Class 9 Artificial Intelligence (417) – CBSE Syllabus 2026–27
This comprehensive educational guide covers the complete Class 9 Artificial Intelligence curriculum (Subject Code 417) prescribed for the 2026–27 academic session by the CBSE Department of Skill Education. It details the course structure across Employability Skills (Part A), Subject Specific AI Skills (Part B), Practical Work in Python (Part C), and Project Work (Part D) aligned with Sustainable Development Goals.
Class 9 Artificial Intelligence Syllabus Overview (2026–27)
The CBSE Class 9 Artificial Intelligence curriculum (Subject Code 417) combines the Inspire and Acquire learning modules. It is designed to develop readiness in learners for appreciating artificial intelligence, understanding its societal impacts, mastering the 6 stages of the AI Project Cycle, building data literacy, applying foundational mathematics, exploring Generative AI, and acquiring hands-on Python programming skills.
Interaction with the three primary realms of AI: Data, Computer Vision (CV), and Natural Language Processing (NLP).
Building critical data literacy, understanding Statistics & Probability for AI, and exploring modern Generative AI principles.
Hands-on programming with Python variables, conditions, loops, and lists, paired with deep discussions on algorithmic bias and ethical AI.
Course Structure & Assessment (Total: 100 Marks, 210 Hours)
The curriculum allocates 50 Marks for Theory (Part A: 10 Marks + Part B: 40 Marks) and 50 Marks for Practical/Project Work (Part C: 35 Marks + Part D: 15 Marks), spanning a total of 210 instructional hours.
| Part / Unit | Unit Title | Theory Hours | Practical Hours | Total Hours | Max Marks |
|---|---|---|---|---|---|
| PART A: Employability Skills (50 Hours, 10 Marks) | |||||
| Unit 1 | Communication Skills-I | 10 Hours Combined | 10 | 2 | |
| Unit 2 | Self-Management Skills-I | 10 Hours Combined | 10 | 2 | |
| Unit 3 | Information & Communication Technology (ICT) Skills-I | 10 Hours Combined | 10 | 2 | |
| Unit 4 | Entrepreneurial Skills-I | 15 Hours Combined | 15 | 2 | |
| Unit 5 | Green Skills-I | 05 Hours Combined | 05 | 2 | |
| Part A Sub-Total | 50 | 10 | |||
| PART B: Subject Specific Skills (160 Hours, 40 Marks) | |||||
| Unit 1 | AI Reflection, Project Cycle and Ethics | 30 | 25 | 55 | 10 |
| Unit 2 | Data Literacy | 22 | 28 | 50 | 10 |
| Unit 3 | Math for AI (Statistics & Probability) | 12 | 13 | 25 | 07 |
| Unit 4 | Introduction to Generative AI | 08 | 12 | 20 | 05 |
| Unit 5 | Introduction to Python | 01 | 09 | 10 | 08 |
| Part B Sub-Total | 73 | 87 | 160 | 40 | |
| PART C: Practical Work in Python (35 Marks) | |||||
| File | Practical File (Minimum 15 Python Programs) | 15 | |||
| Exam | Practical Exam (Any 3 programs on I/O, Operators, Control Flow, Lists) | 15 | |||
| Viva | Viva Voce | 05 | |||
| Part C Sub-Total | 35 | ||||
| PART D: Project Work / Field Visit / Student Portfolio (15 Marks) | |||||
| Project | Any ONE: AI Model Project / IT Industry Field Visit / Student Portfolio (Min 5 activities) related to SDGs | 15 | |||
| Part D Sub-Total | 15 | ||||
| GRAND TOTAL (Theory: 50 Marks + Practical: 50 Marks) | 210 Hours | 100 Marks | |||
Part A – Employability Skills
Employability Skills are integrated into all vocational and skill courses under CBSE to develop workplace readiness, self-reliance, and communication competence.
Communication Skills-I
Verbal and non-verbal communication, effective listening, writing sentences, and overcoming communication barriers in collaborative settings.
Self-Management Skills-I
Developing self-awareness, positive attitude, personal grooming, hygiene, goal setting, and effective time-management habits.
Information & Communication Technology (ICT) Skills-I
Basic computer operations, file management, operating systems, internet navigation, email communication, and digital safety practices.
Entrepreneurial Skills-I
Understanding entrepreneurship concepts, role of entrepreneurs in society, identifying business opportunities, and core entrepreneurial traits.
Green Skills-I
Understanding environmental conservation, sustainable development, green economy concepts, and adopting eco-friendly daily practices.
AI Reflection, Project Cycle and Ethics
Unit 1 lays the bedrock of Artificial Intelligence. It introduces learners to human-machine interaction, the three core domains of AI, the six-stage AI Project Cycle framework, and crucial ethical dilemmas including bias and equitable access.
