Grade 11: units, topics and lessons

1. Level Up!
2. What Are Transformers?
3. Three Words You Need: Weight, Loss, Gradient
4. Weight, Loss, Gradient by Hand
5. The AI Revolution
6. Attention and Steps by Hand
7. Transformer Match!
8. Transformer Inside?
9. Specialist Skills
10. Why Better AI Changes Access
11. Sort the Skills!
12. This Year's Journey
13. Key Concepts Preview
14. Term to Task
15. Attention Cost Lab
16. Attention Cost by Hand
17. How ChatGPT Turned Research into a Product
18. Specialist Diagnosis
19. Learning Path Order
20. Specialist Check!
21. Checkpoint Quiz
22. Explain the Specialist Shift
23. Specialist Terms
24. Welcome Aboard!

• CISCE + statesExplain the brief history and primary elements of AI including Machine Learning (ML) vs Deep Learning (DL)/neural networks, and applications of ML/DL such as image recognition, computer vision, speech recognition and information retrieval
• CISCE + statesInteract with Large Language Models (LLMs) and apply prompt engineering to perform text generation and image creation, and understand how responsive AI agents function in educational contexts
• CSTA (US)Evaluate the impact of equity, access, and influence on the distribution of computing resources in a global society.
• 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).

1. The Legendary Paper
2. The Old Way
3. Enter: Attention
4. Attention or Recurrence?
5. Old vs New Match
6. What Parallel Training Bought
7. Self-Attention, Step by Step
8. Weights by Hand
9. Why This Paper Keeps Showing Up
10. Sort the Features
11. Inside the Transformer
12. Component to Job
13. The Eight Authors
14. Scores to Weights Lab
15. Why Google Translate Needed Better Attention
16. Design Calls
17. Transformer Pipeline
18. Paper Facts Check!
19. Attention Quiz
20. Explain Why the Paper Mattered
21. Paper Vocabulary
22. Attention Master!

• AI4K12 (US)Progress from demonstrating how a computer produces different verb tense/agreement forms (K-2), to experimenting with a speech-to-text system's ability to resolve ambiguous words via context (3-5), to illustrating how word embeddings support reasoning about word meaning, e.g. outlier detection and analogy completion (6-8), to demonstrating how a small context-free grammar parses or generates simple sentences and where it falls short, e.g. subject-verb agreement (9-12).

1. Where Q, K and V Come From
2. The Library Analogy
3. Scaled Dot-Product Attention
4. Scores and Projections by Hand
5. QKV Match!
6. What Scaling Changes
7. One Attention Head by Hand
8. Finish the Head
9. Heads That Specialise
10. Sort Attention Steps!
11. Self vs Cross Attention
12. Self or Cross?
13. Attention Patterns
14. Scaling Lab
15. Lab Readout
16. Autocomplete Against the Clock
17. Attention Pipeline
18. Attention Facts!
19. Self-Attention Quiz
20. Explain Q, K, and V
21. Attention Terms
22. Attention Master!

• 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
• CSTA (US)Describe how current AI models (e.g., LLMs) use data representation.
• CSTA (US)Choose and use an appropriate representation of complex data for processing by an AI algorithm.
• AI4K12 (US)Progress from identifying the features that make objects in a collection unique and organizing them into a feature table (K-2), to constructing a feature-vector representation for a set of objects and showing that similar objects lie close together in feature space (3-5), to explaining how word embeddings represent words as numeric vectors for tasks like machine translation (6-8), to describing how a transformer network maps sequences of word-embedding vectors to output sequences (9-12).
• AI4K12 (US)Progress from illustrating ambiguity via homophones/homonyms resolved by context (K-2), to illustrating pronoun reference ambiguity resolved by world knowledge (3-5), to illustrating prepositional-phrase attachment ambiguity resolved by meaning (6-8), to describing polysemy (multiple word senses) and how statistical NLP systems resolve it less reliably than human common sense (9-12).

1. One Head Not Enough!
2. Team of Experts
3. Multi-Head Architecture
4. Two Heads by Hand
5. Why One Head Blurs
6. Multi-Head Match!
7. Per Head or Shared?
8. Real Numbers!
9. Read a Model Config
10. Heads Learn Different Jobs
11. Sort the Steps!
12. What Heads Learn
13. Is 12 Heads 12× the Work?
14. Count the Weights
15. Head Size Lab
16. How ChatGPT Tracks Multiple Signals at Once
17. Pipeline Order
18. Multi-Head Facts!
19. Multi-Head Quiz
20. Explain Why Multiple Heads Help
21. Multi-Head Terms
22. Multi-Head Master!

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

1. Lost in Order
2. Order Matters!
3. Positional Encoding
4. Order-Blind by Hand
5. Position Match!
6. Order-Blind or Order-Aware?
7. Sine Wave Secret
8. Lost in the Middle
9. Encoding Sort!
10. Adding Positions
11. Two Approaches
12. Fixed or Learned?
13. Position Wave Lab
14. Position Code by Hand
15. Why Translation Apps Care About Word Order
16. Position Bugs in Real Apps
17. Build the Pipeline!
18. Position Check!
19. Position Quiz!
20. Explain Positional Encoding
21. Position Words
22. Position Master!

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

1. The Full Picture
2. Encoder-Decoder
3. Encoder Layers
4. Encoder by the Numbers
5. Architecture Match!
6. The Same Recipe, Scaled Up
7. Decoder Magic
8. Decoder Checkpoint
9. Encoder vs Decoder!
10. Add & Normalize
11. Add & Norm by Hand
12. Output Layer
13. Mask Links Lab
14. Mask and Next Word by Hand
15. Three Families from One Blueprint
16. Pick the Architecture
17. Full Data Flow!
18. Transformer Types!
19. Architecture Quiz!
20. Explain the Full Transformer
21. Transformer Terms
22. Architecture Master!

• CBSEExplain the basic structure and components of a neural network; identify different types of neural networks and their applications; understand machine learning and neural networks through hands-on projects (e.g. TensorFlow Celsius-to-Fahrenheit model, TensorFlow Playground classification) (Unit 6: Understanding Neural Networks).
• CISCE + statesExplain Artificial Neural Networks (ANN) — neurons, input/hidden/output layers, forward and backward propagation, multi-layer perceptron (MLP) — using an example such as handwritten-digit recognition
• England + CambridgeShow understanding of how artificial neural networks have helped with machine learning
• 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.

1. Meet BERT!
2. BERT Origins
3. Encoder Only
4. Which Words Can BERT See?
5. BERT Match!
6. Reader or Writer?
7. Bidirectional Magic
8. Why Search Improved So Much
9. Training Tasks!
10. Masked LM Magic
11. Masking by Hand
12. Fine-Tuning BERT
13. Pre-train or Fine-tune?
14. Masking Lab
15. How Google Search Understands Better Queries
16. Read the Query
17. BERT Pipeline!
18. BERT Can Do This!
19. BERT Quiz!
20. Explain Why BERT Understands Well
21. BERT Vocab
22. BERT Master!

• ADEKFootprint / resource cost of AI
• CSTA (US)Design ways to minimize negative environmental impacts of AI and communicate those ways to others.
• CSTA (US)Students examine security implications and environmental impacts of AI systems within Systems & Security.
• AI4K12 (US)Progress from demonstrating how a computer produces different verb tense/agreement forms (K-2), to experimenting with a speech-to-text system's ability to resolve ambiguous words via context (3-5), to illustrating how word embeddings support reasoning about word meaning, e.g. outlier detection and analogy completion (6-8), to demonstrating how a small context-free grammar parses or generates simple sentences and where it falls short, e.g. subject-verb agreement (9-12).
• AI4K12 (US)Progress from demonstrating how computers recognize emotions in faces via mouth/eye/eyebrow shape (K-2), to illustrating how sentiment analysis judges the emotional tone of text, e.g. movie/restaurant reviews, social media (3-5), to describing how computers combine facial expression, gaze, gesture, body language, tone of voice, and word choice to recognize emotional states (6-8), to identifying ways AI applications modify their behavior in response to detected human emotional states, e.g. adaptive tutoring systems (9-12).
• OECD–EC (Europe)Analyse how design and deployment decisions affect an AI system's resource consumption (Engage 5, advanced).
• OECD–EC (Europe)Consider the ethical consequences of AI systems throughout their life-cycle, including environmental impact and societal impact such as platformisation of work and low-cost labelling labour (AI example 57, competence 4.4).

1. Meet GPT!
2. GPT Evolution
3. Decoder Power
4. Causal Mask by Hand
5. GPT vs BERT!
6. Which Training Target?
7. Next Word Prediction
8. Generation by Hand
9. Why Scale Changed GPT So Fast
10. Attention Types!
11. Training GPT
12. Temperature Control
13. Temperature Lab
14. Temperature by Hand
15. How ChatGPT Keeps a Conversation Going
16. Builder's Choices
17. Generation Pipeline!
18. GPT Can Do This!
19. GPT Quiz!
20. Explain How GPT Generates
21. GPT Vocab
22. GPT Master!

• OECD–EC (Europe)Purposefully integrate AI outputs with original ideas to build a comprehensive solution (Create 1, advanced).

1. Talk to AI!
2. Prompt Basics
3. Good vs Bad
4. Diagnose the Prompt
5. Prompt Match!
6. What Did the Rewrite Add?
7. Prompt Formula
8. CRISP by Hand
9. Why Examples Teach So Fast
10. Pro Techniques!
11. Chain-of-Thought
12. Check the Steps
13. System Prompts
14. System or User?
15. Few-Shot Token Lab
16. How GitHub Copilot Benefits from Better Prompts
17. Build a Prompt!
18. Rate the Prompt!
19. Prompt Quiz!
20. Explain Why Prompting Matters
21. Prompt Vocab
22. Prompt Master!

• CSTA (US)Explain security issues that might lead to compromised computer programs.
• CSTA (US)Compare ways software developers protect devices and information from unauthorized access.

1. Your Own AI!
2. Two Training Phases
3. When to Fine-Tune?
4. How RAG Works
5. Methods Match!
6. Which Training Phase?
7. The Process
8. Why Data Quality Beats Data Quantity
9. Data Matters!
10. Data Format
11. Read the JSONL
12. LoRA Efficiency
13. LoRA Size Lab
14. LoRA by Hand
15. How Support Bots Keep a Brand Voice
16. Maya's Study Helper
17. Fine-Tune Pipeline!
18. Fine-Tune or Not?
19. Fine-Tune Quiz!
20. Explain When Fine-Tuning Helps
21. Fine-Tune Vocab
22. Fine-Tune Master!

• AI4K12 (US)Progress from demonstrating how training data are labeled by class in a tool like Teachable Machine (3-5), to explaining the difference between training a reasoning model and using it on new data (6-8), to illustrating all steps of building a classifier/predictor with machine learning, including choosing features, labeling, cross-validation, and test-set evaluation (9-12). K-2 is marked N/A.

1. Power at Your Fingertips
2. API Basics
3. Where Does the Work Happen?
4. The Big Players
5. Your App or Their Servers?
6. Provider Match!
7. Tiny Settings Change the Whole Experience
8. Key Parameters
9. Parameter Sort!
10. Real API Code
11. Read the Response
12. Tokens & Pricing
13. API Tuning Lab
14. Price a Request by Hand
15. How Notion AI Adds Intelligence Through APIs
16. API Call Pipeline!
17. API Best Practices!
18. Symptom to Fix
19. API Quiz!
20. Explain the API Flow
21. API Vocab
22. API Master!

• 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).

1. The Grand Finale!
2. Unit Journey
3. Warm-Up: Attention by Hand
4. Attention Review
5. Query, Key or Value?
6. Unit Match
7. From GPT-3 to ChatGPT: Human Feedback
8. Architecture Review
9. Model Sort!
10. Skills Review
11. Choose Your Path
12. Pick the Right Tool
13. Chat History Cost Lab
14. History Cost by Hand
15. How GitHub Copilot Uses the Whole Stack
16. Follow One Request
17. AI Timeline
18. Ultimate Test!
19. Final Quiz!
20. Explain the Big Idea
21. Master Vocab
22. Unit 1 Complete

• CBSECommunicate effectively about AI concepts and applications; describe the historical development of AI; differentiate types and domains of AI including their applications; recognise key terminologies/concepts related to machine learning and deep learning; formulate informed opinions on benefits and limitations of AI (Unit 1).
• CISCE + statesDefine AI, trace its evolution and applications across fields, and explain common AI applications (decision making, remote patient monitoring, data analysis, complex problem-solving)
• CSTA (US)Students study the history of computing and AI, emerging technologies, human-AI relationships, and responsible decision-making about technology use.

1. The Creative Revolution
2. Two Types of AI
3. Generate or Classify?
4. Learning to Create
5. Next-Word Odds by Hand
6. GenAI Match
7. GenAI Spread Into Almost Every Format
8. Three Key Architectures
9. Diffusion Step by Step
10. Architecture Sort
11. GenAI in Action
12. Powers and Limits
13. Limits in Your App
14. Generative Design Lab
15. How Canva Turns Prompts Into Designs
16. Pick the Right Model
17. GenAI Timeline
18. Spot the GenAI!
19. Checkpoint Quiz
20. Explain Generative AI
21. GenAI Vocabulary
22. GenAI Explorer!

