CBSE Class 9 Artificial Intelligence Syllabus, Units & Marks
Studying this subject? See the CBSE Class 9 Artificial Intelligence course, or read the units below first.
What is the CBSE Class 9 Artificial Intelligence syllabus?
CBSE Class 9 Artificial Intelligence is organised into 8 units, carrying 50 marks in total. The full unit list, the topics inside each one and the exam pattern are below, taken from the syllabus we teach to.
CBSE Class 9 Artificial Intelligence unit list
| 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 |
| Unit 7 | Part C - Practical Work (Python) | - |
| Unit 8 | Part D - Project Work / Field Visit / Student Portfolio | - |
| Total | 50 |
What each unit covers
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
Part C - Practical Work (Python)
- Practical file of minimum 15 Python programs
- Print/output programs (personal info, patterns, calculations, tables, simple interest)
- Input-based programs (area/perimeter, average marks, discounts, surface area/volume)
- List programs (create, modify, index, slice, extend, sort)
- Conditional and loop programs (voting check, grade check, even/odd, sums)
Part D - Project Work / Field Visit / Student Portfolio
- Create an AI model using Teachable Machine or Machine Learning for Kids
- SDG-linked project: 4Ws problem canvas, system map and spreadsheet data visualization with an AI-enabled solution
- Field visit (physical or virtual) to an industry/IT company using AI, with a report
- Student portfolio of minimum 5 AI activities (e.g. Letter to Future Self, Smart Home Floor Plan, 4Ws canvas, System Map)
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.
Practical and project work
Practical assessment is worth 50 marks: a Python Practical File of minimum 15 programs (15 marks); a Practical Examination of any 3 programs covering input/output, variables, operators, expressions, data types, flow of control/conditions and lists (15 marks); Viva Voce (5 marks); and Project Work / Field Visit / Student Portfolio related to the Sustainable Development Goals (15 marks). Suggested projects include building AI models with Teachable Machine or Machine Learning for Kids, or an SDG-based project using the 4Ws canvas, system maps and spreadsheet data visualization.