CBSE Class 9 Artificial Intelligence, Chapter-wise Weightage 2026-27
Studying this subject? See the CBSE Class 9 Artificial Intelligence course, or the full CBSE Class 9 Artificial Intelligence syllabus.
CBSE Class 9 Artificial Intelligence chapter-wise weightage
The 50 theory marks in CBSE Class 9 Artificial Intelligence are split across 6 units, and they are not split evenly. Unit 1, Part A - Employability Skills, alone carries 10 marks, which is 20% of the paper. Studying every unit for the same number of hours is the most common way students lose easy marks.
| Unit | Topic | Marks |
|---|---|---|
| Unit 1 | Part A - Employability Skills | 10 |
| Unit 2 | Part B - Unit 1: AI Reflection, Project Cycle and Ethics | 10 |
| Unit 3 | Part B - Unit 2: Data Literacy | 10 |
| Unit 4 | Part B - Unit 3: Math for AI (Statistics & Probability) | 7 |
| Unit 5 | Part B - Unit 4: Introduction to Generative AI | 5 |
| Unit 6 | Part B - Unit 5: Introduction to Python | 8 |
| Total | Theory | 50 |
Where should your revision time go?
Weight your revision the way the paper is weighted. Unit 1 deserves the most passes, and the smallest units are worth securing completely rather than skipping, because they are short enough to finish and are usually asked in predictable ways.
Unit-by-unit
Part A - Employability Skills (10 marks)
- Unit 1: Communication Skills-I
- Unit 2: Self-Management Skills-I
- Unit 3: Information and Communication Technology (ICT) Skills-I
- Unit 4: Entrepreneurial Skills-I
- Unit 5: Green Skills-I
Part B - Unit 1: AI Reflection, Project Cycle and Ethics (10 marks)
- AI Reflection: introduction to AI and its daily-life applications
- The three realms/domains of AI: Data, Computer Vision and Natural Language Processing (AI games)
- AI Project Cycle framework: Problem Scoping, Data Acquisition, Data Exploration, Modeling, Evaluation, Deployment
- Problem scoping with the 4Ws problem canvas, goal setting and stakeholders
- Data acquisition, data features and system maps
- Data exploration and visualization using graphical tools
- Modeling: Rule-based and Learning-based models
- Evaluation terms: True Positive, False Positive, True Negative, False Negative
- Deployment of AI solutions
- AI Ethics, AI bias and AI access; advantages and disadvantages of AI
Part B - Unit 2: Data Literacy (10 marks)
- Basics of data literacy and its importance
- Informed decision-making, critical thinking and the Data Literacy Process Framework
- Data privacy vs data security, data breaches and cyber security best practices
- Acquiring data: types of data and acquisition methodologies
- Data preprocessing, processing and interpretation; types and importance of data interpretation
- Project: Interactive Data Dashboard and presentation (e.g. Tableau, Datawrapper)
Part B - Unit 3: Math for AI (Statistics & Probability) (7 marks)
- Importance of Math for AI: Statistics, Linear Algebra, Probability, Calculus
- Finding patterns in numbers and images; number patterns and picture analogy
- Statistics: definition and real-life applications (disaster management, sports, disease prediction, weather forecast)
- Data collection, analysis and interpretation activities
- Probability: calculating probability, types of events
- Applications of probability (sports, weather forecast, traffic estimation)
Part B - Unit 4: Introduction to Generative AI (5 marks)
- Defining Generative AI and classifying its kinds
- How Generative AI works and learns; Generative AI vs Conventional AI
- Types and examples of Generative AI
- Benefits and limitations of Generative AI
- Hands-on with Generative AI tools (e.g. GAN Paint)
- Ethical considerations of using Generative AI
Part B - Unit 5: Introduction to Python (8 marks)
- Introduction to programming through gamified platforms (e.g. CodeCombat)
- Introduction to Python language and its applications
- Python basics: variables, arithmetic/comparison/logical/assignment operators, expressions
- Data types (integer, float, string), type conversion, print() and input() functions
- Flow of control and conditions: if, for and while statements
- Python Lists and simple list operations
Exam pattern
Total 100 marks: Theory 50 + Practical 50. Part A Employability Skills carries 10 theory marks across 5 units; Part B Subject-Specific Skills carries 40 theory marks (Unit 1 AI Reflection, Project Cycle & Ethics 10, Unit 2 Data Literacy 10, Unit 3 Math for AI 7, Unit 4 Generative AI 5, Unit 5 Introduction to Python 8). Practical 50 = Practical File 15 + Practical Examination 15 + Viva Voce 5 + Project/Field Visit/Portfolio 15.