Grade 12: units, topics and lessons

1. The Innovation Lab
2. Innovator Mindset
3. Best Run or Average?
4. AI Today
5. Why Keep a Boring Baseline?
6. Pick the Right Tool
7. Where New AI Ideas Appear First
8. Judge the Claim
9. Research vs Production
10. Building Blocks
11. An Honest Experiment
12. How Sure Is 94%?
13. Margin by Hand
14. Hugging Face: Shared Models, Checkable Claims
15. Close the Gap
16. Innovation Spotter
17. Innovation Quiz
18. Review Maya's Claim
19. Innovator Vocabulary
20. AI Innovator!

• SingaporeLearn about AI: understand what AI is and how it works.

1. The AI Revolution
2. Four Shifts, 2022-2025
3. Why Does 2025 Feel Different?
4. Inside an Agent Loop
5. The AI Industry Stack
6. One AI, Many Modes
7. Technology Families
8. The Ecosystem Moves in Public
9. Recent or Established?
10. Unsolved Problems
11. Tune a 2025 AI System
12. Compounding by Hand
13. Hugging Face Shows the Ecosystem
14. Check the Headline
15. Diagnose the Failure
16. Trend or Overclaim?
17. The AI Revolution Quiz
18. Explain the 2025 Shift
19. Revolution Vocabulary
20. Revolution Mapped

• CSTA (US)Predict how computational innovations that have revolutionized aspects of our culture might evolve.
• Korea, Japan, HKDeepen understanding and skills for realising diverse communication and using information systems and diverse (including large-scale/multimedia) data, and understand the development of information technology and social change including its effect on intellectual activity.

1. AI That Sees & Hears
2. The Modalities
3. Why Do Multiple Senses Matter?
4. Fusion Architecture
5. Pick the Fusion
6. One Idea, Many Forms
7. Famous Models
8. Memes Need Two Brains
9. Applications Sort
10. Cross-Modal Magic
11. Zero-Shot by Hand
12. Balance a Multimodal System
13. Fuse by Hand
14. Be My Eyes Connects Vision and Language
15. Concept Match
16. Multimodal Spotter
17. Multimodal Quiz
18. Explain Multimodal Fusion
19. Multimodal Vocabulary
20. Multimodal Foundations

• 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.

1. AI That Takes Action
2. Agent Anatomy
3. When Does Chat Become Action?
4. The ReAct Pattern
5. Trace the Loop
6. Reasoning Can Leave the Chat Box
7. Agent Types Match
8. One Goal Can Require Many Roles
9. Agent Complexity
10. Agent Challenges
11. Ask First or Go Ahead?
12. Tune an Agent Workflow
13. Compounding by Hand
14. Claude Computer Use Acts on Interfaces
15. Framework Match
16. Agent Spotter
17. Agent Quiz
18. Explain the Agent Loop
19. Agent Vocabulary
20. Agent Expert!

• AI4K12 (US)Progress from illustrating a next possible board state in tic-tac-toe (K-2), to illustrating how a computer represents game play as a linear sequence of board positions connected by legal moves (3-5), to illustrating how a computer solves a maze, finds a map route, or reasons over a knowledge graph by drawing a search tree (6-8), to identifying real-world problems (task planning, scheduling, resource allocation) as search problems and describing their states and operators (9-12).
• 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. Can AI Really Think?
2. Reasoning Types
3. Why Do Steps Improve Answers?
4. CoT In Action
5. One Prompt Can Change the Thinking Style
6. Method Match
7. Voting Can Beat a Single Try
8. Count the Votes
9. Reasoning Needed?
10. Reasoning Limits
11. Tune a Reasoning Strategy
12. Compounding by Hand
13. ChatGPT Gets Better With Structured Reasoning
14. Concept Match
15. What Is Still Unsolved
16. Narrow or General? A Working Test
17. Narrow AI or AGI?
18. Headline Check
19. Reasoning Spotter
20. Reasoning Quiz
21. Explain Chain-of-Thought
22. Reasoning Vocabulary
23. Reasoning Master!

• UAEHow AI systems work; data and design choices
• CISCE + statesDifferentiate broad vs narrow AI and strong vs weak AI, and describe expert systems (e.g. Eliza), computer vision (CV), natural language processing (NLP) and neural networks (NN)
• IBApply computational thinking strategies (decomposition, abstraction, pattern recognition, algorithm design) to solve problems
• IBUnderstand the bias inherent in content produced by an AI tool and be able to critically review AI-generated output
• IBUse AI-generated content as an object of inquiry within Theory of Knowledge to examine how knowledge is produced and evaluated
• CSTA (US)Debate what differences do or should exist between human and artificial intelligence, sentience, consciousness, rights, and responsibilities.
• AI4K12 (US)Progress from demonstrating ways intelligent agents don't understand things the way people do, e.g. no frustration at repeated questions (K-2), to demonstrating what a 'broad AI'/general-AI system could do that current narrow AI cannot (3-5), to defining criteria for consciousness (self-awareness, personal history) and evaluating AI systems or fictional AI characters against them (6-8), to debating alternative perspectives on human vs. artificial intelligence, including the Turing test and artificial superintelligence (9-12).
• OECD–EC (Europe)Evaluate whether an AI output should be accepted, revised, or rejected, and justify the decision (Engage 3, advanced).
• OECD–EC (Europe)Have a disposition to keep learning and stay informed about AI, including how algorithms can be biased and the difference between narrow AI and (hypothetical) general AI (AI example 72, competence 5.4).
• UNESCOGain interdisciplinary foundational knowledge for AI (e.g. artificial neural networks, strong vs weak AI, connections to STEM, languages and social studies) and reflect on AI's reciprocal impact on those subjects (CG4.1.3.3).
• ChinaStudents learn the basic process, implementation principles and core algorithms behind AI systems

1. AI That Writes Code
2. Code Gen Methods
3. How Can AI Write Working Code?
4. AI Coding Tools
5. Comments Can Become Code
6. Capability Match
7. Fast Tasks Are Not the Same as Hard Tasks
8. Difficulty Sort
9. Best Practices
10. Review the Suggestion
11. A Review Routine
12. Tune an AI Coding Workflow
13. Layers by Hand
14. GitHub Copilot Speeds Repetitive Work
15. Risk Match
16. Code Gen Spotter
17. Code Gen Quiz
18. Explain Safe AI Coding
19. Code Gen Vocabulary
20. AI Coder Expert!

• 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
• IBUnderstand the bias inherent in content produced by an AI tool and be able to critically review AI-generated output
• CSTA (US)Students read, evaluate, and critique AI-generated code as part of the Programming strand.
• OECD–EC (Europe)Evaluate whether an AI output should be accepted, revised, or rejected, and justify the decision (Engage 3, advanced).
• UNESCOInterrogate how automation of literature reviews, writing and artistic creation by AI may undermine human thinking and intellectual development, and identify concrete actions to protect themselves and peers from over-reliance on AI outputs (CG4.2.1.3).
• SingaporeLearn with AI: apply AI to support and enhance one's own learning process.
• Australia + NZStudents and educators use generative AI tools in ways designed to enhance teaching and learning outcomes rather than replace core learning processes

1. AI in the Real World
2. Embodied AI Types
3. Why Is Physical AI So Hard?
4. Sense-Think-Act
5. Thinking Has To Become Motion
6. Run the Loop
7. Sensor Match
8. The Hardest Objects Are Often Soft
9. Challenge Sort
10. Hard Problems
11. Tune an Embodied AI System
12. Latency by Hand
13. Waymo Turns Perception Into Driving
14. Which Loop Step?
15. Application Match
16. Embodied Spotter
17. Embodied AI Quiz
18. Explain the Perception-Action Loop
19. Embodied Vocabulary
20. Robotics Expert!

• ADEKRobotics / physical device
• 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 + 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 + 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 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)
• 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
• AI4K12 (US)Progress from locating and identifying computer sensors such as cameras and microphones on everyday devices (K-2), to illustrating how computer sensing differs from human sensing (3-5), to giving examples of intelligent agents (e.g. self-driving cars) combining data from multiple sensors (6-8), to describing the limitations and advantages of specific sensor types such as cameras, microphones, radar, lidar, GPS, and accelerometers (9-12).
• AI4K12 (US)Progress from giving examples of intelligent vs. non-intelligent machines and discussing what makes a machine 'intelligent' (K-2), to demonstrating a machine perception tool (e.g. speech transcription, object recognition) and explaining why it is perception rather than mere sensing (3-5), to giving examples of specialized perception algorithms (face detection, obstacle detection, speech recognition, etc.) (6-8), to explaining how perception algorithms power real-world applications like license plate readers and Google Lens (9-12).
• AI4K12 (US)Progress from 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. AI on Tiny Devices
2. Cloud vs Edge
3. Why Keep AI on the Device?
4. Model Compression
5. Read the Quantizer
6. Huge Models Can Shrink Dramatically
7. Device Match
8. TinyML Fits in Kilobytes
9. Trade-off Sort
10. AI Accelerators
11. Balance an Edge AI Design
12. Size by Hand
13. Face ID Works in Real Time
14. Concept Match
15. Edge AI Spotter
16. Edge AI Quiz
17. Plan an Edge Project
18. Explain Edge AI Trade-offs
19. Edge Vocabulary
20. Edge AI Expert!

• ADEKRobotics / physical device
• IBExplain how core computer hardware, operating systems, and cloud computing concepts underpin computing systems
• England + CambridgeShow understanding of encryption (symmetric/asymmetric cryptography, public/private key), SSL/TLS, digital certification and quantum cryptography purpose/benefits/drawbacks
• England + CambridgeShow understanding of operating system process management (multi-tasking, process states, scheduling algorithms) and virtual memory/paging/segmentation
• 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. The Quantum Frontier
2. Quantum Basics for AI
3. Myth or Mechanism?
4. Why Mix Quantum With AI?
5. What is Quantum Machine Learning?
6. Some Problems May Need a New Kind of Computer
7. The Hybrid Loop
8. Match Quantum Concepts
9. Data Loading Can Cancel the Advantage
10. Sort: Current vs Future QML
11. QML Challenges Today
12. How Fast Noise Adds Up
13. Noise by Hand
14. IBM Quantum Supports Hybrid Research
15. Match QML Applications
16. Spot the Real QML
17. Quantum ML Quiz
18. Explain Quantum ML's Promise
19. Quantum ML Vocabulary
20. Quantum Pioneer!

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

1. The Safety Lab
2. Core AI Safety Areas
3. Why Doing the Wrong Thing Can Still Look Correct
4. Why AI Safety Matters Now
5. Safety Is About Better AI, Not Slower AI
6. Match Safety Concepts
7. A Helpful Model Can Still Hide Risks
8. Sort: Safety vs Capability Research
9. Safety Research Techniques
10. When the Reward Model Is the Proxy
11. How Much Testing Catches a Rare Failure?
12. Rare Failures by Hand
13. Claude Uses Constitutional AI
14. Match Safety Solutions
15. A Release Plan
16. Spot the Safety Issue
17. AI Safety Quiz
18. Explain AI Alignment
19. AI Safety Vocabulary
20. Safety Researcher!

• ADEKAI governance/civic participation
• IBEvaluate ethical implications of AI/ML systems, including bias, fairness, and transparency, as part of the machine learning content
• 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.
• AI4K12 (US)Progress from discussing what makes a system fair vs. unfair through a fair-distribution exercise (K-2), to evaluating whether an AI system meets the design criteria of transparency and explainability (3-5), to evaluating whether an AI system meets the design criteria of accountability and respect for privacy (6-8), to analyzing whether an AI system satisfies the full set of ethical design criteria: fairness, transparency, explainability, accountability, respect for privacy, and adherence to societal values (9-12).
• OECD–EC (Europe)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 Unsolved Mysteries
2. Major Open Problems
3. Why Are Easy Human Skills Still Hard?
4. Why These Problems Are Hard
5. Which Problem Is This?
6. The Frontier Starts Where Benchmarks Stop
7. Match the Open Problem
8. Knowing When Not to Answer Is Still Hard
9. Scope a Project
10. Sort: Solved vs Open
11. More Open Frontiers
12. Tune a Research Breakthrough
13. Compounding by Hand
14. Wolfram Alpha Shows Narrow Strength
15. Match Research Approaches
16. Spot the Open Problem
17. Open Problems Quiz
18. Explain an Open Problem
19. Open Problems Vocabulary
20. Future Researcher!

• ADEKFootprint / resource cost of AI
• IBUse AI-generated content as an object of inquiry within Theory of Knowledge to examine how knowledge is produced and evaluated
• CSTA (US)Debate what differences do or should exist between human and artificial intelligence, sentience, consciousness, rights, and responsibilities.
• 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 what an intelligent agent must 'know' to make sense of a spoken question (K-2), to demonstrating how a text-to-speech/speech-recognition system resolves ambiguity using statistical context (3-5), to classifying an image scene type and describing the domain knowledge needed to understand it (6-8), to analyzing an online image dataset (e.g. ImageNet, Coco) to describe how statistical domain knowledge is derived for a computer vision system (9-12).
• AI4K12 (US)Progress from 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 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).
• AI4K12 (US)Progress from demonstrating ways intelligent agents don't understand things the way people do, e.g. no frustration at repeated questions (K-2), to demonstrating what a 'broad AI'/general-AI system could do that current narrow AI cannot (3-5), to defining criteria for consciousness (self-awareness, personal history) and evaluating AI systems or fictional AI characters against them (6-8), to debating alternative perspectives on human vs. artificial intelligence, including the Turing test and artificial superintelligence (9-12).
• OECD–EC (Europe)Explain how AI systems perform tasks without attributing human traits to them (Engage 2, advanced).
• OECD–EC (Europe)Analyse how design and deployment decisions affect an AI system's resource consumption (Engage 5, advanced).
• OECD–EC (Europe)Assess an AI system's strengths and limitations considering its purpose and potential impacts (Shape 1, 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).
• 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.
• ChinaStudents learn the basic process, implementation principles and core algorithms behind AI systems

1. The AI Journey So Far
2. Unit 1 Key Concepts
3. What Ties the Whole Unit Together?
4. Connecting the Dots
5. Trace One Request
6. Modern AI Is a Stack, Not a Single Trick
7. Match Unit Concepts
8. Safety Connects to Every Capability
9. Sort: Now vs Future
10. More Key Concepts
11. Tune the Modern AI Stack
12. Weak Links by Hand
13. Perplexity Shows the Stack in Practice
14. Design Review
15. Match Problems & Solutions
16. Spot the Cutting Edge
17. Unit 1 Final Quiz
18. Explain the Cutting-Edge Picture
19. Unit 1 Key Terms
20. Unit 1 Complete!

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

1. The Paper Trail
2. Where to Find Papers
3. Why Do Experts Skim Before Reading Deeply?
4. Anatomy of a Research Paper
5. A Paper Has a Shape You Can Learn
6. Match Paper Sections
7. Figures Often Tell the Whole Story
8. Sort: First Pass vs Deep Read
9. Reading Strategies
10. Score a Paper for Deep Reading
11. Score by Hand
12. Papers With Code Helps You Triage Faster
13. Deep Read or Skim?
14. Match Reading Tips
15. Spot Good Paper Habits
16. Which Section Answers It?
17. Paper Reading Quiz
18. Explain Smart Paper Triage
19. Paper Reading Vocabulary
20. Research Reader!

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

1. Decoding the Blueprint
2. The Paper Sections
3. Why Does Standard Structure Matter So Much?
4. Deep Dive: Key Sections
5. Structure Turns Complexity Into Navigation
6. Match Sections to Content
7. The Contributions List Is a Shortcut
8. Sort: Early vs Late Sections
9. More Paper Components
10. Trace a Claim
11. Design a Reader-Friendly Paper
12. Scan Budget by Hand
13. arXiv Works Because Structure Is Familiar
14. Match Paper Elements
15. Reading the Transformer Paper
16. Spot the Section Purpose
17. Paper Structure Quiz
18. Explain Paper Structure
19. Structure Vocabulary
20. Structure Expert!

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

1. The Research Hunt
2. Paper Discovery Tools
3. How Do Researchers Find What Matters?
4. Finding Strategies
5. Order the Search
6. Research Is a Network, Not a Pile
7. Match Discovery Methods
8. Survey Papers Save Huge Amounts of Time
9. Sort: Search vs Subscribe
10. More Discovery Resources
11. Build a Paper Discovery System
12. Precision by Hand
13. Semantic Scholar Surfaces Research Paths
14. Judge What You Found
15. Match Conferences to Fields
16. Spot Good Search Habits
17. Finding Papers Quiz
18. Explain Efficient Paper Discovery
19. Discovery Vocabulary
20. Paper Hunter!

• 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).

