ISC Class 12 Syllabus 2028 Artificial Intelligence

ISC
INDIAN SCHOOL CERTIFICATE
EXAMINATION

YEAR 2028

ARTIFICIAL INTELLIGENCE
(883)

Developed by:
Research, Development and Curriculum Division (RDCD)
CISCE

January 2026
____________________________________________________________________________________________

© Copyright, Council for the Indian School Certificate Examinations
All rights reserved. The copyright to this publication and any part thereof solely vests in the Council for the Indian
School Certificate Examinations. This publication and no part thereof may be reproduced, transmitted, distributed or
stored in any manner whatsoever, without the prior written approval of the Council for the Indian School Certificate
Examinations.

Council for the Indian School Certificate Examinations (CISCE)

MISSION STATEMENT

The Council for the Indian School Certificate
Examinations is committed to serving the nation's
children, through high quality educational
endeavours, empowering them to contribute towards
a humane, just and pluralistic society, promoting
introspective living, by creating exciting learning
opportunities, with a commitment to excellence.

ETHOS OF CISCE

Trust and fair play.
Minimum monitoring.
Allowing schools to evolve their own niche.
Catering to the needs of the children.
Giving freedom to experiment with new ideas
and practices.
Diversity and plurality - the basic strength for
evolution of ideas.
Schools to motivate pupils towards the
cultivation of:
Excellence - The Indian and Global
experience.
Values - Spiritual and cultural - to be the bedrock
of the educational experience.
Schools to have an 'Indian Ethos', strong roots in
the national psyche and be sensitive to national
aspirations.

CLASS XII
There will be two papers in the subject:
Paper I: Theory - 3 hours…70 marks
Paper II: Practical - 3 hours ... 15 marks
Practical File… 15 marks

PAPER I (THEORY) : 70 MARKS
S.No. Unit Total Weightage
(Marks)
1. Applications of AI 10
2. Different paradigms of AI: Neural networks, Machine learning, 15
Deep learning.
3. Practical Implications of ANN 15
4. Practical Implications of Machine Learning (ML) 20
5. Introduction to Computer Vision (CV) 10
Total 70
Note: Key concepts of Class XI need to be revised as a prerequisite.

1. Applications of AI
(i) Natural Language Processing (NLP): Named Entity Recognition (NER)
Identify and classify names of people, organisations, and locations in a news article, Text Summarisation
using Extraction Methods: Summarise a long passage by extracting the most important sentences or
phrases, Spam Email Detection using Machine Learning: Build a model to classify emails as spam or
non-spam based on their content and features.
(ii) Statistics and Probability
Understanding data trends using mean, median, mode, range, variance, and standard deviation;
Probability Basics: Introducing probability as a measure of uncertainty, along with common distributions
like uniform, normal, and binomial.
(iii) Hypothesis Testing
Understanding null and alternative hypotheses, Type I and Type II errors, and their relevance in AI
experiments, Regression Analysis: Introducing simple linear regression for prediction and correlation
coefficient for measuring relationships between variables.

2. Different paradigms of AI: Neural Networks, Machine Learning, Deep Learning
(i) Artificial Neural Networks (ANN)
About Artificial Neural Networks, nodes (neurons), Input layer, Hidden layer, Output layer, Forward and
Backward propagation in ANN, ANN model, Multi-layer perceptron (MLP), Example- Recognising
handwritten digits using a multi-layer perceptron (MLP).
(ii) Machine Learning (ML)
Brief understanding of Machine Learning; An AI paradigm enabling computers to learn from data and
make predictions or decisions without explicit programming. Example- Predicting house prices based on
features like size, location, and number of bedrooms using a decision tree.

6

(iii) Deep Learning (DL):
About Deep Learning, Neural Network utilised for image recognition, pattern recognition, and/or
computer vision. Example- Classifying images into different categories (e.g., cat, dog, bird) using a
Convolution Neural Network (CNN).

3. Practical Implications of ANN
(i) Data Preparation
Use NumPy for numerical computations and Pandas for data manipulation.
Employ Scikit-learn for data preprocessing tasks like scaling, encoding categorical variables, and
splitting data into training and testing sets.
(ii) Building the Neural Network
Choose Keras or TensorFlow: Keras is easy to use and runs on top of TensorFlow, making it beginner
friendly.
(iii) Training the Neural Network
Fit your data to the neural network model using the fit() function, specifying the number of epochs and
batch size.
(iv) Model Evaluation
Evaluate the model's performance on the test data using metrics like accuracy, precision, and recall.
Visualise performance metrics using libraries like Matplotlib to gain insights.
(v) Tuning
Utilise techniques like grid search or random search to tune hyper parameters such as learning rate,
batch size, and number of hidden layers and neurons.

