What is CBSE AICT?
The Central Board of Secondary Education (CBSE) introduced Artificial Intelligence as a Skill Subject to align Indian school education with the demands of the 21st-century digital economy and the goals of NEP 2020 (National Education Policy). This initiative was launched under the Department of Skill Education and is part of the broader “AI For All” national programme.
The subject is formally called Artificial Intelligence and is commonly referred to as AICT (Artificial Intelligence & Computational Thinking). It is available across four classes — IX, X, XI, and XII — through two distinct subject codes based on the school level.
This curriculum is published and maintained by CBSE’s Research & Development Unit at cbseacademic.nic.in/ai.html. It includes downloadable PDFs for Class IX, X, XI, and XII, along with Integration Manuals, Facilitator Guides, Handbooks, and AI Project Showcases.
Why Did CBSE Introduce AI as a Subject?
India’s National Education Policy 2020 emphasises integrating emerging technologies into school education. CBSE introduced AI because the global AI market is projected to exceed $500 billion by 2030, creating millions of high-value jobs. By introducing AI at the school level, CBSE aims to develop the next generation of innovators, critical thinkers, and problem-solvers who can use AI to address real-world challenges in healthcare, agriculture, education, and sustainability.
The curriculum was developed in collaboration with Intel India, leveraging global best practices in AI education. It is designed to be inclusive — accessible to students from all science, commerce, and arts streams.
Subject Codes: 417 & 843
CBSE uses two separate subject codes for the AI curriculum depending on the school level. Understanding these codes is essential for school registration, board examination forms, and mark sheet recognition.
Both Subject Code 417 and 843 are optional skill subjects. A student who takes AI in Class IX (Code 417) progresses to Code 843 in Class XI. Students can choose AI without prior computer science background — the curriculum is designed to be self-contained and beginner-friendly.
Class-Wise Syllabus Breakdown
The CBSE AI curriculum is structured into four parts across all classes. Part A covers Employability Skills (soft skills), Part B covers the core AI Subject Specific Skills, Part C is Practical/Lab work, and Part D is Project/Field Visit. Here is the detailed class-by-class breakdown:
📘 Classes IX & X — Subject Code 417
Class IX AI
Class X AI
📗 Classes XI & XII — Subject Code 843
Class XI AI
Class XII AI
📋 Key Unit Deep Dive
The AI Project Cycle is the foundational framework running across all four classes. Students learn to approach problems systematically like a real AI engineer:
- Problem Scoping: Define the problem using the 4Ws Canvas — What, Who, Where, Why
- Data Acquisition: Identify data requirements, find reliable sources, collect datasets
- Data Exploration: Visualise data using graphs, identify patterns and anomalies
- Modelling: Choose and build an AI model (Decision Tree, Regression, Classification)
- Evaluation: Test the model, calculate accuracy, refine and improve the solution
- Class IX–X: Basics — print, input, variables, arithmetic operators, data types (int, float, string, bool), conditions (if/else), loops (for/while), Lists
- Class XI: Intermediate — Functions, NumPy arrays, Pandas DataFrames, Scikit-learn basics (fit, predict), regression and classification models
- Class XII: Advanced — OOP in Python, file handling, advanced Pandas, full ML pipeline, Orange Data Mining tool, chatbot development with NLP libraries
- Practical: Minimum 15 programs to be submitted in practical file for Classes IX–X
- Ethical Decision Making (Class XI): What makes an AI decision fair? Exploring real cases of AI bias in hiring, healthcare, and policing
- Bias Awareness (Class XI): Types of bias — data bias, algorithmic bias, confirmation bias — and strategies to detect and mitigate them
- AI Values (Class XII): Global AI policy frameworks (EU AI Act, India’s NITI Aayog AI strategy), responsible AI deployment, privacy and security
- Generative AI Ethics: Deepfakes, misinformation, copyright — responsible use of tools like ChatGPT in academic and personal contexts
- Decision Trees: Introduced in Class IX, taught as a visual, intuitive model for classification
- Linear Regression: Class XI — predicting numeric values, calculating MSE and RMSE
- Classification (k-NN): Class XI — classifying data points using nearest neighbour algorithm
- Clustering (k-Means): Class XI — unsupervised grouping of data without labels
- Neural Networks (Concept): Class IX — conceptual introduction through gamification, not coding
- Computer Vision: Class XII — image classification, object detection using Python libraries
- Natural Language Processing: Class XII — tokenization, sentiment analysis, basic chatbot building
The Capstone Project is the highlight of Classes XI and XII. Students build a complete, end-to-end AI solution addressing a real-world societal problem:
- Project Theme: Must align with India’s SDGs or a real community problem
- Deliverables: Full AI model, project documentation (6 marks), demo video (4 marks)
- Tools Used: Python, NumPy, Pandas, Scikit-learn, Orange Data Mining, Jupyter Notebook
- Evaluation: Capstone = 15 marks | Practical File = 10 marks | Lab Test = 10 marks | Viva = 5 marks
- Sample Projects: Crop yield prediction, student dropout prediction, flood risk mapping, healthcare chatbot
Marks & Assessment Scheme
The CBSE AI subject carries a total of 100 marks — equally divided between Theory (50) and Practical (50). This balanced structure ensures students develop both conceptual understanding and hands-on skills.