1. Question Everything
2. Critical Questions to Ask
3. Why Can Strong Claims Still Be Weak Science?
4. Common Problems in Papers
5. Skepticism Makes Research Stronger
6. Match the Problem
7. A Tiny Gain Might Mean Nothing
8. Sort: Good vs Concerning
9. Evaluating Evidence
10. Test the Strength of a Paper's Evidence
11. Trace the Noise
12. Papers With Code Makes Comparisons Harder to Fake
13. Leak or Fair Split?
14. Match Analysis Tools
15. Spot the Red Flags
16. Critical Analysis Quiz
17. What Evidence Would Settle It?
18. Explain Strong Evidence
19. Critical Analysis Vocabulary
20. Critical Thinker!

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

1. The Replication Challenge
2. Why Reproduction Fails
3. Why Doesn't the Same Paper Give the Same Result?
4. Reproduction Best Practices
5. Reproduction Reveals the Invisible Parts
6. Is the Gap Real?
7. Match Reproduction Tools
8. One Data Step Can Change Everything
9. Sort: First Steps vs Later Steps
10. Debugging Reproduction
11. How Many Seeds Is Enough?
12. Seeds by Hand
13. Docker Preserves the Environment
14. When You Can't Re-run It
15. Match Problem to Solution
16. Spot Good Reproduction Practices
17. Reproduction Quiz
18. Explain Reproducibility Challenges
19. Reproduction Vocabulary
20. Reproducibility Expert!

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

1. The Scientific Method
2. Key Experiment Components
3. Why Can a Good Idea Still Produce Bad Evidence?
4. Designing Fair Experiments
5. One Run or Five?
6. A Great Experiment Can Clarify Reality
7. Match Experiment Terms
8. One Seed Can Mislead You
9. Sort: Training vs Test Time
10. Common Experiment Mistakes
11. Balance a Fair Experiment
12. Seeds by Hand
13. Weights & Biases Helps Teams Track Fair Tests
14. Match Data Splits
15. Spot the Experiment Flaw
16. Run the Method
17. Experiment Design Quiz
18. Explain Fair Experiment Design
19. Experiment Vocabulary
20. Experiment Designer!

• CSTA (US)Evaluate the ability of models and simulations to test and support the refinement of hypotheses.

1. The Standard Measures
2. Famous AI Benchmarks
3. Same Benchmark, Different Rules
4. Understanding Datasets
5. Check the Data
6. A Target Everyone Aims At
7. Match Benchmarks to Tasks
8. When a Benchmark Gets Used Up
9. Sort: Images, Text or Both
10. Benchmark Limitations
11. Diagnose the Weakness
12. How Much Could Luck Move a Score?
13. Margin on HumanEval
14. Where Benchmarks and Datasets Live
15. Match Dataset Sources
16. Spot the Benchmark Issue
17. Benchmarks Quiz
18. Choose Between Two Models
19. Benchmark Vocabulary
20. Benchmark Reader

• 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.

1. From Results to Paper
2. Writing Section by Section
3. Why Can Great Research Be Ignored?
4. Writing Clearly
5. Writing Is Part of the Research
6. Match Writing Tips
7. Many Researchers Write the Abstract Last
8. Order Maya's Abstract
9. Sort: Write First vs Last
10. Common Writing Mistakes
11. Claim Only What You Showed
12. Measure a Draft's Reading Grade
13. Reading Grade by Hand
14. Overleaf Supports Collaborative Drafting
15. Match Writing Tools
16. Spot Good Writing
17. Writing Research Quiz
18. Explain Clear Research Writing
19. Writing Vocabulary
20. Paper Plan Complete

• OECD–EC (Europe)Critically evaluate how AI feedback shapes one's ideas and reflect on the creative process (Create 3, advanced).
• UNESCOInterrogate how automation of literature reviews, writing and artistic creation by AI may undermine human thinking and intellectual development, and identify concrete actions to protect themselves and peers from over-reliance on AI outputs (CG4.2.1.3).

1. The Ethics Question
2. Core Ethical Principles
3. Why Can Impressive Results Still Be Wrong to Use?
4. Ethics in AI Research
5. Apply the Principles
6. Responsible Research Protects the Future of the Field
7. Match Ethical Issues
8. Public Data Is Not Always Fair Game
9. Sort: Ethical vs Concerning
10. Ethical Safeguards
11. Balance an Ethical Research Plan
12. Trace the Re-identification Maths
13. Model Cards Add Transparency
14. Plan Your Own Project Ethically
15. Match Ethics Terms
16. Spot the Ethical Issue
17. Research Ethics Quiz
18. Explain Ethical AI Research
19. Ethics Vocabulary
20. India's Welfare AI: Apply the Principles
21. Write the Rule!
22. Same Case, Other Codes
23. Compare the Codes
24. Ethical Researcher!

• UAEImprove AI systems to match societal expectations
• UAEJudge responsible AI use against governance frameworks
• CBSEDemonstrate understanding of the fundamental principles of ethics in AI and the five pillars of AI Ethics; understand AI bias, its sources, real-world implications, and mitigation strategies; identify and apply strategies to promote fairness and transparency; recognise the significance of AI policies in promoting responsible, safe, ethical AI use (Unit 8: AI Ethics and Values).
• CISCE + statesApply an AI code of ethics — avoiding bias, ensuring privacy of users and their data, mitigating environmental impact, and evaluating the effects of designing technology that replicates human life
• CSTA (US)Evaluate the design, motivation, outcomes, and potential impacts of AI systems using ethical design criteria and/or ethical frameworks.
• OECD–EC (Europe)Evaluate how well an AI use aligns with ethical principles and human values across trade-offs (Engage 7, advanced).
• UNESCOCarry out responsible AI practices in compliance with ethical principles and locally applicable regulations, iteratively building and following a personal checklist of lawful and responsible practices (e.g. protecting privacy/copyright, marking AI-generated content, avoiding disinformation, hate speech, or sensitive content about identifiable individuals) (CG4.2.2.1).
• UNESCOGain an age-appropriate understanding of personal, legal and ethical responsibilities when using AI and reinforce self-disciplined behaviours around sensitive personal data, copyrighted materials, deepfake/identifiable-person imagery, and the spread of misinformation, disinformation or hate speech (CG4.2.2.2).
• 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).
• SingaporeLearn to use AI: use AI tools safely and responsibly.

1. Research as a Team
2. Essential Collaboration Tools
3. Why Do Big Research Teams Need So Much Structure?
4. Version Control with Git
5. Read the Trace
6. Tools Let Ideas Move Faster Than Distance
7. Match Tools to Uses
8. A Good Commit Message Saves Future Time
9. Sort: Code vs Writing Tools
10. More Collaboration Tools
11. Build a Strong Research Workflow
12. Trace Thursday's Drop
13. GitHub Coordinates Shared Code
14. Match Git Commands
15. The Pull-Request Cycle
16. Spot Good Collaboration
17. Collaboration Tools Quiz
18. Explain Research Collaboration
19. Collaboration Vocabulary
20. Team Player!

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

1. A World of AI Research
2. Global Research Hubs
3. Why Is AI Research No Longer Concentrated In Just A Few Places?
4. Joining the Global Community
5. Open Research Connects Distant Labs
6. Choose a First Step
7. Match Research Institutions
8. Regional Communities Create Real Momentum
9. Sort: Academic vs Industry
10. Global Research Initiatives
11. Balance a Global Research Ecosystem
12. Overlap by Hand
13. arXiv Helps Research Spread Worldwide
14. Read the Preprint Honestly
15. Match Initiatives
16. Spot Global Opportunities
17. Global AI Research Quiz
18. Explain Global AI Research
19. Global Research Vocabulary
20. Global Researcher!

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

1. The Research Journey
2. Unit 2 Key Topics
3. Why Does Research Move In A Cycle Instead Of A Straight Line?
4. The Research Cycle
5. Research Skills Reinforce One Another
6. Match Research Skills
7. One Loop, In Order
8. One Good Project Can Launch Several More
9. Sort: Research Stages
10. Key Takeaways
11. How Many Runs Do You Need?
12. Spread by Hand
13. GitHub Connects Reading, Building, and Review
14. Match Tools to Tasks
15. Spot the Good Research
16. Review a Draft
17. Unit 2 Final Quiz
18. Explain the Research Cycle
19. Unit 2 Key Terms
20. Unit 2 Review Complete

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

1. The Paper Challenge
2. The Three-Pass Method
3. Why Do Multiple Quick Passes Beat One Slow First Read?
4. Why Read Papers at the Source
5. Pass 1 on the Transformer Paper
6. A Paper Reveals More Each Time You Revisit It
7. Match Reading Depths
8. Appendices Often Hide Critical Details
9. Sort: Pass 1 vs Pass 2
10. Overcoming Common Struggles
11. Budget Your Reading Time
12. Budget by Hand
13. arXiv Supports Source-First Reading
14. Plan Your Project's Reading
15. Match Problem to Solution
16. Spot Effective Reading Habits
17. Advanced Reading Quiz
18. Explain the Three-Pass Method
19. Advanced Reading Terms
20. Paper Reading Pro!

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

1. The Paper Blueprint
2. The IMRAD Structure
3. Why Can Experts Navigate A Long Paper So Quickly?
4. Complete Paper Structure
5. Put the Sections in Order
6. A Shared Structure Makes Research Searchable
7. Match Sections to Content
8. Figure 1 Often Tells the Whole Story
9. Sort: Reading Priority
10. What Each Section Reveals
11. Build a Readable Paper
12. Hunt the Answer
13. Overleaf Encourages Standard Paper Structure
14. Match Paper Types
15. Name the Paper Type
16. Spot Well-Structured Papers
17. Paper Structure Quiz
18. Explain Paper Structure
19. Paper Structure Terms
20. Paper Navigator!

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

1. The Research Goldmine
2. Research Discovery Tools
3. Why Do Researchers Need More Than A Search Box?
4. ArXiv: Day 1 Access
5. Read an arXiv Record
6. Discovery Tools Turn the Firehose Into a Map
7. Match Tools to Uses
8. Citation Trails Work in Two Directions
9. Sort: Preprint vs Published
10. Google Scholar Power Tips
11. Build the Query
12. Adjust a Citation Count for Age
13. Citations per Year by Hand
14. Google Scholar Extends Search Into a Research Network
15. Match Citation Levels
16. Spot Good Research Practices
17. Finding Papers Quiz
18. Explain Paper Discovery Tools
19. Research Discovery Terms
20. Research Explorer!

• 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).

1. Question Everything
2. Critical Reading Levels
3. Why Can A Published Result Still Be Misleading?
4. Evaluating Claims
5. Skepticism Can Make Research Stronger
6. Match Claim Types
7. Limitations Sections Often Matter Most
8. Read the Ablation Table
9. Sort: Good vs Bad Practice
10. Statistical Rigor
11. Tune a Strong Critical Review
12. Trace the Error Cut
13. Papers with Code Helps Inspect Claims
14. Match Quality Indicators
15. Spot Quality Research
16. Critical Analysis Quiz
17. Which Question Catches It?
18. Explain Critical Reading
19. Critical Analysis Terms
20. Critical Thinker!

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

1. The Reproduction Challenge
2. Why ML Reproduction is Hard
3. Why Can The Same Paper Produce Different Numbers?
4. Reproducibility Levels
5. Near Matches Can Still Be Scientific Wins
6. Which Level Was It?
7. Match Reproduction Steps
8. One Missing Hyperparameter Can Change Everything
9. Sort: Normal vs Concerning
10. Solving Common Problems
11. Tune a Reproduction Workflow
12. Accumulation by Hand
13. Weights & Biases Keeps Runs Comparable
14. Maya's Reproduction Report
15. Match Best Practices
16. Spot Good Reproduction Practices
17. Reproduction Quiz
18. Explain Reproducibility
19. Reproduction Terms
20. Reproduction Challenge Complete

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

1. Designing Great Experiments
2. The Scientific Method in ML
3. Why Can A Great Idea Still Produce Weak Evidence?
4. Formulating Research Questions
5. Sharpen the Question
6. A Clean Experiment Can Turn Doubt Into Evidence
7. Match Experiment Components
8. Ablations Often Reveal Surprising Simplicity
9. Read the Ablation
10. Sort: Fair vs Unfair Comparisons
11. Choosing Baselines & Metrics
12. Build a Fair Experiment
13. p by Hand
14. MLflow Organizes Experiment Evidence
15. Match Ablation Benefits
16. Spot Good Experiment Design
17. Experiment Design Quiz
18. Explain Experiment Design
19. Experiment Design Terms
20. Experiment Designer!

• 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)Evaluate the ability of models and simulations to test and support the refinement of hypotheses.

1. Data is Everything
2. Key Vision Benchmarks
3. Why Can A Model Look Great On A Benchmark But Fail In Reality?
4. NLP Benchmarks
5. Shared Benchmarks Let Whole Fields Coordinate
6. Match Benchmarks to Tasks
7. Leaderboards Can Encourage Narrow Optimization
8. Sort: Good vs Bad Practices
9. An Honest Evaluation
10. Dataset Biases to Watch
11. Tune a Strong Dataset
12. Real-World Accuracy by Hand
13. Hugging Face Helps Researchers Inspect Datasets
14. Audit a Dataset Card
15. Match Data Quality Signs
16. Spot Good Dataset Practices
17. Benchmarks Quiz
18. Explain Benchmark Limits
19. Dataset Terms
20. Data Expert!

• ADEKExamine dataset bias, propose fixes
• 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 describing what an intelligent agent must 'know' to make sense of a spoken question (K-2), to demonstrating how a text-to-speech/speech-recognition system resolves ambiguity using statistical context (3-5), to classifying an image scene type and describing the domain knowledge needed to understand it (6-8), to analyzing an online image dataset (e.g. ImageNet, Coco) to describe how statistical domain knowledge is derived for a computer vision system (9-12).
• AI4K12 (US)Progress from illustrating why training a classifier for a broad concept like 'dog' requires large, diverse image data by browsing datasets such as ImageNet or Coco (3-5), to illustrating how objects in images are segmented and labeled with bounding boxes to build an object-recognition training set (6-8), to evaluating a real AI training dataset's size, acquisition/labeling method, storage cost, and time-to-produce, including where bias can enter (9-12). K-2 is marked N/A.
• AI4K12 (US)Progress from 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. Sharing Your Discovery
2. Paper Structure Overview
3. Why Can Strong Results Still Fail To Persuade Reviewers?
4. Writing Each Section
5. Clear Writing Lets Ideas Travel Further
6. Match Sections to Purpose
7. Many Readers Decide Fast
8. Sort: Good vs Bad Writing
9. Which Section Is This?
10. LaTeX Essentials
11. Tune a Clear Research Paper
12. Points, Percent or Errors?
13. Overleaf Supports Clear Collaborative Writing
14. Draft to Camera-Ready
15. Match LaTeX vs Word
16. Spot Good Writing Practices
17. Research Writing Quiz
18. Explain Research Writing
19. Research Writing Terms
20. Research Writer!

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

1. The Foundation of Trust
2. Core Research Principles
3. Why Can One Ethical Mistake Damage So Much Trust?
4. Understanding Plagiarism
5. Paraphrase or Patchwrite?
6. Integrity Lets Knowledge Compound
7. Match Citation Situations
8. Good Citation Habits Start During Reading
9. Sort: Authorship Credits
10. Data Ethics and Privacy
11. Before You Collect Photos
12. How Likely Is a Lucky Win?
13. Luck by Hand
14. Turnitin Checks Writing Similarity
15. Match Ethics Principles
16. Spot Ethical Research Practices
17. Research Ethics Quiz
18. Explain Research Integrity
19. Research Ethics Terms
20. Ethics Champion!

• 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
• OECD–EC (Europe)Assess the ethical implications and justify how an AI-assisted work maintains authenticity (Create 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).
• UNESCOArticulate personal and social responsibilities of citizens in an AI society — from both user and designer perspectives — including ensuring human control and accountability over every step of the AI life cycle (CG4.3.1.2).
• Korea, Japan, HKCultivate an attitude of participating proactively in, and contributing to the development of, the information society while creating new value through appropriate use of information and information technology and adherence to information morals (law, security).
• ChinaStudents understand the ethics and challenges of a smart/AI-driven society, strengthening information-security awareness, responsibility, and dialectical (balanced) views of AI
• 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. Research is a Team Sport
2. Essential Collaboration Tools
3. Why Doesn't Modern Research Collapse Into Chaos?
4. Git Basics for Research
5. Tools Let Teams Think Together
6. Match Tools to Purpose
7. A Good README Saves More Than Time
8. Sort: Good vs Bad Practices
9. From Branch to Main
10. Merge Conflicts, Calmly
11. Read the Markers
12. Team Sprint in Repo Lab
13. Your Team Log
14. Research Workflow
15. Build a Smooth Team Workflow
16. Overlap by Hand
17. GitHub Keeps Shared Research Organized
18. Diagnose the Team
19. Match README Components
20. Spot Good Collaboration Practices
21. Collaboration Tools Quiz
22. Explain Research Collaboration
23. Collaboration Terms
24. Collaboration Pro!

• 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.

