Python Lab, Create tools and capstone projects

1. Your Own Program

• CBSEAcquire introductory Python programming skills: variables, arithmetic/logical/comparison/assignment operators, data types, print()/input(), conditional and iterative statements (if/for/while), and lists, with hands-on practical exercises (Unit 5: Introduction to Python; Part C practical exam).
• CISCE + statesWrite Python programs using variables, data types, operators, conditional statements (if/elif/else), loops (for/while, nested, break/continue/pass) and user-defined 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

Used across grades.

• 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

Used across grades.

• 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

Used across grades.

• 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. Draw a Text Bar Chart

• ADEKBuild a chart/dashboard
• 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. Write Selection Sort
2. Binary Search, Recursively
3. Split the Advisor
4. Reuse Your Helpers: Cinema Chooser

• CBSEAcquire introductory Python programming skills: variables, arithmetic/logical/comparison/assignment operators, data types, print()/input(), conditional and iterative statements (if/for/while), and lists, with hands-on practical exercises (Unit 5: Introduction to Python; Part C practical exam).
• CISCE + statesWrite Python programs using variables, data types, operators, conditional statements (if/elif/else), loops (for/while, nested, break/continue/pass) and user-defined functions
• IBApply object-oriented programming principles (classes, objects, inheritance, encapsulation) as mandatory content for all students
• 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)Create procedures with parameters to organize code and make it easier to reuse.
• CSTA (US)Incorporate existing code, media, and libraries into original programs, and give attribution.
• 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)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)Construct solutions to problems using student-created components, such as procedures, modules and/or objects.
• 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.

Used across grades.

• CSTA (US)Use lists to simplify solutions, generalizing computational problems instead of repeatedly using simple variables.

1. Clean the Log
2. Follow the Links
3. Nearest Neighbours Vote

• England + CambridgeDesign, use and evaluate computational abstractions that model the state and behaviour of real-world problems and physical systems
• CSTA (US)Use lists to simplify solutions, generalizing computational problems instead of repeatedly using simple variables.
• CSTA (US)Collect data using computational tools and transform the data to make it more useful and reliable.
• AI4K12 (US)Progress from drawing a tree by repeatedly splitting branches and placing data at nodes (K-2), to describing the parts of a tree (nodes, parent, leaves) and how they relate (3-5), to describing the parts of a labeled graph (nodes, links, source/target) and how trees are a special case of graphs (6-8), to describing how schemas structure information about people, places, or things within knowledge graphs used by search engines (9-12).
• 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).
• UNESCOAcquire age-appropriate technical skills in AI programming, understanding how supervised, unsupervised and reinforcement-learning algorithms scrape/process data, train, and function (CG4.2.3.2).

Used across grades.

• Korea, Japan, HKUnderstand and apply methods for representing/storing data and for collecting, organising and analysing data (comparison, relation, change, classification), selecting, executing, evaluating and improving the analysis method.

1. Check the AI's Function
2. Test the Grader

• CSTA (US)Systematically test and refine programs using a range of test cases.
• CSTA (US)Document programs in order to make them easier to follow, test, and debug.
• CSTA (US)Students read, evaluate, and critique AI-generated code as part of the Programming strand.
• Australia + NZDevelop, modify and implement software (e.g. webpages using HTML for structure and CSS for presentation) that address defined needs, including validation and testing

Used across grades.

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

1. Look at the Bytecode
2. Read Three Numbers
3. Convert and Compute from Input

• 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. GardenBed: State and Behaviour

• IBApply object-oriented programming principles (classes, objects, inheritance, encapsulation) as mandatory content for all students
• 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)Construct solutions to problems using student-created components, such as procedures, modules and/or objects.
• CSTA (US)Analyze a large-scale computational problem and identify generalizable patterns that can be applied to a solution.
• CSTA (US)Demonstrate ways a given algorithm applies to problems across disciplines.
• AI4K12 (US)Progress from constructing a map of a home, school, or neighborhood and understanding that 'the map is not the territory' (K-2), to demonstrating how different map styles (geometric vs. topological) capture different aspects of the world (3-5), to showing how a game board (tic-tac-toe, chess) can be represented as a plain-language description of squares, relationships, and piece positions (6-8), to describing how to represent a concept as a schema.org-style schema with superconcepts and inherited properties (9-12).
• 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.

Used across grades.

• IBApply object-oriented programming principles (classes, objects, inheritance, encapsulation) as mandatory content for all students
• 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)Construct solutions to problems using student-created components, such as procedures, modules and/or objects.
• 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.

Used across grades.

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

Used across grades.

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

1. Challenge: Split the Pipeline

• IBApply object-oriented programming principles (classes, objects, inheritance, encapsulation) as mandatory content for all students
• 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)Construct solutions to problems using student-created components, such as procedures, modules and/or objects.
• CSTA (US)Demonstrate code reuse by creating programming solutions using libraries and APIs.
• CSTA (US)Modify an existing program to add additional functionality and discuss intended and unintended implications (e.g., breaking other functionality).
• 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.

1. Test and Document Your Function

• CSTA (US)Develop and use a series of test cases to verify that a program performs according to its design specifications.
• CSTA (US)Evaluate key qualities of a program through a process such as a code review.
• Australia + NZDevelop, modify and implement software (e.g. webpages using HTML for structure and CSS for presentation) that address defined needs, including validation and testing

Used across grades.

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

Used across grades.

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

Used across grades.

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

1. Two Tables and a Key
2. Join to Answer a Question
3. Enforce Keys, Then Normalise
4. Triples and Two Hops

• IBDesign and query relational databases using SQL as core (mandatory) content for all students
• IB(HL only) Explain NoSQL database models and their use cases relative to relational databases
• 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)Evaluate the tradeoffs in how data elements are organized and where data is stored.
• AI4K12 (US)Progress from drawing a tree by repeatedly splitting branches and placing data at nodes (K-2), to describing the parts of a tree (nodes, parent, leaves) and how they relate (3-5), to describing the parts of a labeled graph (nodes, links, source/target) and how trees are a special case of graphs (6-8), to describing how schemas structure information about people, places, or things within knowledge graphs used by search engines (9-12).
• Korea, Japan, HKUnderstand and apply methods for representing/storing data and for collecting, organising and analysing data (comparison, relation, change, classification), selecting, executing, evaluating and improving the analysis method.
• Australia + NZAnalyse and visualise data using structured query language (or equivalent) to interrogate relational databases

1. Extend the Client

• CSTA (US)Demonstrate code reuse by creating programming solutions using libraries and APIs.
• CSTA (US)Modify an existing program to add additional functionality and discuss intended and unintended implications (e.g., breaking other functionality).

Used across grades.

