CBSE Class 10 AI: Complete Guide 2026

Starting Your CBSE Class 10 AI Journey? Read This First.

Hey there! 👋

So you’ve picked Artificial Intelligence for Class 10 CBSE as your skill subject. Smart choice! Whether you chose it because “AI sounds cool” or because your school made you, I’m here to make sure you don’t just survive – you THRIVE.

This is the complete Class 10 AI CBSE guide that covers absolutely everything: from understanding the syllabus structure to acing your board exams, from Python practicals to project work, and from “what is this subject even about?” to “how do I score 45+ marks?”

By the end of this guide, you’ll have a crystal-clear roadmap for the entire year. No confusion, no panic, just a solid plan.

Quick reality check: This subject gives you 100 marks (50 theory + 50 practical) that count toward your percentage. Unlike some skill subjects that are just “additional,” Class 10 AI CBSE can actually REPLACE a failed subject under CBSE rules. More on that later.

Why CBSE Introduced AI in Class 10 (And Why It Matters to You)

The Big Picture

CBSE partnered with IBM and introduced AI as a skill subject because:

Future-ready skills: AI jobs are growing 40% annually in India
Practical learning: Unlike theory-heavy subjects, this is hands-on
Problem-solving: You’ll build actual AI projects, not just memorize
College advantage: Many tech colleges give preference to AI students

What Makes Class 10 AI Different

This isn’t Computer Science. You won’t spend the year writing algorithms on paper.

This is AI literacy: Understanding how machines learn, building models using no-code tools (Orange Data Mining), and creating solutions for real-world problems.

The cool part? You can build a disease diagnosis system or sentiment analyzer WITHOUT being a coding genius. That’s the power of modern AI tools.

The Complete CBSE Class 10 AI Syllabus Breakdown

Understanding the structure is 50% of success. Let me break down the Class 10 AI CBSE syllabus into digestible chunks.

The Two-Part Structure

The syllabus has TWO major parts:

PART A: Employability Skills (10 marks)

Communication Skills
Self-Management
ICT Skills
Entrepreneurial Skills
Green Skills

PART B: Subject-Specific AI Content (40 marks)

This is where the actual AI concepts live
7 units covering everything from project cycle to Python

PART C: Practical Work (50 marks)

Practical file with 15 programs
Practical exam
Project work
Viva voce

Part B: Unit-wise Deep Dive (This is the Important Part!)

Let me break down what you’ll actually study in Class 10 AI CBSE:

Unit 1: Revisiting AI Project Cycle & Ethical Frameworks (7 marks)

What you’ll learn:

The 5 stages of AI Project Cycle (Problem Scoping → Data Acquisition → Data Exploration → Modeling → Evaluation)
Three AI domains: Data Science, Computer Vision, Natural Language Processing
Ethical frameworks for AI (Bioethics, fairness, transparency, bias)

Why it matters: This is your foundation. Every project you build will follow this cycle.

Exam pattern:

5 MCQs (1 mark each)
1 short answer (2 marks)
1 long answer (4 marks)

Pro tip: The 4Ws Problem Canvas (What, Who, Where, Why) is GUARANTEED to appear. Practice drawing it.

Unit 2: Advanced Concepts of Modeling in AI (11 marks)

What you’ll learn:

Difference between AI, ML, and Deep Learning
Types of machine learning: Supervised, Unsupervised, Reinforcement
Classification vs Regression models
Neural Networks (this confuses everyone – I’ll help you understand it simply)
How AI makes decisions

Why it matters: Highest marks from a single unit. Master this = score 45+.

Exam pattern:

8 MCQs (1 mark each)
3 short answers (2 marks each)

Danger zone: Neural Networks and CNN are abstract. Use visual learning (I’ll link resources below).

Unit 3: Evaluating Models (10 marks)

What you’ll learn:

Why evaluation matters
Train-test split
Confusion Matrix (True Positive, False Positive, etc.)
Accuracy, Precision, Recall, F1 Score
Bias and transparency issues

Why it matters: Confusion Matrix alone is worth 4-6 marks. Learn this ONE topic well and you’re golden.