1. Your Place in AI Research
2. Types of Research Organizations
3. Why Do Some Important Research Opportunities Stay Hidden?
4. Finding Research in Your Region
5. Your Local Context Can Be a Research Advantage
6. Match Organization Types
7. Open Source Can Be a Real Entry Point
8. Sort: Good vs Bad Strategies
9. Research Opportunities for Students
10. Read Before You Write
11. Design Your Research Path
12. Odds by Hand
13. LinkedIn Surfaces Research Communities
14. Match Research Problems
15. Your First Project Route
16. Spot Good Research Strategies
17. Research Ecosystem Quiz
18. Explain the Research Ecosystem
19. Research Ecosystem Terms
20. Ecosystem Explorer!

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

1. Research Journey Complete
2. Skills You've Mastered
3. Why Do These Different Research Skills Fit Together?
4. One Claim, Checked End to End
5. Twelve Tries, One Winner
6. Read the p-value
7. Match Tools to Questions
8. The Best Researchers Keep Switching Modes
9. Sort: Research Best Practices
10. Quick Reference Guide
11. How Many Tries Until a False Alarm?
12. False Alarms by Hand
13. A Lab Log Keeps Every Try
14. Match Problems to Solutions
15. Spot Research Best Practices
16. Referee the Abstract
17. Unit 2 Final Quiz
18. Explain the Unit 2 Toolkit
19. Unit 2 Key Terms
20. Unit 2 Complete

• 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)Evaluate the ability of models and simulations to test and support the refinement of hypotheses.

1. When AI Needs to Look Things Up
2. How RAG Works
3. Put the Pipeline in Order
4. Why Can't a Language Model Just Memorize Everything?
5. Why RAG Matters
6. Search Can Make Generation More Trustworthy
7. Match RAG Components
8. Good Answers Often Depend on Good Chunks
9. Sort: RAG vs No-RAG Use Cases
10. Vector Search Explained
11. Cosine by Hand
12. Tune a RAG Pipeline
13. ChatGPT Search Shows Retrieval in Action
14. Which Retrieval Fits?
15. Match RAG Challenges
16. Spot Good RAG Design
17. RAG Systems Quiz
18. Explain Why RAG Matters
19. RAG Vocabulary
20. Retrieval, Explained

• ADEKDesign a scalable AI system architecture
• 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 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 identifying the features that make objects in a collection unique and organizing them into a feature table (K-2), to constructing a feature-vector representation for a set of objects and showing that similar objects lie close together in feature space (3-5), to explaining how word embeddings represent words as numeric vectors for tasks like machine translation (6-8), to describing how a transformer network maps sequences of word-embedding vectors to output sequences (9-12).
• AI4K12 (US)Progress from demonstrating how a computer produces different verb tense/agreement forms (K-2), to experimenting with a speech-to-text system's ability to resolve ambiguous words via context (3-5), to illustrating how word embeddings support reasoning about word meaning, e.g. outlier detection and analogy completion (6-8), to demonstrating how a small context-free grammar parses or generates simple sentences and where it falls short, e.g. subject-verb agreement (9-12).
• ChinaStudents learn the basic process, implementation principles and core algorithms behind AI systems

1. Building Your First RAG
2. The RAG Pipeline Steps
3. Why Does Building RAG Fail Even When the Idea Is Correct?
4. Chunking Strategies
5. Chunk Maths by Hand
6. Small Pipeline Choices Change the Whole System
7. Match Tools to Tasks
8. Chunk Overlap Often Prevents Hidden Failures
9. Sort: Retrieval Pipeline Order
10. Code Building Blocks
11. Trace the Chunker
12. Tune a RAG Index
13. LangChain Speeds Up RAG Prototypes
14. Match Chunk Sizes
15. Spot Good RAG Implementation
16. Test the Retriever
17. Building RAG Quiz
18. Explain Building RAG
19. RAG Building Terms
20. How Your Plan Is Judged
21. RAG Builder!

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

1. The Customization Challenge
2. What Are LLMs?
3. Why Can't Prompts Always Replace Fine-Tuning?
4. Fine-Tuning Explained
5. Which Lever?
6. General Models Can Become Specialists
7. Match: Fine-Tuning Concepts
8. Tokens Shape Cost More Than People Expect
9. Sort: Fine-Tuning Approaches
10. Understanding Tokens
11. Count the Training Tokens
12. Tune a Fine-Tuning Strategy
13. OpenAI Fine-Tuning Creates Task-Specific Assistants
14. Plan a Fair Fine-Tune
15. Match: Problem to Fix
16. Spot: When to Fine-Tune?
17. Fine-Tuning Mastery Quiz
18. Explain Fine-Tuning
19. Fine-Tuning Vocabulary
20. Fine-Tuning Expert!

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

1. The Efficiency Revolution
2. What is PEFT?
3. How Can Tiny Updates Change Huge Models?
4. LoRA Deep Dive
5. LoRA by Hand
6. Efficiency Can Expand Who Gets To Build AI
7. Match: PEFT Techniques
8. Adapters Can Be Stored Like Skills
9. Sort: Full vs PEFT Fine-Tuning
10. QLoRA Revolution
11. Will It Fit?
12. Tune a PEFT Setup
13. Hugging Face Makes LoRA Sharing Practical
14. Match: LoRA Hyperparameters
15. Spot: When to Use PEFT
16. Plan a QLoRA Project
17. PEFT Mastery Quiz
18. Explain LoRA and PEFT
19. PEFT Vocabulary
20. PEFT Pioneer!

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

1. Beyond Generic Chatbots
2. Custom Chatbot Architecture
3. Why Doesn't a Generic Chatbot Feel Personal or Useful Enough?
4. How the Parts Are Wired
5. Memory Maths by Hand
6. Architecture Can Turn a Model Into an Assistant
7. Match: Chatbot Components
8. Memory Changes User Experience Immediately
9. Sort: Memory Types
10. Tools & Function Calling
11. Order the Tool-Call Loop
12. Tune a Custom Chatbot
13. Discord Bots Show Custom Chatbot Design in Practice
14. Match: Deployment Options
15. Spot: Custom Chatbot Features
16. Design Maya's Helper
17. Custom Chatbots Quiz
18. Explain Custom Chatbots
19. Chatbot Vocabulary
20. Chatbot Designer

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

1. From Chatbots to Agents
2. The ReAct Pattern
3. Why Does an Agent Need To Think Before Acting?
4. Designing Maya's Agent
5. Trace Maya's Agent
6. Planning Turns Tools Into Real Capability
7. Match: Agent Design Parts
8. Observation Is What Makes Agents Adaptive
9. Sort: Does It Need an Agent?
10. Agent Frameworks
11. Tune an AI Agent
12. Retries by Hand
13. OpenAI Agents SDK Enables Goal-Driven Workflows
14. Guard Maya's Agent
15. Match: Multi-Agent Patterns
16. Spot: Good Agent Design
17. AI Agents Mastery Quiz
18. Explain AI Agents
19. Agent Vocabulary
20. Agent Designer

• ADEKDesign a scalable AI system architecture
• CSTA (US)Develop programs for multiple computing platforms.
• AI4K12 (US)Progress from illustrating a next possible board state in tic-tac-toe (K-2), to illustrating how a computer represents game play as a linear sequence of board positions connected by legal moves (3-5), to illustrating how a computer solves a maze, finds a map route, or reasons over a knowledge graph by drawing a search tree (6-8), to identifying real-world problems (task planning, scheduling, resource allocation) as search problems and describing their states and operators (9-12).
• 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. Beyond Simple Image Recognition
2. Vision Task Hierarchy
3. Why Isn't One Label Enough for an Image?
4. Pick the Output
5. Modern Vision Architectures
6. One Image Can Be Understood in Layers
7. Match: Vision Tasks to Models
8. Foundation Models Changed Vision
9. Sort: Segmentation Types
10. CLIP and Zero-Shot Vision
11. Zero-Shot by Hand
12. IoU: How Good Is a Box?
13. IoU by Hand
14. Segment Anything Makes Pixel-Level Vision Practical
15. Match: Vision Applications
16. Spot: Which Vision Task?
17. Advanced Vision Quiz
18. Explain Advanced Vision
19. Vision Vocabulary
20. Beyond Classification

• CBSEExplain computer vision and its significance in visual data analysis; understand key stages (acquisition, preprocessing, feature extraction, analysis); identify real-world applications in healthcare, surveillance, autonomous vehicles; analyse challenges (ethics, privacy, technical limitations); develop basic skills using OpenCV and deploying ML models online (Unit 3).
• CISCE + statesIntroduce Computer Vision and OpenCV applications in real-world scenarios: image classification, object detection, facial recognition, colour detection and object tracking (brief explanation with examples)
• ChinaStudents learn core AI concepts, theory, and application domains

1. The Voice Revolution
2. Speech Recognition Pipeline
3. Why Is Human Speech Harder Than Typed Text?
4. OpenAI Whisper
5. Sound to Text, in Order
6. Messy Audio Can Become Clean Language
7. Match: Speech Recognition Models
8. Whisper Learned From Massive Audio Variety
9. Sort: Traditional vs End-to-End
10. Beyond Transcription
11. Word Error Rate Lab
12. WER by Hand
13. Whisper Handles Real-World Voices
14. Plan a Fair Speech Test
15. Match: Applications
16. Spot: Speech Recognition Challenges
17. Speech Recognition Quiz
18. Explain Speech Recognition
19. Speech Recognition Vocabulary
20. Chapter Complete

• CISCE + statesDescribe the domain of Natural Language Processing (NLP) — text understanding, text generation, language translation, question answering/chatbots, dialogue systems — and internet search types (navigation, informational, transactional, investigative, voice)
• CISCE + statesApply Named Entity Recognition (NER) to classify names of people/organizations/locations in text, perform text summarization by extraction, and build a spam-email classifier using Machine Learning
• AI4K12 (US)Progress from explaining how images are digitally represented as 2D pixel arrays (3-5), to explaining how sounds are digitally encoded via waveform sampling (6-8), to explaining how radar, lidar, GPS, and accelerometer data are represented and computed (9-12). K-2 is marked N/A for this topic.
• AI4K12 (US)Progress from giving examples of intelligent vs. non-intelligent machines and discussing what makes a machine 'intelligent' (K-2), to demonstrating a machine perception tool (e.g. speech transcription, object recognition) and explaining why it is perception rather than mere sensing (3-5), to giving examples of specialized perception algorithms (face detection, obstacle detection, speech recognition, etc.) (6-8), to explaining how perception algorithms power real-world applications like license plate readers and Google Lens (9-12).
• AI4K12 (US)Progress from giving examples of visual features (parts, textures, colors) used to recognize an object class such as cats (K-2), to illustrating how face detection extracts facial features (3-5), to simulating an edge detector to illustrate feature extraction from images (6-8), to explaining how features (formants, edges, intensity gradients) are extracted from waveforms and images at multiple scales (9-12).
• AI4K12 (US)Progress from describing the sounds that make up spoken language and giving example words for each vowel sound (K-2), to illustrating how sequences of sounds are recognized as candidate words despite unclear signals (3-5), to illustrating how sequences of words are recognized as phrases by resolving ambiguity with higher-level information (6-8), to illustrating the full abstraction hierarchy from waveform to sentence in speech understanding (9-12).
• AI4K12 (US)Progress from discussing why intelligent agents need to understand languages other than English (K-2), to discussing why domain knowledge must be broad enough to cover accents, dialects, and musical genres (3-5), to describing how a vision system can exhibit cultural bias if it lacks knowledge of objects outside its creators' culture, e.g. non-US traffic signs (6-8), to describing technical difficulties (e.g. low-contrast faces, children's speech) in making perception systems work well for diverse groups (9-12).
• AI4K12 (US)Progress from demonstrating what an intelligent assistant like Siri or Alexa can and cannot do (K-2), to demonstrating question types a search engine or assistant can and cannot answer (3-5), to describing NLP tasks such as summarization, sentiment analysis, question answering, and machine translation (6-8), to describing NLP approaches ranging from keyword/template matching to deep neural networks, and when each is appropriate (9-12).
• OECD–EC (Europe)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. How Does It Know?
2. Types of Recommendation
3. How Did the App Predict Maya's Next Click?
4. Collaborative Filtering Deep Dive
5. Taste Can Be Learned From Behavior Patterns
6. Match: Recommendation Approaches
7. The Netflix Prize Changed Recommendation AI
8. Sort: Strengths & Weaknesses
9. Modern Deep Learning RecSys
10. Tune a Recommender
11. YouTube Recommendations Learn From Watch Patterns
12. Match: Real-World Systems
13. Spot: RecSys Challenges
14. Recommendation Systems Quiz
15. Explain Recommendation Systems
16. RecSys Vocabulary
17. RecSys Expert!

• UAEHow AI models human emotion and behaviour
• 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).

1. Predicting the Future
2. Time Series Components
3. If the Future Is Uncertain, Why Do Forecasts Work?
4. Name the Component
5. From Traditional to Deep Learning
6. Time Patterns Hide Inside Ordinary Data
7. Match: Time Series Methods
8. Simple Forecasting Models Often Win First
9. Sort: When to Use What
10. Time Series Applications
11. Tune a Forecast
12. Smooth It by Hand
13. Google Maps Predicts Traffic From Time Patterns
14. Build an Honest Backtest
15. Match: Forecasting Horizons
16. Spot: Time Series Challenges
17. Time Series AI Quiz
18. Explain Time Series Forecasting
19. Time Series Vocabulary
20. Time Series Master!

• UAEHow AI models human emotion and behaviour

1. The AI Olympics
2. Competition Types
3. How Can a Teenager Beat Experts in AI?
4. How Competitions Work
5. Competition Feedback Can Accelerate Learning
6. Match: Kaggle Terminology
7. Tiny Score Gains Often Come From Better Validation
8. Sort: Winning Strategies
9. Competition Skills
10. Read the Folds
11. Is That Leaderboard Jump Real?
12. Kaggle Turns Practice Into Public Benchmarking
13. A Clean Competition Workflow
14. Match: Kaggle Ranks
15. Spot: Competition Best Practices
16. Leak or Legit?
17. Competition Judgement Quiz
18. Explain Cross-Validation
19. Kaggle Vocabulary
20. Competition Champion!

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

1. Advanced Projects Journey Complete
2. Skills You Now Have
3. What Actually Changed Across Unit 3?
4. Match the Symptom to the Part
5. Which Part Fixes It?
6. Many AI Skills Now Fit Into One Mental Model
7. Match Concepts to Chapters
8. Production AI Usually Combines Multiple Patterns
9. Sort: LLM-Based or Specialised?
10. Quick Reference
11. Balance an AI Product Stack
12. Chain by Hand
13. ChatGPT Combines Multiple AI Patterns
14. Match Problems to Solutions
15. Spot Best Practices
16. Plan the Build, in Order
17. Unit 3 Review Quiz
18. Explain Your Design
19. Unit 3 Key Terms
20. How Your Plan Is Judged
21. Unit 3 Review Complete

• 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).
• ChinaStudents learn core AI concepts, theory, and application domains

1. The Knowledge Problem
2. RAG Revealed
3. Why Does a Smart Chatbot Still Make Up Rules?
4. Three RAG Components
5. Search Can Make AI More Trustworthy
6. Component Match
7. Meaning Beats Keywords in Good Retrieval
8. RAG vs Others
9. When to Use?
10. Tune a RAG System
11. Recall@k by Hand
12. A School Bot Could Answer From the Handbook
13. Is the Answer Supported?
14. Semantic vs Keyword
15. Where Did It Fail?
16. Spot the RAG Use!
17. RAG Mastery Quiz
18. Explain Why RAG Helps
19. RAG Vocabulary
20. RAG Master!

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

1. From Theory to Code
2. LangChain Components
3. Why Can't We Just Feed the Whole Book to the Model?
4. The Art of Chunking
5. Trace the Splitter
6. Good Retrieval Starts Long Before the Answer
7. Chunking Match
8. Chunk Design Can Make or Break Retrieval
9. Embeddings, Storage and MMR
10. Database Choice
11. Tune MMR's Balance
12. MMR by Hand
13. LangChain Helps Teams Assemble RAG Faster
14. RAG Troubleshooting
15. Diagnose Maya's Bot
16. RAG Design Decisions
17. RAG Building Quiz
18. Explain Chunking
19. Building Vocabulary
20. RAG Builder!

