ISC Class 12 Syllabus 2027 Artificial Intelligence

ISC YEAR 2027

INDIAN SCHOOL CERTIFICATE
EXAMINATION

ARTIFICIAL
INTELLIGENCE
(883)

February 2025
____________________________________________________________________________________________

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MISSION STATEMENT

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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 II: Practical - 3 hours ... 15 marks
Paper I: Theory - 3 hours ... 70 marks Practical File … 15 marks

PAPER I- THEORY: 70 Marks

S. NO. UNIT TOTAL WEIGHTAGE
1. Applications of AI 10 Marks
2. Different paradigms of AI: Neural networks, Machine 15 Marks
learning, Deep learning.
3. Practical Implications of ANN 15 Marks
4. Practical Implications of Machine Learning (ML) 20 Marks
5. Introduction to Computer Vision (CV) 10 Marks
TOTAL 70 Marks

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PAPER I – THEORY – 70 Marks
Note: Key concepts of Class XI need to be revised like size, location, and number of bedrooms
as a prerequisite. using a decision tree.
(iii) Deep Learning (DL):
1. Applications of AI
About Deep Learning, Neural Network
(i) Natural Language Processing (NLP): utilized for image recognition, pattern
Named Entity Recognition (NER) recognition, and/or computer vision.
Identify and classify names of people, Example- Classifying images into different
organizations, and locations in a news categories (e.g., cat, dog, bird) using a
article, Text Summarization using Convolution Neural Network (CNN).
Extraction Methods: Summarize a long
passage by extracting the most important 3. Practical Implications of ANN
sentences or phrases, Spam Email Detection
(i) Data Preparation
using Machine Learning: Build a model to
classify emails as spam or non-spam based Use NumPy for numerical computations and
on their content and features. Pandas for data manipulation.
Employ Scikit-learn for data preprocessing
(ii) Statistics and Probability
tasks like scaling, encoding categorical
Understanding data trends using mean, variables, and splitting data into training
median, mode, range, variance, and and testing sets.
standard deviation; Probability Basics:
Introducing probability as a measure of (ii) Building the Neural Network
uncertainty, along with common Choose Keras or TensorFlow: Keras is easy
distributions like uniform, normal, and to use and runs on top of TensorFlow,
binomial. making it beginner friendly.
(iii) Hypothesis Testing (iii) Training the Neural Network
Understanding null and alternative Fit your data to the neural network model
hypotheses, Type I and Type II errors, and using the fit() function, specifying the
their relevance in AI experiments, number of epochs and batch size.
Regression Analysis: Introducing simple
(iv) Model Evaluation
linear regression for prediction and
correlation coefficient for measuring Evaluate the model's performance on the test
relationships between variables. data using metrics like accuracy, precision,
and recall.
2. Different paradigms of AI: Neural Networks, Visualize performance metrics using
Machine Learning, Deep Learning libraries like Matplotlib to gain insights.

(i) Artificial Neural Networks (ANN) (v) Tuning
About Artificial Neural Networks, nodes Utilize techniques like grid search or
(neurons), Input layer, Hidden layer, Output random search to tune hyper parameters
layer, Forward and Backward propagation such as learning rate, batch size, and
in ANN, ANN model, Multi-layer perceptron number of hidden layers and neurons.
(MLP), Example- Recognizing handwritten
digits using a multi-layer perceptron (MLP).
(ii) Machine Learning (ML) 4. Practical Implications of Machine Learning
Brief understanding of Machine Learning; (ML)
An AI paradigm enabling computers to learn (i) Predictive Maintenance
from data and make predictions or decisions
Using historical data from machines to
without explicit programming. Example-
predict equipment failure or require
Predicting house prices based on features

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maintenance, helping to minimize downtime PAPER II – PRACTICALS - 30 Marks
and optimize maintenance schedules.
The practical paper of three hours’ duration will be
(ii) Fraud Detection evaluated by the Visiting Examiner appointed
Credit card fraud or identity theft, enabling locally and approved by CISCE.
financial institutions to take proactive
measures to prevent losses. The paper shall consist of three problem statements
/problems from which a candidate has to attempt any
(iii) Medical Diagnosis one. The practical consists of two parts:
Analysing Medical Imaging, Genetic (1) Planning/ Writing Session
Information, and Patient records for (2) Examination Session
diagnosis.
The total time to be spent on the Planning/Writing
5. Introduction to Computer Vision (CV) Session and the Examination session is three hours.
A maximum of 90 minutes is permitted for the
(i) Introduction to CV, Application of Open CV Planning/Writing Session and 90 minutes for the
in real world scenario Examination session.
Image Classification, Object Detection, Candidates are to be permitted to proceed to the
Facial Recognition, Color Detection, Object Examination Session only after the 90 minutes of
Tracking (brief explanation with examples). the Planning / Writing Session are over.
(ii) Image Classification Planning/Writing Session
Implementing a simple image classifier to The candidates will be required to prepare an
distinguish between different categories of algorithm and a handwritten program to solve the
objects, such as cats vs. dogs or fruits vs. problem.
vegetables.
Examination Session
(iii) Object Detection
The program handed in at the end of the
Identification and localization of specific Planning/Writing session shall be returned to the
objects within an image, such as detecting candidates. The candidates will be required to code
cars in a street scene or detecting faces in a and execute the program individually on the
photograph. computer, hardware and show execution to the
(iv) Facial Recognition Visiting Examiner. A printout of the program listing
Building a facial recognition application including output results should be attached to the
that can recognize and identify faces in answer script containing the handwritten program
images or videos, and optionally, associate and hardware results. This should be returned to the
them with known individuals. examiner. The program should be sufficiently
documented so that the material required, circuit
(v) Color Detection diagram/block diagram, algorithm, representation
Identification and classification of objects and development process is clear from reading the
based on their color properties, such as program. Large differences between the planned
sorting colored objects on a conveyor belt or program and the printout will result in loss of marks.
detecting ripe fruits in agriculture.
Teachers should maintain a record of all the
(vi) Object Tracking assignments done as part of the practical work
Track the movement of objects in a video throughout the year and give it due credit at the time
sequence over time, such as tracking the of cumulative evaluation at the end of the year.
trajectory of a ball in sports footage or Students are expected to do a minimum of twenty-
monitoring vehicles in traffic surveillance. five assignments for the year.
The details are as follows:
Details of Assignments to be done during the year

