Class 9 AI Glossary of Important Terms
Understanding important terms is essential for learning Artificial Intelligence. This glossary provides simple explanations of important AI concepts covered in the CBSE Class 9 Artificial Intelligence syllabus.
Students can use this dictionary for:
- Quick revision
- Exam preparation
- Understanding AI concepts
- Learning technical vocabulary
A – Artificial Intelligence Terms
Artificial Intelligence (AI)
Artificial Intelligence is a technology that enables machines to perform tasks that normally require human intelligence.
Examples:
- Voice assistants
- Chatbots
- Recommendation systems
Algorithm
An algorithm is a step-by-step set of instructions used to solve a problem or complete a task.
Example:
A recipe can be considered an algorithm because it contains steps to prepare food.
AI Bias
AI bias occurs when an AI system produces unfair results due to problems in data or system design.
Example:
A model giving unfair predictions because training data was incomplete.
AI Ethics
AI Ethics refers to principles that guide the responsible development and use of Artificial Intelligence.
Important principles:
- Fairness
- Privacy
- Transparency
- Accountability
AI Model
An AI model is a system trained using data to identify patterns and make predictions.
B – AI Terms
Bias
Bias means an unfair preference or judgement.
In AI, bias can affect the fairness of results.
Boolean
A data type that has two values:
- True
- False
Used in programming decisions.
C – AI Terms
Computer Vision
Computer Vision is an AI domain that enables computers to understand images and videos.
Applications:
- Face recognition
- Object detection
- Medical image analysis
Code
A set of instructions written in a programming language to perform tasks.
Example:
Python code.
Classification
The process of placing data into different categories.
Example:
Classifying emails as spam or not spam.
D – AI Terms
Data
Data is a collection of facts and information used by AI systems.
Examples:
- Numbers
- Text
- Images
- Audio
Data Acquisition
The process of collecting data required for an AI project.
Sources:
- Surveys
- Sensors
- Databases
Data Exploration
The process of analyzing collected data to understand patterns and information.
Dataset
A collection of related data used for analysis or training AI models.
Data Visualization
Representing data using charts, graphs, and tables.
Examples:
- Bar graph
- Line graph
- Pie chart
E – AI Terms
Evaluation
The final stage of the AI Project Cycle where the performance of an AI model is checked.
Ethics
Principles that define what is right and responsible.
F – AI Terms
Fairness
An AI principle that ensures systems do not produce unfair or discriminatory results.
Feature
A measurable property or characteristic of data used by AI models.
Example:
Age or height in a dataset.
G – AI Terms
Generative AI
A type of AI that creates new content such as:
- Text
- Images
- Audio
- Code
Green Skills
Skills related to protecting the environment and promoting sustainable practices.
I – AI Terms
ICT
ICT stands for Information and Communication Technology.
It includes:
- Computers
- Internet
- Digital tools
Input
Information provided to a computer or AI system for processing.
Example:
A user’s question given to a chatbot.
L – AI Terms
Loop
A programming structure used to repeat instructions.
Example:
Repeating a task multiple times.
M – AI Terms
Machine Learning
A branch of AI where computers learn patterns from data and improve performance.
Mean
A statistical value calculated by:
Sum of values ÷ Number of values
Median
The middle value of a data set arranged in order.
Mode
The value that occurs most frequently in a dataset.
Modelling
The stage of AI Project Cycle where an AI model is created.
N – AI Terms
Natural Language Processing (NLP)
An AI domain that helps computers understand and process human language.
Applications:
- Translation
- Chatbots
- Voice assistants
O – AI Terms
Output
The result produced by a computer program or AI system.
Example:
A prediction generated by an AI model.
P – AI Terms
Probability
A measure of the possibility of an event occurring.
Formula:
Probability = Favorable outcomes ÷ Total outcomes
Problem Scoping
The first stage of AI Project Cycle where the problem is identified and defined.
Prompt
An instruction or question given to a Generative AI system.
Example:
“Write a summary about Artificial Intelligence.”
Python
A popular programming language used in:
- Artificial Intelligence
- Data Science
- Software Development
R – AI Terms
Renewable Energy
Energy obtained from natural sources that can be renewed.
Examples:
- Solar energy
- Wind energy
S – AI Terms
Structured Data
Data organized in a fixed format, usually tables.
Example:
Student database.
Sustainable Development
Development that meets current needs while protecting resources for future generations.
T – AI Terms
Training Data
Data used to teach an AI model and help it learn patterns.
Transparency
An AI principle that means decisions made by AI should be understandable.
U – AI Terms
Unstructured Data
Data without a fixed format.
Examples:
- Images
- Videos
- Audio files
V – AI Terms
Variable
A name used in programming to store a value.
Example:
name = "AI"
Quick Revision Table
| Term | Meaning |
|---|---|
| AI | Machines performing intelligent tasks |
| Algorithm | Step-by-step instructions |
| Data | Information used by AI |
| Model | System trained using data |
| NLP | Understanding human language |
| Computer Vision | Understanding images/videos |
| Prompt | Instruction given to AI |
| Python | Programming language |
| Bias | Unfair AI result |
| Ethics | Responsible AI use |
How to Use This Glossary for Exams
Students should:
✓ Learn definitions in simple words
✓ Remember examples of each term
✓ Practice writing short answers
✓ Revise before examinations
✓ Use terms correctly in AI project explanations
Summary
The Class 9 AI glossary helps students understand important Artificial Intelligence vocabulary required for CBSE AI Code 417. Learning these terms makes it easier to understand AI concepts, write better answers, and prepare effectively for examinations.