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

1. The Customization Challenge
2. Fine-Tuning Revealed
3. Why Isn't RAG Enough for Expert Style?
4. RAG vs Fine-Tuning
5. A Model Can Keep Its General Skills and Gain Specialization
6. Which Approach?
7. Data Quality Matters More Than Hype
8. Fine-Tuning Approaches
9. Method Match-Up
10. LoRA's Share of One Matrix
11. LoRA Weights by Hand
12. A Support Bot Can Be Fine-Tuned for Brand Voice
13. Fine-Tuning Pitfalls
14. Plan Maya's Fine-Tuning Run
15. Good or Bad Practice?
16. Diagnose the Run
17. Fine-Tuning Quiz
18. Explain Fine-Tuning
19. Fine-Tuning Terms
20. Fine-Tuning Decisions

• UAEHumanitarian and environmental effectiveness

1. The GPU Problem
2. LoRA Explained
3. How Can Tiny Adapters Change a Huge Model?
4. Count the Adapter
5. The PEFT Family
6. Specialization No Longer Belongs Only to Big Labs
7. PEFT Match-Up
8. Tiny Adapter Files Can Be Surprisingly Powerful
9. LoRA Configuration
10. Will It Fit?
11. Resource Match
12. Tune a LoRA Setup
13. Hugging Face Makes Adapter Sharing Practical
14. LoRA Superpowers
15. LoRA Best Practices
16. LoRA & PEFT Quiz
17. Plan the Run
18. Explain LoRA
19. PEFT Vocabulary
20. PEFT Planner

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

1. Beyond Simple Chat
2. Chatbot Ingredients
3. Why Does a Raw LLM Forget the Conversation?
4. Memory Systems
5. Buffer Cost by Hand
6. Conversation Can Feel Continuous Because the App Adds State
7. Memory Match
8. Production Bots Often Combine Memory Types
9. One Turn of Hybrid Memory
10. Chatbot Tools
11. Tool Selection
12. How Fast Does a Full Buffer Grow?
13. ChatGPT Layers Memory and Tools on Top of the Model
14. Bot Personality
15. Design the Shop Bot
16. Chatbot Design
17. Chatbot Quiz
18. Explain Chatbot Memory
19. Chatbot Vocabulary
20. Chatbot Designer

• CBSEDevelop understanding of the complexities of human language and challenges in NLP tasks (emotion detection, sentiment analysis, classification, chatbots); learn techniques/algorithms for NLP tasks and phases of NLP; create a chatbot using a conversational platform (Unit 7: Leveraging Linguistics and Computer Science).
• AI4K12 (US)Progress from 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. Beyond Chatbots
2. Agent Components
3. What Makes an Agent Different From a Tool-Using Chatbot?
4. ReAct Framework
5. Trace the Loop
6. A Model Can Turn Thought Into Action Loops
7. Agent Match-Up
8. Most Agent Failures Come From Bad Guardrails
9. Agent vs Chatbot
10. Task Assignment
11. Tune an Agent Workflow
12. Token Growth by Hand
13. AutoGPT-Style Systems Show Agent Thinking in Action
14. Agent Safety
15. Read the Agent's Log
16. Agent Design
17. Agent Quiz
18. Explain AI Agents
19. Agent Vocabulary
20. Agent Guardrails

• ADEKDesign a scalable AI system architecture
• 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).
• 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. Seeing Like AI
2. Vision Tasks
3. Why Isn't One Label Enough for a Photo?
4. YOLO: Speed King
5. NMS by Hand
6. One Image Can Support Many Kinds of Understanding
7. Vision Match
8. Promptable Vision Models Changed the Field
9. SAM: Segment Anything
10. Model Selection
11. The Frame Budget
12. Frame Budget by Hand
13. Photoshop-Style Background Removal Uses Segmentation
14. Detect, Then Segment
15. CLIP: Vision + Language
16. Self-Supervised Vision: Learning Without Labels
17. Self-Supervised True or False
18. Run the Contrastive Loss
19. Vision Decisions
20. Vision Quiz
21. Explain Vision Tasks
22. Vision Vocabulary
23. Vision Master!

• CBSEExplain computer vision and its significance in visual data analysis; understand key stages (acquisition, preprocessing, feature extraction, analysis); identify real-world applications in healthcare, surveillance, autonomous vehicles; analyse challenges (ethics, privacy, technical limitations); develop basic skills using OpenCV and deploying ML models online (Unit 3).
• CISCE + statesIntroduce Computer Vision and OpenCV applications in real-world scenarios: image classification, object detection, facial recognition, colour detection and object tracking (brief explanation with examples)

1. Teaching AI to Hear
2. Speech to Text Pipeline
3. Why Is Speech Harder Than Clean Text?
4. Whisper: Sizes and Limits
5. Messy Audio Can Become Usable Language
6. Speech Match
7. HuBERT: Learning Speech Without Transcripts
8. HuBERT True or False
9. Run the Masked Frames
10. Whisper Scaled Robust Speech Recognition
11. Speech AI Tasks
12. Task Selection
13. One Turn of a Voice Assistant
14. Tune a Speech System
15. Real-Time Factor by Hand
16. YouTube Captions Depend on Speech Recognition
17. Speech Challenges
18. Plan Maya's Transcriber
19. Speech Decisions
20. Speech Quiz
21. Explain Speech-to-Text
22. Speech Vocabulary
23. Speech Expert!

• AI4K12 (US)Progress from describing the sounds that make up spoken language and giving example words for each vowel sound (K-2), to illustrating how sequences of sounds are recognized as candidate words despite unclear signals (3-5), to illustrating how sequences of words are recognized as phrases by resolving ambiguity with higher-level information (6-8), to illustrating the full abstraction hierarchy from waveform to sentence in speech understanding (9-12).

1. How Does It Know?
2. Two Approaches
3. How Does the App Guess What Maya Will Like?
4. Collaborative Filtering
5. Maya's Taste Twin
6. Taste Patterns Can Be Learned From Massive Interaction Data
7. Recommendation Match
8. Tiny Ranking Gains Matter at Scale
9. Modern Deep Recs
10. Approach Selection
11. Tune a Recommender
12. Cosine by Hand
13. YouTube Rankings Depend on Recommendation Models
14. Pipeline of a Feed
15. Recommendation Challenges
16. Rec System Design
17. Recommendations Quiz
18. Explain Recommendation Systems
19. Rec System Vocabulary
20. Rec System Expert!

• 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).
• 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.
• 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 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 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).
• 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)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).
• 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 Weather Wizard
2. Time Components
3. The Monday Rain Puzzle
4. Real Cycle or Coincidence?
5. ARIMA Explained
6. Difference by Hand
7. The Future Leaves Clues
8. Forecasting Match
9. Patterns Beyond Weather
10. LSTM Networks
11. Method Sorting
12. Tune a Forecast
13. Read the Horizon
14. Forecasting Inside a Weather App
15. Prophet Tool
16. Spot Time Series AI
17. Time Series Quiz
18. Explain the Pattern
19. Forecasting Words
20. Forecasting Toolkit

• CSTA (US)Describe different types of AI algorithms and models, and compare and contrast the strengths and limitations of their reasoning.

1. Data Science Arena
2. Competition Flow
3. The Leaderboard Surprise
4. Feature Magic
5. Features and Folds by Hand
6. Tiny Changes, Huge Jumps
7. Kaggle Match
8. What Winners Focus On
9. Folds in Order
10. Ensemble Power
11. Inside Bagging and Boosting
12. Bagging or Boosting?
13. Run the Ensembles
14. Strategy Sorting
15. Build a Stronger Entry
16. Leaderboard Noise by Hand
17. Why Kaggle Rankings Change Fast
18. Kaggle Medals
19. Spot Good Strategy
20. Kaggle Quiz
21. Explain a Winning Strategy
22. Competition Words
23. Competition Ready!

• 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.

1. Unit 3 Complete!
2. Skills Unlocked
3. Knowledge vs Skill
4. Your Portfolio
5. Projects Become a Portfolio
6. Skills Match
7. How Real Products Work
8. When to Use What
9. Choose the Approach
10. Complexity Sort
11. Design a Stronger AI System
12. Majority Vote by Hand
13. One App, Many AI Layers
14. Pro Tips
15. Symptom → Practice
16. Apply Your Skills
17. Unit 3 Final Quiz
18. Explain System Design
19. Unit 3 Vocabulary
20. Advanced AI Master!

• 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.
• AI4K12 (US)Progress from identifying a problem as a classification problem or a search problem (K-2), to categorizing problems as classification vs. search using examples like Teachable Machine (3-5), to categorizing problems as classification, prediction, combinatorial search, or sequential decision problems (6-8), to categorizing real-world problems across eight reasoning-problem types including heuristic search, adversarial search, logical deduction, and statistical inference (9-12).
• AI4K12 (US)Progress from modeling the use of a classification or search algorithm to solve a simple problem (K-2), to describing differences between decision-tree and neural-network classification algorithms (3-5), to comparing several algorithms that could solve a specific reasoning problem based on input data and decision complexity (6-8), to listing, for each of eight reasoning-problem types, an algorithm that could solve it (9-12).
• OECD–EC (Europe)Compare task complexity, the need for human judgment, and ethical implications when deciding whether to use AI (Manage 1, advanced).

1. From Code to Product
2. The AI Product Framework
3. The Impressive Demo Problem
4. Product-Market Fit for AI
5. Useful Beats Impressive
6. Match: AI Product Successes
7. Invisible AI Wins
8. Sort: Does This Need AI?
9. Indian AI Product Success Stories
10. From Pain Point to MVP
11. Test the Product Fit
12. Score It by Hand
13. A Product People Use Without Thinking About AI
14. Match: Product Thinking Terms
15. Spot: Good vs Bad AI Product Thinking
16. AI Product Thinking Quiz
17. Pressure-Test Three Pitches
18. Explain Product Fit
19. Product Thinking Vocabulary
20. Product Thinker!

• CISCE + statesExplain Machine Learning as computers learning to make predictions/decisions from data without explicit programming, with an example such as predicting house prices using a decision tree
• CSTA (US)Analyze the risks, benefits, and effectiveness of using AI for specific tasks (e.g., coding, brainstorming), including when AI is used to fully automate a process or is used with a human-in-the-loop approach.
• CSTA (US)AI content is integrated (not a separate strand): students learn rule-based vs. data-driven approaches, machine learning basics, and related societal impacts such as bias, within the Algorithms & Design strand.
• OECD–EC (Europe)Compare task complexity, the need for human judgment, and ethical implications when deciding whether to use AI (Manage 1, advanced).
• OECD–EC (Europe)Evaluate the effectiveness of an AI approach considering context, data, efficiency, transparency, and impact (Manage 2, advanced).
• Korea, Japan, HKUnderstand the characteristics of machine learning versus traditional (rule-based) programming, judge which problems are suited to machine-learning-based AI, and collect data appropriate to the problem.

1. Who Actually Wants This?
2. 5 Market Research Methods
3. The Friendly Feedback Trap
4. TAM, SAM, SOM -- Market Sizing
5. Size It by Hand
6. Questions Shape Products
7. Match: Research Method to Situation
8. Signals Worth Trusting
9. Sort: Good vs Bad Research Questions
10. Rewrite the Question
11. Real Market Research Wins
12. Measure the Validation
13. Testing Demand Before Building
14. The Ladder of Evidence
15. Match: Market Sizing Terms
16. Spot: Research Red Flags
17. Market Research Mastery Quiz
18. Explain Market Validation
19. Market Research Vocabulary
20. Market Researcher!

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

1. How Does Free AI Make Money?
2. 6 AI Business Models
3. The Free App Riddle
4. Unit Economics: Does the Maths Work?
5. LTV by Hand
6. Revenue Can Hide Behind the Experience
7. Match: Company to Business Model
8. Why Investors Love Recurring Revenue
9. Sort: Revenue Models by Type
10. Indian Business Models
11. Read the Metrics
12. Tune the Business Model
13. How an AI Assistant Makes Money
14. What the Free Tier Costs
15. Match: Business Terms
16. Spot: Sustainable AI Business Models
17. Business Models Quiz
18. Explain the Revenue Logic
19. Business Model Vocabulary
20. Business Model Mapped

• 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).

1. Build Less, Learn More
2. Famous MVPs and What They Tested
3. The Tiny MVP Shock
4. The AI MVP Playbook
5. A Prototype Can Be a Message
6. Riskiest Assumption First
7. Match: Idea to MVP Shape
8. Speed Creates Insight
9. Sort: MVP vs Over-Engineered
10. Tools and Metrics for an AI MVP
11. What Would the Test Cost?
12. Cost and Footprint by Hand
13. A Video Before the Product
14. Match: Which Metric Is This?
15. Put the Test in Order
16. Spot: Good MVP Decisions
17. MVP Development Quiz
18. Explain MVP Thinking
19. MVP Vocabulary
20. How Your Plan Is Judged
21. MVP Builder!

• 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. The Feature Nobody Asked For
2. 4 Types of User Research
3. The Feature Mismatch
4. User Personas -- Know Your User
5. Users Teach Through Behavior
6. Match: Research Type to Question
7. Patterns in Human Behavior
8. Sort: User Says vs User Does
9. User Research That Changed Products
10. Ask Without Leading
11. Did Version B Really Win?
12. Lift by Hand
13. A Tiny UX Change With Huge Impact
14. Match: Research Tools
15. Spot: Good Research Practices
16. Plan Your Research Sprint
17. User Research Quiz
18. Explain User Research
19. User Research Vocabulary
20. User Researcher!

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

1. 60 Seconds to Change Everything
2. The Perfect Pitch Structure
3. The Invisible Pitch Problem
4. Build the Deck
5. The Elevator Pitch -- 60 Seconds
6. One Line Can Carry the Whole Product
7. Match: Pitch Slide to Content
8. Traction Talks Louder
9. Sort: Strong vs Weak Pitch Lines
10. Pitches That Won in India
11. Size the Market Bottom-Up
12. Finish the Trace
13. Why the Best Pitches Sound Simple
14. Match: Pitch Mistakes to Fixes
15. Spot: Investor-Ready Claims
16. Pitching AI Products Quiz
17. Explain the Pitch Logic
18. Your Own 60 Seconds
19. Pitching Vocabulary
20. Pitch Master!

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

1. Where Does Startup Money Come From?
2. The Funding Journey
3. The Funding Tradeoff
4. Equity, Valuation, and Dilution
5. Valuation by Hand
6. Ownership Is Only Part of the Story
7. Match: Funding Stages
8. Why Grants Matter Early
9. Sort: Bootstrapped vs VC-Funded
10. Where to Find Funding in India
11. Does It Cost Equity?
12. Model the Fundraise
13. Maya's Runway Plan
14. Growing Without Investors
15. Match: Funding Terms
16. Spot: Smart Funding Decisions
17. Funding Basics Quiz
18. Explain Dilution
19. Funding Vocabulary
20. Funding Expert!

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

1. India's AI Revolution
2. India's AI Unicorns
3. Why Does India Produce So Many AI Opportunities?
4. India's AI Advantage: The India Stack
5. Free, Paid or Restricted?
6. India's Complexity Is a Startup Advantage
7. Match: Indian AI Startup to Problem Solved
8. Constraints Can Make Better AI
9. Sort: AI Sectors in India
10. Student-Founded AI Startups
11. How Long Would the Download Take?
12. Minutes on a Weak Link
13. Bhashini Makes Language AI Easier to Build
14. AI Needed, or Rules Suffice?
15. Match: India Stack Components
16. Spot: India-Specific AI Opportunities
17. Indian AI Startups Quiz
18. Explain India's AI Edge
19. Indian AI Ecosystem Vocabulary
20. India 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 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. When AI Breaks the Law
2. 3 Legal Pillars for AI in India
3. The Project That Suddenly Needed a Lawyer
4. Which Pillar?
5. DPDP Act 2023 -- What You Must Do
6. Law Appears Before You Expect It
7. Match: Legal Requirement to Action
8. Tiny Products Can Carry Big Legal Risk
9. Sort: Legal vs Illegal for AI Products
10. Real AI Legal Cases
11. Read the Case
12. Count What a Breach Would Expose
13. Exposure by Hand
14. ChatGPT Shows Why Training Data Matters
15. Match: Legal Terms
16. Spot: Legally Safe AI Practices
17. Legal Considerations Quiz
18. Explain AI Compliance Basics
19. Legal Vocabulary for AI Builders
20. Legal Eagle!

• ADEKAI governance/civic participation
• CSTA (US)Debate laws and regulations that impact the development and use of software.
• AI4K12 (US)Progress from analyzing deepfake images/videos to identify the flaws that currently reveal them, e.g. thispersondoesnotexist.com (3-5), to critiquing uses of AI technology for surveillance or privacy violation, e.g. face recognition, license-plate trackers (6-8), to identifying appropriate areas for AI regulation and evaluating proposed legislation, e.g. autonomous-vehicle laws (9-12). K-2 is marked N/A.
• OECD–EC (Europe)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).
• 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).
• 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).
• 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).
• SingaporeLearn to use AI: use AI tools safely and responsibly.

1. From 100 to 1 Million Users
2. 5 Strategies to Scale AI
3. Why Growth Can Break an AI Product
4. The Economics of AI at Scale
5. Maya's Bill by Hand
6. Tiny AI Costs Multiply Fast
7. Match: Scaling Problem to Solution
8. Most AI Products Mix Fast and Slow Models
9. Sort: Scaling Stages
10. How Indian Companies Scale AI
11. Tune Your Scaling Strategy
12. Read the Bill
13. Notion AI Balances Speed and Cost
14. Put the Scaling Plan in Order
15. Match: Cost Comparison
16. Spot: Smart Scaling Decisions
17. Scaling AI Products Quiz
18. Explain AI Scaling
19. Scaling Vocabulary
20. Scale Master!

