Maya's Capstone Calls Are Judged
24. Challenge Champion!

• UAEDesign AI projects for real-world problems
• ADEKBuild an AI solution to a real problem
• ADEKStakeholder debate / ethical matrix
• CBSEDescribe the stages of the AI project cycle (Define Problem, Collect Data, Test AI Tools, Reflect and Improve); apply no-code tools to tackle real-world problems and reflect on their utility/effectiveness; analyse contributions of AI to fields like healthcare, automation, and education, understanding both benefits and risks; describe AI ethics as values/guidelines for responsible AI (Class 8).
• CBSEDecompose any problem using the 5W1H method; apply Design Thinking methodology; create empathy maps; align problems to Sustainable Development Goals; express a solution to a problem in non-technical words (Unit 4: Introduction to Capstone Project).
• IBGiven a design situation, identify a problem that needs to be solved and analyse the need for a solution
• IBConduct an inquiry into the nature of a design problem to inform the design brief
• IBWrite a detailed design specification that drives the development of a solution
• IBDevelop and present a proposed design solution derived from the specification
• CSTA (US)Discuss issues of bias and accessibility in the design of existing technologies.
• OECD–EC (Europe)Propose specific design changes to improve an AI system for human well-being (Shape 4, intermediate).
• 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).
• UNESCOPractise skills to identify and scope a problem that could be solved by building or training a new AI model, based on a simulation project (CG4.1.4.2).
• UNESCODevelop skills to assess an AI system's needs for data, algorithms, programming languages, software, and computing hardware, including feasibility given regulatory/ethical restrictions and cost (CG4.1.4.3).
• SingaporeApply computational thinking and design-thinking concepts to create a solution for a real-world problem (secondary school students).
• SingaporeDevelop data competencies as one of the nine core NDLP digital competencies.
• ChinaStudents apply a 'needs analysis → technology matching → outcome evaluation' engineering-thinking framework to AI projects

1. The Retrospective
2. Match the Moment
3. What Changed Most?
4. Your Growth
5. Skills Stack Over Time
6. Lessons Learned
7. Reflection Builds Memory
8. The Three Retro Questions
9. Practice or Capstone?
10. Keep or Change?
11. The Unit Timeline
12. Measure Your Growth Mix
13. Duolingo Uses Review Loops
14. Your Reflection
15. Unit Check
16. Retro Quiz
17. Explain Your Growth
18. Retro Term Match
19. Unit 4 Takeaways
20. One More Loop
21. Rules for the Next Loop
22. Keep or Drop the Rule?
23. This Is Governance
24. Draft, Try, Review
25. Build the Rule Set
26. Write Your Class AI Rules
27. Rules Check
28. Unit 4 Complete!

• UAEHow AI is governed through national policies
• ADEKDraft/commit to ethical-use guidelines
• ADEKTake part in shaping AI policy
• ADEKExplain governance as distinct from building
• ADEKAI governance/civic participation
• CBSEDecompose any problem using the 5W1H method; apply Design Thinking methodology; create empathy maps; align problems to Sustainable Development Goals; express a solution to a problem in non-technical words (Unit 4: Introduction to Capstone Project).
• OECD–EC (Europe)Compare AI-generated suggestions to one's own ideas during brainstorming (Create 1, intermediate).
• OECD–EC (Europe)Consider when attribution, permission, or avoiding AI use is the appropriate choice (Create 4, intermediate).
• OECD–EC (Europe)Propose specific design changes to improve an AI system for human well-being (Shape 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).
• 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).
• SingaporeLearn to use AI: use AI tools safely and responsibly.
• Australia + NZStudents understand that generative AI tools should be used in ways that benefit all members of the school community and support wellbeing, not just individual convenience
• Australia + NZStudents 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. Career Discovery
2. Tech Careers
3. Who Builds the Product?
4. Creative AI Jobs
5. Many Paths Check
6. Career Match
7. Whose Job Is It?
8. Impact Careers
9. Skills Revealed
10. Career Sort
11. Career Path Steps
12. Duolingo Needs More Than Coders
13. Build It Together
14. Future AI Jobs
15. Career Check
16. Career Quiz
17. Explain a Career Fit
18. Career Vocabulary
19. Key Takeaways
20. Mindset Check
21. Career Explorer!

