ISC Class 11 Syllabus 2027 Artificial Intelligence

ISC YEAR 2027

INDIAN SCHOOL CERTIFICATE
EXAMINATION

ARTIFICIAL
INTELLIGENCE
(883)

February 2025
____________________________________________________________________________________________

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

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

PAPER I- THEORY: 70 Marks

S. NO. UNIT TOTAL WEIGHTAGE
1. Basic concepts of Artificial Intelligence 08 Marks
Introduction and State of Art of AI, Natural Language
2. 08 Marks
Processing (NLP), and Potential use of AI
3. Mathematics for AI 12 Marks
4. Data Visualization 16 Marks
5. Theoretical and Practical Aspects of Data Processing 08 Marks
6. Data Modelling, Simple Linear Regression 12 Marks
7. Ethical Practices in AI 06 Marks
TOTAL 70 Marks

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PAPER I – THEORY – 70 Marks Alexa),Internet searches such as navigation
searches (related to familiar brands and
1. Basic concepts of Artificial Intelligence platforms, e.g., LinkedIn, YouTube),
(i) Artificial Intelligence informational searches (for learning and
understanding), transactional searches (for
Definition, Evolution, Applications in
purchasing, signing up for services, or
different fields, commonly used AI
downloading apps), investigative searches
applications, benefits – decision making,
(e.g., top-rated web series, movies), and
remote patient monitoring, analysis of data,
voice searches.
solving complex problems, etc.
(iii) Potential use of AI
(ii) Role of data and information, evolution
computing Use of AI in various domains in Word
Processing System like Smart Phones, Web-
Types of data, identification, acquiring and
based Auction sites, Scanner machines, e-
exploring the data, binary logic system,
commerce platforms and social networking
conditional gates, deterministic and
sites (brief explanation).
probabilistic nature of real- life problems
with appropriate examples. (iv) AI and Society
(iii) Overview of Decision making Social benefits of AI: Healthcare
(enhancement in diagnosis treatment plans
Decision making in machines/computers;
and patient care), Transportation
Cyber security in computing and machine
(Autonomous vehicles and transport
intelligence.
management system), Disaster Prediction
(iv) Components of AI project framework (Early warning system and response
Problem scoping, data acquisition, data management) and Agriculture (Precision
exploration, modeling and evaluation (in farming, crop monitoring and yield
brief) prediction).
(v) Overview of Data representation and 3. Mathematics for AI
programming in Python
(i) Matrices
Datatypes, variables, operators,
conditional statements, control statements, Introduction to Matrices, Types of Matrices,
functions. Matrix Operations (Addition, subtraction,
multiplication, transpose).
2. Introduction and State of Art of AI, Natural (ii) Vectors and its applications
Language Processing (NLP), and Potential
Vector arithmetic
use of AI
(iii) Set Theory
(i) Brief History and Primary elements of AI
Introduction to data table joins, Context
Definition of Machine Learning (ML) and
setting, Set Theory and Relational Algebra,
Deep Learning (DL)/Neural Networks.
Set operations.
Application of ML and DL: Image
recognition/processing and Computer (iv) Simple Statistical Concepts
vision, Speech recognition, Information Measures of Central Tendency (Mean,
Retrieval (IR) through Search Engine, etc. Median, Mode), Variance and Standard
(ii) Domain of Natural Language Processing Deviation.
(NLP)
4. Data Visualization
Text understanding, Text generation,
Language translation (e.g., Google (i) Data Visualization using Python
translate), Question answering (Chatbots), Programming.
Dialogue systems (e.g., Siri and

