Class 12 Artificial Intelligence Code 843 Syllabus PDF Download

CBSE Class 12 Artificial Intelligence (Code 843) Latest Syllabus

Class 12 Artificial Intelligence (Code 843) syllabus is very important for both students and teachers as it provides the complete framework for the academic session and CBSE board examination preparation. Before starting preparation, students should carefully go through the latest CBSE AI syllabus 2025–26 to understand the updated topics, unit-wise distribution, and exam pattern. It helps students to focus on the right concepts such as AI project cycle, data science, machine learning, and ethical issues in AI, ensuring better planning and effective revision.

For teachers, the updated syllabus plays a crucial role in designing lesson plans, study material, notes, and classroom activities according to CBSE guidelines. Providing accurate and syllabus-based content ensures that students are learning the correct topics without missing any important concept. At the same time, students can use the syllabus as a checklist to track their preparation, revise important topics, and practice questions accordingly. Download the latest Class 12 AI (Code 843) syllabus PDF to stay updated and boost your preparation with proper guidance and structured learning.

Click the links below to download the updated syllabus for Class 12 Artificial Intelligence (Code 843)

Syllabus	Source	Download
CBSE Class 12 Artificial Intelligence Syllabus 2026-27	CBSE	Available Soon
CBSE Class 12 Artificial Intelligence Syllabus 2025-26	CBSE	PDF Download
CBSE Class 12 Artificial Intelligence Syllabus 2024-25	CBSE	PDF Download
CBSE Class 12 Artificial Intelligence Syllabus 2023-24	CBSE	PDF Download

ARTIFICIAL INTELLIGENCE (SUBJECT CODE 843)

CLASS – XII (SESSION 2025-26)

Total Marks: 100 (Theory 50 Marks + Practical 50 Marks)

Employability Skills (PART A)	Hours	Max Marks
Unit 1: Communication Skills-IV	15	2
Unit 2: Self-Management Skills-IV	10	2
Unit 3: ICT Skills-IV	15	2
Unit 4: Entrepreneurial Skills-IV	10	2
Unit 5: Green Skills-IV	10	2
Total	60	10

Subject Specific Skills (PART B)	Theory	Practical	Max Marks
Unit 1: Python Programming - II*	6	8	*to be evaluated in practicals only
Unit 2: Data Science Methodology: An Analytic Approach to Capstone Project	8	12	8
Unit 3: Making Machines See	6	12	6
Unit 4: AI with Orange Data Mining Tool*	4	18	*to be evaluated in practicals only
Unit 5: Introduction to Big Data and Daata Analytics	7	12	6
Unit 6: Understanding Neural Networks	8	12	8
Unit 7: Generative AI	6	12	7
Unit 8: Data Storytelling	5	4	5
Total	50	100 Hours	40

Practical & Project Work	Max Marks
Capstone Project + Project Documentation (As per the process given in “Project Guidelines”, on page 2 of CBSE IBM Projects Cookbook) Capstone Project = 15 Marks Project Documentation = 6 Marks Video = 4 Marks	25
Practical File	10
Lab Test (Python and Orange Data Mining)	10
Viva Voce (based on Capstone Project + Practical File)	5
Total	50
Grand Total	100

1. Practical File:

I. Mandatory Programs

(i) Python Programs

Minimum 6 programs of Python.

(ii) Orange Data Mining

Minimum 3 programs using Orange Data Mining tool.

(iii) Data Storytelling

Minimum 1 problem to create a Data Story using all steps of Data Storytelling.

II. Optional Programs (For Practical File)

Demonstration of train-test split in Linear Regression using Python.

Chatbot using Google Gemini API.

Orange Data Mining for Data Analytics.

Classification problem using TensorFlow Playground.

Regression problem using TensorFlow Playground.

Sample Programs for Reference

I. Python

1. Pandas DataFrame Program

Write Python code to create a Pandas DataFrame using any sequence data type: 

(a) Display the DataFrame 

(b) Display first 5 records 

(c) Display last 10 records

(d) Display the number of missing values in the dataset

2. CSV Dataset Program

Download dataset in the form of CSV from any public open-source website

Perform the following:

(a) Read CSV file and convert it into Pandas DataFrame

(b) Perform statistical functions on the dataset:

Checking data

Checking missing values

Filling missing data

3. Model Evaluation Program

Write Python code to evaluate a model

II. Orange Data Mining

1. Perform step-wise procedure of Data Visualization using Orange

2. Perform Classification using Orange

3. Evaluate the Classification Model

4. Perform Image Analytics using Orange

5. Write steps to visualize Word Frequencies using Word Cloud

Note: Take snapshots of all steps and outputs and paste them in the practical file

III. Data Storytelling (Sample)

Using available data on student enrollment, attendance, and dropout rates, create a compelling data story that explores the impact of the Mid-Day Meal Scheme (MDMS) since its launch in 1995. Uncover trends, patterns, and correlations in the data to tell a story about how the implementation of the MDMS may have influenced dropout rates in the state over the years. Consider incorporating visualizations, charts, and graphs to effectively communicate your findings. Additionally, analyze any external factors or events that might have played a role in shaping these trends. Your goal is to provide a comprehensive narrative that highlights the relationship between the MDMS and student dropout rates in the state.

IV. Capstone Project

Guidelines

Group size: Minimum 3 and Maximum 5 students

Project must align with any of the SDGs (Sustainable Development Goals)

To be completed in Class XII

Documentation must be prepared

Project Video Requirements (3 Minutes Only)

The video must include:

Problem Statement

SDG Alignment

AI Concepts / Domains / Algorithms Used

Working of the Project

Conclusion

Acknowledgement to the Teacher