• UAEAI applied across sectors
• ADEKCareers
• CBSEDecompose any problem using the 5W1H method; apply Design Thinking methodology; create empathy maps; align problems to Sustainable Development Goals; express a solution to a problem in non-technical words (Unit 4: Introduction to Capstone Project).
• CSTA (US)Compare tradeoffs associated with computing technologies that affect people's everyday activities and career options.
• CSTA (US)Describe the roles that humans play (including in data curation and labeling) in creating and refining AI models.
• AI4K12 (US)Progress from describing jobs that no longer exist due to past technology advances, e.g. farriers replaced by auto mechanics (K-2), to describing how a specific job will change due to AI/robotic technology, e.g. warehouse workers alongside robots (3-5), to predicting a new job type that might arise or an existing job that might change/disappear due to AI adoption (6-8), to investigating the specific skills needed for AI-enabled careers across the workforce, not just for programmers (9-12).
• UNESCOUnderstand human roles across the AI life cycle (researchers, architecture/data engineers, beta testers, ethics/safety regulators, human-AI interface specialists, compliance auditors) and the main ethical issues in using data to train AI systems (CG4.1.3.4).
• Korea, Japan, HKAnalyse how AI adoption is changing the nature of jobs and the skills workers need, and reflect on implications for the student's own future career choices.

1. Show Your Work
2. Portfolio Basics
3. Proof Check
4. What to Include
5. Small Projects Can Prove Big Skills
6. Portfolio Match
7. Present Your Work
8. Process or Just Result?
9. Maya's Library Case Study
10. Where to Build
11. Content Sort
12. Build Order
13. GitHub Shows the Work
14. Pro Tips
15. Portfolio Check
16. Portfolio Quiz
17. Explain One Portfolio Piece
18. Vocabulary Match
19. Key Takeaways
20. Your First Project
21. How Your Portfolio Write-Up Is Judged
22. Portfolio Builder!

• CBSEDecompose any problem using the 5W1H method; apply Design Thinking methodology; create empathy maps; align problems to Sustainable Development Goals; express a solution to a problem in non-technical words (Unit 4: Introduction to Capstone Project).
• IBUse and apply technology effectively to access, process, and communicate information, and to model and create solutions to problems
• IBWhere a school offers digital design (rather than or alongside product design), demonstrate digital-tool competencies such as prototyping, graphic/web design, and multimedia editing through the same four-criterion design cycle
• England + CambridgeUndertake creative projects selecting, using and combining multiple applications across devices to achieve challenging goals, including collecting and analysing data and meeting the needs of known users
• England + CambridgeCreate, re-use, revise and re-purpose digital artefacts for a given audience, with attention to trustworthiness, design and usability
• CSTA (US)Distribute tasks and maintain a project timeline when collaboratively developing computational artifacts.
• CSTA (US)Collaborate with many contributors through strategies such as crowdsourcing or surveys when creating a computational artifact.
• SingaporeKeep digital knowledge current, including awareness of emerging technologies such as AI, as one of the nine core NDLP digital competencies.
• SingaporeDevelop digital information management skills as one of the nine core NDLP digital competencies.
• SingaporeDevelop digital communication, collaboration and participation skills as one of the nine core NDLP digital competencies.
• SingaporeApply the Find-Think-Apply-Create framework when locating, evaluating, using and creating with digital information (exact breakdown of the four components not published on the primary MOE page reviewed).
• SingaporeDevelop digital literacy and technological skills needed to thrive in an increasingly technology-driven world, as a central goal of the Masterplan.
• Australia + 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

1. Ready to Compete
2. Types of Competitions
3. What Should the Team Do First?
4. Why Compete
5. Why Compete Check
6. Benefit Match
7. First Hour or Later?
8. How to Prepare
9. Where to Find
10. Strategy Sort
11. Competition Day
12. Kaggle Makes Iteration Visible
13. The Kaggle Loop
14. Team Success
15. Competition Check
16. Competition Quiz
17. Explain a Winning Move
18. Vocabulary Match
19. Key Takeaways
20. Byte's Challenge
21. Competition Ready!

• CBSEDecompose any problem using the 5W1H method; apply Design Thinking methodology; create empathy maps; align problems to Sustainable Development Goals; express a solution to a problem in non-technical words (Unit 4: Introduction to Capstone Project).