• ADEKClassification vs generation, how generative AI is trained
• CBSEExplain how Generative AI works; differentiate Generative AI and Discriminative AI and identify use cases; explore ethical, social, and legal concerns; gain hands-on experience with AI tools to generate creative/analytical outputs (images, text, video); use an LLM API to design and deploy a functional chatbot (Unit 7: Generative AI).
• CISCE + statesExplain Deep Learning and demonstrate image/pattern recognition using a Convolutional Neural Network (CNN) example (e.g. classifying images of cats, dogs, birds)
• IBDescribe deep learning at an introductory level as a machine learning technique
• OECD–EC (Europe)Select and integrate outputs from multiple AI systems into a final product (Create 2, advanced).
• Korea, Japan, HKApply deep-learning-based computer vision, speech recognition and natural-language-processing technologies, choosing appropriate deep-learning techniques to solve real-life and cross-disciplinary problems.
• Korea, Japan, HKStudents should consciously develop an understanding of how generative AI works, how to apply it to learning, and how to critically evaluate/master its use in each subject area, combining "learning about AI itself", "learning how to use it", and "actively using it in subjects".

1. Word by Word
2. Tokens Revealed
3. Tokens by Hand
4. Predict the Next
5. The Loop by Hand
6. Probability Match
7. Sampling Shapes the Voice You Hear
8. Temperature, Top-K and Top-P
9. Sampling Sort
10. Cuts by Hand
11. Generation Steps
12. Stopping Conditions
13. Sampling Lab
14. Lab by Hand
15. How ChatGPT Streams Answers So Naturally
16. Symptom to Setting
17. Pipeline Order
18. Spot Text Gen!
19. Build a Small Next-Word Loop
20. Checkpoint Quiz
21. Explain the Token Loop
22. Generation Vocab
23. Token Loop Traced

• UAEApply effective prompting strategies
• ADEKClassification vs generation, how generative AI is trained
• CBSEDevelop understanding of the complexities of human language and challenges in NLP tasks (emotion detection, sentiment analysis, classification, chatbots); learn techniques/algorithms for NLP tasks and phases of NLP; create a chatbot using a conversational platform (Unit 7: Leveraging Linguistics and Computer Science).
• CBSEExplain how Generative AI works; differentiate Generative AI and Discriminative AI and identify use cases; explore ethical, social, and legal concerns; gain hands-on experience with AI tools to generate creative/analytical outputs (images, text, video); use an LLM API to design and deploy a functional chatbot (Unit 7: Generative AI).
• Korea, Japan, HKStudents should consciously develop an understanding of how generative AI works, how to apply it to learning, and how to critically evaluate/master its use in each subject area, combining "learning about AI itself", "learning how to use it", and "actively using it in subjects".

1. Pictures from Noise
2. Diffusion Revealed
3. One Denoising Step by Hand
4. Text Becomes Art
5. Training or Generating?
6. Model Match
7. Seeds Make Image Generation Reproducible
8. The Latent Space
9. Latent Size by Hand
10. Parameter Sort
11. The U-Net Brain
12. Prompt Mastery
13. Prompt Doctor for Your App
14. Diffusion Control Lab
15. Denoising Cost by Hand
16. How Adobe Firefly Uses Prompted Image Generation
17. Pipeline Order
18. Spot Image Gen!
19. Checkpoint Quiz
20. Explain Diffusion
21. Image Gen Vocab
22. Image Creator!

• UAEApply effective prompting strategies
• ADEKGAN/diffusion image generation, critique an image

1. Deep Dive Time
2. DDPM Revealed
3. Noise Prediction by Hand
4. Noise Schedules
5. Schedules Check
6. Sampler Match
7. The Sampler Changes the Feel of the Same Model
8. DDIM Breakthrough
9. Steps and Speed by Hand
10. Guidance Sort
11. CFG Formula
12. CFG by Hand
13. ControlNet Power
14. Diffusion Tuning Lab
15. How Stable Diffusion Interfaces Expose Research Controls
16. Tune the App's Defaults
17. Diffusion Timeline
18. Spot Diffusion!
19. Fit a Toy Reverse Process
20. Checkpoint Quiz
21. Explain DDPM Simply
22. Diffusion Vocab
23. Diffusion Master!

• ADEKGAN/diffusion image generation, critique an image

1. The AI Battle
2. Generator vs Discriminator
3. Forger or Detective?
4. The Minimax Game
5. Minimax by Hand
6. GAN Types Match
7. GANs Shaped AI Art Before Diffusion Took Over
8. Training Problems
9. Solutions Sort
10. StyleGAN's Style Space
11. Style Mixing Check
12. GANs vs Diffusion
13. Minimax Value Lab
14. Read the Lab, Pick the Model
15. How This Person Does Not Exist Showed GAN Power
16. Synthetic Faces in Your App
17. GAN Applications
18. Spot the GAN!
19. Checkpoint Quiz
20. Explain How GANs Work
21. GAN Vocabulary
22. GAN Expert!

• ADEKGAN/diffusion image generation, critique an image
• CISCE + statesExplain Deep Learning and demonstrate image/pattern recognition using a Convolutional Neural Network (CNN) example (e.g. classifying images of cats, dogs, birds)
• IBDescribe deep learning at an introductory level as a machine learning technique
• Korea, Japan, HKApply deep-learning-based computer vision, speech recognition and natural-language-processing technologies, choosing appropriate deep-learning techniques to solve real-life and cross-disciplinary problems.

1. Paint Like Picasso
2. Content vs Style
3. Which Layer Holds What?
4. The Gram Matrix
5. Gram Matrix by Hand
6. Content or Style?
7. Style Match
8. One Paper Changed Mobile Photo Filters
9. Optimising the Pixels
10. Speed Sort
11. Modern Style AI
12. Pick the Method
13. Real Applications
14. Style Blend Lab
15. Total Loss by Hand
16. How Prisma Turned Photos Into Instant Art
17. Pipeline Order
18. Spot Style Transfer!
19. Checkpoint Quiz
20. Explain Style Transfer
21. Style Vocabulary
22. Style Master!

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

1. The Art Controversy
2. Training Data Debate
3. Where the Argument Starts
4. Copyright Chaos
5. Who Owns This Image?
6. Ethics Match-Up
7. Different AI Models Face Very Different Ethical Questions
8. Artists vs AI
9. Perspectives Sort
10. Legal Battles
11. Read the Cases
12. Ethical Guidelines
13. Ethics Balance Lab
14. Readiness Score by Hand
15. How Adobe Firefly Changed the Training Debate
16. Your Image App: Design Calls
17. Ethics Priority
18. Ethics Detector
19. Ethics Quiz
20. Explain Responsible AI Art Use
21. Ethics Vocabulary
22. Judge the Case
23. Write the Rule
24. My AI Practice Checklist
25. Which Line Does It Break?
26. My Checklist, Applied
27. Ethics Champion!

• ADEKDeepfakes: explain, spot flaws, set responsible-use rules
• ADEKHuman/AI-generated art, ethics of generative tools
• ADEKStakeholder debate / ethical matrix
• ADEKDraft/commit to ethical-use guidelines
• 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)Evaluate the data used to solve a problem, including its source(s) and whether privacy is protected, if/how the data has been processed, data quality (e.g., accuracy, reliability, validity), what the data represents, and biases.
• CSTA (US)Students build data fluency and examine the societal impacts of large-scale data collection used to train AI systems.
• AI4K12 (US)Progress from 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.
• OECD–EC (Europe)Assess the ethical implications and justify how an AI-assisted work maintains authenticity (Create 4, advanced).
• OECD–EC (Europe)Evaluate how dataset properties affect an AI system's performance and impact, factoring in data source and selection (Shape 3, advanced).
• OECD–EC (Europe)Know that 'deep-fakes' are AI-generated images, video, or audio of events or people that did not really happen, and may be indistinguishable from the real thing (AI example 51, competence 1.2).
• OECD–EC (Europe)Be aware that content shared publicly online can be used to train AI systems (e.g. facial-recognition training on shared photos), which may raise privacy concerns (AI example 45, competence 2.2).
• UNESCOCarry out responsible AI practices in compliance with ethical principles and locally applicable regulations, iteratively building and following a personal checklist of lawful and responsible practices (e.g. protecting privacy/copyright, marking AI-generated content, avoiding disinformation, hate speech, or sensitive content about identifiable individuals) (CG4.2.2.1).
• UNESCOGain an age-appropriate understanding of personal, legal and ethical responsibilities when using AI and reinforce self-disciplined behaviours around sensitive personal data, copyrighted materials, deepfake/identifiable-person imagery, and the spread of misinformation, disinformation or hate speech (CG4.2.2.2).
• SingaporeLearn to use AI: use AI tools safely and responsibly.
• Australia + NZStudents develop an understanding of how generative AI tools work in broad terms, how they can be used appropriately, and when/how such tools are affecting the content and information they see (transparency about AI-generated or AI-assisted content)
• Australia + NZStudents learn that use of generative AI outputs (e.g. in assessable work) must remain open to challenge, retain human agency, and that the student/user remains accountable for decisions made with AI assistance (e.g. academic integrity implications)

1. The Sound Revolution
2. Text-to-Speech Magic
3. How Can AI Speak in a Human Voice?
4. Voice Cloning Tech
5. Cloning Choices
6. Audio Tech Match
7. Debug the Voice
8. AI Music Makers
9. Audio Type Sort
10. Audio AI Architecture
11. Frames by Hand
12. Audio AI Uses
13. Designing a Voice Feature
14. Audio Design Lab
15. Lab by Hand
16. How ElevenLabs Made Voice AI Mainstream
17. TTS Pipeline Order
18. Audio AI Spotter
19. Audio Generation Quiz
20. Explain AI Audio Generation
21. Audio Vocabulary
22. Audio AI Master!

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

1. Movies from Words
2. The Hard Problems
3. Frames by Hand
4. Video Diffusion
5. Patches by Hand
6. Video Model Match
7. Physics Mistakes Are Easy to Notice
8. Inside Sora
9. Video Gen Types
10. Advanced Techniques
11. Name the Technique
12. Video AI in Action
13. Clip Length vs Attention Work
14. Length, Cost and Energy
15. How Runway Makes Video Creation Faster
16. Label the Fake
17. Video Gen Pipeline
18. Video AI Spotter
19. Video Generation Quiz
20. Explain Why Video Is Harder Than Images
21. Video Vocabulary
22. Video Generation Explained

• AI4K12 (US)Progress from giving commonsense explanations of human behavior useful for a robot to know, e.g. choosing a plausible story ending (K-2), to explaining what everyday cultural and naive-physics knowledge a computer needs to understand a story (3-5), to explaining the naive-physics inference rules (mass, volume, conservation, forces) needed to solve a physics reasoning problem (6-8), to explaining the cultural, naive-physics, and folk-psychology knowledge needed to interpret a fable, fairytale, or Winograd-schema sentence (9-12).

1. AI That Sees & Hears
2. CLIP: The Bridge
3. Contrastive Pairs by Hand
4. Embedding Magic
5. Zero-Shot by Hand
6. Multimodal Match
7. Humans Are Multimodal Too
8. Vision-Language AI
9. CLIP or VLM?
10. Task Type Sort
11. Capabilities Revealed
12. Multimodal in Action
13. Design the Feature
14. Tune a Multimodal System
15. Visual Tokens by Hand
16. How ChatGPT Uses Vision in Real Tasks
17. VLM Pipeline Order
18. Multimodal Spotter
19. Multimodal Quiz
20. Explain Why Shared Embeddings Matter
21. Multimodal Vocabulary
22. Multimodal Master!

• 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).

1. AI Goes Global
2. World AI Centers
3. Why Build Locally?
4. Multilingual AI
5. Which Challenge Is It?
6. Regional AI Match
7. Related Languages Can Help Each Other
8. Transfer or Not?
9. Open Source AI
10. AI Approach Sort
11. AI Sovereignty
12. Local AI Solutions
13. The Token Bill of Going Global
14. Token Bill by Hand
15. How Hugging Face Supports Global AI Builders
16. Pick the Build Path
17. Localization Steps
18. Global AI Spotter
19. Global AI Quiz
20. Explain Why Local AI Matters
21. Global AI Vocabulary
22. Global AI Expert!

• CSTA (US)Evaluate the impact of equity, access, and influence on the distribution of computing resources in a global society.
• AI4K12 (US)Progress from discussing why intelligent agents need to understand languages other than English (K-2), to discussing why domain knowledge must be broad enough to cover accents, dialects, and musical genres (3-5), to describing how a vision system can exhibit cultural bias if it lacks knowledge of objects outside its creators' culture, e.g. non-US traffic signs (6-8), to describing technical difficulties (e.g. low-contrast faces, children's speech) in making perception systems work well for diverse groups (9-12).
• OECD–EC (Europe)Be aware that AI systems are typically developed in English-speaking contexts and can work less accurately in non-English contexts, e.g. machine translation between less-used language pairs (AI example 55, competence 2.5).