4. Practical Implications of Machine Learning (ML)
(i) Predictive Maintenance
Using historical data from machines to predict equipment failure or require maintenance, helping to
minimise downtime and optimise maintenance schedules.
(ii) Fraud Detection
Credit card fraud or identity theft, enabling financial institutions to take proactive measures to prevent
losses.
(iii) Medical Diagnosis
Analysing Medical Imaging, Genetic Information, and Patient records for diagnosis.

5. Introduction to Computer Vision (CV)
(i) Introduction to CV, Application of Open CV in real world scenario
Image Classification, Object Detection, Facial Recognition, Color Detection, Object Tracking (brief
explanation with examples).
(ii) Image Classification
Implementing a simple image classifier to distinguish between different categories of objects, such as cats
vs. dogs or fruits vs. vegetables.
(iii) Object Detection
Identification and localisation of specific objects within an image, such as detecting cars in a street scene
or detecting faces in a photograph.
7

(iv) Facial Recognition
Building a facial recognition application that can recognise and identify faces in images or videos, and
optionally, associate them with known individuals.
(v) Color Detection
Identification and classification of objects based on their color properties, such as sorting colored objects
on a conveyor belt or detecting ripe fruits in agriculture.
(vi) Object Tracking
Track the movement of objects in a video sequence over time, such as tracking the trajectory of a ball in
sports footage or monitoring vehicles in traffic surveillance.

PAPER II (PRACTICALS) : 30 MARKS
The practical paper of three hours’ duration will be evaluated by the Visiting Examiner appointed locally and
approved by CISCE.
The paper shall consist of three problem statements /problems from which a candidate has to attempt any one.
The practical consists of two parts:
(1) Planning/ Writing Session
(2) Examination Session
The total time to be spent on the Planning/Writing Session and the Examination session is three hours. A
maximum of 90 minutes is permitted for the Planning/Writing Session and 90 minutes for the Examination
session.
Candidates are to be permitted to proceed to the Examination Session only after the 90 minutes of the
Planning / Writing Session are over.
Planning/Writing Session
The candidates will be required to prepare an algorithm and a handwritten program to solve the problem.
Examination Session
The program handed in at the end of the Planning/Writing session shall be returned to the candidates. The
candidates will be required to code and execute the program individually on the computer, hardware and show
execution to the Visiting Examiner. A printout of the program listing including output results should be attached
to the answer script containing the handwritten program and hardware results. This should be returned to the
examiner. The program should be sufficiently documented so that the material required, circuit diagram/block
diagram, algorithm, representation and development process is clear from reading the program. Large differences
between the planned program and the printout will result in loss of marks.
Teachers should maintain a record of all the assignments done as part of the practical work throughout the year
and give it due credit at the time of cumulative evaluation at the end of the year. Students are expected to do a
minimum of twenty-five assignments for the year.
The details are as follows:
Details of Assignments to be done during the year

Broad Area Number of Assignments

Practical (Programming) 15
Hands-on (Case Study) 10
TOTAL 25
8

List of suggested assignments:
Some sample problems are given below as examples. The problems are of varying levels of difficulty:
1. Tokenization and Text Preprocessing: Demonstrate tokenization techniques using NLTK or spaCy in Python
to break down sentences into individual words or tokens.
2. Named Entity Recognition (NER): Implement NER using spaCy to identify and classify named entities such
as persons, organisations, and locations in text data.
3. Probability Distributions: Explore different probability distributions (e.g., normal, binomial, Poisson) using
Python libraries like NumPy and visualise their properties using Matplotlib.
4. Hypothesis Testing: Perform hypothesis testing (e.g., t-test, chi-square test) on sample data to make
inferences about population parameters.
5. Create a Python script to train a linear regression model using the NumPy library to predict a car's fuel
efficiency (in miles per gallon) based on its engine size (in liters).
6. Write a Python script to train a linear regression model using the NumPy library to predict a student's final
exam score based on their study hours.
7. Implementing Forward and Backward Propagation: Build a simple neural network from scratch in Python to
understand the concepts of forward and backward propagation.
8. Introduction to Hidden Layers: Extend the basic neural network by adding hidden layers and activation
functions to demonstrate the role of hidden la yers in learning complex patterns.
9. Generate attendance monitoring system to track the attendance of the students using Open-CV.
10. Introduction to CPU, GPU, TPU and DPU and their distinction for individual projects/research in AI.
NOTE: This list is indicative only. Teachers and students should use their imagination to create innovative and
original assignments.
EVALUATION OF PROGRAMMING ASSIGNMENTS
Marks (out of 30) should be distributed as given below.
Continuous Evaluation
Candidates will be required to submit a work file containing the practical work related to assignments done during
the year.