1. One App, Five Rulebooks
2. Three Tools for Governing AI
3. Law, Plan or Principles?
4. Why Not One Global Rulebook?
5. The EU: Rules by Risk
6. How Big Can the Fine Get?
7. Which EU Tier?
8. The First Minister for AI
9. The UAE: Strategy, Minister, Charter
10. India: Build Capacity, Guide Use
11. Match the Move
12. The US and China
13. True or False: US and China
14. Global Bodies and Companies
15. Same Values, Different Tools
16. Case 1: The Exam Proctor
17. Case 2: The Hiring Filter
18. Case 3: A Chatbot for Kids
19. Plan a Five-Country Launch
20. When Italy Paused ChatGPT
21. Checkpoint: How the World Governs AI
22. Explain Two Approaches
23. Governance Vocabulary
24. Policy Explorer!

• UAEHow AI is governed through national policies
• UAEAI applied across sectors
• ADEKTake part in shaping AI policy
• ADEKExplain governance as distinct from building
• 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)Identify unintended impacts of an AI use that differ from its immediate goals (Engage 7, intermediate).
• 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).
• UNESCOUnderstand that human accountability is a legal obligation of AI creators and AI service providers, who are answerable for safety incidents, ethical risks in design/training, and misuses of their AI service (CG4.2.1.1).
• Korea, Japan, HKIdentify AI ethical principles and analyse ethical issues raised by real AI applications (bias, fairness, accountability), as a dedicated unit distinct from general 'societal impact'.

1. Inside a Chatbot
2. The Five Parts
3. A Message's Journey
4. Part → Job
5. Who Handles It?
6. The Helpline Autopsy
7. The Journey, In Order
8. StudyBuddy v0 - the Code
9. Run It in Your Head
10. Trace v0
11. Real World: Support Bots
12. Chatbot Claims
13. Knowledge Quiz
14. Vocabulary Match
15. Key Takeaways
16. Chapter Complete!

• CBSEStudy Natural Language Processing concepts and applications as Unit 6 of the Class X curriculum (20 theory + 7 practical hours); exact sub-unit learning outcomes not extracted from the source PDF pages read.
• CBSEDevelop understanding of the complexities of human language and challenges in NLP tasks (emotion detection, sentiment analysis, classification, chatbots); learn techniques/algorithms for NLP tasks and phases of NLP; create a chatbot using a conversational platform (Unit 7: Leveraging Linguistics and Computer Science).
• 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)
• CSTA (US)Document design decisions using text, graphics, presentations, and/or demonstrations in the development of complex programs.
• 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 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).
• OECD–EC (Europe)Accurately describe how a specific AI tool operates (Engage 2, intermediate).
• ChinaStudents learn the basic process, implementation principles and core algorithms behind AI systems
• Australia + NZWithin authentic contexts and taking account of end-users, students independently decompose problems into algorithms, determine when to use different control structures, and document their programs using an organised approach to testing and debugging

1. Matching the Pattern
2. One Question, Many Ways
3. The in Operator
4. Match or No Match?
5. Keyword Teams
6. Check → Meaning
7. StudyBuddy v1 - the Code
8. Assemble the Matcher
9. One Message, Step by Step
10. Which Reply?
11. Real World: Wake Words
12. Matcher Claims
13. Knowledge Quiz
14. Vocabulary Match
15. Key Takeaways
16. Chapter Complete!

• CBSEDevelop understanding of the complexities of human language and challenges in NLP tasks (emotion detection, sentiment analysis, classification, chatbots); learn techniques/algorithms for NLP tasks and phases of NLP; create a chatbot using a conversational platform (Unit 7: Leveraging Linguistics and Computer Science).

1. The Vending Machine Voice
2. Canned Responses
3. Lists + random.choice
4. One Voice or Many?
5. F-string Templates
6. Fill the Slots
7. Pick the Technique
8. The Complete Program
9. Assemble the Replier
10. Predict the Output
11. Reply Claims
12. The Variety Lab
13. Real World: Alexa
14. Reply Quiz
15. Key Vocabulary
16. Chapter Complete!

• ADEKChatbot mechanics, build a simple conversational agent
• CBSEDevelop understanding of the complexities of human language and challenges in NLP tasks (emotion detection, sentiment analysis, classification, chatbots); learn techniques/algorithms for NLP tasks and phases of NLP; create a chatbot using a conversational platform (Unit 7: Leveraging Linguistics and Computer Science).
• 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. Diya Breaks the Bot
2. Wash It First
3. Clean the Message
4. Punctuation + Silence
5. Which Step Fixes It?
6. Typo Tolerance
7. Where Does It Land?
8. The Complete Program
9. Order the Pipeline
10. Predict the Reply
11. Cleaning Claims
12. The Typo Lab
13. Real World: Google Search
14. Janitor Quiz
15. Key Vocabulary
16. Chapter Complete!

