Class 11 Artificial Intelligence (843) – CBSE Syllabus 2026–27
Complete curriculum guide for CBSE Class 11 Artificial Intelligence (Subject Code 843, Job Role: AI Assistant) for session 2026–27. Master AI foundations, Python libraries (NumPy, Pandas, Scikit-Learn), Design Thinking Capstone Projects, Data Literacy, Machine Learning (Regression, KNN, K-Means), NLP & Chatbots, AI Ethics, and practical blueprints.
Total Marks
100 Marks
Theory Weightage
50 Marks (110h)
Practical / Lab
50 Marks (100h)
Total Hours
210 Hours
Class 11 AI Curriculum Blueprint (2026–2027)
The curriculum is structured into three integrated parts: Part A (Employability Skills), Part B (Subject Specific Skills), and Part C (Practical & Capstone Project Work).
Employability Skills
5 foundational units: Communication Skills-III, Self-Management Skills-III, ICT Skills-III, Entrepreneurial Skills-III, and Green Skills-III.
Subject Specific Skills
8 in-depth units covering AI Foundations, Future Careers, Python & Data Libraries, Capstone Methodology, Data Literacy, Machine Learning, NLP, and AI Ethics.
Practical & Capstone Work
Hands-on evaluation: IBM SkillsBuild (5M), Capstone Project (12M), Bootcamp/Internship (7M), Practical File (10M), Lab Exam (10M), and Viva Voce (6M).
Subject Specific Skills – 8 Chapter Modules
Comprehensive study guides containing theory notes, real-world industry case studies, code examples, and official CBSE Teacher Handbook MCQs with complete answer keys.
Introduction: Artificial Intelligence for Everyone
Historical milestones from Alan Turing (1950) to the Dartmouth Conference (1956), AI Winter & 21st-century resurgence. Narrow, Broad, and General AI (ASI). The 3 domains (Data Science, NLP, Computer Vision). Cognitive computing (IBM Watson) and AI vs ML vs DL ladder of complexity.
Core Syllabus Highlights:
- What is AI vs What is NOT AI (Rule-based systems & basic sensors)
- Alan Turing 1950 Imitation Game & John McCarthy 1956 Dartmouth
- AI vs Machine Learning vs Deep Learning comparison & meat sorting case
- Supervised, Unsupervised & Reinforcement Learning (Kitchen robot grid)
Unlocking your Future in AI
Skyrocketing global demand for AI professionals and the evolving job landscape. 9 key job roles (ML Engineer, Data Scientist, BI Developer, Robotics Engineer, NLP Engineer, Computer Vision Engineer, AI Ethicist, AI Consultant). Essential technical toolkit & soft skills across 15+ industries.
Core Syllabus Highlights:
- 9 specialized AI career roles & required qualifications
- Technical toolkit (Python, R, TensorFlow, NumPy, SciPy, Linear Algebra)
- Soft skills: Communication, collaboration, problem-solving & critical thinking
- 15+ industry applications: Healthcare, Finance, Automobile, Agriculture & Media
Python Programming
Master Python fundamentals from character sets, tokens, data types (dynamic typing & casting), and control flow statements to CSV file operations. Dive into core AI packages: NumPy ndarrays, Pandas Series & DataFrames (.loc, .iloc, missing values), and Scikit-learn (Iris dataset & KNN).
Core Syllabus Highlights:
- Python tokens: Keywords, identifiers, literals, operators & punctuators
- Control flow: if-elif-else ladders, for loops, range() & break statements
- CSV manipulation with the Python csv module (reader, writer, writerow)
- NumPy ndarray, Pandas DataFrame operations & Scikit-learn KNN classification
Introduction to Capstone Project
The definitive guide to your AI Capstone journey. The prerequisite question 'Is there a Pattern?' and 5 predictive questions. Problem decomposition in 4 steps, the 5W1H questioning method, and the 5 stages of Design Thinking: Empathize (Empathy Maps), Define, Ideate, Prototype, and Test aligned with UN SDGs.
Core Syllabus Highlights:
- 'Is there a Pattern?' — Core prerequisite for AI problem-solving
- Problem decomposition in 4 structured steps & 5W1H questioning
- 5 Stages of Design Thinking: Empathize, Define, Ideate, Prototype & Test
- 4-quadrant Empathy Maps (Says, Thinks, Does, Feels) & UN SDG alignment
Data Literacy – Collection to Analysis
Understand data as the 21st-century oil. Primary vs. Secondary data collection. The 4 Levels of Measurement (Nominal, Ordinal, Interval, Ratio). Statistical analysis: Central Tendency (Mean, Median, Mode) and Dispersion (Variance, Standard Deviation). Matplotlib data visualization and Matrix mathematics for AI.