Exam pattern:

5 MCQs
1 short answer
1 long answer (usually: “Given this confusion matrix, calculate all metrics”)

Pro tip: Check out our Complete Confusion Matrix Guide – it has every calculation explained step-by-step.

Unit 4: Statistical Data (Practical Only)

What you’ll learn:

Using Orange Data Mining tool (no-code AI)
Analyzing datasets
Building classification and regression models
Interpreting results

Why it matters: This is your practical exam savior. Orange is SO much easier than coding from scratch.

Exam pattern:

Not tested in theory
15 marks in practical exam
You’ll be given a dataset, asked to build a model, and explain results

Essential skill: Learn Orange BEFORE exam month. It takes 2-3 hours to become comfortable.

Learn Orange here: Orange Data Mining Complete Tutorial

Unit 5: Computer Vision (4 marks)

What you’ll learn:

How computers “see” images
Pixels, resolution, RGB vs grayscale
Convolutional Neural Networks (CNN)
Feature extraction, convolution operation
Real-world applications (face recognition, object detection)

Why it matters: Questions are theoretical but require understanding visual concepts.

Exam pattern:

3 MCQs
1 short answer

Cool practical: You’ll use Lobe.ai or Teachable Machine to build an image classifier (like a plant disease detector).

Unit 6: Natural Language Processing (8 marks)

What you’ll learn:

How computers understand human language
Text processing: tokenization, bag of words, TF-IDF
Sentiment analysis
Chatbots (script bot vs smart bot)

Why it matters: NLP projects are easier to explain in viva (everyone understands “analyze if a review is positive or negative”).

Exam pattern:

5 MCQs
1 short answer
1 long answer

Project idea: Sentiment analysis of movie reviews or Twitter data (easy + impressive).

Learn NLP project: Sentiment Analysis Step-by-Step

Unit 7: Advanced Python (Practical Only)

What you’ll learn:

Jupyter Notebook basics
NumPy (arrays, mean, median, mode)
Matplotlib (line charts, scatter plots)
Reading CSV files
Basic image processing with OpenCV

Why it matters: Your 15-program practical file comes from this.

Exam pattern:

Not tested in theory
15 marks in practical exam (you’ll write/debug code)
15 marks for practical file

Struggling with Python? Check our 15 Ready Programs Guide

The CBSE Class 10 AI Marking Scheme (Where Marks Come From)

Understanding this is CRITICAL for strategy.

Theory Paper (50 marks) – 2 hours

Section	Type	Questions	To Answer	Marks
Part A	Employability Skills	5 MCQs	All 5	10 marks
Part B	AI Subject MCQs	24 MCQs	Any 20	20 marks
Part B	Short Answer (2m each)	6 questions	Any 4	8 marks
Part B	Long Answer (4m each)	5 questions	Any 3	12 marks
TOTAL				50 marks

Key insights:

✅ You have CHOICES – don’t panic if one question is tough
✅ MCQs are 30% of theory marks (easy scoring)
✅ Employability Skills are easy 10 marks (don’t skip!)

Practical Exam (50 marks) – Conducted by school

Component	Marks	What It Is
Practical File	15	Your notebook with 15 programs + outputs
Practical Exam	15	Examiner gives you a task (Orange/Python)
Project Work	10	Your capstone AI project
Viva Voce	10	Questions about your project + concepts
TOTAL	50

Key insights:

✅ Practical file is FREE 15 marks (just maintain it properly)
✅ Project work: Start early, choose simple problem
✅ Viva: Confidence matters more than perfection

The Exact Exam Pattern You’ll Face

Theory Paper Structure (Actual Board Exam)

You’ll receive a question paper with:

Section A – Employability Skills (10 marks):

5 questions with assertion-reasoning format
Example: “Assertion: Entrepreneurship involves risk. Reason: Starting a business has uncertainties.”
All 5 are compulsory

Section B – Subject Specific (40 marks):

Part 1: MCQs (20 marks)

24 MCQs given, answer any 20
Topics from all 7 units
1 mark each, no negative marking

Part 2: Short Answer (8 marks)

6 questions given, answer any 4
2 marks each
Expected answer length: 20-30 words

Part 3: Long Answer (12 marks)

5 questions given, answer any 3
4 marks each
Expected answer length: 100-150 words

Pro tip: In MCQs, always attempt all 24 (even if guessing). No negative marking means zero downside!