• CBSEDevelop understanding of the complexities of human language and challenges in NLP tasks (emotion detection, sentiment analysis, classification, chatbots); learn techniques/algorithms for NLP tasks and phases of NLP; create a chatbot using a conversational platform (Unit 7: Leveraging Linguistics and Computer Science).

1. The Goldfish Bot
2. Stateless vs. Remembering
3. Memory or Goldfish?
4. Writing to Memory
5. Match the Memory Moves
6. Anatomy of a Follow-Up
7. StudyBuddy Remembers
8. Rebuild the Memory Bot
9. True or False: Memory Code
10. Replay Diya's Chat
11. Real World: ChatGPT
12. Memory Quiz
13. Vocabulary Match
14. Key Takeaways
15. Memory Keeper!

• CBSEDevelop understanding of the complexities of human language and challenges in NLP tasks (emotion detection, sentiment analysis, classification, chatbots); learn techniques/algorithms for NLP tasks and phases of NLP; create a chatbot using a conversational platform (Unit 7: Leveraging Linguistics and Computer Science).

1. The Homework Disaster
2. Intents vs. Keywords
3. Spot the Intent
4. Keywords Vote, Intents Win
5. Read the Scoreboard
6. Inside the Engine
7. The Intent Engine
8. Rebuild the Engine
9. True or False: The Engine
10. Three Endings
11. The StudyBuddy Pipeline
12. Assemble the Pipeline
13. Intent Quiz
14. Vocabulary Match
15. Key Takeaways
16. Intent Detective!

• ADEKChatbot mechanics, build a simple conversational agent
• CBSEDevelop understanding of the complexities of human language and challenges in NLP tasks (emotion detection, sentiment analysis, classification, chatbots); learn techniques/algorithms for NLP tasks and phases of NLP; create a chatbot using a conversational platform (Unit 7: Leveraging Linguistics and Computer Science).
• 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. The Smart Assistant
2. What Are Entities?
3. Common Entity Types
4. Entity Matching
5. Intent vs Entity
6. Sort It Out!
7. Extraction Techniques
8. Step by Step
9. Duolingo Uses Learner Details To Personalize Practice
10. Why Validate?
11. User Profiles
12. Spot the Entity!
13. Knowledge Quiz
14. Vocabulary Match
15. Key Takeaways
16. Byte's Final Check
17. Chapter Complete!

• CBSEDevelop understanding of the complexities of human language and challenges in NLP tasks (emotion detection, sentiment analysis, classification, chatbots); learn techniques/algorithms for NLP tasks and phases of NLP; create a chatbot using a conversational platform (Unit 7: Leveraging Linguistics and Computer Science).
• 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).
• Korea, Japan, HKUnderstand speech recognition and natural-language-processing techniques and try a word-association-style generative-AI tool to see how language models generate text.

1. Two Chatbots
2. Why Personality?
3. Personality Dimensions
4. Personality Match
5. Good vs Bad
6. Duolingo's Duo Has a Recognizable Voice
7. Empathy Matters
8. Consistency is Key
9. Personality Check
10. Knowledge Quiz
11. Vocabulary Match
12. Chapter Complete!

• CBSEDevelop understanding of the complexities of human language and challenges in NLP tasks (emotion detection, sentiment analysis, classification, chatbots); learn techniques/algorithms for NLP tasks and phases of NLP; create a chatbot using a conversational platform (Unit 7: Leveraging Linguistics and Computer Science).

1. The Crash
2. Why Handle Errors?
3. Error Types
4. Response Match
5. Good vs Bad
6. Best Practices
7. Progressive Help
8. The Recovery Ladder
9. Knowledge Quiz
10. Real World: Google Assistant
11. Vocabulary Match
12. Chapter Complete!

• CBSEDevelop understanding of the complexities of human language and challenges in NLP tasks (emotion detection, sentiment analysis, classification, chatbots); learn techniques/algorithms for NLP tasks and phases of NLP; create a chatbot using a conversational platform (Unit 7: Leveraging Linguistics and Computer Science).

1. The Demo Disaster
2. Why Test?
3. Test Types
4. How Can One Tiny Input Break a Whole Demo?
5. Test Match
6. Good vs Bad
7. Build the Test Plan
8. Automated Testing
9. True or False: Test Like a Pro
10. Knowledge Quiz
11. Real World: ChatGPT
12. Vocabulary Match
13. Chapter Complete!