1. Journey Complete!
2. Image Generation Recap
3. Diffusion, GAN or Style Transfer?
4. Audio & Video Recap
5. Sound and Motion Check
6. Unit Master Match
7. Review Helps Knowledge Transfer
8. Multimodal & Ethics
9. Ethics Case Calls
10. Generation Type Sort
11. Key Takeaways
12. The Future
13. Denoiser Passes per Request
14. Passes and Energy by Hand
15. How Adobe Firefly Brings Many GenAI Ideas Together
16. Design a Small GenAI App
17. A Tool for Each Part
18. Draft the Script
19. Make the Cover
20. Credit Line and What I Changed
21. GenAI Timeline
22. Ultimate Challenge
23. Unit Final Quiz
24. Explain Why Tech and Ethics Belong Together
25. Master Vocabulary
26. Unit 2 Complete!

• CBSECommunicate effectively about AI concepts and applications; describe the historical development of AI; differentiate types and domains of AI including their applications; recognise key terminologies/concepts related to machine learning and deep learning; formulate informed opinions on benefits and limitations of AI (Unit 1).
• CISCE + statesDefine AI, trace its evolution and applications across fields, and explain common AI applications (decision making, remote patient monitoring, data analysis, complex problem-solving)
• CSTA (US)Students study the history of computing and AI, emerging technologies, human-AI relationships, and responsible decision-making about technology use.
• OECD–EC (Europe)Select and integrate outputs from multiple AI systems into a final product (Create 2, advanced).

1. The Gaming Champion
2. The RL Loop
3. Name the Parts
4. Rewards and Episodes
5. Total Reward by Hand
6. RL Parts Match
7. Three Ways to Learn
8. Policy Power!
9. RL Concept Sort
10. Explore vs Exploit
11. Value Functions
12. Epsilon-Greedy Odds
13. Greedy Picks by Hand
14. AlphaGo Learned From Feedback
15. RL Process Order
16. Pick the Family
17. Spot the RL!
18. RL Knowledge Quiz
19. Explain Trial and Error
20. RL Vocabulary
21. RL Pioneer!

• ADEKDesign a scalable AI system architecture
• IBDistinguish supervised, unsupervised, and reinforcement learning approaches to machine learning
• England + CambridgeShow understanding of Deep Learning, Machine Learning and Reinforcement Learning and the reasons for using each, including machine learning categories such as supervised and unsupervised learning
• AI4K12 (US)Progress from identifying a problem as a classification problem or a search problem (K-2), to categorizing problems as classification vs. search using examples like Teachable Machine (3-5), to categorizing problems as classification, prediction, combinatorial search, or sequential decision problems (6-8), to categorizing real-world problems across eight reasoning-problem types including heuristic search, adversarial search, logical deduction, and statistical inference (9-12).
• AI4K12 (US)Progress from describing and giving examples of how people vs. computers learn (K-2), to differentiating human flexible learning from computer pattern-finding/trial-and-error learning (3-5), to contrasting human learning characteristics with narrowly-defined machine learning problems (6-8), to defining supervised, unsupervised, and reinforcement learning and giving human-learning analogues for each (9-12).
• AI4K12 (US)Progress from 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.
• Korea, Japan, HKClassify machine learning into supervised, unsupervised and reinforcement learning, and select the type appropriate to a given problem's characteristics.

1. The Math Behind RL
2. The MDP Recipe
3. The Present-State Puzzle
4. Markov Magic
5. Frame a Delivery Robot
6. MDP Match-Up
7. State, Action or Reward?
8. The Discount Factor
9. Discounted Return by Hand
10. Discount Sorting
11. Transitions Explained
12. The Bellman Equation
13. One Bellman Backup by Hand
14. Balance an MDP
15. Google Maps Thinks in States
16. Value Iteration Steps
17. Value Iteration by Hand
18. Spot the MDP!
19. MDP Mastery Quiz
20. Explain the Markov Idea
21. MDP Vocabulary
22. MDP Master!

• CISCE + statesExplain types of data, data identification/acquisition/exploration, binary logic and conditional gates, and deterministic vs probabilistic nature of real-life problems with examples
• CSTA (US)Analyze a large-scale computational problem and identify generalizable patterns that can be applied to a solution.
• AI4K12 (US)Progress from constructing a map of a home, school, or neighborhood and understanding that 'the map is not the territory' (K-2), to demonstrating how different map styles (geometric vs. topological) capture different aspects of the world (3-5), to showing how a game board (tic-tac-toe, chess) can be represented as a plain-language description of squares, relationships, and piece positions (6-8), to describing how to represent a concept as a schema.org-style schema with superconcepts and inherited properties (9-12).
• AI4K12 (US)Progress from giving examples of symbols encountered in daily life such as restroom icons and card suits (K-2), to explaining hierarchical tree structures such as ancestry trees and taxonomies (3-5), to translating a structure like a game board, road map, or mind map into a labeled graph of nodes and links (6-8), to translating the premises of a syllogism from English into predicate-logic notation and completing the inference (9-12).

1. The Reward Problem
2. Reward vs Return
3. The Wrong Win
4. Reward Types
5. Sparse or Dense?
6. Reward Match-Up
7. Rewards Shape Real Systems
8. Reward Shaping
9. Shaping Loop by Hand
10. Episodic vs Continuing
11. Return Formulas
12. Return by Hand
13. Reward Hacking
14. Tune the Incentives
15. YouTube Needs Better Signals
16. Hack to Fix
17. Reward Design Steps
18. Spot the Issue!
19. Rewards Quiz
20. Explain Reward Hacking
21. Reward Vocabulary
22. Reward Expert!

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

1. The Q Revolution
2. The Q-Table
3. Read the Q-Table
4. The Magic Formula
5. One Update by Hand
6. Q-Learning Match
7. From Tables to Deep RL
8. Off-Policy Magic
9. Targets and the Replay Queue
10. Queue or Stack?
11. Epsilon-Greedy
12. Learning Rate α
13. The Full Algorithm
14. Tune a Q Update
15. FrozenLake Is a Q-Learning Classic
16. Q-Learning Steps
17. Spot Q-Learning!
18. Q-Learning Quiz
19. Explain the TD Error
20. Q Vocabulary
21. Q Master!

• CISCE + statesDistinguish types of motion (linear, angular, circular) and types of joints/links (prismatic, revolute, spherical; rigid, soft) and use links and joints to create specific motion
• CISCE + statesDefine degrees of freedom of a robot and identify them through illustration
• IB(HL only) Implement abstract data types such as stacks, queues, linked lists, and trees
• CSTA (US)Use lists to simplify solutions, generalizing computational problems instead of repeatedly using simple variables.
• CSTA (US)Compare and contrast fundamental data structures and their uses.

1. The Atari Miracle
2. Why Tables Fail
3. Table or Network?
4. DQN Architecture
5. Read the Q-Network
6. DQN Match-Up
7. Stability Was the Real Breakthrough
8. Experience Replay
9. Replay by Hand
10. Target Network
11. Target Stability
12. DQN Training
13. TD Loss Lab
14. DeepMind's Atari Breakthrough
15. TD Target by Hand
16. Debug the DQN
17. DQN Algorithm Steps
18. Spot the DQN!
19. DQN Quiz
20. Explain Stable DQN
21. DQN Vocabulary
22. DQN Decoded

• AI4K12 (US)Progress from describing and giving examples of how people vs. computers learn (K-2), to differentiating human flexible learning from computer pattern-finding/trial-and-error learning (3-5), to contrasting human learning characteristics with narrowly-defined machine learning problems (6-8), to defining supervised, unsupervised, and reinforcement learning and giving human-learning analogues for each (9-12).

1. A Different Approach
2. Value vs Policy
3. Why a Table Breaks
4. Stochastic Policies
5. Sample the Policy
6. Method Match-Up
7. Randomness Can Be Optimal
8. Policy Gradient Theorem
9. Returns by Hand
10. REINFORCE
11. High Variance
12. Baseline by Hand
13. Actor-Critic
14. Tune a Policy Update
15. TD Error by Hand
16. RLHF Uses Policy Gradients
17. Actor-Critic Steps
18. Spot Policy Gradients!
19. Policy Gradient Quiz
20. Explain Policy Gradients
21. PG Vocabulary
22. Policy Master!

• CISCE + statesDistinguish types of motion (linear, angular, circular) and types of joints/links (prismatic, revolute, spherical; rigid, soft) and use links and joints to create specific motion
• CISCE + statesDefine degrees of freedom of a robot and identify them through illustration

1. The Perfect Partnership
2. Actor & Critic
3. Who Does What?
4. Advantage Function
5. The Critic's Verdict by Hand
6. Actor-Critic Match
7. Advantage Is a Smarter Signal
8. A2C: Advantage AC
9. A3C: Async Power
10. N-Step Returns
11. N-Step Returns by Hand
12. GAE: Best Advantages
13. GAE by Hand
14. How Fast Does λ Forget?
15. PPO Builds on Actor-Critic
16. Tuning Calls
17. A2C Algorithm Steps
18. Spot Actor-Critic!
19. Actor-Critic Quiz
20. Explain Advantage Learning
21. AC Vocabulary
22. AC Master!

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

1. Game Masters
2. Historic Victories
3. Timeline Check
4. Games Meet RL
5. RL Playground or Not?
6. AI Hall of Fame
7. Games Are RL Laboratories
8. Self-Play Training
9. Types of Games
10. AlphaGo Secrets
11. AlphaGo's Parts at Work
12. Essential Methods
13. Search Explosion Lab
14. Search Cost by Hand
15. AlphaGo Combined Learning and Search
16. Choose the Right Tool
17. Training Steps
18. RL in Gaming
19. Checkpoint Quiz
20. Explain Self-Play
21. Game AI Terms
22. Game Master!

• 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).

1. Robots Learn Too
2. Robot Challenges
3. Why Not Practise on the Real Robot?
4. Sim-to-Real Transfer
5. A Virtual World Can Teach the Physical One
6. Robotics Concepts
7. Diagnose the Reality Gap
8. Closing the Gap
9. Which Setting Closes It?
10. Robot Skills
11. Robot Stars
12. Safe Robot RL
13. Safety Trade-offs
14. How Much Practice Can a Simulator Buy?
15. Practice Hours by Hand
16. OpenAI's Rubik's Cube Hand
17. Training Pipeline
18. RL or Not?
19. Checkpoint Quiz
20. Explain Sim-to-Real
21. Robotics Terms
22. Robot Master!

• CISCE + statesDistinguish types of motion (linear, angular, circular) and types of joints/links (prismatic, revolute, spherical; rigid, soft) and use links and joints to create specific motion
• CISCE + statesDefine degrees of freedom of a robot and identify them through illustration
• CSTA (US)Evaluate the ability of models and simulations to test and support the refinement of hypotheses.
• CSTA (US)Analyze the risks, benefits, and effectiveness of using AI for specific tasks (e.g., coding, brainstorming), including when AI is used to fully automate a process or is used with a human-in-the-loop approach.
• OECD–EC (Europe)Compare task complexity, the need for human judgment, and ethical implications when deciding whether to use AI (Manage 1, advanced).

1. Teaching AI to Help
2. The Raw AI Problem
3. Why Pretraining Wasn't Enough
4. RLHF Three Steps
5. Which Step Fixes It?
6. RLHF Concepts
7. Alignment Is an Optimization Problem
8. Human Feedback
9. RLHF Pipeline
10. Reward Model
11. PPO Optimization
12. Tune an RLHF System
13. KL Penalty by Hand
14. ChatGPT and Claude Use Preference Training
15. Alignment Is Still Open
16. Oversight Check
17. Full RLHF Process
18. RLHF Applications
19. Checkpoint Quiz
20. Explain RLHF
21. RLHF Vocabulary
22. RLHF Master!

• CISCE + statesDescribe the general block diagram and building blocks of a robot (mechanical, electronic, computational) and identify robots by demonstration/video (industrial arm, humanoid, mobile)
• CISCE + statesExplain gears (role, principles, gear ratio) and classify sensors (internal: position/velocity/acceleration/force; external: contact/non-contact) and their functions in a robotic system
• CISCE + statesDescribe actuators (linear, rotary), control systems (manual vs automatic) and integrate sensors, actuators and controller in a robotic system using a block diagram (input-controller-robot-feedback)
• England + CambridgeDescribe how sensors, microprocessors and actuators combine to create automated systems, and describe advantages/disadvantages for given scenarios (industry, transport, agriculture, weather, gaming, lighting, science)

1. RL Everywhere
2. Major Applications
3. One Pattern, Many Industries
4. Healthcare Revolution
5. Healthcare RL, Honestly
6. Application Match
7. RL-Shaped or Not?
8. Green AI
9. In Use or Research?
10. Case Files
11. Real-World Challenges
12. Spot the Challenge
13. Sample Cost Lab
14. Sample Cost by Hand
15. Google Used RL to Cool Data Centers
16. Deployment Pipeline
17. Your App, Your Reward
18. RL or Not?
19. Checkpoint Quiz
20. Explain Real-World RL
21. Application Terms
22. Field Guide Complete

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

1. RL Journey Review
2. RL Foundations
3. Why So Many RL Styles?
4. RL Algorithms
5. Which Family Fits?
6. Algorithm Match
7. Q-Update by Hand
8. Applications Review
9. Concept Sort
10. Key Techniques
11. Which Trick Fixes It?
12. RLHF Recap
13. Compare RL Strategies
14. Discounting by Hand
15. ChatGPT Shows RL's Reach
16. RLHF as an RL Loop
17. RL Evolution
18. Unit Challenge
19. Unit Final Quiz
20. Explain the Big Picture
21. Master Terms
22. RL Master!

