Class 9 AI Notes 2026-27: CBSE Artificial Intelligence (Code 417)
Looking for simple and complete Class 9 AI notes? You are in the right place. This page covers the full CBSE Artificial Intelligence syllabus for the 2026-27 session. Every unit has its own detailed notes, with MCQs, short questions, and long questions.
All notes are written in easy English and short sentences. They are made for beginners.
What you will find on this page: syllabus overview, marks distribution, unit-wise notes links, quick revision terms, study plan, project ideas, and FAQs.
Quick Links
- About CBSE Class 9 AI (Code 417)
- Class 9 AI Syllabus 2026-27 at a Glance
- Marks Distribution and Exam Pattern
- Unit-Wise Class 9 AI Notes
- Which Units Are Most Important?
- Quick Revision: Key Terms
- How to Study Class 9 AI
- Practical Work and Project Ideas
- Frequently Asked Questions
About CBSE Class 9 AI (Code 417)
Artificial Intelligence is a skill subject in CBSE Class 9. Its subject code is 417. Students take it along with their other subjects.
The course helps students become “AI-ready”. It teaches the three domains of AI: Data, Computer Vision, and Natural Language Processing. It also covers the AI Project Cycle, ethics, data literacy, basic maths for AI, generative AI, and Python.
The course is full of games, activities, and hands-on work. So it is not only about theory.
Class 9 AI Syllabus 2026-27 at a Glance
The subject specific part (Part B) has five units. The table below shows the hours and marks for each unit, as given in the CBSE curriculum for 2026-27.
| Unit | Unit Name | Theory Hours | Practical Hours | Marks |
|---|---|---|---|---|
| 1 | AI Reflection, Project Cycle and Ethics | 30 | 25 | 10 |
| 2 | Data Literacy | 22 | 28 | 10 |
| 3 | Math for AI (Statistics and Probability) | 12 | 13 | 7 |
| 4 | Introduction to Generative AI | 8 | 12 | 5 |
| 5 | Introduction to Python | 1 | 9 | 8 |
| Total | 160 hours | 40 | ||
Marks Distribution and Exam Pattern
Class 9 AI is worth 100 marks in total. These are split into 50 marks for theory and 50 marks for practical.
| Part | What it covers | Marks |
|---|---|---|
| Part A | Employability Skills | 10 |
| Part B | Subject Specific Skills (Units 1 to 5) | 40 |
| Part C | Practical Work (practical file 15, practical exam 15, viva 5) | 35 |
| Part D | Project Work / Field Visit / Student Portfolio | 15 |
| Total | 100 |
Note: Always check the latest circular and sample paper from CBSE and your school. The pattern can be updated.
Unit-Wise Class 9 AI Notes
Click on any unit below to read its full notes. Each page has detailed explanations, diagrams, 20 MCQs, short answer questions, and long answer questions.
Unit 1: AI Reflection, Project Cycle and Ethics (10 Marks)
This is the biggest unit. You learn what AI is, the three domains of AI, and the six stages of the AI Project Cycle. You also learn the 4Ws Problem Canvas, rule-based and learning-based models, the confusion matrix, and AI ethics.
- Three domains of AI: Data, Computer Vision, NLP
- AI Project Cycle: Problem Scoping, Data Acquisition, Data Exploration, Modelling, Evaluation, Deployment
- 4Ws Canvas, Decision Matrix, System Maps
- True Positive, False Positive, True Negative, False Negative
- AI ethics, bias, and privacy
Read Unit 1 Notes: AI Reflection, Project Cycle and Ethics »
Unit 2: Data Literacy (10 Marks)
This unit teaches you how to read, understand, and protect data. You learn the Data Pyramid, data privacy and security, types of data, data acquisition, and data interpretation.
- Data Pyramid (Data, Information, Knowledge, Wisdom)
- Data privacy vs data security, and cyber security tips
- Textual and numeric data, continuous and discrete data
- Data discovery, augmentation, and generation
- Qualitative and quantitative data interpretation
Read Unit 2 Notes: Data Literacy »
Unit 3: Math for AI – Statistics and Probability (7 Marks)
This unit shows why maths is the base of AI. You study statistics and probability with simple, real-life examples.
- Four areas of maths in AI: Statistics, Linear Algebra, Probability, Calculus
- Mean, median, and mode
- Probability formula and the probability scale
- Types of events and real-life applications
Read Unit 3 Notes: Math for AI »
Unit 4: Introduction to Generative AI (5 Marks)
This is the newest and most exciting unit. You learn how AI can create text, images, music, and video. You also learn why it must be used with care.
- Generative AI vs Conventional AI
- Types: GANs, VAEs, RNNs, Transformers
- Tools and examples
- Benefits, limitations, and ethical concerns
Read Unit 4 Notes: Introduction to Generative AI »
Unit 5: Introduction to Python (8 Marks)
In this unit, you start coding in Python. You learn variables, operators, data types, print() and input(), if, for and while, and lists.
Notes for Unit 5: Coming soon.