• ADEKDebug an algorithm/program
• CBSEDevelop understanding of the complexities of human language and challenges in NLP tasks (emotion detection, sentiment analysis, classification, chatbots); learn techniques/algorithms for NLP tasks and phases of NLP; create a chatbot using a conversational platform (Unit 7: Leveraging Linguistics and Computer Science).
• 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.

1. The Boring Bot
2. Why Replies Get Boring
3. The Template Toolkit
4. Why Can Two Correct Answers Feel Completely Different?
5. Template Toolkit Match
6. Strong or Weak?
7. True or False: Templates & Variety
8. A/B Testing at the Booth
9. Run the Improvement Cycle
10. Knowledge Quiz
11. Real World: Khanmigo
12. Vocabulary Match
13. Chapter Complete!

• CBSEDevelop understanding of the complexities of human language and challenges in NLP tasks (emotion detection, sentiment analysis, classification, chatbots); learn techniques/algorithms for NLP tasks and phases of NLP; create a chatbot using a conversational platform (Unit 7: Leveraging Linguistics and Computer Science).

1. Judging Day
2. The Road to Judging Day
3. Which Lesson Built It?
4. Five Parts, One Bot
5. Core Part or Upgrade?
6. StudyBuddy: The Fair Build
7. Assemble the Fair Build
8. The Judges Type...
9. True or False: The Fair Build
10. The Verdict
11. Unit 5 Final Quiz
12. Real World: Amazon Alexa
13. Vocabulary Match
14. Key Takeaways
15. Unit 5 Complete!

• ADEKChatbot mechanics, build a simple conversational agent
• CBSEDevelop understanding of the complexities of human language and challenges in NLP tasks (emotion detection, sentiment analysis, classification, chatbots); learn techniques/algorithms for NLP tasks and phases of NLP; create a chatbot using a conversational platform (Unit 7: Leveraging Linguistics and Computer Science).
• IBPlan the creation of the chosen solution and follow that plan to build a prototype sufficient for testing and evaluation
• 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).
• SingaporeDesign and build a prototype that incorporates artificial intelligence to address a problem.

1. Can Maya Try It?
2. One Message, Many Packets
3. Label or Cargo?
4. Addresses: Where Packets Go
5. Valid Address or Not?
6. Routers: One Hop Closer
7. Route One Packet
8. The Packet Lab
9. Split It in Python
10. Predict the Output
11. Out of Order? Put It Back
12. Rebuild the Message
13. What TCP Adds
14. Who Does What?
15. IP's Job or TCP's Job?
16. Checkpoint: A Message's Trip
17. Real World: traceroute
18. What Arrives Is a Text File
19. Build a Page in Web Page Lab
20. Read the Tags
21. Key Takeaways
22. Chapter Complete!

• IBDescribe network structures, protocols, and security threats (encryption, firewalls, phishing, DDoS attacks)
• England + CambridgeExplain IP addresses, URLs and DNS; distinguish PAN/LAN/WAN and topologies; understand encryption, firewalls, error detection and protocols (TCP/IP, HTTP)
• England + CambridgeUnderstand the difference between the internet and the world wide web, and describe how web pages are located, retrieved and displayed (browser, IP addresses, DNS, web server, HTML)
• CSTA (US)Model the role of protocols in transmitting data across networks and the Internet.
• CSTA (US)Evaluate the scalability and reliability of networks, by describing the relationship between routers, switches, servers, topology, and addressing.
• CSTA (US)Describe the issues that impact network functionality (e.g., bandwidth, load, delay, topology).
• Korea, Japan, HKUnderstand the structure and components of communication networks, the role of protocols, and methods/techniques for ensuring information security; design a small-scale network.
• Australia + NZExamine network protocols, and how networks apply cybersecurity measures to protect data, software and hardware

1. Where Did My Question Go?
2. Client and Server
3. Client or Server?
4. Request, Response and API
5. Reading a URL
6. Name the URL Part
7. JSON: The Shared Language
8. Valid JSON or Not?
9. Reading the Reply
10. Match the Status Code
11. Whose Side Is the Problem?
12. Byte's Question, Step by Step
13. One Full Exchange, Offline
14. Order the Round Trip
15. True or False: The Round Trip
16. Checkpoint: Clients, Servers and APIs
17. Vocabulary Match
18. Key Takeaways
19. Chapter Complete!