• CBSECommunicate effectively about AI concepts and applications; describe the historical development of AI; differentiate types and domains of AI including their applications; recognise key terminologies/concepts related to machine learning and deep learning; formulate informed opinions on benefits and limitations of AI (Unit 1).
• CISCE + statesDefine AI, trace its evolution and applications across fields, and explain common AI applications (decision making, remote patient monitoring, data analysis, complex problem-solving)
• CSTA (US)Students study the history of computing and AI, emerging technologies, human-AI relationships, and responsible decision-making about technology use.

1. Wet Grass at 7 a.m.
2. You Already Know Bayes
3. Bayes by Hand
4. Prior, Likelihood or Posterior?
5. Draw the Network
6. Read the Tables
7. One Row of the Joint
8. Fill the Wet-Grass Rows
9. Inference by Enumeration
10. Order the Enumeration
11. Check with Bayes' Rule
12. Two Suspects
13. Why Networks Save Numbers
14. Independent or Not?
15. The Same Idea in MDPs
16. MDP Meets Network
17. True or False: Networks
18. Checkpoint: Bayesian Networks
19. Chapter Terms
20. Key Takeaways
21. Chapter Complete!

• UAEData characteristics shape AI decisions
• AI4K12 (US)Progress from constructing a map of a home, school, or neighborhood and understanding that 'the map is not the territory' (K-2), to demonstrating how different map styles (geometric vs. topological) capture different aspects of the world (3-5), to showing how a game board (tic-tac-toe, chess) can be represented as a plain-language description of squares, relationships, and piece positions (6-8), to describing how to represent a concept as a schema.org-style schema with superconcepts and inherited properties (9-12).
• AI4K12 (US)Progress from giving examples of symbols encountered in daily life such as restroom icons and card suits (K-2), to explaining hierarchical tree structures such as ancestry trees and taxonomies (3-5), to translating a structure like a game board, road map, or mind map into a labeled graph of nodes and links (6-8), to translating the premises of a syllogism from English into predicate-logic notation and completing the inference (9-12).
• AI4K12 (US)Progress from 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).

1. The Mood Detective
2. Markov Chains, One Step Deeper
3. True or False: Reading the Chain
4. Where Does the Chain Settle?
5. When the State Is Hidden
6. Hidden or Observed?
7. The Full Toy Model
8. Read the Tables
9. The Forward Algorithm by Hand
10. Order the Forward Algorithm
11. Check the Day-2 Numbers
12. Your Turn: Grumpy, Grumpy
13. Checked in Python
14. Sound Step or Bug?
15. From α to a Best Guess
16. Where HMMs Did Real Work
17. Map the Mapping
18. Checkpoint: Hidden Markov Models
19. HMM Vocabulary
20. Chapter Complete!

• UAEData characteristics shape AI decisions
• AI4K12 (US)Progress from demonstrating knowledge of language structure by generating plausible/implausible novel words or unscrambling sentences (K-2), to demonstrating that human language is infinite via sentence extension (3-5), to using a parser to display a sentence's syntactic (parse-tree) structure and explaining the node categories (6-8), to demonstrating how a small context-free grammar can parse or generate simple sentences, and its limits with agreement constraints (9-12).

1. Maya's Exam-Week Problem
2. Variables, Domains, Constraints
3. Name the Parts
4. The Rules as a Function
5. Shrink the Domains
6. Backtracking in Ten Lines
7. How Big Is the Haystack?
8. Read the Solver
9. Trace the First Dead End
10. Run It: 36 Nodes
11. How Backtracking Behaves
12. Seeing the Dead End Sooner
13. Forward Checking
14. Which Check Says No?
15. Timetables Are CSPs
16. Spot the Constraint
17. Same World, Three Modalities
18. Graph, Tree or Rules?
19. My Structure, Written Out
20. Valid or Clash?
21. New System, Same solve()
22. What My Model Hides
23. Checkpoint: Predict the Output
24. Solver Vocabulary
25. Constraint Solver!

• UAEOptimisation: goals, constraints, trade-offs
• 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)
• 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)Analyze a large-scale computational problem and identify generalizable patterns that can be applied to a solution.
• AI4K12 (US)Progress from constructing a map of a home, school, or neighborhood and understanding that 'the map is not the territory' (K-2), to demonstrating how different map styles (geometric vs. topological) capture different aspects of the world (3-5), to showing how a game board (tic-tac-toe, chess) can be represented as a plain-language description of squares, relationships, and piece positions (6-8), to describing how to represent a concept as a schema.org-style schema with superconcepts and inherited properties (9-12).
• AI4K12 (US)Progress from giving examples of symbols encountered in daily life such as restroom icons and card suits (K-2), to explaining hierarchical tree structures such as ancestry trees and taxonomies (3-5), to translating a structure like a game board, road map, or mind map into a labeled graph of nodes and links (6-8), to translating the premises of a syllogism from English into predicate-logic notation and completing the inference (9-12).
• AI4K12 (US)Progress from drawing a simple 3-node search tree showing two possible tic-tac-toe moves (K-2), to drawing a full search tree of next moves for tic-tac-toe or nim and picking the best move (3-5), to modeling breadth-first search on a graph problem such as common-friend detection (6-8), to illustrating and comparing breadth-first, depth-first, and best-first search algorithms for growing a search tree (9-12).

1. Beyond the Model
2. System Components
3. Which Part Failed?
4. Data Pipeline
5. Check or Change?
6. Component Match
7. Most Failures Are System Failures
8. Model Serving
9. Scaling Systems
10. Reliability Patterns
11. Pick the Pattern
12. Design Tradeoffs
13. Uptime Chain Lab
14. Uptime by Hand
15. Netflix Runs More Than a Model
16. Design Review: Your App
17. Design Process
18. Design Decisions
19. Checkpoint Quiz
20. Explain AI System Design
21. System Terms
22. System Architect!

• ADEKDesign a scalable AI system architecture
• CBSEUnderstand Big Data, its types, advantages and disadvantages, and characteristics; explain the concept and significance of Big Data Analytics; analyse future trends; understand mining data streams (Unit 5).
• IBDescribe network structures, protocols, and security threats (encryption, firewalls, phishing, DDoS attacks)
• 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 + CambridgeUnderstand the difference between the internet and the world wide web, and describe how web pages are located, retrieved and displayed (browser, IP addresses, DNS, web server, HTML)
• CSTA (US)Evaluate the scalability and reliability of networks, by describing the relationship between routers, switches, servers, topology, and addressing.
• CSTA (US)Describe the issues that impact network functionality (e.g., bandwidth, load, delay, topology).
• 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).
• Korea, Japan, HKUnderstand the structure and components of communication networks, the role of protocols, and methods/techniques for ensuring information security; design a small-scale network.
• 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)
• ChinaStudents learn the basic process, implementation principles and core algorithms behind AI systems
• Australia + NZExamine network protocols, and how networks apply cybersecurity measures to protect data, software and hardware

1. The Digital Restaurant
2. API Building Blocks
3. Order Through the Waiter
4. REST APIs Explained
5. Read the Request
6. HTTP Methods Match
7. One Pattern, Many Services
8. Monolith vs Microservices
9. API Request Flow
10. API Status Codes
11. Diagnose the Status Code
12. AI Model APIs
13. Retries vs Failures
14. Retries by Hand
15. OpenAI API Exposes Model Power
16. Where Does the Key Live?
17. Build an API Call
18. Spot the API Usage
19. APIs & Microservices Quiz
20. Explain API Requests
21. API Vocabulary
22. API Architect!

• IBDescribe network structures, protocols, and security threats (encryption, firewalls, phishing, DDoS attacks)
• CSTA (US)Describe the issues that impact network functionality (e.g., bandwidth, load, delay, topology).

1. The AI Factory Floor
2. Model Serving Essentials
3. Serving Diagnosis
4. Inference Servers
5. Training or Inference?
6. Serving Technology Match
7. UX Depends on Infrastructure
8. Batching for Speed
9. Batching by Hand
10. Model Deployment Pipeline
11. Model Optimization Techniques
12. Quantization by Hand
13. Deployment Strategies
14. Pick the Rollout
15. Batch Size Lab
16. Serving Has a Speed and an Energy Cost
17. Scaling Strategy Sort
18. Spot the Serving Issue
19. Model Serving Quiz
20. Explain Model Serving
21. Model Serving Vocabulary
22. Factory Floor Complete

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

1. The Speed Demon Framework
2. FastAPI Superpowers
3. Which Feature Fixes It?
4. FastAPI Basics
5. Accepted or 422?
6. FastAPI Component Match
7. Docs From Code
8. Async for AI
9. Waiting by Hand
10. FastAPI Request Lifecycle
11. FastAPI AI Patterns
12. Design a Reliable AI Endpoint
13. Streaming Makes AI Feel Instant
14. Design Review: Study App
15. Streaming LLM Responses
16. Read the Stream
17. Build a FastAPI AI Endpoint
18. Spot the FastAPI Best Practice
19. FastAPI for AI Quiz
20. Explain FastAPI for AI
21. FastAPI Vocabulary
22. Endpoint Reader

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

1. The Shipping Container Revolution
2. Docker Core Concepts
3. Will a Container Fix It?
4. Containers vs Virtual Machines
5. VM or Container?
6. Docker Command Match
7. Smaller Images Usually Move Faster
8. Dockerfile for AI Apps
9. Dockerfile Layer Order
10. Rebuild Time by Hand
11. GPU Support in Docker
12. Tune a Deployment Setup
13. Pull Time by Hand
14. How Docker Desktop Helps Teams Build the Same Way
15. Design Review: Your App
16. Multi-Stage Builds
17. Docker Compose AI Stack
18. Spot the Docker Best Practice
19. Docker for AI Quiz
20. Explain Why Containers Help AI Deployment
21. Docker Vocabulary
22. Docker Captain!

• UAEEffectiveness of AI governance
• IBExplain how core computer hardware, operating systems, and cloud computing concepts underpin computing systems
• England + CambridgeShow understanding of operating system process management (multi-tasking, process states, scheduling algorithms) and virtual memory/paging/segmentation

1. The AI Factories in the Sky
2. The Big Three Cloud Providers
3. Rent, Buy or Which Cloud?
4. Cloud Computing Basics
5. Rent or Buy by Hand
6. Cloud Service Match
7. Where You Store Data Changes Speed and Cost
8. GPU Instances for AI
9. Cloud AI Deployment Steps
10. Managed AI Services
11. Price a Cloud Training Run
12. Spot Pricing by Hand
13. How SageMaker Speeds Up Cloud ML Work
14. Storage for AI Workloads
15. Which Storage Tier?
16. Choose the Right Cloud Service
17. Spot the Cloud Best Practice
18. Design Review: Homework Helper
19. Cloud Platforms Quiz
20. Explain Why Cloud Platforms Help AI Teams
21. Cloud Platform Vocabulary
22. Cloud Planner!

• ADEKFootprint / resource cost of AI
• 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).
• CBSEUnderstand Big Data, its types, advantages and disadvantages, and characteristics; explain the concept and significance of Big Data Analytics; analyse future trends; understand mining data streams (Unit 5).
• IBExplain how core computer hardware, operating systems, and cloud computing concepts underpin computing systems
• 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 + CambridgeShow understanding of operating system process management (multi-tasking, process states, scheduling algorithms) and virtual memory/paging/segmentation
• CSTA (US)Design ways to minimize negative environmental impacts of AI and communicate those ways to others.
• CSTA (US)Students examine security implications and environmental impacts of AI systems within Systems & Security.
• OECD–EC (Europe)Analyse how design and deployment decisions affect an AI system's resource consumption (Engage 5, advanced).
• OECD–EC (Europe)Consider the ethical consequences of AI systems throughout their life-cycle, including environmental impact and societal impact such as platformisation of work and low-cost labelling labour (AI example 57, competence 4.4).

1. From Prototype to Planet-Scale
2. Scaling Fundamentals
3. Diagnose the Slowdown
4. Horizontal Scaling for AI
5. Stateless or Stateful?
6. Scaling Term Match
7. Caches Can Remove Huge Amounts of Work
8. Distributed Training
9. Which Parallelism?
10. Auto-Scaling Response
11. Kubernetes for AI
12. How Many Replicas?
13. Replicas by Hand
14. How Kubernetes Helps AI Services Grow
15. Caching and Edge Deployment
16. Which Cache?
17. Scaling Strategy Selection
18. Spot the Scaling Anti-Pattern
19. Scaling AI Systems Quiz
20. Explain Why Scaling AI Systems Is Hard
21. Scaling Vocabulary
22. Scale Master!

• IBExplain how core computer hardware, operating systems, and cloud computing concepts underpin computing systems
• IBDescribe network structures, protocols, and security threats (encryption, firewalls, phishing, DDoS attacks)
• England + CambridgeShow understanding of operating system process management (multi-tasking, process states, scheduling algorithms) and virtual memory/paging/segmentation
• CSTA (US)Describe the issues that impact network functionality (e.g., bandwidth, load, delay, topology).