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Using matplotlib and seaborn in Python, environmental , importance of AI ethics –the
and Excel charts; Handling missing values, effects of designing technology to replicate
outliers, and inconsistencies in data using human life.
Python's pandas library and Excel's data
cleaning features. PAPER II – PRACTICALS-30 Marks
(ii) Data Visualization using Statistical Graphs The practical paper of three hours duration will be
Types of Graphs-Bar Graph, Histogram, evaluated internally by the school. The paper shall
Scatter plot, Pie graph consist of three problem statements from which a
(iii) Introduction to Dimensionality of Data. candidate has to attempt any one problem statement.
Multi-dimensional data representation and The practical consists of two parts:
visualization using graphs. (1) Planning/Writing Session
(2) Examination Session
5. Theoretical and Practical Aspects of Data The total time to be spent on the Planning/Writing
Processing Session and the Examination session is three hours.
(i) Introduction to Data Cleaning. A maximum of 90 minutes is permitted for the
Data cleaning techniques with Pandas; Planning/Writing Session and 90 minutes for the
Handling duplicates and Inconsistent data. Examination session. Candidates are to be
permitted to proceed to the Examination Session
(ii) Exploring Kaggle Datasets. only after the 90 minutes of the Planning /
Creating and manipulating Data Frames Writing Session are over.
from Kaggle Datasets. Planning/Writing Session
(iii) Data Transformation and Standardization. The candidates will be required to prepare an
Methods for transforming and standardizing algorithm and a handwritten program to solve the
data for analysis. problem.
Examination Session
6. Data Modeling, Simple Linear Regression
The program handed in at the end of the
(i) Introduction to Data Modeling. Planning/Writing session shall be returned to the
A brief understanding of Types of Data candidates. The candidates will be required to do and
Models (Dimensional, relational and entity- execute the program individually on the computer,
relational). hardware and show execution to the examiner. A
printout of the program listing, including output
(ii) Regression analysis.
should be attached to the answer script containing the
Working of Regression (Dependent and handwritten program and hardware results. This
Independent Variables), Types of should be returned to the examiner. The program
Regression (in brief). should be sufficiently documented so that the
(iii) Linear Regression Equation. material required, circuit diagram/block diagram,
algorithm, representation and development process
Least Square Regression Line, Properties of
is clear from reading the program. Large differences
Linear Regression, Regression coefficient,
between the planned program and the printout will
Types of Linear Regression (in brief).
result in loss of marks.
(iv) Solving Linear Equations.
Teachers should maintain a record of all the
Applications of Linear Equations in various assignments done as part of the practical work
contexts. throughout the year and give it due credit at the time
of cumulative evaluation at the end of the year.
7. Ethical Practices in AI Students are expected to do a minimum of twenty
AI code of ethics- avoiding bias, ensuring assignments for the year and ONE project based on
privacy of users and their data, and mitigating the syllabus.

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LIST OF SUGGESTEDASSIGNMENTS: Continuous Evaluation
1. How would you use Matplotlib to create a line Candidates will be required to submit a work file
plot for visualizing the trend of a dataset over containing the practical work related to assignments
time? Write a Python program demonstrating done during the year and ONE project.
this. Programming assignments done 10
2. Using Seaborn, how can you create a scatter throughout the year marks
plot to analyze the relationship between two
Project Work (based on any topic from 5
variables in a dataset? Provide a Python
the syllabus) marks
program illustrating this.
3. Explain how to utilize Matplotlib to generate a Proposed Guidelines for Marking
bar plot for comparing the distribution of
The actual grading will be done by the teacher based
categories in a dataset. Write a Python program
on his/her judgment. One possible way: divide the
for this purpose
outcome for each criterion into one of 3 groups:
4. Write a Python program demonstrating how to excellent, good, poor/unacceptable, then use
identify outliers in a dataset and handle them numeric values for each grade and add to get the
using outlier detection techniques. total.
5. Describe how to create a box plot using Seaborn
Evaluation will be done as follows:
to visualize the distribution of a numerical
variable. Provide a Python program to illustrate Programming Assignments:10 Marks
this.
Criteria Class design Documentation
6. How can you create a pair plot using Seaborn to (Total10 - Execution Practical File
visualize pairwise relationships between marks) (4 marks) (6 marks)
variables in a dataset? Write a Python program Excellent 4 6
demonstrating this.
7. Write a Python program using NumPy to Good 3 4
simulate coin flips and calculate the probability Poor 1 2
of getting heads.
8. Demonstrate how to generate random samples Project Work: 5 marks
from a normal distribution using NumPy and
Criteria Knowledge Functiona Presentation
visualize the distribution using Matplotlib.
(Total and lity and (1 marks)
Provide a Python program for this task.
5 marks) Understand Performa
9. Write a Python program to create a Pandas ing nce
DataFrame from a Kaggle dataset. (1 marks) (3 marks)
10. Perform a hypothesis test to determine if there Excellent 1 3 1
is a significant difference in temperature Good 1 2 1
between two cities. Write a Python script to Poor 1 1 1
conduct the test and interpret the results.
NOTE: This list is indicative only. Teachers and Terminal Evaluation
students should use their imagination to create
innovative and original assignments. Solution to Problem Statement on Hands- 15 marks
On/ Programming
EVALUATION OF PROGRAMMING
ASSIGNMENTS Marks should be given for choice of algorithm and
implementation strategy, documentation, correct
Marks (out of 30) should be distributed as given output on known inputs mentioned in the question
below. paper, correct output for unknown inputs available
only to the examiner.

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