ISC Class 11 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
____________________________________________________________________________________________

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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.

ARTIFICIAL INTELLIGENCE (883)

This subject may be taken with Computer Science but not with Robotics.

Aims:
1. To develop an understanding of concepts and applications in AI.
2. To develop competencies in AI via classroom instruction, laboratory and self-directed project-based learning
approach.
3. To facilitate appreciation, understanding and application of concepts of data science in AI through learning
and engaging in hands-on activities.
4. To introduce concepts of data modelling.
5. To facilitate appreciation, understanding and application of concepts of neural networks and natural language
processing.
6. To introduce concepts of generating predictions from data.

CLASS XI
There will be two papers in the subject:
Paper I: Theory - 3 hours…70 marks
Paper II: Practical - 3 hours ... 15 marks
Project Work … 10 marks
Practical File … 5 marks

PAPER I (THEORY) : 70 MARKS
S.no Unit Total Weightage
(Marks)
1. Basic concepts of Artificial Intelligence 08
2. Introduction and State of Art of AI, Natural Language Processing 08
(NLP), and Potential use of AI
3. Mathematics for AI 12
4. Data Visualization 16
5. Theoretical and Practical Aspects of Data Processing 08
6. Data Modelling, Simple Linear Regression 12
7. Ethical Practices in AI 06
Total 70

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1. Basic concepts of Artificial Intelligence
(i) Artificial Intelligence
Definition, Evolution, Applications in different fields, commonly used AI applications, benefits – decision
making, remote patient monitoring, analysis of data, solving complex problems, etc.
(ii) Role of data and information, evolution computing
Types of data, identification, acquiring and exploring the data, binary logic system, conditional gates,
deterministic and probabilistic nature of real- life problems with appropriate examples.
(iii) Overview of Decision making
Decision making in machines/computers; Cyber security in computing and machine intelligence.
(iv) Components of AI project framework
Problem scoping, data acquisition, data exploration, modeling and evaluation (in brief)
(v) Overview of Data representation and programming in Python
Datatypes, variables, operators, conditional statements, control statements, functions.

2. Introduction and State of Art of AI, Natural Language Processing (NLP), and Potential use of AI
(i) Brief History and Primary elements of AI
Definition of Machine Learning (ML) and Deep Learning (DL)/Neural Networks. Application of ML and
DL: Image recognition/processing and Computer vision, Speech recognition, Information Retrieval (IR)
through Search Engine, etc.
(ii) Domain of Natural Language Processing (NLP)
Text understanding, Text generation, Language translation (e.g., Google translate), Question answering
(Chatbots), Dialogue systems (e.g., Siri and Alexa),Internet searches such as navigation searches (related
to familiar brands and platforms, e.g., LinkedIn, YouTube), informational searches (for learning and
understanding), transactional searches (for purchasing, signing up for services, or downloading apps),
investigative searches (e.g., top-rated web series, movies), and voice searches.
(iii) Potential use of AI
Use of AI in various domains in Word Processing System like Smart Phones, Web-based Auction sites,
Scanner machines, e-commerce platforms and social networking sites (brief explanation).
(iv) AI and Society
Social benefits of AI: Healthcare (enhancement in diagnosis treatment plans and patient care),
Transportation (Autonomous vehicles and transport management system), Disaster Prediction (Early
warning system and response management) and Agriculture (Precision farming, crop monitoring and
yield prediction).

3. Mathematics for AI
(i) Matrices
Introduction to Matrices, Types of Matrices, Matrix Operations (Addition, subtraction, multiplication,
transpose).
(ii) Vectors and its applications
Vector arithmetic
(iii) Set Theory
Introduction to data table joins, Context setting, Set Theory and Relational Algebra, Set operations.

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(iv) Simple Statistical Concepts
Measures of Central Tendency (Mean, Median, Mode), Variance and Standard Deviation.

4. Data Visualization
(i) Data Visualization using Python Programming.
Using matplotlib and seaborn in Python, and Excel charts; Handling missing values, outliers, and
inconsistencies in data using Python's pandas library and Excel's data cleaning features.
(ii) Data Visualization using Statistical Graphs
Types of Graphs-Bar Graph, Histogram, Scatter plot, Pie graph
(iii) Introduction to Dimensionality of Data.
Multi-dimensional data representation and visualization using graphs.

5. Theoretical and Practical Aspects of Data Processing
(i) Introduction to Data Cleaning.
Data cleaning techniques with Pandas; Handling duplicates and Inconsistent data.
(ii) Exploring Kaggle Datasets.
Creating and manipulating Data Frames from Kaggle Datasets.
(iii) Data Transformation and Standardization.
Methods for transforming and standardizing data for analysis.

6. Data Modeling, Simple Linear Regression
(i) Introduction to Data Modeling.
A brief understanding of Types of Data Models (Dimensional, relational and entity-relational).
(ii) Regression analysis.
Working of Regression (Dependent and Independent Variables), Types of Regression (in brief).
(iii) Linear Regression Equation.
Least Square Regression Line, Properties of Linear Regression, Regression coefficient, Types of Linear
Regression (in brief).
(iv) Solving Linear Equations.
Applications of Linear Equations in various contexts.

7. Ethical Practices in AI
AI code of ethics- avoiding bias, ensuring privacy of users and their data, and mitigating environmental ,
importance of AI ethics –the effects of designing technology to replicate human life.

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PAPER II (PRACTICALS) : 30 MARKS
The practical paper of three hours duration will be evaluated internally by the school. The paper shall consist of
three problem statements from which a candidate has to attempt any one problem statement.
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 do and execute the program individually on the computer, hardware and show
execution to the examiner. A printout of the program listing, including output 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 assignments for the year and ONE project based on the syllabus.
List of suggested assignments:
1. How would you use Matplotlib to create a line plot for visualising the trend of a dataset over time? Write a
Python program demonstrating this.
2. Using Seaborn, how can you create a scatter plot to analyse the relationship between two variables in a
dataset? Provide a Python program illustrating this.
3. Explain how to utilise Matplotlib to generate a bar plot for comparing the distribution of categories in a
dataset. Write a Python program for this purpose
4. Write a Python program demonstrating how to identify outliers in a dataset and handle them using outlier
detection techniques.
5. Describe how to create a box plot using Seaborn to visualise the distribution of a numerical variable. Provide
a Python program to illustrate this.
6. How can you create a pair plot using Seaborn to visualise pairwise relationships between variables in a
dataset? Write a Python program demonstrating this.
7. Write a Python program using NumPy to simulate coin flips and calculate the probability of getting heads.
8. Demonstrate how to generate random samples from a normal distribution using NumPy and visualise the
distribution using Matplotlib. Provide a Python program for this task.
9. Write a Python program to create a Pandas DataFrame from a Kaggle dataset.
10. Perform a hypothesis test to determine if there is a significant difference in temperature between two cities.
Write a Python script to conduct the test and interpret the results.

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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 and ONE project.

Programming assignments done throughout the year 10 marks

Project Work (based on any topic from the syllabus) 5 marks

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.
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
Project Work: 5 marks

Criteria Knowledge and Functionality and Presentation
Understanding Performance
(Total 5 marks) (1 mark)
(1 mark) (3 marks)
Excellent 1 3 1
Good 1 2 1
Poor 1 1 1
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.

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