1. The Silent Failures
2. What to Monitor
3. Crash or Drift?
4. Data Drift and Concept Drift
5. Data Drift, Concept Drift, or Neither?
6. Monitoring Tool Match
7. Catching Drift Before the Labels Arrive
8. The Four Golden Signals
9. Percentiles by Hand
10. Incident Response Flow
11. Logging Best Practices
12. How Many Requests Does the Tail Hit?
13. How Grafana Helps Teams See Model Health
14. Building Dashboards
15. Attacks Leave Traces
16. Threat to Defence
17. Alert Priority Assignment
18. Spot the Monitoring Issue
19. Monitoring an AI App: Quiz
20. Explain Why ML Monitoring Matters
21. Monitoring Vocabulary
22. Silent Failures, Caught

• CISCE + statesChoose and construct appropriate statistical graphs (bar graph, histogram, scatter plot, pie graph) and represent multi-dimensional data (introduction to dimensionality)
• CSTA (US)Explain security issues that might lead to compromised computer programs.
• CSTA (US)Compare ways software developers protect devices and information from unauthorized access.
• OECD–EC (Europe)Know that processing of personal data by AI (e.g. voice interactions with a virtual assistant) is subject to regulations such as the EU GDPR and can carry data protection, privacy and security risks (AI example 39, competence 4.2).
• Australia + NZStudents understand that their privacy and personal data must be protected when using generative AI tools, and are taught not to input sensitive personal or others' data into such tools without appropriate safeguards

1. The 87% That Never Launch
2. MLOps Core Components
3. Why Do Great Models Stay Stuck?
4. DevOps vs MLOps
5. Code Bug or ML Problem?
6. MLOps Tool Match
7. Big Data Is Why Features Get Shared
8. Feature Stores
9. Feature Store Diagnosis
10. MLOps Pipeline Flow
11. Model Registry Concepts
12. Count the Models
13. Model Count by Hand
14. How MLflow Organizes Experiments and Models
15. Design Review: Your App
16. CI/CD for ML
17. MLOps Maturity Levels
18. Spot the MLOps Practice
19. MLOps Introduction Quiz
20. Explain Why MLOps Is More Than DevOps
21. MLOps Vocabulary
22. MLOps Pioneer!

• CBSEUnderstand Big Data, its types, advantages and disadvantages, and characteristics; explain the concept and significance of Big Data Analytics; analyse future trends; understand mining data streams (Unit 5).
• England + CambridgeEvaluate models and simulators for real-life scenarios; create relational databases with linked tables; understand Big Data and describe its applications
• CSTA (US)Evaluate and analyze the roles of humans and human decision-making in the creation of AI.
• 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).

1. The Automation Revolution
2. ML-Specific Tests
3. Which Test Catches It?
4. Pipeline Triggers
5. Which Trigger Fires?
6. CI/CD Stage Match
7. Small Validation Gates Prevent Huge Failures
8. GitHub Actions for ML
9. Read the Workflow
10. ML CI/CD Pipeline Order
11. Model Validation Gates
12. Tune an ML Release Pipeline
13. How GitHub Actions Automates ML Checks
14. Deployment Strategies
15. Canary Numbers by Hand
16. Pick the Strategy
17. When to Block Deployment
18. Spot the CI/CD Best Practice
19. CI/CD for ML Quiz
20. Explain Why ML CI/CD Needs More Checks
21. CI/CD Vocabulary
22. Pipeline Architect!

• 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).
• OECD–EC (Europe)Establish checkpoints, monitor AI use against criteria, and adjust roles throughout a problem-solving process (Manage 4, advanced).
• UNESCOBuild technical and social-responsibility skills to make decisions on optimizing, reconfiguring, or shutting down an AI system based on test results and user feedback (three scenarios: optimize datasets/model/interface; reconfigure problem-scoping/architecture; or shut down and remediate where the system violates human rights or harms vulnerable groups) (CG4.3.4.2).

1. The Million-Dollar GPU Bill
2. Where ML Costs Come From
3. Read the Bill by Hand
4. Spot, Reserved and On-Demand
5. Spot Maths
6. Cost Optimization Match
7. Cache Hits Can Feel Like Free Compute
8. Model Optimization for Cost
9. Model Size by Hand
10. Which Bill Does It Cut?
11. Smart Scaling Strategies
12. Scaling Choices
13. Tune an AI Cost Strategy
14. How Redis Helps Reduce AI Bills
15. Caching for Cost
16. Cache Savings by Hand
17. Choose Cost-Effective Options
18. Spot the Cost Waste
19. Cost Optimization Quiz
20. Explain Why AI Cost Optimization Matters
21. Cost Optimization Vocabulary
22. Cost Engineer!

• ADEKDesign a scalable AI system architecture
• CISCE + statesTune model hyperparameters (learning rate, batch size, number of hidden layers/neurons) using techniques such as grid search or random search
• 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)

1. From Notebook to Planet Scale
2. Unit 4 Journey Recap
3. Diagnose Maya's Launch
4. The Production ML Stack
5. Which Layer Broke?
6. Tool to Purpose Match
7. Tiny Weak Spots Can Break Big Systems
8. Key Metrics to Monitor
9. Percentiles by Hand
10. Complete ML Deployment Flow
11. Cost vs Performance Trade-offs
12. Horizontal Scaling Lab
13. Replicas by Hand
14. How a Modern ML Platform Connects the Pieces
15. Production Readiness Checklist
16. Launch Review
17. Problem to Solution Match
18. Production-Ready Assessment
19. Unit 4 Comprehensive Quiz
20. Explain What Makes AI Production-Ready
21. Unit 4 Key Terms
22. Systems Architect!

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

1. The Doctor's AI Assistant
2. Healthcare AI Applications
3. Which Area Is It?
4. Medical Imaging AI
5. Patches by Hand
6. Detect, Classify or Segment?
7. Support Vector Machines: The Widest Street
8. SVM True or False
9. Run the SVM
10. Healthcare AI Match
11. AI Can Warn Before Crisis
12. Drug Discovery with AI
13. Drug Discovery Pipeline
14. Healthcare AI Challenges
15. Balance a Clinical AI System
16. Alarm Maths by Hand
17. A Shortcut to Protein Shapes
18. Predictive Healthcare
19. Keep It Running
20. AI Application Classification
21. Spot Responsible Healthcare AI
22. AI in Healthcare Quiz
23. Explain Healthcare AI's Role
24. Healthcare AI Vocabulary
25. Healthcare AI Pioneer!

• UAEEffectiveness of AI governance
• UAEHuman–AI collaboration across sectors
• 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).
• 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 + statesExplain the potential use of AI in word processing, smartphones, web-based auction sites, scanner machines, e-commerce and social networking, and AI's social benefits in healthcare, transportation, disaster prediction and agriculture
• 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 + 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
• CSTA (US)Describe how artificial intelligence drives many software and physical systems.
• AI4K12 (US)Progress from identifying devices in daily life that use AI, e.g. smartphones, smart assistants (K-2), to describing how AI-powered services (search, recommenders, autonomous vehicles) are used in daily life (3-5), to examining an aspect of daily life predicted to change due to AI, e.g. transportation and self-driving cars (6-8), to explaining the kinds of cultural debates that arise as AI evolves, e.g. AI-generated art, LLM use in homework, data rights (9-12).
• AI4K12 (US)Progress from identifying current uses of AI and how they have impacted society (K-2), to identifying how sectors of society such as manufacturing, retail, and healthcare are changing due to AI (3-5), to comparing AI-driven societal change to previous industrial revolutions (6-8), to predicting how a specific sector of society is likely to change in the short and intermediate term as a result of AI, referencing Amara's Law (9-12).
• OECD–EC (Europe)Deliberately assign parts of a task to human strengths versus AI capabilities (Manage 3, advanced).
• OECD–EC (Europe)Assess an AI system's strengths and limitations considering its purpose and potential impacts (Shape 1, advanced).
• OECD–EC (Europe)States that a foundational understanding of AI's technical functioning and the ability to recognise chances, limits and risks are indispensable for students to use AI responsibly and reflectively ('digital mündig'); calls on the Länder to integrate AI-related informatics competences and 'Bildung in der digitalen Welt' competences into curricula for all subjects and school types, without itself specifying discrete grade-level student outcomes.
• UNESCOUnderstand that human accountability is a legal obligation of AI creators and AI service providers, who are answerable for safety incidents, ethical risks in design/training, and misuses of their AI service (CG4.2.1.1).
• 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. The Money Machines
2. Finance AI Applications
3. The Card Swipe That Looked Normal
4. Algorithmic Trading
5. Trading Signals Check
6. Finance AI Match
7. Pattern, Not Field
8. Fraud Detection
9. Pick the Fraud Tool
10. Credit Decision Flow
11. Alternative Data in Finance
12. Tune a Finance AI System
13. Base Rates by Hand
14. Visa Flags Risk in Real Time
15. Fairness in Financial AI
16. Four-Fifths Check by Hand
17. Case: AI in Insurance Decisions
18. Risk or Proxy?
19. Insurance Regulator Quiz
20. Finance AI Use Case Classification
21. Spot Responsible Finance AI
22. AI in Finance Quiz
23. Explain Finance AI Safeguards
24. Finance AI Vocabulary
25. FinTech AI Expert!

• UAEEffectiveness of AI governance
• UAEHuman–AI collaboration across sectors
• CISCE + statesExplain the potential use of AI in word processing, smartphones, web-based auction sites, scanner machines, e-commerce and social networking, and AI's social benefits in healthcare, transportation, disaster prediction and agriculture
• 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
• IBDistinguish supervised, unsupervised, and reinforcement learning approaches to machine learning
• England + CambridgeShow understanding of Deep Learning, Machine Learning and Reinforcement Learning and the reasons for using each, including machine learning categories such as supervised and unsupervised learning
• CSTA (US)Describe how artificial intelligence drives many software and physical systems.
• AI4K12 (US)Progress from giving examples of symbols encountered in daily life such as restroom icons and card suits (K-2), to explaining hierarchical tree structures such as ancestry trees and taxonomies (3-5), to translating a structure like a game board, road map, or mind map into a labeled graph of nodes and links (6-8), to translating the premises of a syllogism from English into predicate-logic notation and completing the inference (9-12).
• AI4K12 (US)Progress from drawing a tree by repeatedly splitting branches and placing data at nodes (K-2), to describing the parts of a tree (nodes, parent, leaves) and how they relate (3-5), to describing the parts of a labeled graph (nodes, links, source/target) and how trees are a special case of graphs (6-8), to describing how schemas structure information about people, places, or things within knowledge graphs used by search engines (9-12).
• AI4K12 (US)Progress from 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.
• AI4K12 (US)Progress from demonstrating how computers recognize emotions in faces via mouth/eye/eyebrow shape (K-2), to illustrating how sentiment analysis judges the emotional tone of text, e.g. movie/restaurant reviews, social media (3-5), to describing how computers combine facial expression, gaze, gesture, body language, tone of voice, and word choice to recognize emotional states (6-8), to identifying ways AI applications modify their behavior in response to detected human emotional states, e.g. adaptive tutoring systems (9-12).
• AI4K12 (US)Progress from identifying devices in daily life that use AI, e.g. smartphones, smart assistants (K-2), to describing how AI-powered services (search, recommenders, autonomous vehicles) are used in daily life (3-5), to examining an aspect of daily life predicted to change due to AI, e.g. transportation and self-driving cars (6-8), to explaining the kinds of cultural debates that arise as AI evolves, e.g. AI-generated art, LLM use in homework, data rights (9-12).
• AI4K12 (US)Progress from identifying current uses of AI and how they have impacted society (K-2), to identifying how sectors of society such as manufacturing, retail, and healthcare are changing due to AI (3-5), to comparing AI-driven societal change to previous industrial revolutions (6-8), to predicting how a specific sector of society is likely to change in the short and intermediate term as a result of AI, referencing Amara's Law (9-12).
• OECD–EC (Europe)Recognise that AI applications directly interacting with humans and making decisions about their lives (e.g. CV-sorting, exam scoring) can be controversial even where other AI applications are not (AI example 63, competence 2.3).
• UNESCOEvaluate the consequences of institutional use of AI to make high-stakes decisions about people (e.g. profiling students, hiring, loan/assessment scoring) and understand why human accountability must not be ceded to AI in such decisions (CG4.2.1.2).
• Korea, Japan, HKClassify machine learning into supervised, unsupervised and reinforcement learning, and select the type appropriate to a given problem's characteristics.