Which Units Are Most Important?
Look at the marks. Unit 1 and Unit 2 together carry 20 out of 40 marks in Part B. That is half of the subject specific marks. So spend the most time on these two units.
Unit 5 (Python) looks small in theory hours, but it carries 8 marks and also feeds the practical file and practical exam. So do not ignore it.
A good order of study is: Unit 1, then Unit 2, then Unit 5, then Unit 3, and finally Unit 4.
Quick Revision: Key Terms
Use this table for fast revision before your test.
| Term | Simple Meaning | Unit |
|---|---|---|
| Artificial Intelligence | Machines that can learn, think, and take decisions like humans. | 1 |
| Computer Vision | AI that understands pictures and videos. | 1 |
| NLP | AI that understands human language. | 1 |
| Stakeholder | A person affected by the problem. | 1 |
| 4Ws Canvas | Who, What, Where, Why. A tool to study a problem. | 1 |
| Training data | Data used to teach the AI model. | 1 |
| Testing data | New data used to check the model. | 1 |
| Confusion matrix | A table showing true and false predictions. | 1 |
| Bias | When AI treats one group unfairly because of poor data. | 1 |
| Data literacy | The ability to read, understand, and explain data. | 2 |
| Data Pyramid | Data, Information, Knowledge, Wisdom. | 2 |
| Data privacy | Protecting personal data and people’s rights over it. | 2 |
| Data security | Protecting data from theft and misuse. | 2 |
| Primary data | Data we collect ourselves. | 2 |
| Secondary data | Data collected earlier by someone else. | 2 |
| Mean | The average of all values. | 3 |
| Median | The middle value after sorting. | 3 |
| Mode | The value that appears most often. | 3 |
| Probability | Favourable outcomes divided by total outcomes. | 3 |
| Generative AI | AI that creates new content like text, images, and music. | 4 |
| GAN | Two networks (Generator and Discriminator) that compete to create realistic data. | 4 |
| Deepfake | A fake but realistic image or video made by AI. | 4 |
| Prompt | The instruction we give to a Generative AI tool. | 4 |
How to Study Class 9 AI
- Read the unit notes once, slowly. Do not rush. Understand the examples.
- Make your own small notes. Write definitions in your own words.
- Draw the diagrams. The AI Project Cycle, Data Pyramid, and confusion matrix are easy to draw and often asked.
- Solve the MCQs. Do them without looking at the answers first.
- Practise short and long questions. Write the answers in full sentences.
- Try the activities. Play the AI games and try tools like Teachable Machine. Hands-on practice makes concepts stick.
- Revise with the key terms table. Do this a day before the exam.
Practical Work and Project Ideas
Practical work carries a large part of the marks, so start early.
Practical File and Exam (Part C)
You need a practical file with at least 15 Python programs. The practical exam covers simple programs with input and output, variables and operators, flow of control (if, for, while), and lists. You will also face a viva.
Project, Field Visit, or Portfolio (Part D)
You must do any one of these, and it should connect to the Sustainable Development Goals.
- Project idea 1: Build a simple AI model using Teachable Machine or Machine Learning for Kids.
- Project idea 2: Pick a real problem. Make a 4Ws canvas, a system map, and a graph of your data. Then suggest an AI-based solution.
- Field visit: Visit a company that uses AI (in person or online) and write a report.
- Portfolio: Keep a record of at least 5 AI activities, such as a 4Ws canvas, a system map, or a letter to your future self.
Frequently Asked Questions (FAQs)
What is the subject code of AI in CBSE Class 9?
The subject code of Artificial Intelligence in CBSE Class 9 is 417.
How many units are there in Class 9 AI?
Part B has five units: AI Reflection, Project Cycle and Ethics; Data Literacy; Math for AI (Statistics and Probability); Introduction to Generative AI; and Introduction to Python.
What is the marks distribution of Class 9 AI?
The total is 100 marks. Theory is 50 marks and practical is 50 marks. Part B (subject specific skills) is worth 40 marks.
Which unit carries the most marks?
Unit 1 (AI Reflection, Project Cycle and Ethics) and Unit 2 (Data Literacy) carry the most marks. Each is worth 10 marks in Part B.
Is Python included in the Class 9 AI syllabus?
Yes. Unit 5 is Introduction to Python. It carries 8 marks in Part B. You also need a practical file with at least 15 programs.
Is Generative AI part of the Class 9 syllabus?
Yes. Unit 4 is Introduction to Generative AI. It covers types, examples, tools, benefits, limitations, and ethics. It carries 5 marks.
How can I score well in Class 9 AI?
Study Unit 1 and Unit 2 well, practise the diagrams, solve MCQs, and prepare your practical file and project early. Revise with the key terms table before the exam.
Are these Class 9 AI notes free?
Yes. All the notes on this website are free to read.
Start Learning Now
Pick a unit and begin. If you are not sure where to start, start with Unit 1: AI Reflection, Project Cycle and Ethics. Bookmark this page, so that you can come back to all unit notes in one place.