How to Score 45+ in CBSE Class 10 AI (Strategic Study Plan)

The High-Yield Topics (Study These First!)

Based on past 3 years of board papers, these topics appear EVERY year:

Guaranteed Questions (20+ marks):

Confusion Matrix calculations (4-6 marks) – Master it here
AI Project Cycle 4Ws (2-4 marks)
Types of Machine Learning (2-4 marks)
Neural Networks basics (2-4 marks)
Supervised vs Unsupervised learning examples (2 marks)

Frequently Asked (10+ marks): 6. Precision vs Recall scenarios (2-4 marks) 7. CNN layers explanation (2 marks) 8. Bag of Words / TF-IDF (2 marks) 9. Ethical issues in AI (bias, transparency) (2-4 marks) 10. Train-test split concept(2 marks)

Master these 10 topics = Guaranteed 30+ marks in theory alone.

The 12-Week Study Plan

Weeks 1-4: Foundation Building

Week 1-2: Units 1 & 2 (Project Cycle, ML Types, Neural Networks)
Week 3-4: Unit 3 (Evaluation Metrics – spend extra time on Confusion Matrix)
Parallel: Start Orange Data Mining practice (1 hour/week)

Weeks 5-8: Domains & Practicals

Week 5: Unit 5 (Computer Vision basics, CNN)
Week 6: Unit 6 (NLP, text processing)
Week 7: Python programming (write all 15 programs)
Week 8: Complete practical file, start project

Weeks 9-11: Project & Integration

Week 9-10: Complete AI project (data collection, model, documentation)
Week 11: Orange workflows for all three domains (practice 5+ times)
Parallel: Solve MCQs (50+ questions per week)

Week 12: Revision & Mock Tests

Days 1-3: Theory revision (formula sheets, MCQs)
Days 4-5: Practical revision (Orange + Python)
Days 6-7: Full mock tests (theory + practical)

Download the plan: 12-Week Study Schedule PDF

The Practical Exam: What Actually Happens

Many students panic about practicals. Here’s exactly what to expect:

Before Exam Day

Your school will:

Submit your practical file to CBSE (date varies by school)
Schedule practical exam (usually 2-3 weeks before theory boards)
Assign an external examiner

You must prepare:

✅ Practical file with 15 programs (neat, indexed, signed)
✅ Project report (printed, bound)
✅ Confidence in Orange Data Mining
✅ Ability to explain your project

On Exam Day (Step-by-Step)

Duration: 30-45 minutes per student

Part 1: Practical File Check (5 minutes)

Examiner flips through your file
Checks if all 15 programs are there
Asks 1-2 basic questions (“What does this program do?”)

Part 2: Practical Task (15-20 minutes)

You’re given EITHER:
- Option A: Orange task (“Build a model to classify this data”)
- Option B: Python task (“Write a program to calculate mean/median” or “Read this CSV and plot a graph”)

Example Orange Task:

“I’m giving you a dataset of iris flowers. Use Orange to build a classification model that predicts the species. Show me the confusion matrix and tell me the accuracy.”

Example Python Task:

“Write a Python program using NumPy to calculate the mean and standard deviation of this array: [45, 67, 89, 23, 56, 78]”

Part 3: Project Presentation (10 minutes)

Show your project
Explain: Problem → Data → Model → Results
Demonstrate if possible (even if using Orange screenshots)

Part 4: Viva Questions (10 minutes)

Questions about your project
General concepts from syllabus
Practical applications

Sample viva questions:

“Why did you choose this algorithm?”
“What is overfitting? Did your model overfit?”
“Explain the difference between classification and regression.”
“What is the confusion matrix showing?”