1. The Store That Knows You
2. Retail AI Applications
3. Which Retail AI Fits?
4. Recommendation Systems
5. Recommend by Hand
6. Retail AI Match
7. Customer Segments with K-Means
8. Good Fit for K-Means?
9. Run K-Means
10. Tiny Preference Signals Add Up Fast
11. Demand Forecasting
12. Forecast by Hand
13. Customer Purchase Journey
14. In-Store AI
15. Low Risk or Safeguards?
16. Tune a Retail AI Strategy
17. How Amazon Uses Recommendations at Scale
18. Supply Chain AI
19. Routes by Hand
20. AI Solution Mapping
21. Spot Effective Retail AI
22. AI in Retail Quiz
23. Explain How AI Changes Retail Decisions
24. Retail AI Vocabulary
25. Retail AI Analyst

• UAEHow AI models human emotion and behaviour
• 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).
• IBDistinguish supervised, unsupervised, and reinforcement learning approaches to machine learning
• England + CambridgeShow understanding of Deep Learning, Machine Learning and Reinforcement Learning and the reasons for using each, including machine learning categories such as supervised and unsupervised learning
• 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)Describe different types of AI algorithms and models, and compare and contrast the strengths and limitations of their reasoning.
• 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.
• AI4K12 (US)Progress from identifying devices in daily life that use AI, e.g. smartphones, smart assistants (K-2), to describing how AI-powered services (search, recommenders, autonomous vehicles) are used in daily life (3-5), to examining an aspect of daily life predicted to change due to AI, e.g. transportation and self-driving cars (6-8), to explaining the kinds of cultural debates that arise as AI evolves, e.g. AI-generated art, LLM use in homework, data rights (9-12).
• AI4K12 (US)Progress from identifying current uses of AI and how they have impacted society (K-2), to identifying how sectors of society such as manufacturing, retail, and healthcare are changing due to AI (3-5), to comparing AI-driven societal change to previous industrial revolutions (6-8), to predicting how a specific sector of society is likely to change in the short and intermediate term as a result of AI, referencing Amara's Law (9-12).
• 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).
• Korea, Japan, HKClassify machine learning into supervised, unsupervised and reinforcement learning, and select the type appropriate to a given problem's characteristics.

1. The Intelligent Farm
2. Agricultural AI Applications
3. How Can a Drone Spot Trouble Before a Farmer Can?
4. Precision Farming
5. Uniform or Precision?
6. AgriTech AI Match
7. Read the NDVI Map
8. Computer Vision for Crops
9. Field-Test the Model
10. Precision Farming Workflow
11. Livestock AI
12. NDVI Lab
13. NDVI by Hand
14. How John Deere Uses AI in the Field
15. Spray Savings by Hand
16. Climate Adaptation
17. Match Problem to AI Solution
18. Spot Effective AgriTech
19. AI in Agriculture Quiz
20. Explain Why AI Helps Modern Farming
21. AgriTech AI Vocabulary
22. AgriTech Innovator!

• CISCE + statesExplain the potential use of AI in word processing, smartphones, web-based auction sites, scanner machines, e-commerce and social networking, and AI's social benefits in healthcare, transportation, disaster prediction and agriculture
• CSTA (US)Describe how artificial intelligence drives many software and physical systems.
• AI4K12 (US)Progress from identifying devices in daily life that use AI, e.g. smartphones, smart assistants (K-2), to describing how AI-powered services (search, recommenders, autonomous vehicles) are used in daily life (3-5), to examining an aspect of daily life predicted to change due to AI, e.g. transportation and self-driving cars (6-8), to explaining the kinds of cultural debates that arise as AI evolves, e.g. AI-generated art, LLM use in homework, data rights (9-12).
• AI4K12 (US)Progress from identifying current uses of AI and how they have impacted society (K-2), to identifying how sectors of society such as manufacturing, retail, and healthcare are changing due to AI (3-5), to comparing AI-driven societal change to previous industrial revolutions (6-8), to predicting how a specific sector of society is likely to change in the short and intermediate term as a result of AI, referencing Amara's Law (9-12).

1. The Smart Factory
2. Manufacturing AI Applications
3. Read the Warning Signs
4. Predictive Maintenance
5. Maintenance by the Numbers
6. Manufacturing AI Match
7. A Tiny Defect Can Reveal a Big Process Problem
8. AI Quality Control
9. Inspection by the Numbers
10. Manufacturing AI Implementation
11. Industry 4.0 Technologies
12. OEE Lab
13. OEE by Hand
14. How Siemens Uses Digital Twin Thinking
15. AI-Powered Robotics
16. Which Robot Skill?
17. Match Challenge to AI Solution
18. Spot Smart Manufacturing
19. AI in Manufacturing Quiz
20. Explain How AI Improves Manufacturing
21. Manufacturing AI Vocabulary
22. Manufacturing AI Expert!

• CISCE + statesExplain what a robot is, its characteristics, and the benefits of robots over humans (quality, productivity, efficiency, working span, hazardous environments)
• CISCE + statesState a brief history of robotics evolution since 1900 and recite Isaac Asimov's Three Laws of Robotics
• CISCE + statesClassify robots by terrain/field (aerial, ground, underwater), by control (manual, automatic), and identify bio-inspired robots (humanoid, bird, snake, insect)
• CISCE + statesDifferentiate a machine from a robot, and a robot from a cobot (human-in-the-loop, multi-robot systems), tracing the progress from robots to cobots
• CISCE + statesExplain the potential use of AI in word processing, smartphones, web-based auction sites, scanner machines, e-commerce and social networking, and AI's social benefits in healthcare, transportation, disaster prediction and agriculture
• 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
• England + CambridgeUnderstand what is meant by robotics, describe the characteristics of a robot (mechanical structure, electrical components/sensors/actuators/microprocessors, programmability), and the roles robots perform (industry, transport, agriculture, medicine, domestic, entertainment) with advantages/disadvantages
• CSTA (US)Describe how artificial intelligence drives many software and physical systems.
• AI4K12 (US)Progress from identifying devices in daily life that use AI, e.g. smartphones, smart assistants (K-2), to describing how AI-powered services (search, recommenders, autonomous vehicles) are used in daily life (3-5), to examining an aspect of daily life predicted to change due to AI, e.g. transportation and self-driving cars (6-8), to explaining the kinds of cultural debates that arise as AI evolves, e.g. AI-generated art, LLM use in homework, data rights (9-12).
• AI4K12 (US)Progress from identifying current uses of AI and how they have impacted society (K-2), to identifying how sectors of society such as manufacturing, retail, and healthcare are changing due to AI (3-5), to comparing AI-driven societal change to previous industrial revolutions (6-8), to predicting how a specific sector of society is likely to change in the short and intermediate term as a result of AI, referencing Amara's Law (9-12).

1. Your AI Teacher
2. Education AI Applications
3. Which Education AI Fits?
4. Intelligent Tutoring Systems
5. Trace the Tutor
6. EdTech AI Match
7. Small Errors Reveal Big Learning Gaps
8. AI-Powered Assessment
9. Grade the Grader
10. Adaptive Learning Flow
11. AI Learning Tools
12. The Forgetting Curve
13. Forgetting by Hand
14. How Duolingo Uses AI to Adapt Practice
15. Challenges and Ethics
16. Name the Risk
17. Match Problem to AI Solution
18. Spot Effective EdTech
19. AI in Education Quiz
20. Explain How AI Personalizes Education
21. EdTech AI Vocabulary
22. EdTech Analyst

• CSTA (US)Evaluate how AI use impacts an individual's decision making and other behavior.
• AI4K12 (US)Progress from identifying devices in daily life that use AI, e.g. smartphones, smart assistants (K-2), to describing how AI-powered services (search, recommenders, autonomous vehicles) are used in daily life (3-5), to examining an aspect of daily life predicted to change due to AI, e.g. transportation and self-driving cars (6-8), to explaining the kinds of cultural debates that arise as AI evolves, e.g. AI-generated art, LLM use in homework, data rights (9-12).
• AI4K12 (US)Progress from identifying current uses of AI and how they have impacted society (K-2), to identifying how sectors of society such as manufacturing, retail, and healthcare are changing due to AI (3-5), to comparing AI-driven societal change to previous industrial revolutions (6-8), to predicting how a specific sector of society is likely to change in the short and intermediate term as a result of AI, referencing Amara's Law (9-12).
• OECD–EC (Europe)Recognise that AI applications directly interacting with humans and making decisions about their lives (e.g. CV-sorting, exam scoring) can be controversial even where other AI applications are not (AI example 63, competence 2.3).
• UNESCOEvaluate the consequences of institutional use of AI to make high-stakes decisions about people (e.g. profiling students, hiring, loan/assessment scoring) and understand why human accountability must not be ceded to AI in such decisions (CG4.2.1.2).
• UNESCOInterrogate how automation of literature reviews, writing and artistic creation by AI may undermine human thinking and intellectual development, and identify concrete actions to protect themselves and peers from over-reliance on AI outputs (CG4.2.1.3).
• Australia + NZStudents understand that generative AI tools should be used in ways that benefit all members of the school community and support wellbeing, not just individual convenience

1. The Self-Driving Future
2. Transportation AI Applications
3. How Does a Car Know What Happens Next?
4. Autonomous Vehicle AI
5. Which Stage Failed?
6. Transportation AI Match
7. Now or Next?
8. SAE Automation Levels
9. Place the Level
10. Autonomous Vehicle Pipeline
11. Smart Traffic Systems
12. Stopping Distance Lab
13. Stopping Distance by Hand
14. How Waymo Connects Sensing and Driving
15. Challenges in Autonomous Driving
16. Match Challenge to AI Solution
17. Spot Effective Transportation AI
18. Design Review: Routing App
19. Routing Aid When the Map Is Wrong
20. Data Source, Blind Spot
21. Design Review: Aid Routing
22. AI in Transportation Quiz
23. Explain Why Transportation AI Is Complex
24. Transportation AI Vocabulary
25. Transportation AI Expert!

• UAEAI in economic development and social change
• UAEHumanitarian and environmental effectiveness
• CISCE + statesExplain the potential use of AI in word processing, smartphones, web-based auction sites, scanner machines, e-commerce and social networking, and AI's social benefits in healthcare, transportation, disaster prediction and agriculture
• 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)Describe how artificial intelligence drives many software and physical systems.
• 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).
• AI4K12 (US)Progress from identifying devices in daily life that use AI, e.g. smartphones, smart assistants (K-2), to describing how AI-powered services (search, recommenders, autonomous vehicles) are used in daily life (3-5), to examining an aspect of daily life predicted to change due to AI, e.g. transportation and self-driving cars (6-8), to explaining the kinds of cultural debates that arise as AI evolves, e.g. AI-generated art, LLM use in homework, data rights (9-12).
• AI4K12 (US)Progress from identifying current uses of AI and how they have impacted society (K-2), to identifying how sectors of society such as manufacturing, retail, and healthcare are changing due to AI (3-5), to comparing AI-driven societal change to previous industrial revolutions (6-8), to predicting how a specific sector of society is likely to change in the short and intermediate term as a result of AI, referencing Amara's Law (9-12).

1. AI Creates Entertainment
2. Entertainment AI Applications
3. Generate or Personalise?
4. AI in Video Games
5. Seeds and Difficulty by Hand
6. Entertainment AI Match
7. Recommendations Quietly Shape Taste
8. AI in Film & TV
9. Content Recommendation Pipeline
10. Generative AI in Entertainment
11. Tune an Entertainment AI Experience
12. Seed Bits by Hand
13. How Netflix Uses AI to Shape Viewing
14. Feedback Loop by Hand
15. Ethics & Challenges
16. Pick the Objective
17. Which Tool Does the Job?
18. Spot Responsible Entertainment AI
19. AI in Entertainment Quiz
20. Explain How AI Changes Entertainment
21. Entertainment AI Vocabulary
22. Entertainment AI, Explained

• ADEKDeepfakes: explain, spot flaws, set responsible-use rules
• CISCE + statesExplain the potential use of AI in word processing, smartphones, web-based auction sites, scanner machines, e-commerce and social networking, and AI's social benefits in healthcare, transportation, disaster prediction and agriculture
• CSTA (US)Describe how artificial intelligence drives many software and physical systems.
• CSTA (US)Evaluate computational artifacts to maximize their beneficial effects and minimize harmful effects on society.
• CSTA (US)Evaluate the intended and unintended impacts of AI on society (e.g., deep fakes, job loss), including government, education, entertainment, culture, careers, and national security, while considering how these impacts may differ among diverse communities.
• AI4K12 (US)Progress from demonstrating how computers recognize emotions in faces via mouth/eye/eyebrow shape (K-2), to illustrating how sentiment analysis judges the emotional tone of text, e.g. movie/restaurant reviews, social media (3-5), to describing how computers combine facial expression, gaze, gesture, body language, tone of voice, and word choice to recognize emotional states (6-8), to identifying ways AI applications modify their behavior in response to detected human emotional states, e.g. adaptive tutoring systems (9-12).
• AI4K12 (US)Progress from identifying devices in daily life that use AI, e.g. smartphones, smart assistants (K-2), to describing how AI-powered services (search, recommenders, autonomous vehicles) are used in daily life (3-5), to examining an aspect of daily life predicted to change due to AI, e.g. transportation and self-driving cars (6-8), to explaining the kinds of cultural debates that arise as AI evolves, e.g. AI-generated art, LLM use in homework, data rights (9-12).
• AI4K12 (US)Progress from identifying current uses of AI and how they have impacted society (K-2), to identifying how sectors of society such as manufacturing, retail, and healthcare are changing due to AI (3-5), to comparing AI-driven societal change to previous industrial revolutions (6-8), to predicting how a specific sector of society is likely to change in the short and intermediate term as a result of AI, referencing Amara's Law (9-12).
• OECD–EC (Europe)Analyse how AI shapes beliefs and behaviours (Engage 1, advanced level).
• OECD–EC (Europe)Weigh the benefits and drawbacks of data-shaped access to information from predictive AI systems (Engage 4, advanced).
• OECD–EC (Europe)Weigh the benefits and disadvantages of using AI-driven search engines, including privacy and commercial-interest trade-offs (AI example 31, competence 1.1).
• OECD–EC (Europe)Be aware that AI algorithms may embody a commercial or political message rather than only serving the user's information need, with possible negative consequences such as reproducing stereotypes or spreading misinformation (AI example 48, competence 1.2).
• OECD–EC (Europe)Know that 'deep-fakes' are AI-generated images, video, or audio of events or people that did not really happen, and may be indistinguishable from the real thing (AI example 51, competence 1.2).