Classes IX & X — Code 417
| Component | Description | Marks |
|---|---|---|
| Part A — Theory | Employability Skills (MCQ + Short Answer) | 10 |
| Part B — Theory | Subject Specific Skills (Written Theory Paper) | 40 |
| Part C — Practical File | Minimum 15 Python programs submitted | 15 |
| Part C — Practical Exam | Live coding test (3 Python programs) | 15 |
| Part C — Viva Voce | Oral examination on practical work | 5 |
| Part D — Project | Field Visit / Student Portfolio / AI Project | 15 |
| GRAND TOTAL | Theory (50) + Practical (50) | 100 |
Classes XI & XII — Code 843
| Component | Description | Marks |
|---|---|---|
| Part A — Employability Theory | Communication, ICT, Self-Management, Entrepreneurship, Green Skills | 10 |
| Part B — Subject Theory | AI Theory: Concepts, ML Algorithms, Data Science, Ethics | 40 |
| Capstone Project | End-to-end AI solution with documentation | 15 |
| Project Documentation | Written report of project methodology and findings | 6 |
| Project Video | Demo video of the AI project working | 4 |
| Practical File | Python programs + Orange Data Mining tasks | 10 |
| Lab Test | Python and Orange Data Mining hands-on exam | 10 |
| Viva Voce | Based on Capstone Project + Practical File | 5 |
| GRAND TOTAL | Theory (50) + Practical (50) | 100 |
Software Tools & Technologies
The CBSE AI curriculum requires specific software tools for practical work. Schools must provide these tools, and the recommended human-to-machine ratio is 2:1 (2 students per computer) for a batch of 20 students.
Python 3.x
Primary programming language. Used across all 4 classes for data analysis and ML models.
Jupyter Notebook
Interactive coding environment. Default IDE for Python data science tasks in the curriculum.
NumPy
Python library for numerical computing. Used in Class XI–XII for array operations and maths.
Pandas
Data manipulation library. Used for loading, cleaning, and analysing datasets in Class XI–XII.
Scikit-learn
Machine learning library. Used for regression, classification, clustering models in Class XI–XII.
Orange Data Mining
Visual ML tool used in Class XII. Drag-and-drop interface for building ML pipelines without coding.
Matplotlib
Python plotting library for creating charts, graphs, and data visualisations in practicals.
AI For All Platform
Official CBSE AI learning platform at ai-for-all.in — curated resources, activities, and quizzes.
Schools need computers with minimum 4GB RAM, Intel Core i3 or above, internet connectivity for cloud tools, and webcams for AI vision experiments. Recommended batch size is 20 students with 10 computers (2:1 ratio).
Career Pathways After CBSE AI
Students who study CBSE AI gain foundational skills that open doors to India’s fastest-growing career sector. The AI job market in India is expected to create over 1 million new jobs by 2030 across industries.
AI / ML Engineer
Design and build machine learning models for real-world applications across industries.
Data Scientist
Analyse large datasets to extract insights and build predictive models for businesses.
Computer Vision Engineer
Develop systems that can see and understand images — used in healthcare, autonomous vehicles.
NLP Specialist
Build language models, chatbots, and translation systems using Natural Language Processing.
AI Ethics Researcher
Study the social implications of AI — bias, fairness, policy — a fast-growing interdisciplinary field.
Healthcare AI Analyst
Apply AI to medical imaging, diagnosis, patient data analysis, and drug discovery.
After Class XII CBSE AI, students can pursue B.Tech in AI & Data Science, B.Sc. Data Science, BCA with AI specialisation at top institutes including IITs, NITs, BITS Pilani, and IIIT Hyderabad. Many universities now offer direct AI specialisation programmes for students with a CBSE AI background.
Official CBSE AI Resources
Class IX Curriculum PDF
Official CBSE AI Syllabus for Class 9 (Code 417) — 2024-25
Class X Curriculum PDF
Official CBSE AI Syllabus for Class 10 (Code 417) — 2024-25
Class XI Curriculum PDF
Official CBSE AI Syllabus for Class 11 (Code 843) — 2024-25
Class XII Curriculum PDF
Official CBSE AI Syllabus for Class 12 (Code 843) — 2024-25
AI For All Platform
Official CBSE AI learning & practice platform for students
AI Integration Manual
How AI integrates with core subjects like Maths, Science, English