How to Score Full Marks in Practicals

For Practical File (15 marks):

✅ All 15 programs present
✅ Neat handwriting or printout
✅ Proper index and page numbers
✅ Every program has: Title, Code, Output, Explanation
✅ Teacher signature on each program

For Practical Exam (15 marks):

✅ Complete the task in time
✅ Explain your steps clearly
✅ Handle errors calmly (debugging shows understanding)
✅ Show understanding, not just memorization

For Project (10 marks):

✅ Clear problem statement (aligned with SDGs is bonus)
✅ Real dataset (not made-up numbers)
✅ Working model (even simple is fine)
✅ Professional report format

For Viva (10 marks):

✅ Confident body language
✅ Admit if you don’t know (don’t bluff)
✅ Connect answers to your project
✅ Use correct terminology

Get project templates: 10 Ready AI Projects

Project Work: How to Choose & Execute

Your project is 10 marks, but more importantly, it’s your showcase piece. Choose wisely.

The Perfect Project Formula

Good projects have these 4 qualities:

Simple problem: Don’t try to cure cancer with AI
Available data: You can find/collect it easily
Explainable: You understand how it works
Impressive: Solves a real problem

10 Project Ideas (Easy to Hard)

Beginner Level:

Spam Email Classifier (using Bag of Words)
Movie Review Sentiment Analysis (positive/negative)
Student Grade Predictor (based on study hours, attendance)
Flower Species Classification (Iris dataset – classic)

Intermediate Level: 5. Face Mask Detector (Computer Vision with Teachable Machine) 6. Crop Disease Identifier(image classification) 7. Fake News Detector (NLP with TF-IDF) 8. Heart Disease Prediction (classification with medical data)

Advanced Level: 9. Coral Bleaching Detection (Computer Vision with Orange) 10. Student Dropout Prediction(regression with multiple features)

Full project walkthrough: Complete Project Guide

Project Report Format (Follow This Exactly)

Your report should have these sections:

1. Cover Page

Project Title
Your Name, Class, Roll Number
School Name
Session: 2025-26

2. Certificate

School principal’s certificate
Teacher’s signature

3. Acknowledgement

Thank teachers, parents (keep it short)

4. Table of Contents

5. Introduction (1 page)

What problem are you solving?
Why is it important?
SDG connection (bonus points)

6. Problem Scoping (1-2 pages)

4Ws Canvas (What, Who, Where, Why)
Goal statement

7. Data Acquisition (1 page)

Where did you get data?
How many samples?
What features?

8. Data Exploration (1-2 pages)

Data visualization (graphs, charts)
Insights from data

9. Modeling (2-3 pages)

Which algorithm did you use? (Classification/Regression)
Why this algorithm?
Screenshots of Orange workflow or Python code

10. Evaluation (1-2 pages)

Confusion matrix
Accuracy, Precision, Recall
Is the model good enough?

11. Conclusion (1 page)

What did you learn?
Future improvements

12. References

List datasets, tools, websites used

Total: 10-15 pages (not including code printouts)

Download template: Project Report Template

Special Topic: Can AI Replace a Failed Subject?

This is a common question, and the answer is YES, with conditions.

The CBSE Replacement Rule

According to CBSE guidelines:

If you FAIL in one main subject (Science, Math, Social Science), AND you PASS in AI:

Your AI marks will replace the failed subject for pass percentage calculation
You still qualify for promotion to Class 11
You get a “Pass” certificate

Example:

You score 30/100 in Math (FAIL)
You score 80/100 in AI (PASS)
CBSE considers you PASSED overall
Your percentage is calculated using AI marks instead of Math

BUT there’s a catch:

You still need to appear for Math compartment exam (usually July)
Some Class 11 streams (like Science with Math) might still require Math pass
Check your target stream requirements

Full explanation: AI Subject Replacement Rules

Common Mistakes Students Make (And How to Avoid Them)

Mistake #1: Ignoring Part A (Employability Skills)

Why it’s bad: You’re throwing away FREE 10 marks.