• IBWrite programs in a chosen language (Java or Python, selected at school/department level) that implement given algorithms
• IB(HL only) Implement abstract data types such as stacks, queues, linked lists, and trees
• CSTA (US)Compare and contrast fundamental data structures and their uses.
• CSTA (US)Compare multiple programming languages and discuss how their features make them suitable for solving different types of problems.

Used across grades.

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

Used across grades.

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

1. Binary Search, Step by Step
2. Merge Sort
3. Look Inside Python

• IBWrite programs in a chosen language (Java or Python, selected at school/department level) that implement given algorithms
• 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)Compare multiple programming languages and discuss how their features make them suitable for solving different types of problems.

1. Stack: Match the Brackets
2. Linked List: Reverse in Place
3. Binary Search Tree

• IBWrite programs in a chosen language (Java or Python, selected at school/department level) that implement given algorithms
• IBApply object-oriented programming principles (classes, objects, inheritance, encapsulation) as mandatory content for all students
• IB(HL only) Implement abstract data types such as stacks, queues, linked lists, and trees
• CSTA (US)Compare and contrast fundamental data structures and their uses.
• CSTA (US)Construct solutions to problems using student-created components, such as procedures, modules and/or objects.
• CSTA (US)Compare multiple programming languages and discuss how their features make them suitable for solving different types of problems.

Used across grades.

• CBSEExtend Python programming skills as Unit 7 (Advance Python, 10 practical hours), building on Class IX Python basics; exact sub-unit content not extracted from the source PDF pages read.
• CISCE + statesUse Python packages/libraries (NumPy, Pandas, SciPy, Matplotlib), lists, tuples and string operations/built-in string functions in programs
• CSTA (US)Use lists to simplify solutions, generalizing computational problems instead of repeatedly using simple variables.

1. Save Your Own Log
2. Edit a Log and Resave as JSON
3. Load a Semicolon File
4. Rank Survival Rates and Interpret Them

• ADEKPython analysis of a people-rating/ranking dataset
• CBSEAnalyse data (numbers/images), find patterns/relations, and understand the role of Math (statistics, linear algebra, probability, calculus) in AI; understand statistics and probability concepts and apply them to real-life scenarios such as disaster management, sports, disease prediction, weather forecast, traffic estimation (Unit 3: Math for AI).
• CBSEExtend Python programming skills as Unit 7 (Advance Python, 10 practical hours), building on Class IX Python basics; exact sub-unit content not extracted from the source PDF pages read.
• CISCE + statesUse Python packages/libraries (NumPy, Pandas, SciPy, Matplotlib), lists, tuples and string operations/built-in string functions in programs
• CSTA (US)Evaluate the tradeoffs in how data elements are organized and where data is stored.
• Korea, Japan, HKUnderstand and apply methods for representing/storing data and for collecting, organising and analysing data (comparison, relation, change, classification), selecting, executing, evaluating and improving the analysis method.
• Australia + NZAnalyse and visualise data using structured query language (or equivalent) to interrogate relational databases

1. Clean a Real Messy File
2. Refactor Without Changing the Answer

• CBSEExtend Python programming skills as Unit 7 (Advance Python, 10 practical hours), building on Class IX Python basics; exact sub-unit content not extracted from the source PDF pages read.
• CISCE + statesUse Python packages/libraries (NumPy, Pandas, SciPy, Matplotlib), lists, tuples and string operations/built-in string functions in programs
• 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)Collect data using computational tools and transform the data to make it more useful and reliable.
• 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 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.

1. Tell the Canteen Story with Context
2. Choose Charts for Two Questions
3. Read the Shape of a Loaded Column
4. Build a Monitoring Dashboard

• ADEKBuild a chart/dashboard
• ADEKBuild a data story (data + visuals + narrative for an audience)
• CBSERecognise the importance of data visualisation and discover different visualisation methods; build an interactive dashboard and presentation (Unit 2 sub-topic: Project Interactive Data Dashboard).
• CBSEExtend Python programming skills as Unit 7 (Advance Python, 10 practical hours), building on Class IX Python basics; exact sub-unit content not extracted from the source PDF pages read.
• CISCE + statesRepresent explored data using bar graphs, line graphs, scatter plots and pie charts; apply modelling approaches — linear regression (prediction), rule-based, supervised learning and unsupervised learning (brief)
• CISCE + statesUse Python packages/libraries (NumPy, Pandas, SciPy, Matplotlib), lists, tuples and string operations/built-in string functions in programs
• CISCE + statesChoose and construct appropriate statistical graphs (bar graph, histogram, scatter plot, pie graph) and represent multi-dimensional data (introduction to dimensionality)
• CSTA (US)Create interactive data visualizations using software tools to help others better understand real-world phenomena.
• Korea, Japan, HKUnderstand and apply methods for representing/storing data and for collecting, organising and analysing data (comparison, relation, change, classification), selecting, executing, evaluating and improving the analysis method.

1. Simulate, Then Check Reality
2. A Permutation Test

• CBSEAnalyse data (numbers/images), find patterns/relations, and understand the role of Math (statistics, linear algebra, probability, calculus) in AI; understand statistics and probability concepts and apply them to real-life scenarios such as disaster management, sports, disease prediction, weather forecast, traffic estimation (Unit 3: Math for AI).
• CSTA (US)Create computational models that represent the relationships among different elements of data collected from a phenomenon or process.
• Korea, Japan, HKUnderstand methods for modelling social/natural phenomena and evaluate/improve a model through simulation, having experienced solving the same problem with and without a computer.

Used across grades.

• CBSEExtend Python programming skills as Unit 7 (Advance Python, 10 practical hours), building on Class IX Python basics; exact sub-unit content not extracted from the source PDF pages read.
• CISCE + statesUse Python packages/libraries (NumPy, Pandas, SciPy, Matplotlib), lists, tuples and string operations/built-in string functions in programs
• AI4K12 (US)Progress from demonstrating, with instructor help, how to train a computer to recognize something using a tool like Teachable Machine (K-2), to training a classification model and examining its accuracy on new inputs (3-5), to training and evaluating a classification or prediction model on a tabular dataset (6-8), to using supervised or unsupervised learning on real-world data and quantitatively evaluating the results on a nontrivial test set (9-12).
• 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).

Used across grades.

• UNESCOAcquire age-appropriate technical skills in AI programming, understanding how supervised, unsupervised and reinforcement-learning algorithms scrape/process data, train, and function (CG4.2.3.2).

