AI Journey 10

Step into the next level of Artificial Intelligence with the Grade 10 CBSE curriculum! This engaging course empowers students to deepen their understanding of AI concepts, ethics, and real-world applications. Learners will explore advanced AI project cycles, experiment with data science and machine learning tools, and create meaningful projects using computer vision and natural language processing. Through hands-on activities—including Python programming—students will develop analytical skills, ethical reasoning, and the confidence to build AI solutions for today’s world.

Key Features:

• Advanced exploration of the AI Project Cycle, including problem scoping, data handling, modeling, and deployment
• Deep dive into ethics and responsible AI (bias, fairness, privacy)
• Hands-on experience with open-source AI tools (Orange, Teachable Machine, Lobe, etc.)
• Introduction to computer vision (image classification, object detection) and NLP (chatbots, sentiment analysis)
• Real-world projects and portfolio development linked to the UN Sustainable Development Goals
• Beginner-to-intermediate Python programming for data handling and AI

Who should join?
Grade 10 students who are curious about technology, interested in coding and real-life applications, and ready to take their AI skills to the next level. No advanced coding required—just a growth mindset!

Learning Objectives – Artificial Intelligence (CBSE Grade 10)

By the end of this course, students will be able to:

1. Describe key concepts, domains, and the project cycle of Artificial Intelligence, including advanced modeling and evaluation techniques.
2. Apply ethical frameworks to real-world AI challenges, identifying issues such as bias, fairness, transparency, and privacy.
3. Collect, analyze, and visualize data using modern, no-code and low-code AI tools.
4. Develop and evaluate machine learning models using classification, regression, and clustering techniques.
5. Build practical projects in Computer Vision (e.g., image classification, feature extraction) using accessible tools and Python libraries.
6. Experiment with Natural Language Processing to create and test chatbots, perform text analysis, and understand how AI processes language.
7. Write and run basic-to-intermediate Python programs for data science, image processing, and simple AI applications.
8. Collaborate effectively, communicate results, and document the AI project cycle through reports, presentations, and a personal portfolio.
9. Reflect on the societal and environmental impact of AI, connecting classroom learning to the Sustainable Development Goals (SDGs).
10. Demonstrate readiness for advanced AI study and 21st-century tech careers through practical problem-solving, ethical reasoning, and digital fluency.