Solution: Spend 2 hours before exam revising Part A. It’s common sense + basic definitions.

Key topics:

Communication barriers (physical, semantic, psychological)
SMART goals (Specific, Measurable, Achievable, Relevant, Time-bound)
Types of entrepreneurs (solo, partnership, family)
Green skills (3Rs: Reduce, Reuse, Recycle)

Mistake #2: Memorizing Without Understanding

Why it’s bad: Viva will expose you. Examiners ask “Why?” not “What?”

Solution: For every concept, understand:

What problem does it solve?
How does it work (basic idea)?
Real-world example?

Example: Don’t just memorize: “Precision = TP / (TP + FP)” Understand: “Precision tells me: when I predict positive, how often am I correct? High precision = fewer false alarms.”

Mistake #3: Starting Project in Exam Month

Why it’s bad: Good projects need iteration. You can’t do quality work in 1 week.

Solution:

September-October: Choose topic, collect data
November-December: Build model, iterate
January: Complete report, prepare viva

Starting in February = guaranteed stress + subpar project.

Mistake #4: Focusing Only on Theory

Why it’s bad: Practicals are 50% marks. Many students lose 15-20 marks here.

Solution:

Practice Orange 5+ times BEFORE exam
Run every Python program yourself (don’t just copy)
Explain concepts out loud (teach a friend)

Mistake #5: Ignoring MCQ Strategy

Why it’s bad: MCQs are 30 marks. Poor strategy = lost marks.

Solution:

Attempt ALL 24 MCQs (no negative marking)
If stuck: eliminate obviously wrong options, then guess
Don’t overthink – first instinct is often correct
Time management: 30 seconds per MCQ = 12 minutes for all MCQs

Practice MCQs here: 50 Board-Style MCQs

The Last Week Strategy (Battle-Tested Plan)

You have 7 days before theory exam. Here’s the optimal plan:

Day 7 (One Week Before):

Morning (3 hours): Confusion Matrix, Precision, Recall, F1 Afternoon (2 hours): AI Project Cycle (4Ws, stages)Evening (2 hours): 50 MCQs practice

Day 6:

Morning (3 hours): ML types, Classification vs Regression, Decision Trees Afternoon (2 hours): Neural Networks, CNN basics Evening (2 hours): Employability Skills (all units)

Day 5:

Morning (3 hours): NLP (Bag of Words, TF-IDF, Sentiment Analysis) Afternoon (2 hours): Computer Vision (Pixels, RGB, Convolution) Evening (2 hours): 50 MCQs practice

Day 4:

Morning (3 hours): Train-test split, Overfitting, Bias Afternoon (2 hours): Orange Data Mining practice (build 2-3 models) Evening (2 hours): Python revision (run 5-7 key programs)

Day 3:

Full-length Mock Test (2 hours):

Attempt a complete theory paper
Time yourself strictly
Check answers, note weak areas

Evening (3 hours): Revise weak areas from mock test

Day 2:

Morning (2 hours): Formula sheet revision (write formulas 5 times) Afternoon (2 hours): Diagram practice (Confusion Matrix, Neural Network, 4Ws Canvas) Evening (2 hours): Previous year board papers (2-3 papers)

Day 1 (Night Before):

Morning (2 hours): Light revision – MCQs only Afternoon: Relax, watch AI explanation videos Evening: Read formula sheet once, sleep early

NO NEW TOPICS on Day 1! Your brain needs consolidation time.

Download: Last Week Revision Notes

Resources to Supplement Your Learning

Official CBSE Resources

Must-have documents:

CBSE Class 10 AI Curriculum 2025-26 – Download the official syllabus PDF
Sample Question Papers (Released by CBSE in January)
Previous Year Papers (2023, 2024, 2025)

Tools You Must Know

Orange Data Mining

Download: orangedatamining.com
Our tutorial: Orange Complete Guide

Teachable Machine (for Computer Vision)

Web tool: teachablemachine.withgoogle.com
No installation needed, works in browser

Lobe.ai (Alternative CV tool)

Download: lobe.ai
Easier than Teachable Machine for beginners

Jupyter Notebook (for Python)

Install via Anaconda: Our Setup Guide

AISkillsIndia.in Resources

Concept Guides:

Practical Help:

Exam Prep:

Need 1-on-1 help? Try our AI Doubt Solver – Get answers in 5 minutes.