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10. Introduction to CPU, GPU, TPU and DPU and
Broad Area Number of
their distinction for individual
Assignments
projects/research in AI.
Practical 15
(Programming) NOTE: This list is indicative only. Teachers and
students should use their imagination to create
Hands-on (Case Study) 10 innovative and original assignments.

TOTAL 25 EVALUATION OF PROGRAMMING
ASSIGNMENTS
Marks (out of 30) should be distributed as given
LIST OF SUGGESTED ASSIGNMENTS: below.
Some sample problems are given below as examples. Continuous Evaluation
The problems are of varying levels of difficulty:
Candidates will be required to submit a work file
1. Tokenization and Text Preprocessing:
containing the practical work related to assignments
Demonstrate tokenization techniques using
done during the year.
NLTK or spaCy in Python to break down
sentences into individual words or tokens. Programming assignments done 10
2. Named Entity Recognition (NER): Implement throughout the year (Internal marks
NER using spaCy to identify and classify Evaluation)
named entities such as persons, organizations, Programming assignments done 5
and locations in text data. throughout the year (Visiting Examiner) marks
3. Probability Distributions: Explore different
probability distributions (e.g., normal,
binomial, Poisson) using Python libraries like Proposed Guidelines for Marking
NumPy and visualize their properties using The actual grading will be done by the teacher based
Matplotlib. on his/her judgment. One possible way: divide the
4. Hypothesis Testing: Perform hypothesis testing outcome for each criterion into one of 3 groups:
(e.g., t-test, chi-square test) on sample data to excellent, good, poor/unacceptable, then use
make inferences about population parameters. numeric values for each grade and add to get the
5. Create a Python script to train a linear total.
regression model using the NumPy library to Evaluation will be done as follows:
predict a car's fuel efficiency (in miles per
gallon) based on its engine size (in liters). Programming Assignments: 10 Marks
6. Write a Python script to train a linear regression Criteria Class design Documentation
model using the NumPy library to predict a (Total 10 - Execution Practical File
student's final exam score based on their study marks) (4 marks) (6 marks)
hours. Excellent 4 6
7. Implementing Forward and Backward Good 3 4
Propagation: Build a simple neural network
from scratch in Python to understand the Poor 1 2
concepts of forward and backward propagation.
8. Introduction to Hidden Layers: Extend the basic
neural network by adding hidden layers and Terminal Evaluation
activation functions to demonstrate the role of
hidden layers in learning complex patterns. Solution to Problem Statement on 15 marks
9. Generate attendance monitoring system to track Hands-On/ Programming
the attendance of the students using Open-CV.

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Marks should be given for choice of algorithm and II. Software Requirements:
implementation strategy, documentation, correct Operating System: Windows 10 or Linux
output on known inputs mentioned in the question (Ubuntu recommended for AI applications)
paper, correct output for unknown inputs available for flexibility in using open-source libraries.
only to the examiner. Programming Tools:
Note: Algorithm should be expressed clearly using  Python (latest version): Primary
any standard scheme such as a pseudo code. programming language for AI
applications.
LABORATORY REQUIREMENTS  IDEs:
- Jupyter Notebook or Google Colab
(For a class of 30 students)
(for cloud-based Python coding).
I. Hardware Requirements: - PyCharm or VS Code for offline
Group of 2 Students: 1 Desktop along with programming.
Webcam  AI Libraries:
- NumPy, Pandas, and Matplotlib: For
1. Webcam Detailed Description:
data manipulation and visualization.
Video Capture Resolution 1080p - Scikit-learn: For machine learning
Maximum Focal Length 1080 algorithms.
Maximum Aperture 2 f - TensorFlow or PyTorch: For deep
Reference: Logitech Brio 100 Full HD learning and neural networks.
Webcam - OpenCV: For computer vision
2. Desktop Configuration projects.
- NLTK or spaCy: For natural language
Detailed Description:
processing (NLP) projects.
Dell Optiplex 3000 Desktop:
 Database Tools: MySQL or SQLite for
12th Generation Intel Core I5-12500
storing and managing datasets.
Processor Intel B660 Chipset
 Cloud Platforms:
2GB NVidai Dedicated Graphics
- Google Colab or Amazon Web
ENERGY STAR Qualified
Services (AWS) for running large AI
8GB, DDR4, Non-ECC Ram 3200 Mhz
models.
M.2 512GB Gen 4 PCIe NVMe Solid
- GitHub for version control and
State Drive
collaborative projects.
Power Cord 1M for India Dell USB
Keyboard & Optical Mouse III. AI Kits (Optional but Beneficial):
Windows 11 Pro OEM with License  Raspberry Pi AI Kits: For experimenting
Dell 24” Display with AI on hardware devices.
Wifi Card + HDMI 1 Mtr. Cable
 Robotics kits: For AI integration into
3 Years onsite Warranty robotics projects.
Micro-Tech UPS - 600 VA (15 mins)

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

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