• England + CambridgeExplain IP addresses, URLs and DNS; distinguish PAN/LAN/WAN and topologies; understand encryption, firewalls, error detection and protocols (TCP/IP, HTTP)
• England + CambridgeUnderstand the difference between the internet and the world wide web, and describe how web pages are located, retrieved and displayed (browser, IP addresses, DNS, web server, HTML)
• CSTA (US)Incorporate existing code, media, and libraries into original programs, and give attribution.
• CSTA (US)Model the role of protocols in transmitting data across networks and the Internet.
• CSTA (US)Apply multiple methods of encryption to model the secure transmission of information.
• CSTA (US)Evaluate the scalability and reliability of networks, by describing the relationship between routers, switches, servers, topology, and addressing.
• Korea, Japan, HKUnderstand the structure and components of communication networks, the role of protocols, and methods/techniques for ensuring information security; design a small-scale network.
• Australia + NZExamine network protocols, and how networks apply cybersecurity measures to protect data, software and hardware

1. Two Facts Are Not Enough
2. What Is a Model Endpoint?
3. Parts of a Model Call
4. Status Codes and Rate Limits
5. Wait or Fix?
6. Keys Are Secrets
7. Safe or Leak?
8. A Pretend Endpoint in Python
9. Check, Then Retry Once
10. Predict the Caller
11. Waiting and Paying
12. The Cost Lab
13. Thirty Students, One Key
14. Good Message or Bad?
15. Checkpoint: Calling a Model
16. Real World: AI Features in Apps
17. Vocabulary Match
18. Key Takeaways
19. Chapter Complete!

• CSTA (US)Incorporate existing code, media, and libraries into original programs, and give attribution.

1. The Failed Demo
2. Why the Demo Failed
3. Why Did One Demo Survive the Judge's Curveball?
4. 5 Planning Steps
5. Plan the Festival App
6. Step Match
7. Good vs Bad
8. Curiosity: Builders Sketch First Even When They Know the Tools
9. Mistake Clinic
10. Feature Priority
11. Festival Features: Must or Nice?
12. Experiment: Planning Time vs Build Time
13. Real World: Trello
14. Planning Claims
15. Knowledge Quiz
16. Key Vocabulary
17. Chapter Complete!

• CSTA (US)Decompose problems and subproblems into parts to facilitate the design, implementation, and review of programs.
• CSTA (US)Create prototypes that use algorithms to solve computational problems by leveraging prior student knowledge and personal interests.
• CSTA (US)Design and iteratively develop computational artifacts for practical intent, personal expression, or to address a societal issue by using events to initiate instructions.
• CSTA (US)Systematically design and develop programs for broad audiences by incorporating feedback from users.
• Australia + NZStudents use an iterative process to design, develop, document, and test basic computer programs; apply design principles and usability heuristics to their own designs and evaluate user interfaces against them
• Australia + NZStudents independently design and produce a digital outcome for an authentic context, considering end-users, and evaluating the outcome's fitness for purpose

1. The Rainy Decision
2. What We're Building
3. Weather Database
4. Weather Match
5. Code Approaches
6. The Complete Program
7. Assemble the Advisor
8. Part → Job
9. Trace the Inputs
10. Adding Weather Types
11. Design Claims
12. Experiment: Grow the Database
13. Real World: Google Weather
14. Knowledge Quiz
15. Key Vocabulary
16. Chapter Complete!

• CBSEOrganise and represent data in various forms (text, numbers, images, sounds); recognise simple patterns in data and make decisions based on observations (Class 6); collect/organise structured data and create bar/line/pie charts (Class 7).
• England + CambridgeDesign, use and evaluate computational abstractions that model the state and behaviour of real-world problems and physical systems
• Korea, Japan, HKUse a simulation tool (e.g. a traffic-light decision simulator) to explore how an AI system makes sequential decisions under simulated conditions.
• ChinaStudents collect, organize and analyze data, and solve real-world problems through project-based learning

1. The Homework Helper
2. What We're Building
3. Nested Structure
4. Feature Match
5. Good vs Bad Design
6. Accessing Nested Data
7. The Complete Program
8. Assemble the Buddy
9. Predict the Result
10. Design Claims
11. Experiment: Grow the Knowledge Base
12. Real World: Duolingo
13. Knowledge Quiz
14. Key Vocabulary
15. Chapter Complete!