1. k-NN or Tree: Bake-off
2. k-NN on Iris

• CBSEExtend Python programming skills as Unit 7 (Advance Python, 10 practical hours), building on Class IX Python basics; exact sub-unit content not extracted from the source PDF pages read.
• CISCE + statesUse Python packages/libraries (NumPy, Pandas, SciPy, Matplotlib), lists, tuples and string operations/built-in string functions in programs
• CSTA (US)Select and use an appropriate AI algorithm for a classification task (e.g., KNN, decision tree).
• AI4K12 (US)Progress from demonstrating, with instructor help, how to train a computer to recognize something using a tool like Teachable Machine (K-2), to training a classification model and examining its accuracy on new inputs (3-5), to training and evaluating a classification or prediction model on a tabular dataset (6-8), to using supervised or unsupervised learning on real-world data and quantitatively evaluating the results on a nontrivial test set (9-12).
• UNESCOAcquire age-appropriate technical skills in AI programming, understanding how supervised, unsupervised and reinforcement-learning algorithms scrape/process data, train, and function (CG4.2.3.2).
• SingaporeUse the nearest neighbour method for a classification task with two quantitative features to demonstrate the basic principles behind ML.

1. Cluster Iris without Labels

• CBSEDifferentiate types of machine learning methods (supervised: regression, classification/k-NN; unsupervised: clustering/k-means); understand the concept behind each method; apply methods to develop solutions for day-to-day situations, e.g. Pearson correlation and linear regression in MS Excel/Python (Unit 6: Machine Learning Algorithms).
• AI4K12 (US)Progress from explaining how reinforcement learning lets a computer learn from trial-and-error experience with a reward signal, e.g. a grid-world navigation demo (3-5), to explaining the difference between supervised learning (told the correct output) and reinforcement learning (told only how well it's doing) (6-8), to selecting the appropriate learning-algorithm family (supervised, unsupervised, or reinforcement) for a given reasoning problem (9-12). K-2 is marked N/A.
• UNESCODevelop an age-appropriate understanding of the three types of AI algorithms — supervised learning, unsupervised learning, and reinforcement learning — and how the data behind them is acquired and labelled (CG4.1.3.2).
• UNESCOAcquire age-appropriate technical skills in AI programming, understanding how supervised, unsupervised and reinforcement-learning algorithms scrape/process data, train, and function (CG4.2.3.2).

Used across grades.

• 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).

1. Audit a Shortlist

• ADEKPython analysis of a people-rating/ranking dataset
• CBSEDevelop proficiency using the Orange Data Mining tool - navigate its interface, employ its features, execute data analysis tasks; demonstrate ability to apply Orange across AI domains including data science, computer vision, and NLP through hands-on projects (Unit 4: AI with Orange Data Mining Tool, practicals only).

Used across grades.

• CISCE + statesTune model hyperparameters (learning rate, batch size, number of hidden layers/neurons) using techniques such as grid search or random search
• CSTA (US)Select and use an appropriate AI algorithm for a classification task (e.g., KNN, decision tree).
• SingaporeUse the nearest neighbour method for a classification task with two quantitative features to demonstrate the basic principles behind ML.

1. Train an MLP for Real

• 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.

Used across grades.

• 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.
• 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.
• 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).

Used across grades.

• 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.
• 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.
• 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).
• 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.

1. Audit the Rows Before Modelling
2. Make One Change and Log the Decision
3. Wrap the Trained Model as a Predictor
4. Check an AI-Written Pipeline

• CBSEExtend Python programming skills as Unit 7 (Advance Python, 10 practical hours), building on Class IX Python basics; exact sub-unit content not extracted from the source PDF pages read.
• CISCE + statesUse Python packages/libraries (NumPy, Pandas, SciPy, Matplotlib), lists, tuples and string operations/built-in string functions in programs
• IBPlan the creation of the chosen solution and follow that plan to build a prototype sufficient for testing and evaluation
• CSTA (US)Using a dataset and a systematic process, develop an AI model to generate for classification or prediction, and articulate the assumptions made at each step: (1) develop a question solvable with AI, (2) collect or curate data, (3) evaluate the data, (4) train an AI model on the data, (5) evaluate the model, and (6) iteratively improve the model.
• CSTA (US)Students read, evaluate, and critique AI-generated code as part of the Programming strand.
• AI4K12 (US)Progress from demonstrating, with instructor help, how to train a computer to recognize something using a tool like Teachable Machine (K-2), to training a classification model and examining its accuracy on new inputs (3-5), to training and evaluating a classification or prediction model on a tabular dataset (6-8), to using supervised or unsupervised learning on real-world data and quantitatively evaluating the results on a nontrivial test set (9-12).
• 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.
• 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).
• Korea, Japan, HKUnderstand and apply methods for representing/storing data and for collecting, organising and analysing data (comparison, relation, change, classification), selecting, executing, evaluating and improving the analysis method.
• Australia + NZDevelop, modify and implement software (e.g. webpages using HTML for structure and CSS for presentation) that address defined needs, including validation and testing

1. Reuse a Real Feature Extractor
2. Turn Sound into a Spectrogram

• CBSEAcquire introductory Python programming skills: variables, arithmetic/logical/comparison/assignment operators, data types, print()/input(), conditional and iterative statements (if/for/while), and lists, with hands-on practical exercises (Unit 5: Introduction to Python; Part C practical exam).
• CISCE + statesClassify types of data (audio, visual, numeric, text) and explain how data becomes information within the AI Project Cycle
• CISCE + statesWrite Python programs using variables, data types, operators, conditional statements (if/elif/else), loops (for/while, nested, break/continue/pass) and user-defined functions
• 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
• CSTA (US)Translate between different bit representations of real-world phenomena, such as characters, numbers, and images.
• AI4K12 (US)Progress from explaining how images are digitally represented as 2D pixel arrays (3-5), to explaining how sounds are digitally encoded via waveform sampling (6-8), to explaining how radar, lidar, GPS, and accelerometer data are represented and computed (9-12). K-2 is marked N/A for this topic.
• AI4K12 (US)Progress from giving examples of visual features (parts, textures, colors) used to recognize an object class such as cats (K-2), to illustrating how face detection extracts facial features (3-5), to simulating an edge detector to illustrate feature extraction from images (6-8), to explaining how features (formants, edges, intensity gradients) are extracted from waveforms and images at multiple scales (9-12).
• AI4K12 (US)Progress from 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.
• 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).

