CBSE Class 10 Artificial Intelligence (417)

1. Part A: Employability Skills – II

Communication Skills-II · Self-Management Skills-II · ICT Skills-II · Entrepreneurial Skills-II · Green Skills-II

• Communication Skills: How to Share Meaning Clearly and Kindly – Communication is sharing meaning between people. A sender encodes an idea into a message, sends it through a channel, and a receiver decodes it and gives feedback. Barriers such as noise, hard words, strong emotions and cultural differences can block or bend the message. We send meaning with words (verbal), with face, eyes, gestures, posture and tone (non-verbal), and with pictures (visual). Active listening — attention, patience, questions and paraphrasing — proves the message arrived. Good communicators adapt to context: formal or informal, spoken or written, and use polite requests, kind refusals, sincere apologies and assertive (not aggressive or passive) language.
• Self-Management: Taking Charge of Your Feelings, Goals and Time – Self-management is the skill of guiding your own feelings, thoughts and actions so you can reach your goals. It starts with self-awareness: noticing what you feel and how strong it is. Then you pause before reacting (Stop, Think, Act) and use calming tools such as slow breathing. Your beliefs and thoughts shape your feelings and actions, so changing an unhelpful thought ('I'm useless') into a helpful one ('I can't do it yet') changes what you do. Good self-managers set SMART goals, plan their time by importance, build healthy habits and bounce back from setbacks (resilience). These skills help in school, friendships and future careers.
• ICT Basics: Using Computers, the Cloud and Learning Platforms Safely – ICT means information and communication technology: the devices, software and networks we use to create, store and share information. Every computer takes input, processes it, gives output and stores data. Storage is measured in bytes, and each bigger unit (KB, MB, GB, TB) is about 1000 times the one before. Good digital learners keep files in clearly named folders, use learning platforms to get and submit work, store files in the cloud and on devices, back up with the 3-2-1 rule and protect accounts with long passphrases and two-step login. ICT also lets you make creative projects such as videos, websites and podcasts.

2. Part B: Revisiting AI Project Cycle & Ethical Frameworks for AI

Revisiting AI Project Cycle & Ethical Frameworks for AI

• AI Ethics: Using Artificial Intelligence Fairly and Safely – AI systems learn patterns from data and then make decisions. AI ethics asks whether those decisions are fair, safe and respectful. The main issues are bias (unfair data gives unfair results), privacy (personal data needs consent and protection), transparency (people should know why an AI decided something), accountability (a human stays responsible), safety and misuse (deepfakes, false information), and social impact (jobs, the digital divide, the environment). Responsible AI means checking all of these before and after an AI is used.

3. Part B: Advanced Concepts of Modeling in AI

Advanced Concepts of Modeling in AI

• Machine Learning – Machine learning (ML) is a way for computers to learn a rule from examples instead of being told the rule. We give data made of features (inputs) and, in supervised learning, labels (answers). The machine fits a model: regression predicts a number, a decision tree asks yes/no questions to pick a class, and k-means clustering groups unlabelled data. We train on most of the data, test on data it never saw, and measure accuracy or error. A model that only memorises (overfits) fails on new data.

4. Part B: Evaluating Models

Evaluating Models

• Model Evaluation: How Good Is a Model? – A model is a simple copy of the real world that makes predictions. To evaluate it, we compare its predictions with the real answers. In machine learning we split data into a training set (to learn) and a test set (to check on new data). A confusion matrix counts true positives (TP), false positives (FP), false negatives (FN) and true negatives (TN). Accuracy = (TP + TN) ÷ total. Precision = TP ÷ (TP + FP). Recall = TP ÷ (TP + FN). F1 = 2PR ÷ (P + R). Science models (like the inverse-square law or the particle model) are judged the same way: do predictions match measurements, and where does the model stop working?

5. Part B: Statistical Data (practical only)

Statistical Data (practical only)

• Statistics: Mean, Median and Mode of Grouped Data – When data is put into classes (like marks 20–30), we cannot see each value. We use the middle of each class to find the mean, the running total to find the median, and the tallest bar to find the mode. All three tell us the 'centre' of the data in different ways.

6. Part B: Computer Vision

Computer Vision

• Computer Vision: How Machines See – Computer vision (CV) is the part of AI that lets computers understand images and videos. A digital image is a grid of pixels; each pixel is a number (0–255 for grey) or three numbers (R, G, B) for colour. Resolution is width × height in pixels. CV finds features such as edges, corners and colours, then does tasks like classification (what), object detection (where) and segmentation (which pixels). It is used in face unlock, self-driving cars, medical scans, farming, shops and traffic cameras.

7. Part B: Natural Language Processing

Natural Language Processing

Coming soon

8. Part B: Advance Python

Advance Python

Coming soon