FAQ: Your Questions Answered

Q1: Is Class 10 AI difficult?

A: It’s DIFFERENT, not difficult. If you’re expecting traditional math/science, you’ll struggle. But if you embrace the practical, hands-on nature, it’s actually easier than subjects like Math or Science.

Difficulty level: 6/10 (compared to Math: 8/10, Science: 7/10)

Q2: Do I need to be good at coding?

A: Not really! The Python part is basic (arrays, charts, reading files). If you can understand logic, you can code these programs. Plus, 50% of practicals use Orange Data Mining, which is NO CODE.

Q3: Can I score 90+ in AI?

A: Yes, but it’s rare. Most toppers score 80-85. Why? Because practicals involve subjective evaluation (project quality, viva performance). But 45+ (90%) is very achievable.

Realistic targets:

With moderate effort: 35-40 (70-80%)
With dedicated effort: 40-45 (80-90%)
With exceptional effort: 45-48 (90-96%)

Q4: What if I fail in AI?

A: You can appear for compartment exam (usually in July). BUT – if you fail AI AND pass another subject, that subject can cover for AI in percentage calculation (reverse of the replacement rule).

Better question: How to NOT fail? Follow this guide, maintain practical file, attempt all questions. Failing is nearly impossible if you put in basic effort.

Q5: Which project is easiest?

A: Sentiment Analysis (NLP) or Iris Flower Classification (Data Science). Both have:

Readily available datasets
Simple logic (positive/negative sentiment OR classify flower species)
Easy to explain in viva
Can be done in Orange in 2 hours

Avoid: Anything with “prediction” in healthcare (too complex for Class 10 level unless you really understand it).

Q6: Is AI better than IT/Computer Science as skill subject?

A: AI is newer and more relevant to current industry trends. IT/CS teach basics (MS Office, networking) which you might already know. AI teaches cutting-edge concepts that colleges and employers value.

Choose AI if: You want to learn something genuinely new and future-focused. Choose IT if: You want a guaranteed easy subject with familiar content.

Full comparison: AI vs IT vs CS

Q7: Do I need a powerful computer for AI practicals?

A: No! Orange Data Mining and Teachable Machine work on ANY computer (even 4GB RAM). Python might be slow on very old laptops, but it’ll still work.

Minimum requirements:

Windows 7 or later / macOS / Linux
4GB RAM
50GB free space
Internet (for downloading tools)

Q8: Can I do AI without taking coaching?

A: Absolutely! This subject is designed to be self-learnable with the right resources. Use:

Your school textbook
Free online tools (Orange, Teachable Machine)
This guide + AISkillsIndia.in articles
YouTube tutorials

Coaching might help IF: You’re completely lost and your school teacher is unavailable. But it’s not necessary.

Your Action Plan: Next Steps

Okay, you’ve read this far (impressive dedication!). Here’s what to do NOW:

This Week:

Day 1 (Today):

Download Orange Data Mining
Download CBSE Class 10 AI syllabus PDF
Bookmark this guide
Join our Telegram Group for updates

Day 2:

Watch 1-2 Orange Data Mining tutorials
Create your practical file (buy register, make index)
List 3 project ideas you’re interested in

Day 3:

Read Unit 1 from textbook (AI Project Cycle)
Practice drawing 4Ws Canvas 3 times
Solve 10 MCQs from Unit 1

Day 4-7:

Start Unit 2 (Modeling concepts)
Write your first Python program in Jupyter
Research datasets for your chosen project

This Month:

Week 1-2:

Complete Units 1, 2, 3 (theory + notes)
Write 5 Python programs in practical file
Practice Orange with 2 sample datasets