1. The AI Entrepreneur
2. AI Startup Landscape
3. Why Do Some Great AI Ideas Still Fail?
4. AI Business Models
5. Moat or No Moat?
6. Business Model Match
7. Strong or Weak Signal?
8. The Startup Journey
9. Evidence or Vanity?
10. Startup Funding Stages
11. AI Startup Challenges
12. Runway Lab
13. Runway by Hand
14. How Hugging Face Grew Around AI Builders
15. Building AI Products
16. Launch Review
17. Match Challenge to Strategy
18. Spot Smart Startup Decisions
19. AI Startups Quiz
20. Explain What Makes an AI Startup Viable
21. Startup Vocabulary
22. AI Entrepreneur!

• AI4K12 (US)Progress from identifying devices in daily life that use AI, e.g. smartphones, smart assistants (K-2), to describing how AI-powered services (search, recommenders, autonomous vehicles) are used in daily life (3-5), to examining an aspect of daily life predicted to change due to AI, e.g. transportation and self-driving cars (6-8), to explaining the kinds of cultural debates that arise as AI evolves, e.g. AI-generated art, LLM use in homework, data rights (9-12).
• AI4K12 (US)Progress from identifying current uses of AI and how they have impacted society (K-2), to identifying how sectors of society such as manufacturing, retail, and healthcare are changing due to AI (3-5), to comparing AI-driven societal change to previous industrial revolutions (6-8), to predicting how a specific sector of society is likely to change in the short and intermediate term as a result of AI, referencing Amara's Law (9-12).

1. Your AI Career
2. Technical AI Roles
3. Which Technical Role?
4. Skills for AI Careers
5. Which Skill Is Doing the Work?
6. Role to Responsibility Match
7. Projects Often Matter More Than Titles
8. Non-Technical AI Roles
9. One App, Many Roles
10. AI Career Path Progression
11. Where AI Professionals Work
12. Plan Your Portfolio Hours
13. Portfolio Maths by Hand
14. How GitHub Helps AI Careers Become Visible
15. Building Your AI Profile
16. Evidence or Claim?
17. Match Interest to Role
18. Spot Good Career Moves
19. AI Careers Quiz
20. Explain How to Grow Toward an AI Career
21. AI Career Vocabulary
22. Career Explorer!

• ADEKCareers, future of work
• CBSEArticulate the demand for AI professionals and diverse career opportunities; identify requisite skills and tools for AI careers; understand roles/responsibilities of AI professionals across industries; evaluate own interests/skills to determine career pathways in AI (Unit 2: Unlocking your Future in AI).
• AI4K12 (US)Progress from identifying devices in daily life that use AI, e.g. smartphones, smart assistants (K-2), to describing how AI-powered services (search, recommenders, autonomous vehicles) are used in daily life (3-5), to examining an aspect of daily life predicted to change due to AI, e.g. transportation and self-driving cars (6-8), to explaining the kinds of cultural debates that arise as AI evolves, e.g. AI-generated art, LLM use in homework, data rights (9-12).
• AI4K12 (US)Progress from identifying current uses of AI and how they have impacted society (K-2), to identifying how sectors of society such as manufacturing, retail, and healthcare are changing due to AI (3-5), to comparing AI-driven societal change to previous industrial revolutions (6-8), to predicting how a specific sector of society is likely to change in the short and intermediate term as a result of AI, referencing Amara's Law (9-12).
• AI4K12 (US)Progress from describing jobs that no longer exist due to past technology advances, e.g. farriers replaced by auto mechanics (K-2), to describing how a specific job will change due to AI/robotic technology, e.g. warehouse workers alongside robots (3-5), to predicting a new job type that might arise or an existing job that might change/disappear due to AI adoption (6-8), to investigating the specific skills needed for AI-enabled careers across the workforce, not just for programmers (9-12).
• OECD–EC (Europe)Be aware that AI-based technologies can replace some human functions, potentially causing job losses or reallocations while also creating new jobs (AI example 60, competence 2.4).

1. The AI Advisor
2. Types of AI Consulting
3. Which Service Fits?
4. The Consulting Process
5. Which Phase?
6. Consulting Terms Match
7. Sometimes the Best Consulting Move Is Asking a Better Question
8. Why Companies Hire AI Consultants
9. AI Consulting Engagement
10. AI Consulting Skills
11. Tune an AI Consulting Strategy
12. POC Savings by Hand
13. How Accenture Turns AI Ideas Into Business Projects
14. Challenges in AI Consulting
15. The Hard Conversation
16. Match Challenge to Approach
17. Spot Good Consulting Practices
18. In Scope or Scope Creep?
19. AI Consulting Quiz
20. Explain Why AI Consulting Needs More Than Coding
21. Consulting Vocabulary
22. AI Consultant!

• AI4K12 (US)Progress from identifying devices in daily life that use AI, e.g. smartphones, smart assistants (K-2), to describing how AI-powered services (search, recommenders, autonomous vehicles) are used in daily life (3-5), to examining an aspect of daily life predicted to change due to AI, e.g. transportation and self-driving cars (6-8), to explaining the kinds of cultural debates that arise as AI evolves, e.g. AI-generated art, LLM use in homework, data rights (9-12).
• AI4K12 (US)Progress from identifying current uses of AI and how they have impacted society (K-2), to identifying how sectors of society such as manufacturing, retail, and healthcare are changing due to AI (3-5), to comparing AI-driven societal change to previous industrial revolutions (6-8), to predicting how a specific sector of society is likely to change in the short and intermediate term as a result of AI, referencing Amara's Law (9-12).

1. AI Everywhere
2. Unit 5 Journey Recap
3. Same Problem, New Industry
4. Common Themes Across Industries
5. Name the Pattern
6. Industry AI Match
7. Most Industry AI Starts With a Narrow Decision, Not a Robot
8. Transport to Consulting
9. AI Adoption Journey
10. Five Industry Challenges
11. Diagnose the Stalled Project
12. Is the Model Worth It?
13. Net Saving by Hand
14. One Cloud, Many Industries
15. AI Industry Ecosystem
16. Who Does What in Your App?
17. Match Industry to Key Challenge
18. Unit 5 True or False
19. Unit 5 Apply Quiz
20. Explain What Connects AI Across Industries
21. Unit 5 Key Terms
22. Unit 5 Connected

• 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
• AI4K12 (US)Progress from identifying devices in daily life that use AI, e.g. smartphones, smart assistants (K-2), to describing how AI-powered services (search, recommenders, autonomous vehicles) are used in daily life (3-5), to examining an aspect of daily life predicted to change due to AI, e.g. transportation and self-driving cars (6-8), to explaining the kinds of cultural debates that arise as AI evolves, e.g. AI-generated art, LLM use in homework, data rights (9-12).
• AI4K12 (US)Progress from identifying current uses of AI and how they have impacted society (K-2), to identifying how sectors of society such as manufacturing, retail, and healthcare are changing due to AI (3-5), to comparing AI-driven societal change to previous industrial revolutions (6-8), to predicting how a specific sector of society is likely to change in the short and intermediate term as a result of AI, referencing Amara's Law (9-12).
• ChinaStudents learn core AI concepts, theory, and application domains

1. An AI to End Hunger?
2. Goals, Targets, Your Problem
3. Goal, Target or Your Problem?
4. SDG Projects Around the World
5. Which Goal Does It Serve?
6. The App Nobody Opened
7. Where Will Your Data Come From?
8. Usable or Warning Sign?
9. Baseline First, Then Build
10. Try It: Test the Canteen Forecast
11. What Did the Forecast Show?
12. Impact and Footprint
13. Footprint Calculator
14. Same Job Size, Very Different Energy
15. Evidence or Just a Claim?
16. Limits and Who Could Be Harmed
17. Limit, Harm or Footprint?
18. Match the Risk to a Safeguard
19. When a Fraud Model Hurt Families
20. Order the SDG Project
21. Checkpoint: SDG Research-and-Build
22. Pitch Your SDG Project
23. SDG Project Terms
24. SDG Builder!

• UAEHumanitarian and environmental effectiveness
• ADEKFootprint / resource cost of AI
• CBSEComplete a group Capstone Project (3-5 students) aligned with an SDG, following the design-thinking process (define problem, understand users, brainstorm/design solution, identify and collect data, build prototype, test solution, reflect individually and as a team, create a 3-minute video pitch); produce project documentation per the CBSE-IBM AI Project Cookbook (Part C).
• 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
• 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.
• 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)Design ways to minimize negative environmental impacts of AI and communicate those ways to others.
• CSTA (US)Students examine security implications and environmental impacts of AI systems within Systems & Security.
• AI4K12 (US)Progress from describing how AI can be used to solve a societal problem, e.g. Code.org's AI for Oceans classifying wildlife vs. debris (K-2), to designing a solution to a societal problem using AI technology, e.g. environmental protection, energy conservation (3-5), to researching a societal problem and describing how AI perception/reasoning/learning technologies could address it, referencing the UN Sustainable Development Goals (6-8), to evaluating an actual AI-for-social-good project in terms of the problem addressed and its real or potential impact (9-12).
• OECD–EC (Europe)Analyse how design and deployment decisions affect an AI system's resource consumption (Engage 5, advanced).
• OECD–EC (Europe)Consider the ethical consequences of AI systems throughout their life-cycle, including environmental impact and societal impact such as platformisation of work and low-cost labelling labour (AI example 57, competence 4.4).

1. Your AI Project
2. What Makes a Good Capstone
3. Which Quality Is Missing?
4. Choosing Your Project
5. Too Big, Right-Sized, Too Small
6. Project Planning Match
7. Many Great Capstones Start as One Careful MVP
8. Project Ideas by Domain
9. Compare and Merge Two AI Plans
10. Your Ideas First
11. Ask for Five
12. Merge It
13. Project Planning Phases
14. Common Pitfalls to Avoid
15. Name the Pitfall
16. Hour Budget Lab
17. Hour Budget by Hand
18. How GitHub Helps a Capstone Look Real
19. Planning Your Timeline
20. Match Phase to Activities
21. Spot Good Project Decisions
22. Capstone Planning Quiz
23. Explain Why Scope Matters in a Capstone
24. Open Brief: Choose Your Own Problem
25. Ready Brief or Not Yet?
26. Who Else Is Working on This?
27. Use the Initiatives
28. SWOT Your Capstone
29. Build Maya's SWOT
30. Plan It as a Gantt Chart
31. Read the Gantt
32. Project Planning Vocabulary
33. Project Planner!

• UAEDesign AI solutions with systematic design processes
• CBSEDecompose any problem using the 5W1H method; apply Design Thinking methodology; create empathy maps; align problems to Sustainable Development Goals; express a solution to a problem in non-technical words (Unit 4: Introduction to Capstone Project).
• CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
• 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)Plan and develop programs for broad audiences using a software lifecycle process.
• OECD–EC (Europe)Purposefully integrate AI outputs with original ideas to build a comprehensive solution (Create 1, advanced).

1. Finding Your Data
2. Data Sources
3. Which Source Fits?
4. Evaluating Data Quality
5. Which Check Fails?
6. Data Terms Match
7. A Tiny Labeling Mistake Can Ripple Through a Whole Project
8. Common Data Problems
9. Gaps and Imbalance by Hand
10. Data Preparation Steps
11. Data Cleaning Techniques
12. How Many Rows Survive?
13. Rows Left by Hand
14. How Kaggle Helps Builders Start With Real Datasets
15. Train-Test Split
16. Stratify and Leakage by Hand
17. Data Quality Problems
18. Spot Good Data Practices
19. Data Preparation Quiz
20. Explain What Makes Data Ready for AI
21. No-Code: Explore, Model, Score
22. The Same Task in Code
23. What the Blocks Hid
24. Data Preparation Vocabulary
25. Data Preparer!

• ADEKExamine dataset bias, propose fixes
• 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).
• CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
• CSTA (US)Evaluate the data used to solve a problem, including its source(s) and whether privacy is protected, if/how the data has been processed, data quality (e.g., accuracy, reliability, validity), what the data represents, and biases.
• CSTA (US)Students build data fluency and examine the societal impacts of large-scale data collection used to train AI systems.
• AI4K12 (US)Progress from examining a labeled dataset to identify problems that could cause incorrect predictions, e.g. an all-green 'healthy foods' set (K-2), to examining training-data features/labels for potential sources of bias such as Amazon's biased resume sorter (3-5), to explaining how unbalanced training data biases a classifier, e.g. faces or loan applications (6-8), to investigating demographic imbalances in training data using data-visualization tools such as histograms or Pandas (9-12).
• OECD–EC (Europe)Evaluate how dataset properties affect an AI system's performance and impact, factoring in data source and selection (Shape 3, advanced).

