CBSE Class 9 Artificial Intelligence
A complete digital coursebook for Class 9 students covering AI foundations, project cycle, data literacy, visualization, AI mathematics, and ethics with GenAI. Includes quizzes, activities, mini-projects, and external lab links.
Infographic slides
Interactive games
Teacher-ready view
Fullscreen learner mode
Animated slide stage • games • app blocks • journal tasks
Course onboarding, digital journal setup, and how to use interactive widgets, projects, and quizzes through this book.
- Understand how to navigate this digital AI coursebook
- Set up your learning journal for project evidence
- Track progress, points, and chapter completion requirements
AI foundations and the three domains: Data for AI, NLP, and Computer Vision.
- Define AI using the data + algorithm concept
- Differentiate CV, NLP, and Data for AI
- Map common apps to the right AI domain
From problem scoping to deployment using the six-stage AI project cycle.
- Order and explain all six AI project cycle stages
- Scope real problems using the 4Ws framework and generate a problem statement
- Distinguish training data from testing data and explain why the split matters
- Classify AI prediction outcomes using the confusion matrix (TP/TN/FP/FN)
- Calculate and interpret accuracy and recall for a binary classifier
Data pyramid, privacy vs security, and practical data handling for AI workflows.
- Explain DIKW transformation from data to wisdom
- Differentiate privacy and security using real scenarios
- Classify qualitative vs quantitative and clean datasets
From raw numbers to compelling visual stories — chart types, interpretation, misleading visuals, and a hands-on Tableau project.
- Explain why the human brain processes visuals 60,000x faster than text
- Choose the correct chart type for any data scenario
- Identify and critique misleading data visualizations
- Build a packed bubble chart dashboard in Tableau Public
- Write a data story with insights from your own visualization
Pattern recognition, statistics workflow, compound probability, conditional probability, Bayesian thinking, and AI decision thresholds.
- Use pattern thinking as a base for AI understanding
- Represent data as vectors and matrices and explain how linear algebra powers AI recommendations
- Describe statistics stages from collection to conclusions
- Choose and calculate mean, median, mode, range, and standard deviation for the correct context
- Calculate basic probabilities using AND, OR, and Complement rules
- Apply conditional probability and Bayesian thinking to update beliefs with evidence
- Explain how thresholds convert probability into responsible AI decisions
Ethics foundations, five AI ethics principles, real-world case analysis, deepfakes, GenAI fundamentals, responsible creation lab, and course capstone.
- Name and apply the five core AI ethics principles to real scenarios
- Identify bias, privacy, accountability, inclusion, and transparency failures in AI systems
- Differentiate conventional AI and generative AI and explain how GANs work
- Use prompt engineering and critically evaluate AI-generated outputs
- Apply the SIFT verification framework to deepfakes and AI-generated misinformation
- Explain accountability chains when AI systems cause harm