Sub-Unit 1: AI Reflection & Three AI Realms
Students discover how AI influences everyday lives, smart homes, and future career pathways. They explore the 3 foundational domains/realms:
Sub-Unit 2: The 6 Stages of the AI Project Cycle
The AI Project Cycle provides an iterative, structured problem-solving framework:
Identifying community problems, drawing mind maps, stakeholder identification, and filling the 4Ws Problem Canvas (Who, What, Where, Why).
Determining reliable data sources, discovering data features, and constructing System Maps to understand relational dependencies.
Understanding why visualization is vital, utilizing graphical tools (Data Viz Catalogue), Top 10 song prediction activity, and spreadsheet charting.
Differentiating between Rule-based AI models (developer-crafted if-then rules) and Learning-based models (machine learns from training data).
Testing model performance through the Confusion Matrix: True Positive (TP), False Positive (FP), True Negative (TN), and False Negative (FN).
Case study on Preventable Blindness, and deploying an AI model for Personalized Education in real-world scenarios.
AI Ethics, Bias & Access
Students participate in Ethics Awareness roleplays, explore autonomous vehicle moral choices via MIT’s Moral Machine, examine training data collection bias, discuss the digital divide, and conduct a Balloon Debate analyzing whether AI creates more benefits or harms for society.
Data Literacy
Data is the fuel of modern artificial intelligence. Unit 2 trains students to become data-literate citizens by understanding how data enables evidence-based decision making, differentiating privacy from security, and creating interactive data dashboards.
1. Basics of Data Literacy
- Definition & societal impact of data literacy
- Data Literacy Process Framework
- Data Privacy vs. Data Security
- Data breach risks & unauthorized access
- Cybersecurity best practices & cyber safety
- Impact of news articles analysis
2. Acquire, Process & Interpret
- Types of data & acquisition methodologies
- Best practices for data collection
- Data features & data preprocessing
- Data processing & cleaning
- Methods and types of data interpretation
- Trend analysis & visualization exercises
3. Interactive Dashboards
- Significance of visual communication
- Building charts with Tableau Public
- Data visualization using Datawrapper
- Structuring visual narratives & dashboards
- Presenting evidence-driven insights
Math for AI (Statistics & Probability)
Unit 3 demystifies the mathematical pillars underpinning artificial intelligence algorithms. It focuses on finding numerical patterns and mastering real-world applications of Statistics and Probability.
1. Four Pillars of Math in AI
Introduces learners to how mathematical branches enable AI models to recognize patterns:
Activities: Number patterns and picture analogies.
2. Statistics in Real Life
Definition of statistics and practical applications across:
- Disaster Management & early warnings
- Sports analytics and team strategy
- Diseases prediction & public health
- Weather forecasting models
3. Probability & Events
Foundations of calculating event probabilities and classifying event types:
- Calculating probability of an event
- Types of events (certain, impossible, likely)
- Sports match prediction calculations
- Weather forecast probability percentages
- Traffic estimation & route planning
Introduction to Generative AI
Generative AI represents a seismic shift in technology. Unit 4 introduces students to the concepts, architectures, and ethical implications of models capable of creating new text, audio, images, and video from natural language prompts.
Key Generative AI Concepts
- Definition & Classification: What is GenAI and how it classifies and generates new data instances.
- Generative AI vs. Conventional AI: Comparing discriminative models (identifying existing patterns) vs. generative models (synthesizing new content).
- How GenAI Learns: Training on vast datasets, recognizing multi-modal semantic relationships, and generating plausible outputs.
- Types & Examples: Text generators, synthetic voice, image generation, and video synthesis tools.
Hands-on Activities & Ethics
- Real vs. AI-Generated Image Guessing: Training the human eye to spot synthetic artifacts and deepfakes.
- GAN Paint Hands-On: Interactive exploration of Generative Adversarial Network manipulation.
- Benefits & Limitations: Productivity acceleration vs. hallucinations and factual errors.
- Ethical Considerations: Copyright, intellectual property, misinformation risks, and responsible prompt engineering.
Introduction to Python
Python is the dominant programming language of AI and data science. Unit 5 provides a friendly, hands-on on-ramp to programming fundamentals through interactive compilers and gamified environments like CodeCombat.