Week 3-4:

Complete Units 5, 6 (CV + NLP)
Finalize project topic
Start data collection for project

Next 3 Months:

Month 1-2: Complete syllabus, practical file, start project Month 3: Complete project, revision, mock tests Month 4 (Exam month): Final revision, practical exam, theory boards

Final Words: You’ve Got This! 💪

Look, CBSE Class 10 AI might seem overwhelming right now – new subject, unfamiliar concepts, practical exams, project work. But here’s the truth:

Thousands of students just like you have scored 40+ marks in AI. They’re not geniuses. They just:

Started early (not in exam month)
Understood concepts (not just memorized)
Practiced tools (Orange, Python)
Chose simple projects (not overambitious)
Asked for help when stuck

You have this ENTIRE guide. You have all the resources linked. You have a clear roadmap.

All you need now is execution.

Start today. Even if it’s just 30 minutes. Install Orange. Read one unit. Write one program.

Small steps, consistent effort, massive results.

Good luck with your Class 10 AI CBSE journey! 🚀

If this guide helped you, share it with your classmates. Let’s make AI easier for everyone.

Starting Your CBSE Class 10 AI Journey? Read This First.

Why CBSE Introduced AI in Class 10 (And Why It Matters to You)

The Big Picture

What Makes Class 10 AI Different

The Complete CBSE Class 10 AI Syllabus Breakdown

The Two-Part Structure

Part B: Unit-wise Deep Dive (This is the Important Part!)

Unit 1: Revisiting AI Project Cycle & Ethical Frameworks (7 marks)

Unit 2: Advanced Concepts of Modeling in AI (11 marks)

Unit 3: Evaluating Models (10 marks)

Unit 4: Statistical Data (Practical Only)

Unit 5: Computer Vision (4 marks)

Unit 6: Natural Language Processing (8 marks)

Unit 7: Advanced Python (Practical Only)

The CBSE Class 10 AI Marking Scheme (Where Marks Come From)

Theory Paper (50 marks) – 2 hours

Practical Exam (50 marks) – Conducted by school

The Exact Exam Pattern You’ll Face

Theory Paper Structure (Actual Board Exam)

How to Score 45+ in CBSE Class 10 AI (Strategic Study Plan)

The High-Yield Topics (Study These First!)

The 12-Week Study Plan

Weeks 1-4: Foundation Building

Weeks 5-8: Domains & Practicals

Weeks 9-11: Project & Integration

Week 12: Revision & Mock Tests

The Practical Exam: What Actually Happens

Before Exam Day

On Exam Day (Step-by-Step)

How to Score Full Marks in Practicals

Project Work: How to Choose & Execute

The Perfect Project Formula

10 Project Ideas (Easy to Hard)

Project Report Format (Follow This Exactly)

Special Topic: Can AI Replace a Failed Subject?

The CBSE Replacement Rule

Common Mistakes Students Make (And How to Avoid Them)

Mistake #1: Ignoring Part A (Employability Skills)

Mistake #2: Memorizing Without Understanding

Mistake #3: Starting Project in Exam Month

Mistake #4: Focusing Only on Theory

Mistake #5: Ignoring MCQ Strategy

The Last Week Strategy (Battle-Tested Plan)

Day 7 (One Week Before):

Day 6:

Day 5:

Day 4:

Day 3:

Day 2:

Day 1 (Night Before):

Resources to Supplement Your Learning

Official CBSE Resources

Tools You Must Know

AISkillsIndia.in Resources

FAQ: Your Questions Answered

Q1: Is Class 10 AI difficult?

Q2: Do I need to be good at coding?

Q3: Can I score 90+ in AI?

Q4: What if I fail in AI?

Q5: Which project is easiest?

Q6: Is AI better than IT/Computer Science as skill subject?

Q7: Do I need a powerful computer for AI practicals?

Q8: Can I do AI without taking coaching?

Your Action Plan: Next Steps

This Week:

This Month:

Next 3 Months:

Final Words: You’ve Got This! 💪

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