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

1. The Algorithm That Discriminated
2. Five Tensions in AI Business
3. How Can Fair Code Produce Unfair Results?
4. Building Ethical AI: The FATE Checklist
5. Bias Grows When Decisions Are Automated
6. Match: Ethical Problem to Solution
7. History Can Quietly Train Unfair AI
8. Selection Rates by Hand
9. Sort: Ethical vs Unethical AI Practices
10. Real Ethical Failures in AI
11. Order a Bias Audit
12. Four-Fifths Check on Maya's Shortlist
13. Amazon's Hiring AI Became a Warning Sign
14. Judge Maya's Tool
15. Match: FATE Framework
16. Spot: Responsible AI Decisions
17. AI Ethics in Business Quiz
18. Explain Responsible AI
19. AI Ethics Vocabulary
20. Chapter Complete

• ADEKDeepfakes: explain, spot flaws, set responsible-use rules
• ADEKFacial recognition: privacy/safety/fairness tension
• CBSEDemonstrate understanding of the fundamental principles of ethics in AI and the five pillars of AI Ethics; understand AI bias, its sources, real-world implications, and mitigation strategies; identify and apply strategies to promote fairness and transparency; recognise the significance of AI policies in promoting responsible, safe, ethical AI use (Unit 8: AI Ethics and Values).
• CISCE + statesApply an AI code of ethics — avoiding bias, ensuring privacy of users and their data, mitigating environmental impact, and evaluating the effects of designing technology that replicates human life
• IBEvaluate ethical implications of AI/ML systems, including bias, fairness, and transparency, as part of the machine learning content
• CSTA (US)Debate laws and regulations that impact the development and use of software.
• CSTA (US)Evaluate the design, motivation, outcomes, and potential impacts of AI systems using ethical design criteria and/or ethical frameworks.
• AI4K12 (US)Progress from evaluating the ways a decision impacts different people differently (K-2), to evaluating how an AI technology, e.g. speech or facial recognition, has different effects on different demographic groups (3-5), to evaluating how various stakeholders' conflicting goals and values shape AI system design decisions (6-8), to explaining how the use of AI systems (e.g. health-risk algorithms) has led to documented disparate impacts on different groups (9-12).
• 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 analyzing deepfake images/videos to identify the flaws that currently reveal them, e.g. thispersondoesnotexist.com (3-5), to critiquing uses of AI technology for surveillance or privacy violation, e.g. face recognition, license-plate trackers (6-8), to identifying appropriate areas for AI regulation and evaluating proposed legislation, e.g. autonomous-vehicle laws (9-12). K-2 is marked N/A.
• OECD–EC (Europe)Analyse how AI shapes beliefs and behaviours (Engage 1, advanced level).
• OECD–EC (Europe)Analyse how institutional deployment of AI can serve particular interests and amplify societal bias (Engage 6, advanced).
• OECD–EC (Europe)Evaluate how well an AI use aligns with ethical principles and human values across trade-offs (Engage 7, advanced).
• OECD–EC (Europe)Be aware that the data on which AI depends may include biases which can be automated and worsened by AI use (AI example 49, 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).
• 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).
• 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).
• UNESCOGain evidence-based insight into the pervasive adoption of AI as infrastructure of social activity, and critique challenges AI poses to inclusion, justice and sustainability, including AI-amplified bias against women, marginalised ethnic groups and socio-economically disadvantaged people (CG4.3.1.1).
• SingaporeLearn to use AI: use AI tools safely and responsibly.
• Australia + NZStudents understand that generative AI tools should be used in ways that benefit all members of the school community and support wellbeing, not just individual convenience
• Australia + NZStudents are taught that generative AI tools must be used, and their outputs treated, in ways that are accessible, fair and respectful to all users, being alert to potential bias in AI outputs

1. Maya's Entrepreneurship Journey Complete
2. Chapters 61–71 in Six Cards
3. What Turns an AI Builder Into an Entrepreneur?
4. The AI Entrepreneur's Checklist
5. Which Box Is Unchecked?
6. Many Skills Become One Launch System
7. Grand Review: Match Concepts
8. Great Startups Blend Product, Business, and Ethics
9. Sort: First Steps vs Later Steps
10. Your AI Startup Toolkit
11. Check Maya's Runway
12. Runway by Hand
13. Niramai Solved a Real Indian Problem First
14. One Build-Measure-Learn Cycle
15. Match: Problem to Indian AI Solution
16. Final Challenge: Ready to Launch?
17. Chapters 61–71 Quiz
18. Explain AI Entrepreneurship
19. Key Terms, Chapters 61–71
20. Chapters 61–71 Reviewed

• ADEKDraft/commit to ethical-use guidelines
• 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).

1. The Startup Dream
2. Project vs Product
3. Why Doesn't a Great Project Become a Great Product?
4. Problem-First
5. Problems Create Better Products Than Features
6. Mindset Match
7. Many Smart Teams Still Build the Wrong Thing
8. AI Opportunities
9. Mistake Sorting
10. Test Product Thinking Strength
11. Funnel by Hand
12. Canva Turned Creative Friction Into a Product
13. Validate First
14. Questions That Reveal Pain
15. Spot Good Ideas
16. Maya's Validation Plan
17. Product Quiz
18. Explain Product Thinking
19. Startup Words
20. Product Thinker!

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

1. The Reality Check
2. TAM SAM SOM
3. Why Did the Investor Reject a 'Huge' Market?
4. Size It, Then Doubt It
5. Know Your Competition
6. A Small SOM Can Still Build a Big Company
7. Research Match
8. Competitors Can Reveal Hidden Opportunity
9. Build Personas
10. Check the AI's Persona
11. Research Methods
12. Stress-Test Your Market Research
13. What One Customer Costs
14. Physics Wallah Understood a Clear Segment First
15. Validation Tricks
16. Spot Good Research
17. Research Quiz
18. Explain TAM, SAM, and SOM
19. Research Words
20. Research Pro!

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

1. The Money Question
2. Subscription Model
3. Why Did Users Love the Product but Not Pay?
4. Freemium Strategy
5. Who Pays for the Free Users?
6. Revenue Design Shapes Product Design
7. Model Match
8. Free Users Can Be Your Most Expensive Users
9. Unit Economics
10. Is Jake Ready to Scale?
11. Metrics Sorting
12. Tune Your Business Model
13. ChatGPT Plus Made AI Revenue Understandable
14. Pricing Strategies
15. Pricing Calls
16. Spot Good Models
17. Business Model Quiz
18. Explain AI Business Models
19. Business Terms
20. Business Savvy!

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

1. The Building Trap
2. MVP Defined
3. Why Did the Perfect Product Fail?
4. MoSCoW Method
5. Ugly Can Be a Competitive Advantage
6. Hypothesis First
7. Feature Match
8. No-Code Tools Can Beat Slow Custom Builds
9. Build Approaches
10. Priority Sorting
11. Optimize Your MVP Plan
12. NPS by Hand
13. Dropbox Validated Demand Before Building Big
14. MVP Metrics
15. Read Lisa's Beta Numbers
16. Spot Good MVPs
17. MVP Quiz
18. Explain MVP Thinking
19. MVP Terms
20. How Your Plan Is Judged
21. Ship It Fast!

• 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).

1. Maya's Discovery
2. Market Gaps Revealed
3. How Does a Frustrating Day Become a Business Idea?
4. The Three Pillars
5. Boring Problems Often Hide Great Startups
6. Pillar Match
7. Which Leg Breaks?
8. Shared Pain Is the Real Signal
9. Evaluation Framework
10. Honest or Overclaim?
11. Strong vs Weak
12. Does the AI Actually Save Time?
13. Net Hours by Hand
14. Niramai Started With a Serious Healthcare Gap
15. Sources of Ideas
16. Spot the Opportunity
17. Knowledge Check
18. Explain a Strong Opportunity
19. Key Vocabulary
20. Opportunity Master!

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

1. James Saves Months
2. MVP Revealed
3. Why Can Building Less Teach You More?
4. Build-Measure-Learn
5. A Tiny Product Can Save Months of Waste
6. Match the Steps
7. Users Often Care About Different Features Than Founders
8. Behaviour or Opinion?
9. Five MVP Approaches
10. MVP vs Full Product
11. Tune an MVP Strategy
12. Loops by Hand
13. Dropbox Proved Demand Before Building Everything
14. MVP Mistakes
15. Ranking People Responsibly
16. Smart MVP Choices
17. Knowledge Check
18. Explain MVP Learning
19. Key Vocabulary
20. MVP Master!

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

1. Priya's Pricing Crisis
2. AI Economics Revealed
3. Why Can Your Best Customer Be Unprofitable?
4. Unit Economics by Hand
5. Value-Based Pricing
6. AI Pricing Must Respect Reality
7. Pricing Match
8. Value Can Matter More Than Cost Plus Margin
9. Six Business Models
10. B2B vs B2C Sort
11. Test a Pricing Model
12. Trace the Margin
13. OpenAI Uses Tiers to Match Value and Cost
14. Price Your Own Project
15. Advanced Strategies
16. Smart Pricing Choices
17. Knowledge Check
18. Explain AI Pricing
19. Key Vocabulary
20. Monetization Master!

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

1. Chen's $15K Lesson
2. GTM Strategy Revealed
3. Why Did Broad Marketing Fail So Badly?
4. Three GTM Motions
5. Proof Beats Hype in AI Marketing
6. GTM Motion Match
7. Narrow Positioning Can Create Faster Growth
8. Narrow vs Broad Entry
9. Sort Market Entry
10. Strengthen a GTM Plan
11. Funnel by Hand
12. HubSpot Grew by Matching Message to Customer Journey
13. Your Project's GTM
14. Positioning Strategies
15. A One-Page GTM Plan
16. Smart GTM Choices
17. Knowledge Check
18. Explain GTM Strategy
19. Key Vocabulary
20. GTM Master!

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

1. Sofia's Viral Nightmare
2. The Scaling Trilemma
3. Why Can Going Viral Hurt an AI Product?
4. Horizontal vs Vertical
5. Growth Is a Technical and Business Problem
6. Scaling Match
7. Redundancy Can Be More Valuable Than Raw Power
8. Cost Optimization
9. Build vs Buy Sort
10. Sofia's Bill by Hand
11. Balance a Scaling Plan
12. Availability by Hand
13. Netflix Shows Why Resilience Matters at Scale
14. Scaling Challenges
15. Diagnose the Symptom
16. Smart Scaling Choices
17. Knowledge Check
18. Explain AI Scaling Tradeoffs
19. Key Vocabulary
20. Scaling Master!

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

1. Alex's 30 Rejections
2. Funding Stages Revealed
3. Why Did Investors Ignore the Amazing Tech?
4. What Investors Want
5. Size It Bottom-Up
6. Technology Matters Most When It Strengthens the Business
7. Funding Match
8. A Smaller Equity Share Can Still Create More Value
9. Funding Sources Deep Dive
10. Bootstrap vs VC Sort
11. Stress-Test a Fundraising Story
12. Check Alex's Numbers
13. Figma Raised Around Product Love and Growth Potential
14. Fundraising Mistakes
15. Evidence or Assertion?
16. Smart Pitch Choices
17. Knowledge Check
18. Explain Fundraising Readiness
19. Key Vocabulary
20. Investor Lens

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

1. Liam's Team Problem
2. Three Core Capabilities
3. Why Can a Brilliant AI Team Still Fail to Ship?
4. Research vs Product
5. Settle the Argument
6. Team Diversity Creates Better AI Products
7. Role Match
8. Healthy Tension Can Improve a Product
9. Hiring Timeline
10. Generalist vs Specialist
11. Balance an AI Team
12. Weakest Link by Hand
13. OpenAI Grew Through More Than Research Alone
14. Team Challenges
15. Diagnose the Symptom
16. Smart Team Choices
17. Knowledge Check
18. Explain AI Team Building
19. Key Vocabulary
20. Team Builder!

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

1. Nina's $85M Decision
2. Exit Paths Revealed
3. Why Would Someone Reject $85 Million?
4. Why Companies Acquire
5. An Exit Is a Strategic Choice, Not Just a Payday
6. Exit Timeline Match
7. Buyers Often Want More Than Just the Product
8. Read the Buyer's Motive
9. Exit Path Comparison
10. IPO vs Acquisition Sort
11. Test an Exit Strategy
12. Expected Value by Hand
13. Instagram Shows the Power of a Strategic Acquisition
14. Exit Considerations
15. Before the Offer Arrives
16. Smart Exit Choices
17. Knowledge Check
18. Explain Exit Strategy
19. Key Vocabulary
20. Entrepreneur Complete!

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

1. A Million AI Jobs, Zero Candidates
2. Top AI Career Paths
3. Why Are So Many AI Jobs Open at Once?
4. Read the Headline Number
5. The AI Career Roadmap for Students
6. Skills Can Outweigh Credentials in AI Hiring
7. Match: Role to Daily Work
8. Different AI Careers Need Different Strengths
9. Sort: Skills by Career Path
10. Which Role Fits?
11. Rough AI Salary Bands in India (2025)
12. Plan Your Project Hours
13. Plan by the Numbers
14. LinkedIn Shows How Demand Clusters Around Real Skills
15. Match: Company to AI Focus
16. Spot: Career-Building Activities
17. AI Career Landscape Quiz
18. Explain the AI Talent Gap
19. AI Career Vocabulary
20. Career Navigator!

• 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).
• CSTA (US)Evaluate and analyze the roles of humans and human decision-making in the creation of AI.

1. The Portfolio That Got the Interview
2. 5 Projects Every AI Portfolio Needs
3. Why Did Five Projects Beat Two Hundred Resumes?
4. The README -- Your Project's First Impression
5. Audit the Results Line
6. A Portfolio Tells the Story of Your Growth
7. Match: Portfolio Strength to Project
8. Different Projects Signal Different Career Strengths
9. Sort: Strong vs Weak Portfolio Items
10. Portfolio Project Ideas for Indian Students
11. How Sure Is 81%?
12. Checkable or Unbacked?
13. GitHub Gives Recruiters Direct Proof
14. Match: README Section to Content
15. Assemble Maya's README
16. Spot: Portfolio Best Practices
17. Portfolio Building Quiz
18. Explain Portfolio Value
19. Portfolio Vocabulary
20. Portfolio Builder!

• SingaporeDevelop digital literacy and technological skills needed to thrive in an increasingly technology-driven world, as a central goal of the Masterplan.
• 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. Your Digital Identity
2. GitHub Profile Optimization
3. Why Can a Messy GitHub Profile Hide Good Work?
4. Git Workflow for AI Projects
5. GitHub Is Part Resume, Part Portfolio, Part Reputation
6. Ship a Feature, In Order
7. Match: Git Command to Purpose
8. Small Git Habits Create Stronger Signals
9. Sort: Good vs Bad Git Practices
10. Profile and Commit Check
11. Contributing to Open Source
12. Why Weights Bloat a Repo
13. Weights in History
14. GitHub Profiles Often Decide Who Gets a Closer Look
15. Match: What Goes in .gitignore
16. Spot: GitHub Profile Red Flags
17. GitHub Best Practices Quiz
18. Explain GitHub's Career Value
19. GitHub Vocabulary
20. GitHub Pro!

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

1. 6 Seconds to Impress
2. The AI Resume Template
3. The 6-Second Resume Mystery
4. The XYZ Formula for Impact
5. Why One Strong Bullet Beats Ten Weak Ones
6. Label the X, Y and Z
7. Match: Weak vs Strong Resume Bullets
8. Sort: Include vs Exclude on AI Resume
9. What Resume Bots Actually Notice
10. ATS -- The Robot Reading Your Resume
11. Inside the ATS
12. Keyword Coverage
13. Coverage by Hand
14. How LinkedIn Recruiters Scan Profiles
15. Match: Resume Section Order
16. Spot: Resume Mistakes
17. AI Resume Quiz
18. Explain Resume Impact
19. Resume Vocabulary
20. Resume Expert!

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

1. The Interview That Changed Everything
2. 5 Types of AI Technical Questions
3. The Project Deep-Dive Mystery
4. The STAR Method for Technical Answers
5. Build a STAR Answer
6. The Same Project Can Tell Two Very Different Stories
7. Match: Question to Best Answer Approach
8. Sort: Good vs Bad Interview Answers
9. Why One Follow-Up Question Can Expose Weak Preparation
10. Survive the Follow-Up
11. Common AI Interview Questions (With Answers)
12. The Screening Trade-off
13. Precision and Recall by Hand
14. How LeetCode Helps, and Where It Doesn't
15. Match: ML Concept to Intuitive Explanation
16. Spot: Interview Preparation Best Practices
17. Technical Interview Quiz
18. Explain Technical Depth
19. Interview Vocabulary
20. Interview Ready!