1. Python Basics
- Variables & Naming Rules
- Data Types:
int,float,str - Type Conversion (
int(),float(),str()) - Arithmetic & Assignment Operators
- Comparison & Logical Operators
print()andinput()functions
2. Flow of Control
- Decision making with
if,elif,else - Relational condition evaluations
- Iterative loops with
for - Conditional loops with
while - Range sequences:
range(start, stop, step) - Loop accumulation & counting algorithms
3. Python Lists
- Creating lists with square brackets
[] - Zero-based positive indexing
- Negative indexing (
-1,-2...) - List slicing:
list[start to end] - Adding & deleting elements (
append,extend,remove,del) - List length
len()and sortingsort()
Part C – Practical Work & Suggested Python Program List
Students are required to maintain a laboratory Practical File containing at least 15 Python programs. The practical examination evaluates 3 programs (15 marks), the submitted file (15 marks), and viva voce (5 marks).
Official PRINT & Expression Programs (CBSE Suggested List)
- Personal Information: Print personal details like Name, Father's Name, Class, and School Name.
- Star Patterns: Print multi-line star pyramid and inverted patterns using multiple print commands.
- Square of 7: Calculate and print the square of number 7 (
7 ** 2). - Sum of Numbers: Compute the sum of two numbers 15 and 20.
- Unit Conversion: Convert length given in kilometers to meters (
km * 1000). - Multiplication Table: Print the table of 5 up to five terms (
5, 10, 15, 20, 25). - Simple Interest: Calculate SI where
P = 2000, R = 4.5, T = 10(SI = (P * R * T) / 100).
Project Work / Field Visit / Student Portfolio (Relate to SDGs)
Students must relate their practical project work to the United Nations Sustainable Development Goals (SDGs). Any one of the following three options must be submitted:
Suggested AI Project
- Create an AI model using Google Teachable Machine or Machine Learning for Kids.
- Choose a sustainable development goal topic.
- Create a 4Ws Problem Canvas.
- Identify data features and build a System Map.
- Visualize data using spreadsheets.
- Suggest an AI-enabled prototype solution.
Suggested Field Visit
Visit an industry or IT organization that creates or deploys Artificial Intelligence solutions. Present a structured field report summarizing the AI technologies observed. (Visit can be conducted in physical or virtual mode).
Student Portfolio
Maintain a documented record of at least 5 AI activities completed throughout the session, such as:
- Letter to Future Self
- Smart Home Floor Plan
- Future Job Advertisement
- Research on AI for SDGs in Sectors
- 4Ws Canvas & System Maps
Laboratory Equipment & Software Specifications
Hardware Specifications (Batch of 20, 2:1 Ratio)
- Intel Core i5-7300U Processor or equivalent
- 8GB DDR4 RAM (2400MHz or above)
- 500 GB HDD (7200 rpm) / Integrated graphics
- 18.5" LED Monitor with HDMI, in-built speaker
- Full HD Webcam, Headphones with Mic
- Dual Band Wireless Connectivity (Min 800 Mbps)
Software Specifications
- Operating System: Any modern OS with Anti-Virus
- Web Browser: Google Chrome
- Productivity Suite: Google Workspace / Office Suite
- Anaconda Navigator Distribution
- Python 3.x environment
- Intel OpenVINO tools for vision processing
What Students Learn in Class 9 Artificial Intelligence
By completing the Class 9 AI (417) curriculum, learners progress across foundational, mathematical, technical, and ethical milestones:
Appreciate AI in everyday life and explore Computer Vision, Data Statistics, and Natural Language Processing.
Scope community issues with 4Ws canvas, acquire data, explore visualizations, compare rule-based vs learning-based models, and evaluate matrices.
Analyze data trends with Tableau, master Statistics and Probability concepts, and understand modern Generative AI principles.
Build computational programs using variables, operators, conditional checks, while/for loops, and list manipulation algorithms.
Explore Free Practice Questions, Worksheets & Courses
Strengthen your Class 9 AI preparation with TeacherColab's interactive resources:
Free AI & ML Worksheets
Step-by-step interactive worksheets covering Classification, Features & Labels, Model Accuracy, and NLP.
Free Python Worksheets
Hands-on coding worksheets for Python Variables, Conditionals, For/While Loops, Functions, and Lists.
Generative AI Tutorial
Student-friendly tutorial on how Generative AI works, prompt engineering tips, and ethical guidelines.
Online AI Quiz
Fast, 10-question self-assessment quiz on AI terminology, computer vision, and machine learning basics.
Live 1-on-1 Python Classes
Live mentoring with experienced coding educators to accelerate Python mastery and project creation.
Frequently Asked Questions on Class 9 AI (417)
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Curriculum Reference
This page is based on the CBSE Artificial Intelligence (Subject Code 417), Class IX, Curriculum for Session 2026–2027, Department of Skill Education, Central Board of Secondary Education (CBSE).
TeacherColab is an independent educational platform and is not affiliated with or endorsed by CBSE.