• England + CambridgeDesign, use and evaluate computational abstractions that model the state and behaviour of real-world problems and physical systems

1. The Playlist Problem
2. What We're Building
3. Why Doesn't a Playlist Feel Repetitive Every Time?
4. Random Selection Magic
5. Feature Match
6. Good vs Bad Design
7. Key Features
8. The Full Program
9. Rebuild the Recommender
10. True or False: The Code
11. Real World: Spotify
12. Experiment: Tune the Playlist Engine
13. Knowledge Quiz
14. Vocabulary Match
15. Chapter Complete!

• England + CambridgePlan statistical investigations, design databases/spreadsheets with cell referencing and functions, and know that data is used to solve problems across industries
• CSTA (US)Incorporate existing code, media, and libraries into original programs, and give attribution.

1. The Bedtime Story Crisis
2. What We're Building
3. How Can a Small Program Create Thousands of Stories?
4. Template Magic
5. Element Match
6. Good vs Bad Design
7. Story Components
8. The Full Program
9. Assemble the Generator
10. Combinatorial Power
11. Experiment: Multiply the Story Universe
12. True or False: The Generator
13. Knowledge Quiz
14. Vocabulary Match
15. Chapter Complete!

• 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).

1. The Unbeatable Opponent
2. What is Game AI?
3. Why Does the AI Feel Smart Without Being Perfect?
4. The Priority System
5. Strategy Order
6. Good vs Bad AI
7. How AI Thinks
8. The AI's Brain in Code
9. Which Rule Fires?
10. Match the Logic
11. Spot Game AI!
12. Real World: Chess.com
13. Game AI Quiz
14. Key Terms
15. Game AI Champion!

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

1. The Study Challenge
2. Quiz System Secrets
3. Mystery: What Makes a Quiz Feel Smart?
4. Question Structure
5. Build a Question
6. Good Quiz Design
7. Quiz Components
8. The Full Program
9. Rebuild the Quiz Master
10. Connect the Logic
11. True or False: The Code
12. Real World: Duolingo
13. Experiment: Tune the Quiz Experience
14. Quiz Master Quiz
15. Vocabulary Match
16. Quiz Master Champion!

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

1. The Missing Money
2. Tracker Features
3. Mystery: Where Does the Money Really Go?
4. Expense Structure
5. Data Parts Match
6. Smart Tracking
7. Analysis Methods
8. The Full Program
9. Rebuild the Tracker
10. Connect Analysis
11. True or False: The Code
12. Real World: YNAB
13. Experiment: Tune the Expense Tracker
14. Tracker Quiz
15. Vocabulary Match
16. Finance Champion!

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

1. The Innovation Fair
2. Pick Your Fair Project
3. Mystery: Why Did Arjun's Rebuilt Demo Survive?
4. The Framework, Applied
5. Plan Your Entry
6. Maya's Features: Must or Nice?
7. Fair-Day Checklist
8. Rescue the Plan
9. True or False: Fair Ready?
10. Real World: Trello
11. Experiment: Tune the Project Idea
12. Fair Strategy Quiz
13. Vocabulary Match
14. Fair Ready!

• ADEKBuild an AI solution to a real problem
• CSTA (US)Seek and incorporate feedback from team members and users to refine a solution that meets user needs.
• Korea, Japan, HKConstruct and apply an AI system using a variety of data ([9정04-03]).
• Korea, Japan, HKDesign, develop and present a group AI project (e.g. an AI-robotics solution to an everyday/pandemic-related problem such as an automatic soap dispenser or light switch) applying knowledge, skill and — in the culminating Booklet-3 project — attitudes from the whole module.

1. First Feature Victory
2. Development Phases
3. Mystery: Why Does Building Slowly Often Finish Faster?
4. Debug Like a Pro
5. Development Order
6. Debug the Crash
7. Smart Development
8. Common Errors
9. Experiment: Tune the Build Process
10. Real World: Visual Studio Code
11. Error Solutions
12. Review AI's Code
13. Spot Good Practices!
14. Development Quiz
15. Key Terms
16. Developer Champion!

• ADEKDebug an algorithm/program
• CSTA (US)Decompose problems and subproblems into parts to facilitate the design, implementation, and review of programs.
• CSTA (US)Seek and incorporate feedback from team members and users to refine a solution that meets user needs.
• CSTA (US)Systematically test and refine programs using a range of test cases.
• CSTA (US)Distribute tasks and maintain a project timeline when collaboratively developing computational artifacts.
• CSTA (US)Document programs in order to make them easier to follow, test, and debug.
• CSTA (US)Collaborate with many contributors through strategies such as crowdsourcing or surveys when creating a computational artifact.