Core Syllabus Highlights:
- Structured, semi-structured & unstructured data formats
- 4 Levels of Measurement: Nominal, Ordinal, Interval & Ratio scales
- Statistical measures with Python statistics library & Dog height case study
- Matplotlib charts (Line, Bar, Histogram, Scatter, Pie) & Matrix operations (m x n, transpose)
Machine Learning Algorithms
In-depth study of Supervised Learning: Correlation (Positive, Negative, Zero), Causation vs Correlation, Pearson's r (-1 to +1), Linear Regression best-fit line (y = a + bx + e) & Least Squares Method. Classification with K-Nearest Neighbors (KNN). Unsupervised Learning: Clustering methods & K-Means step-by-step.
Core Syllabus Highlights:
- Pearson's Correlation Coefficient r formula & Excel functions (=slope, =intercept)
- Simple vs Multiple Linear Regression line of best fit & Python implementation
- Classification types (Binary, Multi-Class, Multi-Label, Imbalanced) & KNN steps
- 4 Clustering methods & K-Means Clustering algorithm (centroids & variance)
Leveraging Linguistics & Computer Science
Bridge linguistics and computer science. Why human language is difficult (ambiguity, sarcasm, metaphors, Groucho Marx elephant joke). Sentence segmentation, tokens, entities, relationships, concepts. Rule-based vs. AI-powered chatbots (frontend, backend, intents, entities, dialog trees). Five Phases of NLP.
Core Syllabus Highlights:
- Human language complexity: Ambiguity, classification riddles & confidence scores
- Chatbot architecture: Frontend messaging channel vs Backend dialog memory
- Intents (actions), Entities (nouns) & Dialog trees (IF/THEN flowcharts)
- 5 Phases of NLP: Lexical, Syntactic, Semantic, Discourse Integration & Pragmatic
AI Ethics and Values
Explore the moral compass of AI. Five Pillars of AI Ethics: Explainability, Fairness, Robustness, Transparency, and Privacy. Sources of AI bias (Training data, Algorithmic, Cognitive) with real-world case studies. AI governance (IBM, Microsoft, Google, EU guidelines), Moral Machine dilemmas, and Survival of the Best Fit.
Core Syllabus Highlights:
- The Five Pillars of AI Ethics: Explainability, Fairness, Robustness, Transparency, Privacy
- Sources of AI bias: Training data, Algorithmic & Cognitive bias in healthcare and law enforcement
- Mitigating bias: IBM AI Fairness 360 toolkit (70+ metrics, 10+ mitigating algorithms)
- Moral Machine Game: Autonomous vehicles collision dilemmas & ethical trade-offs
Practical Examination & Capstone Project Blueprint
The 50-mark internal & external lab evaluation criteria prescribed in the official CBSE Class 11 curriculum for academic session 2026–2027.
IBM SkillsBuild Certification
Completion of designated course badge (Artificial Intelligence Fundamentals, Python for Data Science, or Machine Learning with Python).
AI Capstone Project
Comprehensive team project adhering to the Design Thinking framework: 5W1H problem definition, Empathy Map, SDG alignment, prototype & pitch.
Bootcamps / Internships
Documented participation in hackathons, AI bootcamps, school innovation challenges, or industry workshops with verified certificates.
Practical File (15 Activities)
Neat record file documenting at least 15 prescribed practical activities covering Python programs, statistics, Excel regressions, and empathy maps.
Lab Test / Written Exam
Practical coding examination covering Python control flow, NumPy array creation, Pandas DataFrame operations, and Matplotlib plotting.
Viva Voce
Viva questions on practical file activities, Python library methods, machine learning algorithms, and the student's capstone project.
Class 11 AI High-Yield Recall Points
Frequently Asked Questions (FAQ)
Master Class 11 Artificial Intelligence with 1:1 Live Mentorship
Get 1-on-1 personalized guidance from certified AI educators. Master Python programming, NumPy array operations, Pandas DataFrames, Scikit-learn machine learning pipelines, Capstone project mentoring, and score 100/100 in CBSE Class 11 AI exams.