1. Screen Real Biopsies

• UAEOptimisation: goals, constraints, trade-offs
• CBSEDifferentiate types of machine learning methods (supervised: regression, classification/k-NN; unsupervised: clustering/k-means); understand the concept behind each method; apply methods to develop solutions for day-to-day situations, e.g. Pearson correlation and linear regression in MS Excel/Python (Unit 6: Machine Learning Algorithms).
• CISCE + statesApply ML to real-world case studies: predictive maintenance from historical machine data, and credit-card/identity-theft fraud detection
• CISCE + statesApply ML to medical diagnosis by analysing medical imaging, genetic information and patient records
• IBWrite programs in a chosen language (Java or Python, selected at school/department level) that implement given algorithms
• CSTA (US)Demonstrate code reuse by creating programming solutions using libraries and APIs.
• CSTA (US)Modify an existing program to add additional functionality and discuss intended and unintended implications (e.g., breaking other functionality).
• CSTA (US)Compare multiple programming languages and discuss how their features make them suitable for solving different types of problems.
• AI4K12 (US)Progress from analyzing a decision-tree-building game and how the learning algorithm adds/replaces nodes (3-5), to comparing how decision-tree learning (add a node) differs from neural-network learning (adjust every weight) (6-8), to describing how decision-tree, neural-net, regression, and reinforcement-learning algorithms each adjust their own type of internal representation (9-12). K-2 is marked N/A.
• UNESCOAcquire age-appropriate technical skills in AI programming, understanding how supervised, unsupervised and reinforcement-learning algorithms scrape/process data, train, and function (CG4.2.3.2).

1. Analogies and Odd-One-Out
2. A Tiny Grammar
3. Parse It

• CBSEAcquire introductory Python programming skills: variables, arithmetic/logical/comparison/assignment operators, data types, print()/input(), conditional and iterative statements (if/for/while), and lists, with hands-on practical exercises (Unit 5: Introduction to Python; Part C practical exam).
• CBSEExplain the importance of data literacy in AI; identify data collection methods and applications; comprehend matrices and their operations; apply basic data analysis techniques (mean, median, mode, standard deviation, variance); visualise data using line/bar/histogram/scatter/pie charts with matplotlib (Unit 5: Data Literacy - Data Collection to Data Analysis).
• CISCE + statesWrite Python programs using variables, data types, operators, conditional statements (if/elif/else), loops (for/while, nested, break/continue/pass) and user-defined functions
• CISCE + statesPerform matrix operations (addition, subtraction, multiplication, transpose) and vector arithmetic/applications relevant to AI
• CISCE + statesApply Set Theory to data-table joins and relational algebra, and calculate simple statistical measures (mean, median, mode, variance, standard deviation)
• CISCE + statesAnalyse data trends using mean, median, mode, range, variance and standard deviation; apply probability basics including uniform, normal and binomial distributions
• CISCE + statesFormulate null and alternative hypotheses, identify Type I and Type II errors, and apply simple linear regression and correlation coefficient for prediction and relationship measurement
• 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
• AI4K12 (US)Progress from constructing a map of a home, school, or neighborhood and understanding that 'the map is not the territory' (K-2), to demonstrating how different map styles (geometric vs. topological) capture different aspects of the world (3-5), to showing how a game board (tic-tac-toe, chess) can be represented as a plain-language description of squares, relationships, and piece positions (6-8), to describing how to represent a concept as a schema.org-style schema with superconcepts and inherited properties (9-12).
• AI4K12 (US)Progress from demonstrating knowledge of language structure by generating plausible/implausible novel words or unscrambling sentences (K-2), to demonstrating that human language is infinite via sentence extension (3-5), to using a parser to display a sentence's syntactic (parse-tree) structure and explaining the node categories (6-8), to demonstrating how a small context-free grammar can parse or generate simple sentences, and its limits with agreement constraints (9-12).
• AI4K12 (US)Progress from 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 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.
• UNESCOAcquire age-appropriate technical skills in AI programming, understanding how supervised, unsupervised and reinforcement-learning algorithms scrape/process data, train, and function (CG4.2.3.2).
• 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).

1. Thermostat Controller

• ADEKRobotics / physical device
• ADEKRobotics / physical device
• CBSEAcquire introductory Python programming skills: variables, arithmetic/logical/comparison/assignment operators, data types, print()/input(), conditional and iterative statements (if/for/while), and lists, with hands-on practical exercises (Unit 5: Introduction to Python; Part C practical exam).
• 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 + statesWrite Python programs using variables, data types, operators, conditional statements (if/elif/else), loops (for/while, nested, break/continue/pass) and user-defined functions
• 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 + statesUse TinkerCAD to visualize, design and create robot components (joints: revolute, prismatic; RR mechanism) and visualize motion of designed components
• CISCE + statesBuild a simple robotic system in TinkerCAD, for example a wheeled mobile robot up to four wheels, integrating single-board-computer coding
• CISCE + statesBuild an AI-powered smart-home/robotics capstone (e.g. a smart door that opens via face recognition) integrating pre-trained ML models with Arduino hardware, a webcam and a servo motor
• CISCE + statesConstruct circuits using sensors and actuators (e.g. Arduino, IR sensor, servo motor) and control electronic devices via computer programming, e.g. building an automated hand-triggered sanitiser dispenser
• England + CambridgeDescribe how sensors, microprocessors and actuators combine to create automated systems, and describe advantages/disadvantages for given scenarios (industry, transport, agriculture, weather, gaming, lighting, science)
• England + 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
• 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)Translate between different bit representations of real-world phenomena, such as characters, numbers, and images.
• 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 explaining how images are digitally represented as 2D pixel arrays (3-5), to explaining how sounds are digitally encoded via waveform sampling (6-8), to explaining how radar, lidar, GPS, and accelerometer data are represented and computed (9-12). K-2 is marked N/A for this topic.
• AI4K12 (US)Progress from giving examples of intelligent vs. non-intelligent machines and discussing what makes a machine 'intelligent' (K-2), to demonstrating a machine perception tool (e.g. speech transcription, object recognition) and explaining why it is perception rather than mere sensing (3-5), to giving examples of specialized perception algorithms (face detection, obstacle detection, speech recognition, etc.) (6-8), to explaining how perception algorithms power real-world applications like license plate readers and Google Lens (9-12).
• AI4K12 (US)Progress from giving examples of visual features (parts, textures, colors) used to recognize an object class such as cats (K-2), to illustrating how face detection extracts facial features (3-5), to simulating an edge detector to illustrate feature extraction from images (6-8), to explaining how features (formants, edges, intensity gradients) are extracted from waveforms and images at multiple scales (9-12).
• 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.
• 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).
• 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.
• 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.
• ChinaUpper-primary/junior students apply smart-hardware applications (example: Haidian Experimental Primary School)
• Australia + NZUnderstand how mathematical skills and concepts — pattern recognition, spatial reasoning, chance, data and algorithms — are applied to train AI systems

1. Three Methods, One Dataset
2. Find the Formants

• CBSEDifferentiate types of machine learning methods (supervised: regression, classification/k-NN; unsupervised: clustering/k-means); understand the concept behind each method; apply methods to develop solutions for day-to-day situations, e.g. Pearson correlation and linear regression in MS Excel/Python (Unit 6: Machine Learning Algorithms).
• AI4K12 (US)Progress from giving examples of visual features (parts, textures, colors) used to recognize an object class such as cats (K-2), to illustrating how face detection extracts facial features (3-5), to simulating an edge detector to illustrate feature extraction from images (6-8), to explaining how features (formants, edges, intensity gradients) are extracted from waveforms and images at multiple scales (9-12).
• UNESCOAcquire age-appropriate technical skills in AI programming, understanding how supervised, unsupervised and reinforcement-learning algorithms scrape/process data, train, and function (CG4.2.3.2).