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

1. The Whiteboard Challenge
2. 5 Essential Coding Patterns for AI Roles
3. The Pattern Recognition Mystery
4. The Problem-Solving Framework
5. Order the Method
6. Trace It by Hand
7. One Good Pattern Can Unlock Hundreds of Problems
8. Match: Problem to Pattern
9. Sort: Python vs Data Structure Knowledge
10. Why Efficient Thinking Feels Like Magic
11. Practice Platforms & How to Use Them
12. Tune a Coding Interview Approach
13. Big-O by Hand
14. How LeetCode Builds Interview Muscle Memory
15. Match: Python Trick to Its Use
16. Spot: Good Coding Interview Practices
17. Coding Interview Quiz
18. Explain Coding Patterns
19. Coding Interview Vocabulary
20. Code Warrior!

• IBApply computational thinking strategies (decomposition, abstraction, pattern recognition, algorithm design) to solve problems
• IBConstruct and trace algorithms expressed in pseudocode, independent of a specific programming language's syntax
• IB(HL only) Implement abstract data types such as stacks, queues, linked lists, and trees
• England + CambridgeUnderstand several key algorithms that reflect computational thinking (e.g. sorting and searching); use logical reasoning to compare the utility of alternative algorithms for the same problem
• England + CambridgeFollow and create algorithms presented as flowcharts using standard symbols (start, stop, process, decision, connector), including multiple variables
• England + CambridgeFollow, edit and create algorithms using flowcharts and pseudocode; use selection statements and Boolean logic; progress from linear to binary search
• 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 flowcharts and/or pseudocode to address complex problems as algorithms.
• 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)Compare and contrast fundamental data structures and their uses.
• CSTA (US)Illustrate the flow of execution of a recursive algorithm.
• AI4K12 (US)Progress from drawing a tree by repeatedly splitting branches and placing data at nodes (K-2), to describing the parts of a tree (nodes, parent, leaves) and how they relate (3-5), to describing the parts of a labeled graph (nodes, links, source/target) and how trees are a special case of graphs (6-8), to describing how schemas structure information about people, places, or things within knowledge graphs used by search engines (9-12).
• AI4K12 (US)Progress from drawing a simple 3-node search tree showing two possible tic-tac-toe moves (K-2), to drawing a full search tree of next moves for tic-tac-toe or nim and picking the best move (3-5), to modeling breadth-first search on a graph problem such as common-friend detection (6-8), to illustrating and comparing breadth-first, depth-first, and best-first search algorithms for growing a search tree (9-12).

1. Design It Like You'd Build It
2. The 5-Step ML System Design Framework
3. The System Design Mystery
4. Order the Framework
5. Common ML System Design Problems
6. A Great ML System Can Fail With a Great Model
7. Match: System to Key Design Decision
8. Sort: Online vs Offline Components
9. Why Offline Accuracy Cannot Be Your Final Answer
10. Review a Project Design
11. Real ML Systems at Indian Companies
12. Balance an ML System
13. Precision by Hand
14. Why Netflix-Style Thinking Shows Up Everywhere
15. Match: ML Metric to Use Case
16. Spot: Good ML System Design Practices
17. ML System Design Quiz
18. Explain ML System Design
19. ML System Design Vocabulary
20. How Your Plan Is Judged
21. System Architect!

• ADEKDesign a scalable AI system architecture
• 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).
• 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. The Question That Stumped Maya
2. 5 Must-Prepare Behavioral Questions
3. The Behavioral Interview Mystery
4. The STAR Method Deep Dive
5. Rebuild Maya's Answer
6. Ordinary Experiences Become Powerful When Framed Well
7. Match: Question to What It Really Tests
8. Sort: Strong vs Weak STAR Responses
9. Why Interviewers Keep Returning to Failure Stories
10. How to Discuss Failures Like a Pro
11. Strengthen a STAR Story
12. Action Share by Hand
13. Why Amazon Keeps Asking STAR Questions
14. Evidence You Can Defend
15. Match: Situation to Best Response Strategy
16. Spot: Effective Behavioral Interview Answers
17. Behavioral Interview Quiz
18. Explain the STAR Method
19. Behavioral Interview Vocabulary
20. Story Master!

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

1. Choosing the Right Path
2. Top AI/CS Programs in India
3. The Silent Tiebreaker
4. How AI Knowledge Strengthens Your Application
5. Fit Beats Rank
6. Where a Portfolio Counts
7. Match: University to Unique Strength
8. Sort: Indian vs International Application Requirements
9. Signal Matters
10. Application Tips from Successful Students
11. Fit Score Lab
12. Weighted Score by Hand
13. Alumni Trail
14. Strong or Weak Evidence
15. Match: Entrance Exam to University
16. Spot: Smart Application Strategies
17. University Application Quiz
18. Choice Logic
19. University Application Vocabulary
20. Application Ready!

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

1. From Student to Researcher
2. Five Routes Into Research
3. The Missing Language
4. How to Read an AI Research Paper
5. Which Pass Does It Need?
6. Questions Become Papers
7. Match: Research Tool to Its Job
8. Sort: Good First Project or Too Ambitious?
9. Research Has Friction
10. Three Student Project Cases
11. Project Scope Lab
12. Viability by Hand
13. Notebook Launchpad
14. Match: Research Term to Meaning
15. From Gap to Write-up
16. Spot: Good Research Practices
17. Research Opportunities Quiz
18. Research Pitch
19. Research Vocabulary
20. Research-ready

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

1. The Connection That Changed Everything
2. 5 Platforms to Build Your AI Network
3. The Unexpected Reply
4. Networking Without Being Awkward
5. Build a Cold Message
6. Tiny Posts, Big Doors
7. Match: Networking Action to Outcome
8. Sort: Effective vs Ineffective Networking
9. Trust Builds Slowly
10. Honest and Safe Outreach
11. AI Meetups and Conferences in India
12. Network Momentum
13. Replies by Hand
14. Profile Signal
15. Match: Platform to Best Use Case
16. Spot: LinkedIn Profile Best Practices
17. Networking Quiz
18. Network Plan
19. Networking Vocabulary
20. Community Builder!

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

1. Maya's Career Readiness Check
2. Unit 5 Key Concepts Review
3. One Offer Only
4. The Career Readiness Checklist
5. Order a STAR Answer
6. Habits Compound
7. Match: Interview Type to Preparation Strategy
8. Sort: Do Now vs Do Later Career Actions
9. Proof Travels Fast
10. Common Mistakes to Avoid
11. Readiness Meter
12. Trace the Readiness Score
13. Portfolio Proof
14. Claims a Reviewer Can Check
15. Match: Career Skill to Where You Learned It
16. Spot: Career-Ready Students
17. Unit 5 Review Quiz
18. Gap Check
19. Unit 5 Key Terms Review
20. Unit 5 Review Complete

• 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).
• 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. Career Dreams!
2. Market Explosion!
3. Read the Number
4. Why Alex Advanced
5. From Project to Interview
6. Top AI Careers
7. Jobs Multiply
8. Career Match!
9. Two Paths
10. Skills Transfer
11. Path Sorter!
12. Career Compass
13. Funnel by Hand
14. Recommendation Work
15. Hot AI Fields!
16. Spot the Job!
17. Career Quiz!
18. Career Fit
19. Career Words!
20. Future Pro!

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

1. Portfolio Power!
2. Key Ingredients!
3. The 7-Second Portfolio Test
4. Portfolio Parts
5. Less Can Show More
6. Portfolio Match!
7. Good vs Bad
8. The One-Line Pitch
9. Proof Beats Claims
10. Sort It Out!
11. Tune Your Portfolio Strength
12. Points or Percent?
13. GitHub as a Portfolio Front Door
14. Pin or Leave Off?
15. README Secrets!
16. Spot the Pro!
17. Portfolio Quiz!
18. Explain Portfolio Proof
19. Portfolio Words!
20. Portfolio Pro!

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

1. GitHub Pro!
2. Profile Power!
3. The Quiet GitHub Advantage
4. Clean Repos!
5. Your Commits Tell a Story
6. Commit or Ignore?
7. GitHub Match!
8. Pro vs Amateur
9. Small Repo Signals Matter
10. Leaked Key Drill
11. Sort the Pros!
12. Build Better Repo Hygiene
13. GitHub Profiles as Living Resumes
14. Power Features!
15. Your First Pull Request
16. Clean Up a Repo in Repo Lab
17. Push Protection Said No
18. Spot the Pro!
19. GitHub Quiz!
20. Explain Repo Hygiene
21. GitHub Words!
22. GitHub Master!

• 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.
• SingaporeDevelop digital literacy and technological skills needed to thrive in an increasingly technology-driven world, as a central goal of the Masterplan.

1. Resume Magic!
2. Resume Rules!
3. The Invisible Resume Problem
4. Resume Sections
5. One Bullet Can Do More
6. Section Match!
7. Power Bullets!
8. Numbers Create Trust
9. Assemble a Bullet
10. Bullet Sorter!
11. Get the Percentage Right
12. Change by Hand
13. LinkedIn Jobs Uses Searchable Signals
14. Beat the Robots!
15. Tailor It Honestly
16. Spot the Pro!
17. Resume Quiz!
18. Explain ATS Keywords
19. Resume Words!
20. Resume Pro!

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

1. Portfolio Story!
2. Portfolio Parts!
3. Why One Portfolio Stays With You
4. Format Choice!
5. Format Changes the Story
6. Portfolio Match!
7. Pro Tips!
8. Build BusCast's Case Study
9. Process Makes Work Stick
10. Sort the Tips!
11. How Much Time Does Each Project Get?
12. Divide the Reviewer's Time
13. Behance Rewards Clear Presentation
14. Expert Secrets!
15. Publish or Keep Private?
16. Spot the Pro!
17. Portfolio Quiz!
18. Explain Portfolio Format
19. Portfolio Words!
20. Portfolio Pro!

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

1. Interview Win!
2. The STAR Method!
3. Same Skills, Different Outcome
4. Build a STAR Answer
5. Interview Types!
6. Stories Beat Labels
7. Question Match!
8. Pro Tips!
9. Questions Change the Interview
10. Sort the Tips!
11. Strengthen Your Interview Answer
12. Timing by Hand
13. AI in One-Way Video Interviews
14. Expert Secrets!
15. AI in the Interview Room
16. Spot the Pro!
17. Interview Quiz!
18. Explain STAR Answers
19. Interview Words!
20. Interview Pro!

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

1. Communication Win!
2. Four Pillars!
3. When Communication Fails Fast
4. Channel Choice!
5. One Message, Many Signals
6. Channel Match!
7. Email Pro!
8. Build the Email
9. Listening Builds Trust
10. Email Sorter!
11. What Length Costs
12. Length by Hand
13. Slack Rewards Clear Signals
14. Expert Skills!
15. Three Styles
16. Spot the Pro!
17. Communication Quiz!
18. Explain Channel Choice
19. Communication Words!
20. Communication Pro!

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

1. Networking Power!
2. Network Truths!
3. The Internship That Came Later
4. Networking Tips!
5. Chat to Connection
6. Networks Grow Quietly
7. Setting Match!
8. Do vs Don't!
9. Questions Make You Memorable
10. Sort the Moves!
11. Strengthen a New Connection
12. Reach by Hand
13. LinkedIn Extends Real Meetings
14. Evidence and Honest Drafts
15. Pro Techniques!
16. Spot the Pro!
17. Networking Quiz!
18. Explain Follow-Up Value
19. Network Words!
20. Network Pro!

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

1. Overwhelmed Intern
2. Time vs Energy
3. Why More Hours Did Less
4. Eisenhower Matrix
5. Rest Can Raise Output
6. Productivity Match
7. Busy vs Productive
8. Focus Has a Cost
9. Sort the Tasks
10. AI Triage Check
11. Build a Better Workday
12. Buffer by Hand
13. Google Calendar Can Protect Deep Work
14. Pro Strategies
15. Plan a Project Day
16. Spot the Productive
17. Productivity Quiz
18. Explain Productive Focus
19. Productivity Words
20. Productivity Master!

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

1. Paycheck Surprise
2. Paycheck Secrets
3. The Missing Paycheck Money
4. Paycheck by Hand
5. The 50/30/20 Rule
6. Tiny Early Choices Compound
7. Priority Match
8. 401(k) vs Roth IRA
9. Automation Beats Intention
10. Sort the Spending
11. Start Early vs Start Late
12. Compounding by Hand
13. Employer Match Is Real Compensation
14. Money Habits
15. When AI Does Your Money Maths
16. Smart or Not?
17. Finance Quiz
18. Explain Net Pay Planning
19. Money Words
20. Paycheck Decoded

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

1. The Right Thing
2. Ethics Explained
3. Why the Small Choice Matters
4. Gray Areas
5. Reputation Compounds Too
6. Ethics Match
7. AI at Work: Where Is the Line?
8. Long-Term Wins
9. Silence Sends Signals
10. Sort the Actions
11. Test an Ethical Decision
12. Best of Many by Hand
13. Compliance Hotlines Exist for a Reason
14. Building Character
15. Raising a Concern
16. Spot the Ethics
17. Ethics Quiz
18. Explain Ethical Courage
19. Ethics Words
20. Ethics Champion!

• UAEEffectiveness of AI governance
• UNESCOArticulate personal and social responsibilities of citizens in an AI society — from both user and designer perspectives — including ensuring human control and accountability over every step of the AI life cycle (CG4.3.1.2).
• Korea, Japan, HKCultivate an attitude of participating proactively in, and contributing to the development of, the information society while creating new value through appropriate use of information and information technology and adherence to information morals (law, security).
• ChinaStudents understand the ethics and challenges of a smart/AI-driven society, strengthening information-security awareness, responsibility, and dialectical (balanced) views of AI

1. Jordan's Pivot
2. Types of Transitions
3. The Hidden Career Advantage
4. Transferable Skills
5. A Detour Can Become an Edge
6. Timeline Match
7. Push vs Pull
8. Stories Make Pivots Make Sense
9. Sort the Advice
10. Plan a Stronger Pivot
11. Runway by Hand
12. LinkedIn Helps Reframe Your Path
13. Say It in Their Words
14. Read the Screener
15. Best Practices
16. Spot the Smart Move
17. Transition Quiz
18. Explain Transferable Skills
19. Transition Words
20. Transition Master!

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

1. The Big Idea
2. 5 Steps to Choosing Your Capstone Topic
3. Testable or Vague?
4. Why the Smaller Idea Wins
5. Capstone Project Scope and Timeline
6. Constraints Can Improve Creativity
7. Match: Capstone Domain to Project Idea
8. Sort: Realistic vs Overambitious Capstone Scope
9. Good Ideas Need Filters
10. Inspiring Capstone Projects by Indian Students
11. Question the Cases
12. Score Your Capstone Idea
13. Trace the Fit Score
14. GitHub Projects Help Scope Work
15. Match: Project Phase to Key Deliverable
16. Spot: Good Capstone Project Qualities
17. Capstone Vision Quiz
18. Explain Capstone Scope
19. The Open Brief: Your Problem, Your Evidence
20. Open Brief Check
21. Regional Initiatives: Map Your Field
22. Initiatives in Your Design
23. SWOT to Actions
24. Match Maya's SWOT Pairs
25. The Ethics Log
26. Phase to Check
27. My Ethics Log
28. Capstone Vocabulary
29. Capstone Visionary!

• UAEDesign AI solutions with systematic design processes
• 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 + 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)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.
• 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)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).
• 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. Standing on the Shoulders of Giants
2. How to Conduct a Literature Review
3. The Shortcut Hidden in Research
4. Pass 1 Triage
5. Finding and Using Research Papers
6. One Paper Can Reframe Everything
7. Match: Research Tool to Best Use
8. Sort: Good vs Bad Literature Review Practices
9. Citations Map the Field
10. Turning Research Gaps into Your Contribution
11. Sharpen the Gap Statement
12. Strengthen a Literature Review
13. Budget the Three Passes
14. Google Scholar Turns Search Into Strategy
15. Match: Paper Section to Its Content
16. Spot: Strong Research Contributions
17. Literature Review Quiz
18. Explain Research Gaps
19. Research Component Vocabulary
20. Research Scholar!

• 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)

1. The Blueprint Before the Building
2. 5 Key Architecture Decisions for Your Capstone
3. The Cost of Skipping the Diagram
4. System Architecture Diagrams
5. Trace the Data Flow
6. Every Diagram Is an Abstraction
7. Kept or Left Out?
8. Every Box Hides a Tradeoff
9. Match: Component to Tool Choice
10. Sort: Transfer Learning vs Train From Scratch
11. Constraints Shape Design
12. Paper Numbers vs Real Constraints
13. Architecture Patterns for Common Capstone Types
14. How Many Photos per Minute?
15. Throughput by Hand
16. FastAPI Makes Architecture Visible
17. Match: Architecture Decision to Justification
18. Spot: Sound Architecture Decisions
19. Technical Architecture Quiz
20. Explain System Tradeoffs
21. The Gantt Plan
22. Read the Critical Path
23. Deliverable: System Prompt and Test Suite
24. Build and Test in Prompt Lab
25. What the Suite Shows
26. Architecture Vocabulary
27. How Your Plan Is Judged
28. Your Blueprint
29. Score Your Blueprint
30. System Architect!

