CBSE Class 9 Artificial Intelligence Syllabus 2025-26: FREE PDF Download

The Central Board of Secondary Education (CBSE) has structured the Class 9 Artificial Intelligence (AI) syllabus for the academic year 2025-26 to introduce students to the foundational concepts of AI, emphasizing both theoretical understanding and practical application. This curriculum aims to equip students with the skills necessary to navigate and contribute to the evolving landscape of artificial intelligence.

Objectives of the Course:

The AI curriculum is designed to:

1. Develop a readiness for understanding and appreciating Artificial Intelligence and its applications through interactive learning methods.
2. Introduce students to the three domains of AI in an age-appropriate manner.
3. Enable students to construct the meaning of AI through engaging hands-on activities.
4. Revisit AI domains, project cycles, and ethical considerations.
5. Highlight the importance of mathematics for AI, data literacy, and generative AI.
6. Introduce basic programming skills using Python.

Course Structure:

The syllabus is divided into four main parts:

• Part A: Employability Skills
• Part B: Subject-Specific Skills
• Part C: Practical Work
• Part D: Project Work/Field Visit/Student Portfolio

Part A: Employability Skills

This section focuses on essential skills that enhance students’ employability prospects:

1. Communication Skills-I: Effective verbal and non-verbal communication techniques.
2. Self-Management Skills-I: Time management, goal setting, and personal development strategies.
3. Information and Communication Technology Skills-I: Proficiency in using digital tools and platforms.
4. Entrepreneurial Skills-I: Understanding entrepreneurship and developing innovative thinking.
5. Green Skills-I: Awareness of sustainable practices and environmental responsibility.

Part B: Subject-Specific Skills

This section delves into core Artificial Intelligence concepts:

1. AI Reflection, Project Cycle, and Ethics: Understanding AI concepts, the stages involved in developing AI projects, and ethical considerations.
2. Data Literacy: Introduction to data handling, analysis, and interpretation.
3. Math for AI (Statistics & Probability): Fundamental mathematical concepts relevant to AI applications.
4. Introduction to Generative AI: Exploring the basics of generative models in AI.
5. Introduction to Python: Basic programming concepts using Python.

Part C: Practical Work

Practical sessions are designed to reinforce theoretical knowledge through hands-on experience:

• Practical File: Documenting a minimum of 15 Python programs.
• Practical Examination: Assessing proficiency in simple programs using input and output functions, variables, arithmetic operators, expressions, data types, flow of control and conditions, and lists.
• Viva Voce: Oral examination to evaluate understanding and application of concepts.

Part D: Project Work/Field Visit/Student Portfolio

Students are encouraged to undertake projects or field visits related to Artificial Intelligence applications:

• Project Work/Field Visit: Engaging in real-world case studies related to AI, preferably linked to Sustainable Development Goals.
• Portfolio/Student Portfolio: Compiling a portfolio that showcases their learning journey, including project reports and reflections.

This structured syllabus aims to balance theoretical understanding with practical application, ensuring that students gain a robust foundation in Artificial Intelligence. For detailed information and updates, students and educators are advised to refer to official CBSE resources.