1. Grid vs Random Search
2. Stats on Real Patients
3. Test It
4. Simulate an Outbreak
5. Diffusion in One Dimension

• ADEKGAN/diffusion image generation, critique an image
• CBSEExplain the importance of data literacy in AI; identify data collection methods and applications; comprehend matrices and their operations; apply basic data analysis techniques (mean, median, mode, standard deviation, variance); visualise data using line/bar/histogram/scatter/pie charts with matplotlib (Unit 5: Data Literacy - Data Collection to Data Analysis).
• CISCE + statesPerform matrix operations (addition, subtraction, multiplication, transpose) and vector arithmetic/applications relevant to AI
• CISCE + statesApply Set Theory to data-table joins and relational algebra, and calculate simple statistical measures (mean, median, mode, variance, standard deviation)
• CISCE + statesAnalyse data trends using mean, median, mode, range, variance and standard deviation; apply probability basics including uniform, normal and binomial distributions
• CISCE + statesFormulate null and alternative hypotheses, identify Type I and Type II errors, and apply simple linear regression and correlation coefficient for prediction and relationship measurement
• CISCE + statesTune model hyperparameters (learning rate, batch size, number of hidden layers/neurons) using techniques such as grid search or random search
• CSTA (US)Demonstrate code reuse by creating programming solutions using libraries and APIs.
• CSTA (US)Modify an existing program to add additional functionality and discuss intended and unintended implications (e.g., breaking other functionality).
• CSTA (US)Evaluate the ability of models and simulations to test and support the refinement of hypotheses.

1. Challenge: Your Assistant’s Front End

• CSTA (US)Develop programs for multiple computing platforms.
• Australia + NZStudents use an iterative process to design, develop, document, and test advanced computer programs, e.g. developing a GUI-based program that responds to user inputs (button clicks, text input, drop-down/radio selections)

1. Undo with a Stack
2. A Print Queue with deque
3. BFS on a Grid and a Map
4. A Linked List of Nodes
5. Count a Tree Recursively
6. Walk a Decision Tree
7. Challenge: Pick the Structure

• England + CambridgeShow understanding of how graphs can be used to aid AI, and use A* and Dijkstra's algorithms to perform searches on a graph (candidates not required to write the search algorithms themselves)
• CSTA (US)Use lists to simplify solutions, generalizing computational problems instead of repeatedly using simple variables.
• AI4K12 (US)Progress from drawing a tree by repeatedly splitting branches and placing data at nodes (K-2), to describing the parts of a tree (nodes, parent, leaves) and how they relate (3-5), to describing the parts of a labeled graph (nodes, links, source/target) and how trees are a special case of graphs (6-8), to describing how schemas structure information about people, places, or things within knowledge graphs used by search engines (9-12).
• AI4K12 (US)Progress from drawing a simple 3-node search tree showing two possible tic-tac-toe moves (K-2), to drawing a full search tree of next moves for tic-tac-toe or nim and picking the best move (3-5), to modeling breadth-first search on a graph problem such as common-friend detection (6-8), to illustrating and comparing breadth-first, depth-first, and best-first search algorithms for growing a search tree (9-12).

Used across grades.

• UAEDesign AI projects for real-world problems
• ADEKTrain/test a model, report performance
• ADEKResearch an SDG, build/evaluate a solution
• CBSEDevelop proficiency using the Orange Data Mining tool - navigate its interface, employ its features, execute data analysis tasks; demonstrate ability to apply Orange across AI domains including data science, computer vision, and NLP through hands-on projects (Unit 4: AI with Orange Data Mining Tool, practicals only).
• CSTA (US)Test and refine computational artifacts to reduce bias and equity deficits.
• CSTA (US)Create and evaluate an appropriate AI algorithm (e.g., a decision tree classifier) to accomplish a task.
• CSTA (US)Using a dataset and a systematic process, develop an AI model to generate for classification or prediction, and articulate the assumptions made at each step: (1) develop a question solvable with AI, (2) collect or curate data, (3) evaluate the data, (4) train an AI model on the data, (5) evaluate the model, and (6) iteratively improve the model.
• CSTA (US)Evaluate an AI model (e.g., using a model card) to determine the model's features as well as its biases, explainability, fairness, privacy, accuracy, and transparency.
• CSTA (US)Train, iteratively improve, and then develop a model card for an AI model with ethical considerations for end users and others impacted: fairness, bias, safety, security, intellectual property, privacy, robustness, explainability, accuracy, transparency, and accountability.
• 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 creating a model card for a classification or prediction model documenting its intended use, training data, accuracy, and limitations (3-5), to constructing an ethical matrix of stakeholders and their values for an AI system and evaluating alignment (6-8), to designing an AI system using a full ethical design process that considers all stakeholders' values across fairness, transparency, explainability, accountability, privacy, and societal values (9-12). K-2 is marked N/A.
• UNESCODevelop a critical attitude to ethics-by-design principles behind existing AI systems/algorithms by auditing selected tools, proposing an ethics label, and recommending remedies for identified violations of ethical principles or regulations (CG4.3.2.2).
• UNESCOPractise critiquing AI systems by auditing whether a model enhances or weakens human capacities, agency and consciousness, checking the extent of its explainability and data-privacy protection, and measuring system performance and its broader societal/environmental impact (CG4.3.4.1).
• Australia + NZRecognise how the identification and mitigation of bias is a critical aspect of machine learning, and critically evaluate the reliability, validity and ethics of AI-generated outputs and predictive strategies

Used across grades.

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

Used across grades.

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

Used across grades.

• UAEUse generative AI models to create new content
• UAEUse AI tools to work more effectively
• UAEApply effective prompting strategies
• OECD–EC (Europe)Experiment with multiple AI tools to develop diverse ideas (Create 2, intermediate).
• OECD–EC (Europe)Select and integrate outputs from multiple AI systems into a final product (Create 2, advanced).
• OECD–EC (Europe)Refine work through iterative exchanges with an AI system (Create 3, intermediate).
• OECD–EC (Europe)Critically evaluate how AI feedback shapes one's ideas and reflect on the creative process (Create 3, advanced).
• OECD–EC (Europe)Know how to formulate unambiguous search queries so a conversational agent or smart speaker can respond as required (AI example 29, competence 1.1).
• 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).

