Artificial Intelligence
Become AI-Ready: explore the AI Project Cycle, data literacy, generative AI and Python the official CBSE 417 way.
A complete CBSE Class 9 Artificial Intelligence (Subject Code 417) course aligned to the official 2025-26 Department of Skill Education curriculum. Learners explore the three AI domains, the AI Project Cycle and ethics, data literacy, statistics and probability for AI, generative AI, and beginner Python programming, with hands-on activities and a practical file. Graded out of 100 marks (50 Theory + 50 Practical) and built to prepare students for the AI 417 board assessment.
What you'll learn
- ✓Identify Artificial Intelligence and describe its real-life applications across the Data, Computer Vision and Natural Language Processing domains
- ✓Apply the AI Project Cycle (Problem Scoping, Data Acquisition, Data Exploration, Modeling, Evaluation, Deployment) using tools like the 4Ws canvas and system maps
- ✓Reason about AI ethics, AI bias and AI access, and relate AI projects to the Sustainable Development Goals
- ✓Practise data literacy: acquiring, processing and interpreting data, and creating data visualizations and dashboards
- ✓Use statistics and probability concepts for AI, including recognizing patterns in numbers and images
- ✓Explain how Generative AI works, its types, benefits, limitations and ethical considerations
- ✓Write beginner Python programs using variables, operators, expressions, data types, input()/print(), conditionals, loops and lists
- ✓Build a basic AI model with no-code tools and present an SDG-linked AI project, portfolio or field-visit report
Full syllabus
Mapped to the official CBSE curriculum.
01Part A - Employability Skills+
- 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
02Part B - Unit 1: AI Reflection, Project Cycle and Ethics+
- 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
03Part B - Unit 2: Data Literacy+
- 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)
04Part B - Unit 3: Math for AI (Statistics & Probability)+
- 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)
05Part B - Unit 4: Introduction to Generative AI+
- 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
06Part B - Unit 5: Introduction to Python+
- 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
07Part 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)
08Part 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)
Tools you'll use
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 / project
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.
Who it's for
Class 9 students taking AI (417) as their skill subject who want a structured, board-aligned introduction to AI and Python, including learners with no prior coding experience.
What's included
- ✓Live, interactive online classes with Kajal Ma'am (teaching computer subjects since 2006)
- ✓Chapter-wise Kwick Notes covering all five Part-B units plus Employability Skills
- ✓Solved questions and board-pattern answer practice for the AI 417 theory paper
- ✓Topic-wise assignments and worksheets for every unit, including Python practice sets
- ✓Regular doubt-solving sessions across theory, math-for-AI and Python coding
- ✓Guidance to build the Python Practical File of minimum 15 programs (15 marks)
- ✓Hands-on help with the SDG project, no-code AI model (Teachable Machine / ML for Kids), 4Ws canvas and student portfolio
- ✓Viva voce preparation and full practical-exam rehearsal before the board assessment
Why study Artificial Intelligence?
CBSE Class 9 Artificial Intelligence (Subject Code 417) is a Department of Skill Education subject graded out of 100 marks — 50 theory and 50 practical — making it one of the most scoring subjects on the Class 9 report card when prepared methodically. It lays the official foundation for the Class 10 AI (417) curriculum and builds genuine data literacy, the AI Project Cycle, statistics and probability for AI, generative AI awareness and beginner Python — skills that matter far beyond the exam. Because half the marks come from a practical file, project and viva, structured guidance turns this into an easy high-scoring subject while building real understanding of how AI actually works. Studying it early gives students a clear, ethics-aware head start in a field that now touches nearly every career path.
The AI Project Cycle, data literacy and beginner Python introduced here are the same building blocks used in data science, machine learning, software engineering and IT degrees later on. Students gain an early, ethics-aware understanding of how AI systems are scoped, trained and evaluated, which feeds directly into computer-science streams in Class 11-12 and into engineering and CS undergraduate study. It also builds transferable analytical and problem-solving habits valued across the wider technology industry.
Learn directly from Kajal Ma'am
An MCA who has taught computer subjects since 2006, Kajal Mehta personally mentors every batch — turning dense theory into clear, exam-ready understanding.
Course FAQs
Is this CBSE Class 9 AI course fully online and live?+
Who can join this Class 9 Artificial Intelligence (417) course?+
How are the fees structured — is there a group and a one-to-one option?+
Is the course aligned to the official CBSE AI 417 syllabus and exam pattern?+
Do you help with the practical file, project and viva?+
Can we try a class before enrolling?+
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