Class 10 Artificial Intelligence (417) – CBSE Syllabus 2026–27
Complete chapter-by-chapter curriculum guide for CBSE Class 10 Artificial Intelligence (Subject Code 417) for Academic Session 2026–27. Covers Employability Skills (Part A, 10 Marks), Subject Specific Skills across 7 Units (Part B, 40 Marks), Practical Examination (Part C, 35 Marks), and Capstone Project Work (Part D, 15 Marks) mapped to the UN Sustainable Development Goals.
Course Objectives & Learning Outcomes
Course Focus & Goals
- Build multi-sensorial, experiential readiness for Artificial Intelligence and its real-world societal impact.
- Deepen understanding of the 3 AI domains: Statistical Data, Computer Vision (CV), and Natural Language Processing (NLP).
- Apply the 6-stage AI Project Cycle framework from problem scoping to model evaluation and deployment.
- Master Advance Python skills with NumPy, Matplotlib, Pandas, and OpenCV for scientific data and image handling.
- Analyze ethical frameworks, bioethics, AI bias, transparency, and accountability in AI decision-making.
Key Competencies Acquired
- Distinguish between Rule-based vs Learning-based AI models and Supervised vs Unsupervised vs Reinforcement learning.
- Understand Artificial Neural Networks (ANN), Perceptron mathematical decision modeling, and CNN layers.
- Construct Confusion Matrices and calculate Accuracy, Precision, Recall / Sensitivity, and F1-Score.
- Implement No-Code AI workflows using Orange Data Mining, Google Teachable Machine, and Lobe.ai.
- Execute NLP text normalisation, Bag of Words document vectorization, and TF-IDF numerical calculations.
Course Structure & Marks Distribution (Session 2026–27)
Total Marks: 100 (50 Theory + 50 Practical) | Total Instructional Hours: 210 Hours
| Part | Unit Name | Theory (Hrs) | Practical (Hrs) | Max Marks |
|---|---|---|---|---|
| Part A: Employability Skills | Unit 1: Communication Skills-II | 10 | 2 | |
| Unit 2: Self-Management Skills-II | 10 | 2 | ||
| Unit 3: Information & Communication Technology Skills-II | 10 | 2 | ||
| Unit 4: Entrepreneurial Skills-II | 10 | 2 | ||
| Unit 5: Green Skills-II | 10 | 2 | ||
| Part A Total | 50 Hours | 10 Marks | ||
| Part B: Subject Specific Skills | Unit 1: Revisiting AI Project Cycle & Ethical Frameworks for AI | 11 | 4 | 7 |
| Unit 2: Advanced Concepts of Modeling in AI | 18 | 7 | 11 | |
| Unit 3: Evaluating Models | 21 | 4 | 10 | |
| Unit 4: Statistical Data (Assessed through Practicals) | — | 28 | — | |
| Unit 5: Computer Vision (CV) | 10 | 20 | 4 | |
| Unit 6: Natural Language Processing (NLP) | 20 | 7 | 8 | |
| Unit 7: Advance Python (Assessed through Practicals) | — | 10 | — | |
| Part B Total | 80 | 80 | 40 Marks | |
| Part C: Practical & Project Work | Practical File with minimum 15 Programs | Lab record | 15 | |
| Practical Examination (Units 4, 5, 6, 7 hands-on) | Hands-on Exam | 15 | ||
| Viva Voce (Practical examination) | Oral test | 5 | ||
| Project Work / Field Visit / Student Portfolio (on SDGs) | Project Submission | 10 | ||
| Viva Voce (related to Project Work) | Oral test | 5 | ||
| Part C Total | Practical Work | 50 Marks | ||
| GRAND TOTAL (Theory 50 + Practical 50) | 210 Hours | 100 Marks | ||
Class 10 Artificial Intelligence – All Units & Sections
Every unit contains complete notes, exercises, official questions from the handbook, MCQs with answer keys, Q&As, quick revision, and FAQs.
Revisiting AI Project Cycle & Ethical Frameworks
Recapitulate the 6 stages of the AI Project Cycle, explore the 3 domains of AI, and study ethical frameworks including Bioethics in healthcare.
Advanced Concepts of Modeling in AI
AI vs ML vs DL Venn diagram, Rule-Based vs Learning-Based models, Supervised (Classification/Regression), Unsupervised (Clustering/Association), and Neural Networks.
Evaluating Models
Train-test split, Overfitting, Accuracy vs Error formulas, Confusion Matrix (TP, TN, FP, FN), Precision, Recall / Sensitivity, F1-Score, and ethical evaluation.
Statistical Data & No-Code AI
No-Code vs Low-Code vs High-Code, Descriptive Statistics (Mean, Median, Mode, Variance), and hands-on Orange Data Mining Palmer Penguins case study.
Computer Vision (CV)
Image pixels, RGB channels, resolution, CV tasks (detection, segmentation), convolutions and kernel matrices, and CNN layers (Conv, ReLU, Pooling, FC).
Natural Language Processing (NLP)
5 stages of NLP (lexical to pragmatic), script bots vs smart bots, text normalisation, stemming vs lemmatization, Bag of Words, and TF-IDF calculation.
Advance Python Programming
Jupyter Notebook, NumPy for statistical arrays, Matplotlib line and scatter charts, Pandas CSV exploration, and OpenCV image manipulation.
Employability Skills (Class 10)
All 5 mandatory employability units: Communication-II, Self-Management-II, ICT Skills-II, Entrepreneurial Skills-II, and Green Skills-II.
Practical Examination & Project Work
Complete 15+ program practical file guidelines, hands-on examination breakdown across Units 4-7, Viva questions, and Capstone projects on UN SDGs.
Minimum Equipment & Software Specifications
Prescribed for a batch of 20 students with a human-machine ratio of 2:1 (From Page 10 of CBSE Class 10 Syllabus).
System Hardware Specifications
- Processor: Intel® Core™ i5-7300U or equivalent (SYSmark® 2018 rating 750+)
- Form Factor: Ultra Small Form Factor (USFF) chassis < 1 Litre
- Memory: 8GB DDR4 – 2400MHz or above
- Storage: 500 GB HDD – 7200 rpm or fast SSD
- Display: 18.5" LED Monitor with HDMI & in-built speaker
- Peripherals: Full HD Webcam, Headphones with Mic, Optical Mouse, Keyboard with numpad
- VPU Support: Integrated or support for Vision Processing Unit (VPU) for machine vision
Software & Platform Stack
- Operating System: Windows 10/11 or modern Linux with active antivirus
- Browser: Google Chrome (for web-based AI tools)
- Python Distribution: Python 3.9+ via Anaconda Navigator Distribution
- Core Libraries: NumPy, Pandas, Matplotlib, OpenCV (cv2), NLTK, spaCy
- No-Code Tools: Orange Data Mining, Google Teachable Machine, Lobe.ai
- Intel Tools: Intel OpenVINO toolkit for accelerated computer vision
- Productivity: Google Workspace / Microsoft Excel with Analysis ToolPak
Frequently Asked Questions (Class 10 AI)
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Curriculum Reference
This page is based on the CBSE Artificial Intelligence (Subject Code 417), Class X, Curriculum for Session 2026–2027 and the CBSE–Intel Artificial Intelligence Curriculum Facilitator Handbook, Department of Skill Education, Central Board of Secondary Education (CBSE).
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