Used across grades.

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

Used across grades.

• ADEKExamine dataset gaps/bias

Used across grades.

• UAELearn from data; algorithms make decisions

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

• UAEEvaluate model performance

Used across grades.

• UAEHow advanced models process information (neural networks)
• UAENeural network design and performance
• AI4K12 (US)Progress from analyzing a decision-tree-building game and how the learning algorithm adds/replaces nodes (3-5), to comparing how decision-tree learning (add a node) differs from neural-network learning (adjust every weight) (6-8), to describing how decision-tree, neural-net, regression, and reinforcement-learning algorithms each adjust their own type of internal representation (9-12). K-2 is marked N/A.
• AI4K12 (US)Progress from illustrating that a 1-3 neuron network is a function that computes weighted-sum/threshold outputs (3-5), to illustrating the layered structure (input/hidden/output) of a neural network and its transfer functions (6-8), to describing feed-forward, 2D convolutional, recurrent, and generative adversarial network architectures and their typical uses, e.g. deepfakes (9-12). K-2 is marked N/A.
• AI4K12 (US)Progress from demonstrating how weights are assigned in a neural network to produce a desired input/output behavior (3-5), to demonstrating a learning rule (e.g. perceptron rule) that adjusts weights of a one-layer network in response to errors (6-8), to training a multilayer neural network with the backpropagation algorithm and describing how weights and hidden-unit outputs change during learning (9-12). K-2 is marked N/A.
• Korea, Japan, HKExplain how a neural network is built from perceptron units (inputs, weightings, bias, activation function) and how it "learns" via backpropagation to adjust weights to fit training data.

Used across grades.

• ADEKDesign a scalable AI system architecture
• CBSEDevelop proficiency using the Orange Data Mining tool - navigate its interface, employ its features, execute data analysis tasks; demonstrate ability to apply Orange across AI domains including data science, computer vision, and NLP through hands-on projects (Unit 4: AI with Orange Data Mining Tool, practicals only).
• UNESCOAcquire methodological and technical skills to evaluate frameworks/components (e.g. TensorFlow, PyTorch, Scikit-learn) and configure a scalable, maintainable, reusable prototype AI architecture (data, algorithm, model and interface layers) for a defined problem, considering open-source options (CG4.2.4.1).
• UNESCOAcquire the interdisciplinary technical skills and project-management competencies needed to build a prototype AI system for a simple specific task (e.g. a chatbot), including leveraging computing resources, selecting/enhancing AI models (e.g. hyperparameter optimization), and balancing cost against computing needs and environmental impact (CG4.2.4.2).
• ChinaSenior secondary students progress to applied innovation: refining AI algorithm models and interdisciplinary systems thinking (continuation of the 9-10 strand into upper senior grades)

Used across grades.

• ADEKRobotics / physical device
• ADEKRobotics / physical device
• CISCE + statesUse TinkerCAD to visualize, design and create robot components (joints: revolute, prismatic; RR mechanism) and visualize motion of designed components
• CISCE + statesBuild a simple robotic system in TinkerCAD, for example a wheeled mobile robot up to four wheels, integrating single-board-computer coding
• CISCE + statesBuild an AI-powered smart-home/robotics capstone (e.g. a smart door that opens via face recognition) integrating pre-trained ML models with Arduino hardware, a webcam and a servo motor
• CISCE + statesConstruct circuits using sensors and actuators (e.g. Arduino, IR sensor, servo motor) and control electronic devices via computer programming, e.g. building an automated hand-triggered sanitiser dispenser
• CSTA (US)Create and evaluate an appropriate AI algorithm (e.g., a decision tree classifier) to accomplish a task.
• 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 giving examples of visual features (parts, textures, colors) used to recognize an object class such as cats (K-2), to illustrating how face detection extracts facial features (3-5), to simulating an edge detector to illustrate feature extraction from images (6-8), to explaining how features (formants, edges, intensity gradients) are extracted from waveforms and images at multiple scales (9-12).
• AI4K12 (US)Progress from 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.
• 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).
• ChinaUpper-primary/junior students apply smart-hardware applications (example: Haidian Experimental Primary School)

Used across grades.

• UAEAI-manipulated media: safety and security
• ADEKHuman/AI-generated art + ethics
• ADEKGenerative image tools (GAN/diffusion), deepfakes
• ADEKGAN/diffusion image generation, critique an image
• OECD–EC (Europe)Experiment with multiple AI tools to develop diverse ideas (Create 2, 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).

Used across grades.

• ADEKRobotics / physical device
• ADEKRobotics / physical device
• ADEKRobotics / physical device
• CISCE + statesDescribe the general block diagram and building blocks of a robot (mechanical, electronic, computational) and identify robots by demonstration/video (industrial arm, humanoid, mobile)
• CISCE + statesExplain gears (role, principles, gear ratio) and classify sensors (internal: position/velocity/acceleration/force; external: contact/non-contact) and their functions in a robotic system
• CISCE + statesDescribe actuators (linear, rotary), control systems (manual vs automatic) and integrate sensors, actuators and controller in a robotic system using a block diagram (input-controller-robot-feedback)
• CISCE + statesUse TinkerCAD to visualize, design and create robot components (joints: revolute, prismatic; RR mechanism) and visualize motion of designed components
• CISCE + statesBuild a simple robotic system in TinkerCAD, for example a wheeled mobile robot up to four wheels, integrating single-board-computer coding
• CISCE + statesBuild an AI-powered smart-home/robotics capstone (e.g. a smart door that opens via face recognition) integrating pre-trained ML models with Arduino hardware, a webcam and a servo motor
• CISCE + statesConstruct circuits using sensors and actuators (e.g. Arduino, IR sensor, servo motor) and control electronic devices via computer programming, e.g. building an automated hand-triggered sanitiser dispenser
• 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)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.
• 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).
• ChinaUpper-primary/junior students apply smart-hardware applications (example: Haidian Experimental Primary School)

Used across grades.

• CSTA (US)Create and evaluate an appropriate AI algorithm (e.g., a decision tree classifier) to accomplish a task.
• AI4K12 (US)Progress from analyzing a decision-tree-building game and how the learning algorithm adds/replaces nodes (3-5), to comparing how decision-tree learning (add a node) differs from neural-network learning (adjust every weight) (6-8), to describing how decision-tree, neural-net, regression, and reinforcement-learning algorithms each adjust their own type of internal representation (9-12). K-2 is marked N/A.
• UNESCOAcquire age-appropriate technical skills in AI programming, understanding how supervised, unsupervised and reinforcement-learning algorithms scrape/process data, train, and function (CG4.2.3.2).

Used across grades.