• ADEKDesign a scalable AI system architecture
• 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
• IBEvaluate the success of a computational solution against its own requirements and identify possible improvements
• IBCollaborate with students from other Group 4 (sciences) subjects on shared scientific investigation and collaborative-sciences project skills
• CSTA (US)Implement an artificial intelligence algorithm to play a game against a human opponent or solve a problem.
• CSTA (US)Analyze a large-scale computational problem and identify generalizable patterns that can be applied to a solution.
• CSTA (US)Demonstrate code reuse by creating programming solutions using libraries and APIs.
• CSTA (US)Plan and develop programs for broad audiences using a software lifecycle process.
• 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)Modify an existing program to add additional functionality and discuss intended and unintended implications (e.g., breaking other functionality).
• CSTA (US)Evaluate key qualities of a program through a process such as a code review.
• 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 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).
• OECD–EC (Europe)Critically evaluate how AI feedback shapes one's ideas and reflect on the creative process (Create 3, advanced).
• 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).
• 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, 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).
• 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 to develop AI applications and transfer that capability to solve problems in other real-life contexts

1. The First Line of Code
2. Setting Up Your Development Environment
3. Why Start So Simple?
4. Building Your Baseline Model
5. Floor by Hand
6. Errors Can Be Useful
7. Match: Task Type to Baseline Approach
8. Sort: Do First vs Do Later in Development
9. Good Foundations Look Repetitive
10. Common Baseline Pitfalls and Solutions
11. Baseline Build Order
12. Strengthen Your Baseline Setup
13. Weights & Biases Makes Baselines Comparable
14. Diagnose the Run
15. Match: Metric to What It Measures
16. Spot: Good Foundation Practices
17. Foundation and Baseline Quiz
18. Explain Baseline Value
19. Development Phase 1 Vocabulary
20. Foundation Ready

• 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 + statesVisualize data using Python (matplotlib, seaborn) and Excel charts, and clean data (handle missing values, outliers, inconsistencies) with pandas and Excel data-cleaning features
• CISCE + statesClean data, explore Kaggle datasets, and create/manipulate data frames from Kaggle datasets
• CISCE + statesApply data transformation and standardization methods for analysis
• CISCE + statesPrepare data using NumPy and Pandas, and apply Scikit-learn for preprocessing tasks such as scaling, encoding categorical variables and train/test splitting
• CISCE + statesTune model hyperparameters (learning rate, batch size, number of hidden layers/neurons) using techniques such as grid search or random search
• IB(HL only) Apply data preprocessing and feature selection techniques when preparing data for a machine learning model
• IBApply object-oriented programming principles (classes, objects, inheritance, encapsulation) as mandatory content for all students
• CSTA (US)Construct solutions to problems using student-created components, such as procedures, modules and/or objects.
• UNESCOAcquire age-appropriate knowledge and skills on datasets — applying tools/languages to acquire, clean and transform data into a form suitable for storing, processing and analysing (e.g. SQL, NoSQL, SparkSQL, Apache Flink) (CG4.2.3.1).
• Korea, Japan, HKCollect data for machine learning, identify missing values and outliers, select attributes needed for the problem, and preprocess data into a usable form.

1. From 82% to 93% -- The Iteration Game
2. 5 Techniques to Boost Model Performance
3. What Unlocked the Jump?
4. The Improvement Cycle
5. Read the Error Counts
6. Tiny Changes Can Move Big Scores
7. Match: Problem to Best Improvement Technique
8. Sort: High-Impact vs Low-Impact Improvements
9. Error Logs Hide Clues
10. Three Illustrative Capstone Logs
11. Read the Logs
12. Tune an Improvement Plan
13. Ensemble Vote by Hand
14. Netflix Refines Recommendations in Loops
15. Match: Hyperparameter to Its Effect
16. Spot: Good Iteration Practices
17. Improvement and Refinement Quiz
18. Explain the Refinement Loop
19. Improvement Phase Vocabulary
20. Refinement Toolkit

• 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
• 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.
• CSTA (US)Evaluate the ability of models and simulations to test and support the refinement of hypotheses.

1. From Notebook to Production
2. 5 Steps to Deploy Your Capstone
3. Why Did the Great Model Fail Live?
4. Code Quality for Your Capstone
5. Ship It Safely
6. Deployment Starts After Accuracy
7. Match: Deployment Platform to Best Use Case
8. Sort: Clean Code vs Code Smell
9. Polish Prevents Panic
10. Deployment Stories from Indian Student Projects
11. Balance a Deployment Plan
12. Model Size by Hand
13. Google Maps Plans for Bad Connections
14. Match: Error Scenario to Graceful Handling
15. Read the respond() Code
16. The User Round
17. Blocker, Major or Minor?
18. Iteration Log
19. Spot: Production-Ready Practices
20. Deployment and Polish Quiz
21. Explain Deployment Readiness
22. Deployment Vocabulary
23. Deployment Master!

• 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.
• CSTA (US)Develop programs for multiple computing platforms.
• CSTA (US)Students read, evaluate, and critique AI-generated code as part of the Programming strand.
• 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. The Bug That Went Live
2. 4 Types of Tests for AI Projects
3. Why Did the Demo Break if Tests Passed?
4. Writing Your First Unit Tests
5. Run the Tests in Your Head
6. Tests Catch Invisible Risks
7. Match: Test Type to What It Catches
8. Sort: Critical vs Nice-to-Have Tests
9. Edge Cases Count as Real Users
10. Edge Cases That Broke Real AI Systems
11. Order Maya's Test Plan
12. Is the Model Honest About Its Confidence?
13. Calibration by Hand
14. The Full Evaluation Sheet
15. Which Criterion?
16. My Evaluation Sheet
17. PhonePe Tests Failure Paths First
18. Match: Testing Term to Definition
19. Spot: Good Testing Practices
20. Testing and Validation Quiz
21. Explain Test Coverage
22. Testing Vocabulary
23. Quality Guardian!

• 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).
• OECD–EC (Europe)Design evaluation criteria for an AI system, compare its performance across inputs/users, and propose improvements (Shape 2, advanced).

1. Your Work Deserves to Be Known
2. The Standard Paper Structure
3. Why Was the Strong Project Hard to Judge?
4. Assemble an Abstract
5. Writing Tips for Technical Papers
6. Structure Makes Ideas Travel
7. Match: Paper Section to Its Purpose
8. Sort: Strong vs Weak Technical Writing
9. Edit Like a Reviewer
10. The Abstract Gets Read First
11. Creating Effective Tables and Figures
12. Read the Figure
13. Points or Percent?
14. GitHub Rewards Clear Documentation
15. Match: Writing Principle to Example
16. Spot: Good Paper Writing Practices
17. Crediting AI in Your Paper
18. Fix the Reference
19. My AI-Use Statement
20. Technical Writing Quiz
21. Explain Paper Structure
22. Technical Writing Vocabulary
23. Paper Plan Ready

• 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).
• 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
• OECD–EC (Europe)Assess the ethical implications and justify how an AI-assisted work maintains authenticity (Create 4, advanced).
• 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. From Builder to Storyteller
2. The Perfect 10-Minute Presentation Structure
3. Check the Hook
4. Why Did Judges Forget the Best Project?
5. Slide Design Principles
6. Stories Carry Technical Ideas
7. Match: Talk Section to Its Line
8. Sort: Effective vs Ineffective Slide Design
9. People Remember Demos
10. Handling Q&A Like a Pro
11. Budget Your Minutes
12. Timing by Hand
13. Google Slides Supports Confident Delivery
14. Match: Tough Question to Strong Response
15. Honest or Overclaim?
16. Deliver and Record
17. Score Your Talk
18. Self-Assessment with Timestamps
19. Spot: Effective Presentation Techniques
20. Presentation Preparation Quiz
21. Explain Presentation Flow
22. Presentation Vocabulary
23. Talk Plan Ready

• 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).
• 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. Exhibition Day
2. Setting Up Your Demo Booth
3. Why Do Some Booths Stay Crowded?
4. Making Your Demo Unforgettable
5. Honest Claim or Inflated?
6. A Minute Can Open a Crowd
7. Match: Visitor Type to Best Approach
8. Sort: Before Exhibition vs During Exhibition
9. Visitors Judge Fast
10. Run the Judge Walkthrough
11. Getting and Using Feedback
12. Design a Stronger Booth
13. Failure Odds by Hand
14. LinkedIn Turns Booth Chats into Opportunities
15. Match: Exhibition Challenge to Solution
16. Spot: Exhibition Best Practices
17. Capstone: A Full Model Card
18. Why the Card's Accuracy Is Optimistic
19. Train, Graduate, Test on Others
20. Read the Evaluation
21. Which Section?
22. Explain a Miss
23. Explainability and Privacy Check
24. Two New Card Sections
25. Defend Your Card to a Judge
26. Exhibition Preparation Quiz
27. Explain Exhibition Strategy
28. Exhibition Vocabulary
29. Exhibition Champion!

• 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).
• 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)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)Using a dataset and a systematic process, develop an AI model to generate for classification or prediction, and articulate the assumptions made at each step: (1) develop a question solvable with AI, (2) collect or curate data, (3) evaluate the data, (4) train an AI model on the data, (5) evaluate the model, and (6) iteratively improve the model.
• CSTA (US)Evaluate an AI model (e.g., using a model card) to determine the model's features as well as its biases, explainability, fairness, privacy, accuracy, and transparency.
• CSTA (US)Train, iteratively improve, and then develop a model card for an AI model with ethical considerations for end users and others impacted: fairness, bias, safety, security, intellectual property, privacy, robustness, explainability, accuracy, transparency, and accountability.
• AI4K12 (US)Progress from giving examples of intelligent vs. non-intelligent machines and discussing what makes a machine 'intelligent' (K-2), to demonstrating a machine perception tool (e.g. speech transcription, object recognition) and explaining why it is perception rather than mere sensing (3-5), to giving examples of specialized perception algorithms (face detection, obstacle detection, speech recognition, etc.) (6-8), to explaining how perception algorithms power real-world applications like license plate readers and Google Lens (9-12).
• AI4K12 (US)Progress from creating a model card for a classification or prediction model documenting its intended use, training data, accuracy, and limitations (3-5), to constructing an ethical matrix of stakeholders and their values for an AI system and evaluating alignment (6-8), to designing an AI system using a full ethical design process that considers all stakeholders' values across fairness, transparency, explainability, accountability, privacy, and societal values (9-12). K-2 is marked N/A.
• UNESCODevelop a critical attitude to ethics-by-design principles behind existing AI systems/algorithms by auditing selected tools, proposing an ethics label, and recommending remedies for identified violations of ethical principles or regulations (CG4.3.2.2).
• UNESCOCustomize assessment methods/instruments to test self-crafted AI tools for robustness and ease of use, organise peer assessment and shared feedback, and build collaborative co-creator skills (CG4.3.3.3).
• UNESCOPractise critiquing AI systems by auditing whether a model enhances or weakens human capacities, agency and consciousness, checking the extent of its explainability and data-privacy protection, and measuring system performance and its broader societal/environmental impact (CG4.3.4.1).
• SingaporeLearn beyond AI: develop distinctly human capabilities and dispositions that AI cannot replicate.
• Korea, Japan, HKCapture varied phenomena as information and its connections, and apply information and information technology appropriately, effectively and creatively toward problem discovery and solution (content areas include content creation for communication, data processing, and programming to build information systems).
• ChinaStudents learn to develop AI applications and transfer that capability to solve problems in other real-life contexts

1. Looking in the Mirror
2. 5 Dimensions of Capstone Reflection
3. Why Do Mistakes Matter Later?
4. Writing Your Reflection Report
5. Honesty Can Be Impressive
6. Order the Report
7. Match: Capstone Challenge to Lesson Learned
8. Sort: Growth Mindset vs Fixed Mindset Responses
9. Reflection Finds Patterns
10. Three Growth Stories (Composites)
11. Evidence, Cause or Change?
12. Strengthen a Reflection Report
13. Score It by Hand
14. Notion Helps Growth Stay Visible
15. Match: Evidence to Dimension
16. Spot: Meaningful Reflection Statements
17. Reflection and Growth Quiz
18. Explain Reflective Growth
19. Reflection Vocabulary
20. Reflective Practitioner!

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

1. The Journey of a Lifetime
2. Your Journey: Grade 6 to Grade 12
3. What Skill Outlasts Every Tool?
4. The AI Future You'll Shape
5. Impact First?
6. Your Future Is Bigger Than a Toolkit
7. Match: What You Learned to How It Shapes the Future
8. Sort: Skills That Last vs Skills That Change
9. Learning Habits Compound
10. Lifelong Learning: How to Stay Current in AI
11. Three-Pass Reading
12. Build a Learning Routine
13. Hours by Hand
14. Coursera Makes Lifelong Learning Practical
15. Match: AI Domain to India's Greatest Need
16. Spot: Wisdom for the Road Ahead
17. Final Quiz - Your AI Journey
18. Explain Lifelong Learning
19. Words for the Journey Ahead
20. Chapter 120 Complete

• 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).
• SingaporeLearn beyond AI: develop distinctly human capabilities and dispositions that AI cannot replicate.

1. The Council Decision
2. Ethics Frameworks
3. Who Answers When AI Acts?
4. AI Principles
5. Principles Can Disagree
6. Four Lenses on the Drone
7. Principles Match
8. Regulation Debate
9. Governance Is More Than One Law
10. Build the Proposal
11. Sort the Challenges
12. How Much Does Review Catch?
13. Trace the Oversight Maths
14. ChatGPT Shows Why Guardrails Matter
15. Future Challenges
16. Ethics Spotlight
17. Ethics Quiz
18. Case: AI at the Border
19. AI Acts or Human Decides?
20. Fill the Matrix: Grid Alerts
21. Your Matrix Verdict
22. Explain AI Governance
23. From Council to Classroom
24. How a Guideline Gets Adopted
25. Who Else Does My AI Use Affect?
26. Convenience, Community or Wellbeing?
27. Draft Your School's AI-Use Guideline
28. Guideline Check
29. Build Your Own Matrix
30. Hear Another Side
31. Ethics Words
32. Council Briefing Complete

