Course Overview
This curriculum combines Inspire and Acquire modules to develop readiness for understanding and appreciating Artificial Intelligence and its applications. The course focuses on:
- Understanding AI through games, activities, and multi-sensorial learning
- Introducing three domains of AI in an age-appropriate manner
- Constructing meaning of AI through interactive participation
- Exploring AI domains, project cycle, and ethics
- Introducing importance of Math for AI, data literacy, and generative AI
Recommended AI Learning Resources
Curriculum Units
AI Reflection, Project Cycle and Ethics
Learning Outcomes
- Identify and appreciate AI applications in daily life
- Recognize three domains of AI: Data, Computer Vision, and NLP
- Understand the AI project cycle framework
- Identify stakeholders and ethical issues in AI projects
Examples
AI Domains through Games
Explore AI concepts through interactive games:
- Rock, Paper, Scissors: Based on data patterns
- Semantris: Based on Natural Language Processing
- Quick Draw: Based on Computer Vision
AI Project Cycle
Steps in developing an AI project:
- Problem Scoping
- Data Acquisition
- Data Exploration
- Modeling
- Evaluation
- Deployment
Data Literacy
Learning Outcomes
- Define data literacy and recognize its importance
- Differentiate between data privacy and security
- Determine methods to acquire and process data
- Recognize the importance of data visualization
Examples
Data Security & Privacy
Key concepts in data protection:
- Use strong passwords and two-factor authentication
- Regularly update software and systems
- Be cautious of phishing attempts
- Understand data encryption methods
Data Visualization
Creating effective data visualizations:
- Choose appropriate chart types for data
- Use consistent color schemes
- Label axes and provide clear titles
- Avoid misleading representations
Math for AI (Statistics & Probability)
Learning Outcomes
- Analyze data to find relations and patterns
- Understand concepts of statistics in real life
- Apply probability in various scenarios
- Recognize the importance of math in AI
Examples
Statistics in AI
Applications of statistics:
- Disaster management predictions
- Sports performance analysis
- Disease outbreak forecasting
- Weather pattern analysis
Probability Concepts
Probability in real-world scenarios:
- Predicting sports match outcomes
- Weather forecasting models
- Traffic flow estimations
- Risk assessment in finance
Introduction to Generative AI
Learning Outcomes
- Define generative AI and classify different types
- Explain how generative AI works
- Apply generative AI tools to create content
- Understand ethical considerations of generative AI
Examples
Generative AI Tools
Popular generative AI applications:
- Text generation (ChatGPT, Bard)
- Image creation (DALL-E, Midjourney)
- Music composition (AIVA, Amper)
- Video generation (Synthesia, Runway)
Ethical Considerations
Important ethical aspects:
- Copyright and ownership issues
- Potential for creating misinformation
- Bias in training data
- Impact on creative professions
Introduction to Python
Learning Outcomes
- Learn basic programming skills
- Understand Python syntax and structure
- Work with variables, data types, and operators
- Implement control flow and conditions
- Use Python lists for data storage
Examples
Basic Python Program
Simple program to calculate area:
# Calculate area of rectangle
length = float(input("Enter length: "))
width = float(input("Enter width: "))
area = length * width
print("Area of rectangle:", area)
Working with Lists
Manipulating lists in Python:
# Create and modify a list
students = ["Arjun", "Sonakshi", "Vikram"]
print("Original list:", students)
# Add a new student
students.append("Jay")
print("After adding:", students)
# Remove the second student
del students[1]
print("After removal:", students)
Question Bank
What are the three main domains of AI?
Answer: The three main domains of Artificial Intelligence are:
- Data: Involves collecting, processing, and analyzing data
- Computer Vision: Enables machines to interpret visual information
- Natural Language Processing (NLP): Allows machines to understand and generate human language
Explain the steps in the AI project cycle.
Answer: The AI project cycle consists of:
- Problem Scoping: Define the problem and set goals
- Data Acquisition: Collect relevant data
- Data Exploration: Analyze and visualize data
- Modeling: Create AI models (rule-based or learning-based)
- Evaluation: Test model performance
- Deployment: Implement the solution
What is the difference between data privacy and data security?
Answer:
- Data Privacy: Concerns the proper handling of sensitive data including when, how, and to what extent it's shared
- Data Security: Focuses on protecting data from unauthorized access, breaches, and cyber attacks
Write a Python program to calculate the average of three numbers.
Answer:
# Program to calculate average of three numbers
num1 = float(input("Enter first number: "))
num2 = float(input("Enter second number: "))
num3 = float(input("Enter third number: "))
# Calculate average
average = (num1 + num2 + num3) / 3
# Display result
print("The average is:", average)
What is generative AI? Give two examples.
Answer: Generative AI refers to artificial intelligence systems that can create new content such as text, images, music, or video. Examples include:
- DALL-E: Creates images from textual descriptions
- ChatGPT: Generates human-like text based on prompts
Explain the importance of statistics in AI with two real-world examples.
Describe the ethical considerations that should be taken into account when developing an AI system.
Write a Python program to create a list of 5 student names and then perform the following operations:
- Print the entire list
- Remove the second student from the list
- Add a new student at the end of the list
- Print the modified list
Differentiate between rule-based and learning-based models in AI. Provide one example of each.
What is data visualization? Explain any three types of charts used for data visualization and when each should be used.
Course Information
- Subject Code 417
- Class IX
- Total Marks 100
- Theory Marks 50
- Practical Marks 50
- Total Hours 210
Resources & Links
Upcoming Activities
15
Sep
AI Ethics Debate
Balloon Debate on AI Impact22
Sep
Python Coding Challenge
Solve 10 programming problems30
Sep
Data Visualization Project
Submit your data analysis project
Featured AI Tools