• UAEUse generative AI models to create new content
• ADEKHuman/AI-generated art + ethics
• ADEKHuman/AI-generated art, ethics of generative tools

Used across grades.

• IBCollaborate with students from other Group 4 (sciences) subjects on shared scientific investigation and collaborative-sciences project skills
• CSTA (US)Design and develop computational artifacts working in team roles using collaborative tools.
• CSTA (US)Use version control systems, integrated development environments (IDEs), and collaborative tools and practices (code documentation) in a group software project.
• CSTA (US)Use tools and methods for collaboration on a project to increase connectivity of people in different cultures and career fields.
• Australia + NZPlan and manage projects individually and collaboratively, including using project management tools/strategies to sequence production activities

Used across grades.

• CBSEDevelop proficiency using the Orange Data Mining tool - navigate its interface, employ its features, execute data analysis tasks; demonstrate ability to apply Orange across AI domains including data science, computer vision, and NLP through hands-on projects (Unit 4: AI with Orange Data Mining Tool, practicals only).

Used across grades.

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

Used across grades.

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

Used across grades.

• England + CambridgeShow understanding of how graphs can be used to aid AI, and use A* and Dijkstra's algorithms to perform searches on a graph (candidates not required to write the search algorithms themselves)
• AI4K12 (US)Progress from drawing a simple 3-node search tree showing two possible tic-tac-toe moves (K-2), to drawing a full search tree of next moves for tic-tac-toe or nim and picking the best move (3-5), to modeling breadth-first search on a graph problem such as common-friend detection (6-8), to illustrating and comparing breadth-first, depth-first, and best-first search algorithms for growing a search tree (9-12).

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

• UAEUse generative AI models to create new content
• 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)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).

Used across grades.

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

Used across grades.

• UAEDesign AI projects for real-world problems
• ADEKTrain/test a model, report performance
• AI4K12 (US)Progress from creating a model card for a classification or prediction model documenting its intended use, training data, accuracy, and limitations (3-5), to constructing an ethical matrix of stakeholders and their values for an AI system and evaluating alignment (6-8), to designing an AI system using a full ethical design process that considers all stakeholders' values across fairness, transparency, explainability, accountability, privacy, and societal values (9-12). K-2 is marked N/A.

Used across grades.

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

Used across grades.

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

Used across grades.

• OECD–EC (Europe)Experiment with multiple AI tools to develop diverse ideas (Create 2, 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).

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

• ADEKHuman/AI-generated art, ethics of generative tools

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

• CBSEAnalyse data (numbers/images), find patterns/relations, and understand the role of Math (statistics, linear algebra, probability, calculus) in AI; understand statistics and probability concepts and apply them to real-life scenarios such as disaster management, sports, disease prediction, weather forecast, traffic estimation (Unit 3: Math for AI).
• Korea, Japan, HKUnderstand and apply methods for representing/storing data and for collecting, organising and analysing data (comparison, relation, change, classification), selecting, executing, evaluating and improving the analysis method.

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

• UAEData characteristics shape AI decisions
• UAEDesign AI solutions with systematic design processes
• ADEKBuild a data story (data + visuals + narrative for an audience)
• CBSERecognise the importance of data visualisation and discover different visualisation methods; build an interactive dashboard and presentation (Unit 2 sub-topic: Project Interactive Data Dashboard).
• CISCE + statesRepresent explored data using bar graphs, line graphs, scatter plots and pie charts; apply modelling approaches — linear regression (prediction), rule-based, supervised learning and unsupervised learning (brief)
• IBPlan the creation of the chosen solution and follow that plan to build a prototype sufficient for testing and evaluation
• CSTA (US)Create interactive data visualizations using software tools to help others better understand real-world phenomena.
• CSTA (US)Using a dataset and a systematic process, develop an AI model to generate for classification or prediction, and articulate the assumptions made at each step: (1) develop a question solvable with AI, (2) collect or curate data, (3) evaluate the data, (4) train an AI model on the data, (5) evaluate the model, and (6) iteratively improve the model.
• Korea, Japan, HKUnderstand and apply methods for representing/storing data and for collecting, organising and analysing data (comparison, relation, change, classification), selecting, executing, evaluating and improving the analysis method.
• ChinaStudents construct/build simple AI algorithm models and develop cross-disciplinary solutions using accumulated AI knowledge

Used across grades.

• 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.

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

• 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).

Used across grades.

• 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

Used across grades.

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

Used across grades.

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

Used across grades.

• ADEKDesign a scalable AI system architecture
• UNESCOAcquire methodological and technical skills to evaluate frameworks/components (e.g. TensorFlow, PyTorch, Scikit-learn) and configure a scalable, maintainable, reusable prototype AI architecture (data, algorithm, model and interface layers) for a defined problem, considering open-source options (CG4.2.4.1).
• UNESCOAcquire the interdisciplinary technical skills and project-management competencies needed to build a prototype AI system for a simple specific task (e.g. a chatbot), including leveraging computing resources, selecting/enhancing AI models (e.g. hyperparameter optimization), and balancing cost against computing needs and environmental impact (CG4.2.4.2).

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

• UAEApply effective prompting strategies
• OECD–EC (Europe)Select and integrate outputs from multiple AI systems into a final product (Create 2, advanced).

Used across grades.

• UAEApply effective prompting strategies
• ADEKGAN/diffusion image generation, critique an image
• OECD–EC (Europe)Critically evaluate how AI feedback shapes one's ideas and reflect on the creative process (Create 3, advanced).

Used across grades.

• OECD–EC (Europe)Select and integrate outputs from multiple AI systems into a final product (Create 2, advanced).

Used across grades.

• CSTA (US)Analyze a large-scale computational problem and identify generalizable patterns that can be applied to a solution.

Used across grades.

• CSTA (US)Analyze a large-scale computational problem and identify generalizable patterns that can be applied to a solution.

Used across grades.

• ADEKDesign a scalable AI system architecture
• UNESCOAcquire methodological and technical skills to evaluate frameworks/components (e.g. TensorFlow, PyTorch, Scikit-learn) and configure a scalable, maintainable, reusable prototype AI architecture (data, algorithm, model and interface layers) for a defined problem, considering open-source options (CG4.2.4.1).
• UNESCOAcquire the interdisciplinary technical skills and project-management competencies needed to build a prototype AI system for a simple specific task (e.g. a chatbot), including leveraging computing resources, selecting/enhancing AI models (e.g. hyperparameter optimization), and balancing cost against computing needs and environmental impact (CG4.2.4.2).
• ChinaSenior secondary students progress to applied innovation: refining AI algorithm models and interdisciplinary systems thinking (continuation of the 9-10 strand into upper senior grades)

Used across grades.

• UAEHuman–AI collaboration across sectors

Used across grades.

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

Used across grades.