Programming assignments done throughout the year (Internal 10 marks
Evaluation)

Programming assignments done throughout the year (Visiting 5 marks
Examiner)

Proposed Guidelines for Marking
The actual grading will be done by the teacher based on his/her judgment. One possible way: divide the outcome
for each criterion into one of 3 groups: excellent, good, poor/unacceptable, then use numeric values for each grade
and add to get the total.

9

Evaluation will be done as follows:
Programming Assignments: 10 Marks
Criteria (Total 10 marks) Class design - Execution Documentation Practical File
(4 marks) (6 marks)
Excellent 4 6
Good 3 4
Poor 1 2
Terminal Evaluation

Solution to Problem Statement on Hands-On/ Programming 15 marks

Marks should be given for choice of algorithm and implementation strategy, documentation, correct output on
known inputs mentioned in the question paper, correct output for unknown inputs available only to the examiner.
Note: Algorithm should be expressed clearly using any standard scheme such as a pseudo code.

LABORATORY REQUIREMENTS
(For a class of 30 students)

I. Hardware Requirements:
Group of 2 Students: 1 Desktop along with Webcam
1. Webcam Detailed Description:
Video Capture Resolution 1080p
Maximum Focal Length 1080
Maximum Aperture 2 f
Reference: Logitech Brio 100 Full HD Webcam
2. Desktop Configuration
Detailed Description:
Dell Optiplex 3000 Desktop:
12th Generation Intel Core I5-12500
Processor Intel B660 Chipset
2GB NVidai Dedicated Graphics
ENERGY STAR Qualified
8GB, DDR4, Non-ECC Ram 3200 Mhz
M.2 512GB Gen 4 PCIe NVMe Solid State Drive
Power Cord 1M for India Dell USB Keyboard & Optical Mouse
Windows 11 Pro OEM with License
Dell 24” Display
Wifi Card + HDMI 1 Mtr. Cable
3. Years onsite Warranty
Micro-Tech UPS - 600 VA (15 mins)

10

II. Software Requirements:
Operating System: Windows 10 or Linux (Ubuntu recommended for AI applications) for flexibility in using
open-source libraries.
Programming Tools:
• Python (latest version): Primary programming language for AI applications.
• IDEs:
- Jupyter Notebook or Google Colab (for cloud-based Python coding).
- PyCharm or VS Code for offline programming.
• AI Libraries:
- NumPy, Pandas, and Matplotlib: For data manipulation and visualization.
- Scikit-learn: For machine learning algorithms.
- TensorFlow or PyTorch: For deep learning and neural networks.
- OpenCV: For computer vision projects.
- NLTK or spaCy: For natural language processing (NLP) projects.
• Database Tools: MySQL or SQLite for storing and managing datasets.
• Cloud Platforms:
- Google Colab or Amazon Web Services (AWS) for running large AI models.
- GitHub for version control and collaborative projects.
III. AI Kits (Optional but Beneficial):
• Raspberry Pi AI Kits: For experimenting with AI on hardware devices.
• Robotics kits: For AI integration into robotics projects.

11

SAMPLE TABLE FOR PRACTICAL WORK

Assessment of Assessment of the Practical Examination TOTAL MARKS
Practical File (To be evaluated by the Visiting Examiner only) (Total Marks are to
Unique be added and
Identification Internal Visiting Algorithm Python Program Hard Output entered by the
S. No. Number (Unique Evaluation Examiner with internal Copy Visiting Examiner)
ID) of the 10 Marks 5 Marks Documentation (printout)
candidate 3 Marks 7 Marks 2 Marks 3 Marks 30 Marks

1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

Name of the Visiting Examiner:_________________________________
Signature: _______________________________
Date:___________________________________

12