1. Inspiring Presentation
2. Presentation Parts
3. Mystery: Why Do Some Presentations Win Attention Instantly?
4. Impressive Demos
5. Presentation Order
6. Do's and Don'ts
7. Q&A Mastery
8. Strong or Weak Answer?
9. Experiment: Tune the Presentation
10. Real World: Canva
11. Presentation Skills
12. Spot Great Presenting!
13. Presentation Quiz
14. Key Terms
15. Presenter Champion!

• ADEKBuild an AI solution to a real problem
• IBUse and apply technology effectively to access, process, and communicate information, and to model and create solutions to problems
• IBWhere a school offers digital design (rather than or alongside product design), demonstrate digital-tool competencies such as prototyping, graphic/web design, and multimedia editing through the same four-criterion design cycle
• England + CambridgeUndertake creative projects selecting, using and combining multiple applications across devices to achieve challenging goals, including collecting and analysing data and meeting the needs of known users
• England + CambridgeCreate, re-use, revise and re-purpose digital artefacts for a given audience, with attention to trustworthiness, design and usability
• CSTA (US)Seek and incorporate feedback from team members and users to refine a solution that meets user needs.
• SingaporeKeep digital knowledge current, including awareness of emerging technologies such as AI, as one of the nine core NDLP digital competencies.
• SingaporeDevelop digital information management skills as one of the nine core NDLP digital competencies.
• SingaporeDevelop digital communication, collaboration and participation skills as one of the nine core NDLP digital competencies.
• SingaporeApply the Find-Think-Apply-Create framework when locating, evaluating, using and creating with digital information (exact breakdown of the four components not published on the primary MOE page reviewed).
• SingaporeDevelop digital literacy and technological skills needed to thrive in an increasingly technology-driven world, as a central goal of the Masterplan.
• Korea, Japan, HKConstruct and apply an AI system using a variety of data ([9정04-03]).
• Korea, Japan, HKDesign, develop and present a group AI project (e.g. an AI-robotics solution to an everyday/pandemic-related problem such as an automatic soap dispenser or light switch) applying knowledge, skill and — in the culminating Booklet-3 project — attitudes from the whole module.
• 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

1. The Coding Journey
2. Your Amazing Growth
3. Review Your Skills
4. Grade 9 Preview
5. This Year or Next?
6. Where These Skills Lead
7. Skill to Career
8. Year Review Quiz
9. Grade 8 Champion!

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

1. Global Goals Week
2. 17 Goals, One Plan
3. Match the Goal
4. Why Test the Poster First?
5. Research the Local Problem
6. Too Big or Just Right?
7. Simple Fix or AI?
8. AI Projects Around the World
9. AI's Job or a Person's?
10. Design a Small AI Helper
11. Order the Project Path
12. Small Can Be Lighter
13. Lighter or Heavier?
14. Try It: Train a Mini Bin Buddy
15. What Did Your Bin Buddy Show?
16. Plan Your Evidence
17. The Evidence Lab
18. Fair Test or Not?
19. Real World: Flood Hub
20. Present Your Project
21. Checkpoint: Your SDG Project
22. Pitch Your Own SDG Project
23. New Vocabulary
24. Chapter Complete!

• UAEGlobal challenges: sustainability, humanitarian aid, social impact
• UAEUse generative AI models to create new content
• UAEDesign AI projects for real-world problems
• ADEKResearch an SDG, build/evaluate a solution
• ADEKFootprint / resource cost of AI
• 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).
• IBDesign tests to evaluate a solution, carry out those tests, and objectively evaluate the solution's success
• IBIdentify areas where a design solution could be improved and explain the solution's impact on the client or target audience
• CSTA (US)Create a program using available AI tools, AI plugins, APIs, and/or AI models, with ethical considerations for the model's end users and others impacted: fairness, bias, and accuracy, and then create a model card.
• 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.
• 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)Experiment with multiple AI tools to develop diverse ideas (Create 2, intermediate).
• OECD–EC (Europe)Propose specific design changes to improve an AI system for human well-being (Shape 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).
• UNESCOCritically evaluate and leverage free/open-source AI datasets (e.g. MNIST, CIFAR, ImageNet) and AI/algorithm libraries (e.g. Teachable Machine, PyTorch, Keras) to address authentic, task-based problems (CG4.2.3.3).
• Australia + NZEvaluate existing and student-developed digital solutions and information systems, and their suitability for personal, environmental and social criteria, against needs and requirements