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Many parents hear that their child's school "offers AI" and are not sure what that means: a coding club, an elective, or a board subject with marks. In CBSE schools it can be a board subject, under two subject codes. This post explains what each code covers in 2026-27, how the marks split, how it fits with other subjects, and works through one calculation students meet in Class 10.
What are subject codes 417 and 843?
CBSE's Department of Skill Education lists Artificial Intelligence (417) for Classes 9 and 10 and Artificial Intelligence (843) for Classes 11 and 12. They are skill subjects, which are offered alongside the usual academic subjects. Whether your child can take one depends on whether their school offers it, so ask the school first.
All four curriculum documents for 2026-27 give the same overall split: Total Marks 100, Theory 50 and Practical 50.
What does Class 9 and 10 AI (417) cover?
The Class 9 curriculum has two parts in the theory paper:
Part A, Employability Skills (10 marks): communication, self-management, ICT, entrepreneurial and green skills.
Part B, Subject Specific Skills (40 marks): AI reflection, project cycle and ethics (10), data literacy (10), maths for AI covering statistics and probability (7), introduction to generative AI (5) and introduction to Python (8).
The 50 practical marks are a practical file of at least 15 programs (15), a practical exam (15), a viva (5) and a project, field visit or portfolio related to the Sustainable Development Goals (15).
The Class 10 curriculum keeps the same 10 marks for employability skills. Its subject units are the AI project cycle and ethical frameworks (7), advanced concepts of modelling (11), evaluating models (10), computer vision (4) and natural language processing (8), with statistical data and advanced Python assessed in practicals. The practical half is a file of at least 15 programs (15), a practical exam (15), a viva (5), and project work (10) with its own viva (5).
What does Class 11 and 12 AI (843) cover?
The Class 11 curriculum lists the job role as AI Assistant. Its eight subject units include Python programming, data literacy, machine learning algorithms, linguistics and computer science, AI ethics and an introduction to a capstone project. Practical marks include an industry certification (5), a capstone project (12), bootcamps or internship (7), a practical file (10), a lab test (10) and a viva (6).
The Class 12 curriculum covers Python II, data science methodology, computer vision ("Making Machines See"), the Orange data mining tool, big data, neural networks, generative AI and data storytelling. The practical half is a capstone project with documentation and a video (25), a practical file (10), a lab test (10) and a viva (5).
How does it fit with other subjects?
For Classes 11 and 12, the CBSE Senior Secondary scheme of studies for 2026-27 lists AI (843) among skill electives with 50 theory and 50 practical marks, and says skill electives can be offered along with any subject, as per the scheme of studies.
Classes 9 and 10 are changing. The Secondary scheme for 2026-27 lists Artificial Intelligence among optional subjects. It also says Computational Thinking and AI (CT & AI) will be introduced in Classes 9 to 12 as modules provided by NCERT in 2027-28, and as a subject in 2027-28, with details to follow. CT & AI is a separate plan from the 417 skill subject. Check the current scheme with your school each year.
Worked example: a confusion matrix from Class 10
Class 10 Unit 3, Evaluating Models, asks students to build a confusion matrix and calculate accuracy, precision, recall and F1 score. Here is one with sample numbers.
A model checks 100 photos of leaves for disease. Of the 50 diseased leaves, it flags 40 correctly (true positives) and misses 10 (false negatives). Of the 50 healthy leaves, it wrongly flags 5 (false positives) and clears 45 (true negatives).
Accuracy = (TP + TN) / total = (40 + 45) / 100 = 0.85, or 85%.
Precision = TP / (TP + FP) = 40 / 45 ≈ 0.889.
Recall = TP / (TP + FN) = 40 / 50 = 0.80.
F1 score = 2 × P × R / (P + R). Using exact fractions, P = 8/9 and R = 4/5, so F1 = 2 × (32/45) / (76/45) = 64 / 76 ≈ 0.842.
The lesson the unit wants: 85% accuracy sounds good, but the model still misses one diseased leaf in five. If missing disease is costly, recall is the metric to watch. The curriculum's activity "Decide the appropriate metric to evaluate the AI model" is about exactly this choice.
Common mistakes students and parents make
Treating AI as computer science. It is a skill subject with its own code; CS is 083 and IP is 065. Our guide to choosing Computer Science or Informatics Practices compares those two.
Ignoring the practical half. Fifty marks depend on files, projects and vivas done through the year.
Confusing precision and recall. Precision divides by everything the model flagged; recall divides by everything that was really positive.
Assuming every school offers it. Availability is up to the school.
A practice routine for AI students
Weekly: add two programs to the practical file, with comments, rather than writing 15 in the last month.
Fortnightly: make up a confusion matrix and calculate all four metrics by hand.
Monthly: write half a page on how your project relates to a Sustainable Development Goal or a real user.
If Python errors keep stopping your practical file, the Fermi Problem Library has a step-by-step solver that explains the reasoning rather than printing an answer.
This week, download your class's curriculum PDF from the links above and tick off the units your school has finished.