• UAEEffectiveness of AI governance
• UAEJudge responsible AI use against governance frameworks
• UAEHuman–AI collaboration across sectors
• ADEKFacial recognition: privacy/safety/fairness tension
• ADEKStakeholder debate / ethical matrix
• ADEKDraft/commit to ethical-use guidelines
• ADEKAI governance/civic participation
• CBSEDemonstrate understanding of the fundamental principles of ethics in AI and the five pillars of AI Ethics; understand AI bias, its sources, real-world implications, and mitigation strategies; identify and apply strategies to promote fairness and transparency; recognise the significance of AI policies in promoting responsible, safe, ethical AI use (Unit 8: AI Ethics and Values).
• CISCE + statesIdentify applications and benefits of AI across commercial, industry, medical/healthcare, defence, banking, entertainment, transport, security and agriculture domains
• CISCE + statesApply an AI code of ethics — avoiding bias, ensuring privacy of users and their data, mitigating environmental impact, and evaluating the effects of designing technology that replicates human life
• IBEvaluate ethical implications of AI/ML systems, including bias, fairness, and transparency, as part of the machine learning content
• 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
• IBUse AI-generated content as an object of inquiry within Theory of Knowledge to examine how knowledge is produced and evaluated
• 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 computational artifacts to maximize their beneficial effects and minimize harmful effects on society.
• CSTA (US)Debate laws and regulations that impact the development and use of software.
• CSTA (US)Debate what differences do or should exist between human and artificial intelligence, sentience, consciousness, rights, and responsibilities.
• CSTA (US)Evaluate the design, motivation, outcomes, and potential impacts of AI systems using ethical design criteria and/or ethical frameworks.
• 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 constructing a map of a home, school, or neighborhood and understanding that 'the map is not the territory' (K-2), to demonstrating how different map styles (geometric vs. topological) capture different aspects of the world (3-5), to showing how a game board (tic-tac-toe, chess) can be represented as a plain-language description of squares, relationships, and piece positions (6-8), to describing how to represent a concept as a schema.org-style schema with superconcepts and inherited properties (9-12).
• AI4K12 (US)Progress from demonstrating ways intelligent agents don't understand things the way people do, e.g. no frustration at repeated questions (K-2), to demonstrating what a 'broad AI'/general-AI system could do that current narrow AI cannot (3-5), to defining criteria for consciousness (self-awareness, personal history) and evaluating AI systems or fictional AI characters against them (6-8), to debating alternative perspectives on human vs. artificial intelligence, including the Turing test and artificial superintelligence (9-12).
• AI4K12 (US)Progress from 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 analyzing deepfake images/videos to identify the flaws that currently reveal them, e.g. thispersondoesnotexist.com (3-5), to critiquing uses of AI technology for surveillance or privacy violation, e.g. face recognition, license-plate trackers (6-8), to identifying appropriate areas for AI regulation and evaluating proposed legislation, e.g. autonomous-vehicle laws (9-12). K-2 is marked N/A.
• OECD–EC (Europe)Evaluate how well an AI use aligns with ethical principles and human values across trade-offs (Engage 7, advanced).
• OECD–EC (Europe)Assess the ethical implications and justify how an AI-assisted work maintains authenticity (Create 4, advanced).
• OECD–EC (Europe)Design improvements to an AI system based on technical constraints, ethics, and user needs to promote societal benefit (Shape 4, advanced).
• UNESCOIllustrate dilemmas around AI and identify the main reasons behind ethical conflicts that AI creators and companies face (e.g. data collection vs. ownership, profit vs. privacy, safety vs. speed of iteration) (CG4.1.2.1).
• UNESCOCarry out responsible AI practices in compliance with ethical principles and locally applicable regulations, iteratively building and following a personal checklist of lawful and responsible practices (e.g. protecting privacy/copyright, marking AI-generated content, avoiding disinformation, hate speech, or sensitive content about identifiable individuals) (CG4.2.2.1).
• UNESCOGain an age-appropriate understanding of personal, legal and ethical responsibilities when using AI and reinforce self-disciplined behaviours around sensitive personal data, copyrighted materials, deepfake/identifiable-person imagery, and the spread of misinformation, disinformation or hate speech (CG4.2.2.2).
• UNESCOApply an integral, ethics-by-design set of principles across the whole AI life cycle, e.g. simulating the role of a 'chief ethics officer' drafting a checklist of ethical criteria to audit each step of an AI system's design (CG4.3.2.1).
• UNESCOCultivate social responsibilities to uphold 'ethics by design' in regulations on AI — evaluating how selected local AI regulations align with ethics-by-design, and drafting proposals to modify or create new regulations governing ethics by design (CG4.3.2.3).
• UNESCONurture a self-identity as a co-creator and 'driver' of the next generation of AI technology, critically analyse the long-term impacts of AI systems on social relations and behaviours, and discuss how regulations/policies should adapt to enhance AI governance (e.g. by engaging with local/online communities of AI co-creators) (CG4.3.4.3).
• SingaporeLearn to use AI: use AI tools safely and responsibly.
• SingaporeGive examples of common personal and business tasks that can be performed well by AI: face recognition, voice recognition, image classification and spam filtering.
• Australia + NZStudents understand that generative AI tools should be used in ways that benefit all members of the school community and support wellbeing, not just individual convenience
• Australia + NZStudents learn that use of generative AI outputs (e.g. in assessable work) must remain open to challenge, retain human agency, and that the student/user remains accountable for decisions made with AI assistance (e.g. academic integrity implications)

1. The AI Radiologist
2. Medical AI Uses
3. Why Isn't Accuracy Enough?
4. Data Challenges
5. One Early Signal Can Save a Life
6. Success Match
7. AI vs Doctors
8. Trace a Screening Round
9. Second Opinions Scale Well
10. Sort the Roles
11. What Share of Flags Are Real?
12. Google Health: Lab Score vs Clinic Reality
13. AI Breakthroughs
14. Case and Lesson
15. Healthcare Spotlight
16. Healthcare Quiz
17. Plan an Honest Project
18. Explain Human-AI Care
19. Healthcare Words
20. Healthcare Champion!

• ADEKPython analysis of a people-rating/ranking dataset
• CBSEExplain the importance of data literacy in AI; identify data collection methods and applications; comprehend matrices and their operations; apply basic data analysis techniques (mean, median, mode, standard deviation, variance); visualise data using line/bar/histogram/scatter/pie charts with matplotlib (Unit 5: Data Literacy - Data Collection to Data Analysis).
• CISCE + statesPerform matrix operations (addition, subtraction, multiplication, transpose) and vector arithmetic/applications relevant to AI
• CISCE + statesApply Set Theory to data-table joins and relational algebra, and calculate simple statistical measures (mean, median, mode, variance, standard deviation)
• CISCE + statesAnalyse data trends using mean, median, mode, range, variance and standard deviation; apply probability basics including uniform, normal and binomial distributions
• CISCE + statesFormulate null and alternative hypotheses, identify Type I and Type II errors, and apply simple linear regression and correlation coefficient for prediction and relationship measurement
• CISCE + 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 evaluating the ways a decision impacts different people differently (K-2), to evaluating how an AI technology, e.g. speech or facial recognition, has different effects on different demographic groups (3-5), to evaluating how various stakeholders' conflicting goals and values shape AI system design decisions (6-8), to explaining how the use of AI systems (e.g. health-risk algorithms) has led to documented disparate impacts on different groups (9-12).
• OECD–EC (Europe)Analyse how institutional deployment of AI can serve particular interests and amplify societal bias (Engage 6, advanced).
• OECD–EC (Europe)Deliberately assign parts of a task to human strengths versus AI capabilities (Manage 3, advanced).
• UNESCOGain evidence-based insight into the pervasive adoption of AI as infrastructure of social activity, and critique challenges AI poses to inclusion, justice and sustainability, including AI-amplified bias against women, marginalised ethnic groups and socio-economically disadvantaged people (CG4.3.1.1).

1. Smart Grid Hero
2. AI Mitigation
3. Why Can Cleaner Power Be Unstable?
4. AI Adaptation
5. Timing Can Cut Emissions
6. Cause or Harm?
7. Sector Match
8. The Carbon Paradox
9. Forecasts Become Climate Tools
10. Footprint by Hand
11. Sort the Impact
12. Stabilize a Renewable Grid
13. Read the Grid Model
14. Protecting the Grid
15. Grid Defence Check
16. Google Uses AI to Cool Data Centers
17. Climate Breakthroughs
18. Climate Spotlight
19. Climate Quiz
20. Explain Climate Coordination
21. Climate Words
22. Climate Champion!

• UAEHuman–AI collaboration across sectors
• ADEKFootprint / resource cost of AI
• CISCE + statesIdentify applications and benefits of AI across commercial, industry, medical/healthcare, defence, banking, entertainment, transport, security and agriculture domains
• 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.
• 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).
• SingaporeGive examples of common personal and business tasks that can be performed well by AI: face recognition, voice recognition, image classification and spam filtering.

1. Universal Translator
2. Transformer Power
3. How Can One Model Handle So Many Tasks?
4. Attention by Hand
5. NLP Capabilities
6. Context Is a Superpower
7. NLP Task Match
8. BERT vs GPT
9. Meaning Beats Keywords
10. Sort the Models
11. Increase Language Understanding
12. Long Context by Hand
13. Google Translate Depends on Context
14. NLP Applications
15. Test a Translator
16. NLP Spotlight
17. NLP Quiz
18. Explain Transformer Power
19. NLP Words
20. Language Models, Deeper

• CISCE + statesDescribe the domain of Natural Language Processing (NLP) — text understanding, text generation, language translation, question answering/chatbots, dialogue systems — and internet search types (navigation, informational, transactional, investigative, voice)
• CISCE + statesApply Named Entity Recognition (NER) to classify names of people/organizations/locations in text, perform text summarization by extraction, and build a spam-email classifier using Machine Learning
• IBUse AI-generated content as an object of inquiry within Theory of Knowledge to examine how knowledge is produced and evaluated
• CSTA (US)Debate what differences do or should exist between human and artificial intelligence, sentience, consciousness, rights, and responsibilities.
• 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).
• AI4K12 (US)Progress from demonstrating how a computer produces different verb tense/agreement forms (K-2), to experimenting with a speech-to-text system's ability to resolve ambiguous words via context (3-5), to illustrating how word embeddings support reasoning about word meaning, e.g. outlier detection and analogy completion (6-8), to demonstrating how a small context-free grammar parses or generates simple sentences and where it falls short, e.g. subject-verb agreement (9-12).
• AI4K12 (US)Progress from demonstrating what an intelligent assistant like Siri or Alexa can and cannot do (K-2), to demonstrating question types a search engine or assistant can and cannot answer (3-5), to describing NLP tasks such as summarization, sentiment analysis, question answering, and machine translation (6-8), to describing NLP approaches ranging from keyword/template matching to deep neural networks, and when each is appropriate (9-12).
• 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).
• AI4K12 (US)Progress from demonstrating ways intelligent agents don't understand things the way people do, e.g. no frustration at repeated questions (K-2), to demonstrating what a 'broad AI'/general-AI system could do that current narrow AI cannot (3-5), to defining criteria for consciousness (self-awareness, personal history) and evaluating AI systems or fictional AI characters against them (6-8), to debating alternative perspectives on human vs. artificial intelligence, including the Turing test and artificial superintelligence (9-12).
• OECD–EC (Europe)Explain how AI systems perform tasks without attributing human traits to them (Engage 2, 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.

1. AI Eyes
2. Vision Powers
3. Why Can AI Miss What Humans See?
4. Layers of Seeing
5. Reuse the Layers
6. Pixels Become Meaning
7. Vision Match
8. Two Ways to See
9. Plan the Test
10. Small Visual Shifts Matter
11. Vision Sort
12. Improve Vision Reliability
13. Split the Score
14. Google Photos Shows the Power of Tagging
15. Future Vision
16. Vision Detector
17. Vision Quiz
18. Explain Vision Robustness
19. Vision Words
20. Vision Tester

• AI4K12 (US)Progress from locating and identifying computer sensors such as cameras and microphones on everyday devices (K-2), to illustrating how computer sensing differs from human sensing (3-5), to giving examples of intelligent agents (e.g. self-driving cars) combining data from multiple sensors (6-8), to describing the limitations and advantages of specific sensor types such as cameras, microphones, radar, lidar, GPS, and accelerometers (9-12).
• AI4K12 (US)Progress from demonstrating figure/ground segmentation in an image (K-2), to illustrating how occlusion changes the outlines of objects (3-5), to describing how edge detectors compose into more complex feature detectors such as letter/shape detectors (6-8), to demonstrating how perceptual reasoning at a higher abstraction level (objects, surfaces, boundaries) draws on lower levels (contours, edges, pixels) (9-12).
• AI4K12 (US)Progress from describing what an intelligent agent must 'know' to make sense of a spoken question (K-2), to demonstrating how a text-to-speech/speech-recognition system resolves ambiguity using statistical context (3-5), to classifying an image scene type and describing the domain knowledge needed to understand it (6-8), to analyzing an online image dataset (e.g. ImageNet, Coco) to describe how statistical domain knowledge is derived for a computer vision system (9-12).
• AI4K12 (US)Progress from 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. Learning by Doing
2. RL Building Blocks
3. Why Did the Agent Pick a Strange Shortcut?
4. Explore or Exploit?
5. Reward Signals Shape Strategy
6. RL Timeline
7. Two Ways to Learn
8. Trying More Can Teach More
9. Learning Sort
10. Plan an RL Mini-Project
11. Tune an RL Agent
12. Exploration by Hand
13. YouTube Recommendations Learn from Rewards
14. RL in Action
15. Loophole or Sound?
16. RL Detector
17. RL Quiz
18. Explain RL Rewards
19. RL Words
20. RL, Project-Ready

• 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)Evaluate the ability of models and simulations to test and support the refinement of hypotheses.
• 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 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 Wrong Prediction
2. Safety Dimensions
3. Why Can Safe-Looking AI Still Fail?
4. Confident but Wrong
5. Alignment Challenge
6. Knowing Limits Is a Safety Skill
7. Name the Failure
8. Two Safety Paths
9. Safe Failure Is Still Design
10. Safety Sort
11. Order a Safety Plan
12. How Many Slip Through?
13. Trace the Slip-Through
14. Waymo Builds Safety Around Edge Cases
15. Safety Research
16. Safety Detector
17. Safety Quiz
18. Explain AI Safety Limits
19. Safety Words
20. Safety, Mapped

• 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).
• 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. Jobs Transformed
2. Four Impact Types
3. Why Didn't Marcus Get Replaced?
4. Skills That Matter
5. Split the Job
6. Work Can Shift Without Disappearing
7. Risk Match
8. Two Revolutions
9. New Tools Create New Roles
10. Revolution Sort
11. Plan for Career Resilience
12. Trace the Week
13. LinkedIn Shows Emerging AI Roles
14. Headline Check
15. Case Files: Energy and Government
16. Advise or Answer?
17. Case Check
18. Emerging Careers
19. Future Detector
20. Future Quiz
21. Explain AI Career Change
22. Work Words
23. Future Ready!

• UAEJudge responsible AI use against governance frameworks
• UAEAI in economic development and social change
• ADEKCareers, future of work
• CSTA (US)Evaluate computational artifacts to maximize their beneficial effects and minimize harmful effects on society.
• CSTA (US)Predict how computational innovations that have revolutionized aspects of our culture might evolve.
• CSTA (US)Evaluate how AI use impacts an individual's decision making and other behavior.
• 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 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)Deliberately assign parts of a task to human strengths versus AI capabilities (Manage 3, advanced).
• 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).
• SingaporeLearn beyond AI: develop distinctly human capabilities and dispositions that AI cannot replicate.
• Korea, Japan, HKDeepen understanding and skills for realising diverse communication and using information systems and diverse (including large-scale/multimedia) data, and understand the development of information technology and social change including its effect on intellectual activity.

1. Art Partner
2. AI Creates
3. Who Is the Artist Here?
4. How Generation Works
5. Creativity Can Be a Dialogue
6. Mechanism Match
7. Diffusion, Step by Step
8. Tool vs Creator
9. Iteration Changes Imagination
10. Creativity Sort
11. The Curation Odds
12. Keeper Odds by Hand
13. Adobe Firefly: generation inside an editing workflow
14. Art Debates
15. Credit Your Project
16. Claim Check
17. Art Partner Check
18. Explain Human-AI Art
19. Creative Words
20. Chapter Complete

• ADEKClassification vs generation, how generative AI is trained
• ADEKGAN/diffusion image generation, critique an image
• ADEKHuman/AI-generated art, ethics of generative tools
• 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).
• 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 how AI use impacts an individual's decision making and other behavior.
• OECD–EC (Europe)Purposefully integrate AI outputs with original ideas to build a comprehensive solution (Create 1, advanced).
• OECD–EC (Europe)Select and integrate outputs from multiple AI systems into a final product (Create 2, advanced).
• OECD–EC (Europe)Assess the ethical implications and justify how an AI-assisted work maintains authenticity (Create 4, advanced).
• UNESCOCarry out responsible AI practices in compliance with ethical principles and locally applicable regulations, iteratively building and following a personal checklist of lawful and responsible practices (e.g. protecting privacy/copyright, marking AI-generated content, avoiding disinformation, hate speech, or sensitive content about identifiable individuals) (CG4.2.2.1).
• UNESCOGain an age-appropriate understanding of personal, legal and ethical responsibilities when using AI and reinforce self-disciplined behaviours around sensitive personal data, copyrighted materials, deepfake/identifiable-person imagery, and the spread of misinformation, disinformation or hate speech (CG4.2.2.2).
• SingaporeLearn to use AI: use AI tools safely and responsibly.
• 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".
• 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. Quantum Promise
2. Quantum World
3. Why Isn't Quantum Already Everywhere?
4. Quantum AI Tasks
5. A Different Kind of Computing
6. Bit vs Qubit
7. Where Might Quantum Help?
8. Then vs Now
9. Specialized Beats Universal
10. Era Sort
11. Test Quantum Readiness
12. Crossover by Hand
13. IBM Quantum Makes Exploration Possible
14. Quantum Research
15. Quantum Detector
16. Read the Claim
17. Quantum Quiz
18. Explain Quantum Limits
19. Quantum Words
20. Quantum Master!

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

1. Your AI Path
2. AI Careers
3. Which Path Fits Best?
4. Skills You Need
5. There Is No Single AI Career
6. Career Match
7. Read the Job Ad
8. Academic vs Industry
9. Career Fit Is Multidimensional
10. Path Sort
11. Budget Your Practice Time
12. Hours by Hand
13. Kaggle Helps People Find Their Strengths
14. Learning Journey
15. Order a Project
16. Future Detector
17. Future Quiz
18. Explain AI Path Choice
19. Career Words
20. Path Ready!

• 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).

1. Graduation Day
2. Covered, and Not Covered
3. Covered or Not Covered?
4. Honest or Overclaim?
5. Final Quiz
6. Capstone Exit Checklist
7. Where This Can Lead
8. Career Claim Check
9. Course Complete

• ADEKCareers, future of work
• UNESCOArticulate personal and social responsibilities of citizens in an AI society — from both user and designer perspectives — including ensuring human control and accountability over every step of the AI life cycle (CG4.3.1.2).
• Korea, Japan, HKCultivate an attitude of participating proactively in, and contributing to the development of, the information society while creating new value through appropriate use of information and information technology and adherence to information morals (law, security).
• ChinaStudents understand the ethics and challenges of a smart/AI-driven society, strengthening information-security awareness, responsibility, and dialectical (balanced) views of AI