• UAEApply effective prompting strategies
• UAEDesign AI solutions with systematic design processes
• IBDesign, develop, and document a self-identified computational solution (~35 hours) as the internal assessment, including a problem specification, planning, system overview, development, and evaluation
• CSTA (US)Implement an artificial intelligence algorithm to play a game against a human opponent or solve a problem.
• CSTA (US)Plan and develop programs for broad audiences using a software lifecycle process.
• CSTA (US)Develop and use a series of test cases to verify that a program performs according to its design specifications.
• CSTA (US)Evaluate key qualities of a program through a process such as a code review.
• OECD–EC (Europe)Purposefully integrate AI outputs with original ideas to build a comprehensive solution (Create 1, advanced).
• OECD–EC (Europe)Select and integrate outputs from multiple AI systems into a final product (Create 2, 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)Design improvements to an AI system based on technical constraints, ethics, and user needs to promote societal benefit (Shape 4, advanced).
• 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).
• UNESCOApply an integral, ethics-by-design set of principles across the whole AI life cycle, e.g. simulating the role of a 'chief ethics officer' drafting a checklist of ethical criteria to audit each step of an AI system's design (CG4.3.2.1).
• SingaporeLearn to use AI: use AI tools safely and responsibly.
• Korea, Japan, HKCapture varied phenomena as information and its connections, and apply information and information technology appropriately, effectively and creatively toward problem discovery and solution (content areas include content creation for communication, data processing, and programming to build information systems).
• ChinaStudents learn to develop AI applications and transfer that capability to solve problems in other real-life contexts

Used across grades.

• IBEvaluate the success of a computational solution against its own requirements and identify possible improvements
• UNESCOCustomize assessment methods/instruments to test self-crafted AI tools for robustness and ease of use, organise peer assessment and shared feedback, and build collaborative co-creator skills (CG4.3.3.3).

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

• IBCollaborate with students from other Group 4 (sciences) subjects on shared scientific investigation and collaborative-sciences project skills
• CSTA (US)Use version control systems, integrated development environments (IDEs), and collaborative tools and practices (code documentation) in a group software project.

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

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

Used across grades.

• UAEApply effective prompting strategies
• UAEDesign AI solutions with systematic design processes
• IBDesign, develop, and document a self-identified computational solution (~35 hours) as the internal assessment, including a problem specification, planning, system overview, development, and evaluation
• IBEvaluate the success of a computational solution against its own requirements and identify possible improvements
• IBCredit and reference any AI-generated content used in coursework in the body of the text and in the bibliography, per IB academic integrity requirements
• IBUnderstand that any AI-generated work included, even in part, in a piece of assessment is not considered the student's own unless it is clearly credited in the text and referenced in the bibliography
• IBFollow the IB Academic Integrity policy's guidance on the ethical use of AI tools (per its dedicated appendix) when producing assessed work
• CSTA (US)Implement an artificial intelligence algorithm to play a game against a human opponent or solve a problem.
• CSTA (US)Plan and develop programs for broad audiences using a software lifecycle process.
• CSTA (US)Develop and use a series of test cases to verify that a program performs according to its design specifications.
• CSTA (US)Evaluate key qualities of a program through a process such as a code review.
• OECD–EC (Europe)Select and integrate outputs from multiple AI systems into a final product (Create 2, 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)Assess the ethical implications and justify how an AI-assisted work maintains authenticity (Create 4, advanced).
• OECD–EC (Europe)Design improvements to an AI system based on technical constraints, ethics, and user needs to promote societal benefit (Shape 4, advanced).
• UNESCOApply an integral, ethics-by-design set of principles across the whole AI life cycle, e.g. simulating the role of a 'chief ethics officer' drafting a checklist of ethical criteria to audit each step of an AI system's design (CG4.3.2.1).
• UNESCOCustomize assessment methods/instruments to test self-crafted AI tools for robustness and ease of use, organise peer assessment and shared feedback, and build collaborative co-creator skills (CG4.3.3.3).
• SingaporeLearn beyond AI: develop distinctly human capabilities and dispositions that AI cannot replicate.
• Korea, Japan, HKCapture varied phenomena as information and its connections, and apply information and information technology appropriately, effectively and creatively toward problem discovery and solution (content areas include content creation for communication, data processing, and programming to build information systems).
• ChinaStudents learn to develop AI applications and transfer that capability to solve problems in other real-life contexts
• 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)

Used across grades.

• IBDesign, develop, and document a self-identified computational solution (~35 hours) as the internal assessment, including a problem specification, planning, system overview, development, and evaluation
• IBEvaluate the success of a computational solution against its own requirements and identify possible improvements
• CSTA (US)Plan and develop programs for broad audiences using a software lifecycle process.
• CSTA (US)Evaluate an AI model (e.g., using a model card) to determine the model's features as well as its biases, explainability, fairness, privacy, accuracy, and transparency.
• CSTA (US)Train, iteratively improve, and then develop a model card for an AI model with ethical considerations for end users and others impacted: fairness, bias, safety, security, intellectual property, privacy, robustness, explainability, accuracy, transparency, and accountability.
• AI4K12 (US)Progress from creating a model card for a classification or prediction model documenting its intended use, training data, accuracy, and limitations (3-5), to constructing an ethical matrix of stakeholders and their values for an AI system and evaluating alignment (6-8), to designing an AI system using a full ethical design process that considers all stakeholders' values across fairness, transparency, explainability, accountability, privacy, and societal values (9-12). K-2 is marked N/A.
• OECD–EC (Europe)Design improvements to an AI system based on technical constraints, ethics, and user needs to promote societal benefit (Shape 4, advanced).
• UNESCOApply an integral, ethics-by-design set of principles across the whole AI life cycle, e.g. simulating the role of a 'chief ethics officer' drafting a checklist of ethical criteria to audit each step of an AI system's design (CG4.3.2.1).
• UNESCODevelop a critical attitude to ethics-by-design principles behind existing AI systems/algorithms by auditing selected tools, proposing an ethics label, and recommending remedies for identified violations of ethical principles or regulations (CG4.3.2.2).
• UNESCOCustomize assessment methods/instruments to test self-crafted AI tools for robustness and ease of use, organise peer assessment and shared feedback, and build collaborative co-creator skills (CG4.3.3.3).
• UNESCOPractise critiquing AI systems by auditing whether a model enhances or weakens human capacities, agency and consciousness, checking the extent of its explainability and data-privacy protection, and measuring system performance and its broader societal/environmental impact (CG4.3.4.1).

Used across grades.

• UAEJudge responsible AI use against governance frameworks
• ADEKStakeholder debate / ethical matrix
• ADEKDraft/commit to ethical-use guidelines
• ADEKAI governance/civic participation
• 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