Generative AI and Rule-Based Models in AI

Artificial Intelligence Facilitator Handbook (Class 9)

• This handbook is a curated resource on Artificial Intelligence (AI) for Class 9 students, facilitated by Intel.
• It's designed to equip educators and students with the knowledge and skills to thrive in an AI-driven world.
• The handbook is supported by the Central Board of Secondary Education (CBSE).

Acknowledgements

• Patrons: Rahul Singh, IAS, Chairperson, Central Board of Secondary Education
• Guidance and Support: Dr. Biswajit Saha, Director (Skill Education & Training), CBSE; Ms. Shweta Khurana, Senior Director, Government Partnerships & Initiatives, International Government Affairs Group, Intel
• Education Value Adder, Curator, and Coordinator: Sh. Ravinder Pal Singh, Joint Secretary, Department of Skill Education, CBSE; Ms. Saloni Singhal, Program Manager APJ, Intel Digital Readiness Programs; Ms. Sarita Manuja, Educational Consultant & Program Director, NHES; Ms. Shatarupa Dasgupta, National Program Manager, Intel Digital Readiness Program
• Content Curation Team: Ms. Ambika Saxena, Intel Al for Youth Coach; Ms. Prachi Chandra, Intel Al for Youth Coach; Ms. Shilpa Sethi, Ms. Shipra Panigrahi, Ms. Sonu Lohchab, Ms. Ritu Debnath, Ms. Anshu Banerjee, Ms. Yukti, Ms. A. Sayeesubbulakshmi

About the Book

• AI is fundamental to the global digital economy.
• Recognizing this, nations are working strategically to leverage AI's transformative potential.
• This comprehensive facilitator handbook, supported by Intel and CBSE, aims to enhance the understanding of Al concepts and their practical applications in students.

Key Features

• Enhanced Content: Clearer explanations and examples for deeper comprehension.
• Real-Life Examples: Illustrations of AI in practical scenarios to improve understanding.
• Al-enabled Social Impact Solutions: Encourage student-led solutions for addressing social issues.
• Use Case Walkthroughs: Focuses on how AI concepts are used in various real-world domains.

CBSE Grade IX Al Curriculum (2024-25)

• Unit 1 - Al Reflection: Covers the concept of Al and its domains, including the Al Project Cycle. Includes an activity section where the learners can try the Al activities.
• Unit 2 - Data Literacy: This focuses on the different kinds of data like quantitative (numeric) vs qualitative (categorical). Includes activities on finding different data sources.
• Unit 3 - Math for Al (Statistics & Probability): Discusses the importance of math in Al and explains concepts like probability and statistics. Includes exercises on spotting patterns in numbers and images and how math can help find solutions to problems.
• Unit 4 - Generative Al: An introduction to Generative Al techniques, tools, use-cases and ethical considerations.
• Evaluation: Covers the process of model testing and choosing from a variety of approaches, explaining the different terms involved in model evaluation like True Positive, False Positive, Precision, Accuracy etc.
• Deployment: Covers the process of putting the Al model into real world practice.

Activity: Game Time

• Students perform Al-related activities like Rock–Paper–Scissors, Semantris, and Quick, Draw. These real-world examples help students engage with practical applications.

Pair Activity: Reflect and Analyze

• Students analyze the relationships between Al domains like Natural Language Processing, Computer Vision, and Data for Al.

Revision Time: Quiz Time

• Covers important questions and answers on AI topics. This section is focused on questions and answers for important concepts and terminology.

1.2.2 Data Acquisition

• This section focuses on understanding how to acquire data from reliable and authentic data sources.
• The session includes creating and using System Maps.

1.2.3 Data Exploration

• Data Visualization Techniques: Students learn about data visualization types, including bar charts, line graphs, scatter plots, pie charts and how to use suitable plots.

1.2.4 Modeling

• Al, ML and DL: Students are able to understand the differences between Al, ML and DL. This includes learning about the different approaches for Al (rule-based vs learning-based).
• Discusses the process of selecting an Al model and training it for specific tasks.

1.2.5 Evaluation

• Model Evaluation Techniques: The section is focused on how models are tested for accuracy and reliability by applying testing data to the model for evaluation. Covers concepts like true positive, true negative, false positive and false negative.

1.2.6 Deployment

• Explains how an Al model or solution is used in a real-world situation or deployed.
• Shows how the model can be implemented in practical situations such as in mobile and website applications, self-driving cars, etc.

Unit 1.3 - Ethics and Morality

• Aims to promote ethical considerations in Al.
• Includes ethical scenarios about personal data and Al's possible impact.

2.1 Basics of Data Literacy

• Data and its Importance: Exploring practical aspects of data's usefulness, including different kinds of data, like quantitative (like numbers) and qualitative (like descriptions). Includes a pyramid model illustrating how data moves from raw facts to wisdom.
• Data Interpretation: This section covers the different techniques for data interpretation or how to understand the data.
• Data Presentation: Different techniques used to present data including using charts, graphs, tables etc.
• Data Privacy & Security: Introduces the concepts of Data Privacy and Data security.

2.2 Acquiring Data, Processing, and Interpreting Data

• Data Acquisition: Discusses the steps involved in acquiring data, including data discovery, augmentation, and generation. Describes good and bad data characteristics.
• Data Processing: Explains how raw data gets processed to help create meaning from the data that was collected.
• Data Interpretation: Shows how data is analyzed to get trends, patterns, and possible conclusions. How to prepare appropriate data presentations for effective communication.

2.3 Project Interactive Data Dashboard & Presentation

• Data Visualization: Covers the use of visualization tools (especially Tableau, and Microsoft Excel for generating charts).

3.1: Importance of Math for Al

• Focuses on how math like statistics and probability are used in Al. Includes an activity on identifying patterns.

3.2 Statistics

• Explores how statistics are helpful for Al and how it's used in everyday problems. Introduces different activities like identifying the average value in a list of numbers or drawing a line graph of data points.
• Data types (Quantitative/qualitative): Understanding the differences between these data types.
• Statistical applications (real-world scenarios): Analyzing and interpreting data to solve problems related to weather forecasting, sports, or disease prediction.

3.3 Probability

• Probability Concepts: Introduces basic concepts like impossible, unlikely, likely, and certain events. Includes activities for applying probability to real-world situations (games, scenarios).

Unit 4: Generative Artificial Intelligence

• Introduction to Generative Al: Explains generative Al's components, working, applications and related tools; like ChatGTP, Gemini
• Generative Al vs Conventional Al: Shows the differences.
• Hands-on Activity: Introduces tools to try the generative AI models.
• Ethical Considerations of using Generative Al: Discusses the associated ethical issues around the use of generative Al. This section is focused on the importance of ethical considerations.

Explore the world of Generative AI and rule-based approaches in this quiz. Understand the mathematical concepts, training processes, and the significance of binary data in AI modeling. Test your knowledge on the adaptive nature of AI models and their applications in various fields.