1. Choosing Your Model
2. Model Types
3. Why Can a Simpler Model Win?
4. Matching Model to Problem
5. Name the Problem Type
6. Beyond K-Means: DBSCAN, Hierarchical, Spectral
7. Whose Rule Is This?
8. Run DBSCAN and Friends
9. Training Terms Match
10. Pretrained Models Already Carry a Huge Head Start
11. The Training Process
12. One Update by Hand
13. Model Development Workflow
14. Training Pitfalls
15. Diagnose the Training Run
16. Learning Rate and the Loss
17. How Hugging Face Speeds Up Model Training Choices
18. Transfer Learning
19. Freeze or Fine-Tune?
20. Match Problem to Model
21. Spot Good Training Practices
22. Model Training Quiz
23. Explain How to Choose a Model Wisely
24. Training Vocabulary
25. Model Trainer!

• 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 + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
• CISCE + statesTune model hyperparameters (learning rate, batch size, number of hidden layers/neurons) using techniques such as grid search or random search
• IBDistinguish supervised, unsupervised, and reinforcement learning approaches to machine learning
• England + CambridgeShow understanding of Deep Learning, Machine Learning and Reinforcement Learning and the reasons for using each, including machine learning categories such as supervised and unsupervised learning
• CSTA (US)Select and use an appropriate AI algorithm for a classification task (e.g., KNN, decision tree).
• AI4K12 (US)Progress from modeling the use of a classification or search algorithm to solve a simple problem (K-2), to describing differences between decision-tree and neural-network classification algorithms (3-5), to comparing several algorithms that could solve a specific reasoning problem based on input data and decision complexity (6-8), to listing, for each of eight reasoning-problem types, an algorithm that could solve it (9-12).
• AI4K12 (US)Progress from 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 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.
• 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).
• Korea, Japan, HKClassify machine learning into supervised, unsupervised and reinforcement learning, and select the type appropriate to a given problem's characteristics.

1. From Model to App
2. Application Types
3. Pick the App Type
4. User Interface Design
5. Which Rule Is Broken?
6. Application Terms Match
7. Design for Every User
8. Barrier and Fix
9. Check Your Interface
10. Which Fix Comes First?
11. Your Design Brief
12. Review the Designs
13. Follow One Request
14. Sometimes the Biggest Product Upgrade Is Not the Model
15. Quick Demo Tools
16. Application Development Steps
17. Performance Considerations
18. Balance an AI App Experience
19. Speed Maths by Hand
20. How Streamlit Turns a Model Into Something People Can Try
21. Model Serving Best Practices
22. Trace the Validator
23. Choose the Right Approach
24. Spot Good App Practices
25. AI Application Quiz
26. Explain What Makes an AI App Usable
27. Your Build: a System Prompt
28. What Kind of Test?
29. Build It in Prompt Lab
30. What Did Your Tests Show?
31. Application Vocabulary
32. App Designer!

• UAEImprove AI systems to match societal expectations
• CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
• CISCE + statesChoose and construct appropriate statistical graphs (bar graph, histogram, scatter plot, pie graph) and represent multi-dimensional data (introduction to dimensionality)
• 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)Evaluate and refine computational artifacts to make them more usable and accessible.
• 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 programs for multiple computing platforms.
• 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)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).
• 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).
• Korea, Japan, HKUnderstand and apply the concepts and methods of information design (including universal design, usability and accessibility) needed for effective communication with a given audience, and evaluate/improve created content.
• 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 + NZDesign the user experience of a digital system generating or processing data, and evaluate the suitability of user interfaces for defined purposes and end-users, applying accessibility standards for diverse audiences
• 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. Does It Really Work?
2. Evaluation Metrics
3. Confusion Matrix by Hand
4. Choosing the Right Metric
5. Lower the Threshold
6. AUC by Hand
7. Metric Terms Match
8. The Most Useful Test Cases Are Often the Awkward Ones
9. Beyond Basic Metrics
10. Testing Process
11. Testing Strategies
12. Tune an Evaluation Plan
13. F1 and Groups by Hand
14. How Weights & Biases Helps Teams Compare Model Runs
15. Presenting Results Honestly
16. Honest Report Check
17. Match Scenario to Priority Metric
18. Spot Good Testing Practices
19. Testing and Evaluation Quiz
20. Explain Why Honest Evaluation Matters
21. Evaluation Vocabulary
22. Quality Evaluator!

• CBSEIntegrate Data Science Methodology steps into the Capstone Project; identify the best way to represent a solution to a problem; understand the importance of validating machine learning models; use key evaluation metrics (MSE, RMSE, Precision, Recall, F1, Accuracy) for various machine learning tasks (Unit 2).
• CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
• CISCE + 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
• AI4K12 (US)Progress from demonstrating, with instructor help, how to train a computer to recognize something using a tool like Teachable Machine (K-2), to training a classification model and examining its accuracy on new inputs (3-5), to training and evaluating a classification or prediction model on a tabular dataset (6-8), to using supervised or unsupervised learning on real-world data and quantitatively evaluating the results on a nontrivial test set (9-12).
• OECD–EC (Europe)Assess an AI system's strengths and limitations considering its purpose and potential impacts (Shape 1, advanced).
• OECD–EC (Europe)Design evaluation criteria for an AI system, compare its performance across inputs/users, and propose improvements (Shape 2, advanced).
• OECD–EC (Europe)States that a foundational understanding of AI's technical functioning and the ability to recognise chances, limits and risks are indispensable for students to use AI responsibly and reflectively ('digital mündig'); calls on the Länder to integrate AI-related informatics competences and 'Bildung in der digitalen Welt' competences into curricula for all subjects and school types, without itself specifying discrete grade-level student outcomes.

1. Tell Your Story
2. Documentation Types
3. Which Document Answers This?
4. Writing a Great README
5. README Doctor
6. Documentation Terms Match
7. A README Often Decides Whether Someone Keeps Reading
8. Code Documentation
9. Read the Docstring
10. Presentation Structure
11. Presentation Tips
12. Tune a Project Presentation
13. Time Budget by Hand
14. How GitHub Makes Project Communication Visible
15. Writing the Project Report
16. Place the Report Sentence
17. Match Content to Document
18. Spot Good Documentation Practices
19. Documentation Quiz
20. Explain Why Project Storytelling Matters
21. Documentation Vocabulary
22. Communicator!

• ADEKBuild a data story (data + visuals + narrative for an audience)
• CBSEUnderstand the benefits and elements of storytelling; appreciate the role of data storytelling in data analysis/science/AI; learn to combine data, visuals, and narrative to present complex information effectively using the Narrative Structure of a Data Story (Freytag's Pyramid); create an effective data story from given data, and understand ethics in data storytelling (Unit 8).
• CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
• 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.
• CSTA (US)Students read, evaluate, and critique AI-generated code as part of the Programming strand.
• SingaporeDevelop digital literacy and technological skills needed to thrive in an increasingly technology-driven world, as a central goal of the Masterplan.

1. AI Responsibility
2. Ethical Considerations
3. Which Lens Spots the Problem?
4. Bias in AI Projects
5. Break Down the Accuracy
6. Which Bias Is It?
7. Ethics Terms Match
8. Bias Can Hide Even When Overall Accuracy Looks Strong
9. Privacy Considerations
10. Minimise the Data
11. Ethical Review Process
12. Making It Right
13. How an Average Hides a Group
14. How Hugging Face Model Cards Support Responsible Sharing
15. Your Ethical Statement
16. Check the Model Card
17. Fix, Warn or Refuse?
18. Spot Ethical Practices
19. Ethics Quiz
20. Explain What Responsible AI Means in Practice
21. From Principles to Policy
22. Keep, Rewrite or Drop?
23. Enforcement, Exceptions, Review
24. When a Rule Is Broken
25. Teeth, Door or Clock?
26. Draft Your Governance Clause
27. Governance Check
28. Ethics Vocabulary
29. Ethical Builder!

• UAEEffectiveness of AI governance
• UAEJudge responsible AI use against governance frameworks
• ADEKDraft/commit to ethical-use guidelines
• ADEKAI governance/civic participation
• CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
• IBEvaluate ethical implications of AI/ML systems, including bias, fairness, and transparency, as part of the machine learning content
• England + CambridgeShow understanding of encryption (symmetric/asymmetric cryptography, public/private key), SSL/TLS, digital certification and quantum cryptography purpose/benefits/drawbacks
• CSTA (US)Evaluate and analyze the roles of humans and human decision-making in the creation of AI.
• 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 discussing what makes a system fair vs. unfair through a fair-distribution exercise (K-2), to evaluating whether an AI system meets the design criteria of transparency and explainability (3-5), to evaluating whether an AI system meets the design criteria of accountability and respect for privacy (6-8), to analyzing whether an AI system satisfies the full set of ethical design criteria: fairness, transparency, explainability, accountability, respect for privacy, and adherence to societal values (9-12).
• 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).
• UNESCOCategorise general safety risks of AI and typical AI incidents, deepen knowledge of human rights to data protection and privacy and creators' legal responsibility to collect data only with informed consent, and practise strategies to ensure personal data is collected, used, shared, archived and deleted only with consent (CG4.2.2.3).
• 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).
• 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).
• 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).
• UNESCONurture a self-identity as a co-creator and 'driver' of the next generation of AI technology, critically analyse the long-term impacts of AI systems on social relations and behaviours, and discuss how regulations/policies should adapt to enhance AI governance (e.g. by engaging with local/online communities of AI co-creators) (CG4.3.4.3).
• Australia + NZStudents understand that generative AI tools should be used in ways that benefit all members of the school community and support wellbeing, not just individual convenience

1. Going Live
2. Deployment Options
3. Why Can Something Work Locally and Fail After Launch?
4. Preparing for Deployment
5. Commit or Keep Out?
6. Deployment Terms Match
7. Why Did the Launch Fail?
8. Easy Deployment for Students
9. Deployment Process
10. Sharing Your Work
11. Make Your Project Website
12. Ready to Share?
13. Share It Well
14. Tune Deployment Readiness
15. Downtime by Hand
16. How Hugging Face Spaces Makes ML Sharing Fast
17. Maintaining Deployed Apps
18. After Launch: What Now?
19. Match Project to Platform
20. Spot Good Deployment Practices
21. Deployment Quiz
22. Explain What Makes a Project Ready to Go Live
23. Deployment Vocabulary
24. Launch Planner!

• CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
• 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. Learning from Others
2. Value of Peer Review
3. Name the Blind Spot
4. How to Give Good Feedback
5. Upgrade the Comment
6. Feedback Terms Match
7. A Question Can Beat a Verdict
8. How to Receive Feedback
9. Reply to the Review
10. Feedback Process
11. What to Look For
12. How Much Do Extra Reviewers Help?
13. Extra Eyes by Hand
14. How GitHub Makes Review More Useful
15. Code Review Best Practices
16. Review This Function
17. Test and Document Your Function
18. Hand It to a Reviewer
19. Review a Real-Looking Pull Request
20. Good vs. Bad Feedback
21. Spot Good Feedback Practices
22. Peer Review Quiz
23. Explain What Makes Feedback Useful
24. Feedback Vocabulary
25. Feedback Expert!

• CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
• 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.
• 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.

1. Show Time!
2. Showcase Elements
3. Why Is That Booth Empty?
4. The Perfect Elevator Pitch
5. Pitch, Q&A or Cut?
6. Showcase Terms Match
7. First Impressions in a Showcase Happen Fast
8. Fix This Pitch
9. Creating Visual Displays
10. Showcase Day Timeline
11. Handling Different Audiences
12. Tune a Showcase Pitch
13. Pitch Timing by Hand
14. How LinkedIn Extends a Showcase Beyond One Day
15. Demo Day Tips
16. Demo Rescue
17. Match Audience to Approach
18. Spot Good Showcase Practices
19. Showcase Quiz
20. Write Your Own Pitch
21. Showcase Vocabulary
22. Showcase Star!

• CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
• CSTA (US)Implement an artificial intelligence algorithm to play a game against a human opponent or solve a problem.
• 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

1. The Journey Continues
2. Learning Resources
3. Reading Resources Critically
4. Building Your AI Network
5. Learn, Build or Share?
6. Resource Match
7. Teaching Others Often Strengthens Your Own Learning
8. Paths After Your Capstone
9. Pick the Next Path
10. Skill Building Path
11. Career Development
12. Learning Hours Calculator
13. Learning Hours by Hand
14. How Kaggle Keeps Learning Practical
15. Staying Current
16. Learn Now or Note It
17. Match Goal to Resource
18. Spot Good Learning Habits
19. Continuous Learning Quiz
20. Explain How to Keep Growing in AI
21. Learning Vocabulary
22. Lifelong Learner!

• CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)
• UNESCOReflect on personal goals and a lifelong-learning, self-actualization attitude toward AI as it becomes pervasive in work, life and social practices (CG4.3.1.3).

1. The Grand Finale
2. Your Capstone Journey
3. Lifecycle Order
4. Which Phase Fixes It?
5. Grand Finale Quiz
6. Post-Mortem by Hand
7. Capstone Vocabulary
8. AI Graduate!

• CISCE + statesGive an overview of decision making in machines/computers, cybersecurity in computing, and machine intelligence, plus the components of the AI project framework (problem scoping, data acquisition, data exploration, modelling and evaluation, in brief)