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CBSE Computational Thinking & AI · 2026–27

Class 6 · Artificial Intelligence
Chapter 1 Teaching Pack

Introduction to Artificial Intelligence and Everyday Examples — everything needed to teach the chapter, ready to print.

Complete sample Lesson plan Worksheet + answer key Unplugged activity Project rubric Evidence record

Where this chapter sits

Class 6 carries 100 hours across the year: 40 hours Computational Thinking, 20 hours Artificial Intelligence, and 40 hours interdisciplinary projects. The AI component has four chapters; this is the first.

#	Chapter	Learning focus	Suggested
1	Introduction to AI and Everyday Examples	Meaning of AI, AI in daily life, AI vs automation, human vs machine intelligence, types of learning in AI	6 periods
2	Basic Data Concepts	Understanding, types, collecting, organising and representing data	5 periods
3	Simple Pattern Recognition and Decision Making	Identifying patterns, observations and conclusions, decision making	5 periods
4	Ethics and Digital Responsibility	Responsible use, online safety, privacy, passwords, digital footprints	4 periods

Assessment note The CBSE framework asks for continuous, project- and activity-based assessment from Class 6, not a single end test. This changes what evidence you need to keep — see Section 06.

Lesson plan · 6 periods

Period 1 — What does "intelligent" actually mean?

Open before defining anything. Ask: "Is a calculator intelligent? Is a dog? Is a newborn baby?" Let them argue for five minutes. Most classes split, and the disagreement is the lesson.

Draw out the three abilities that matter: learning, thinking, and solving problems. A calculator does none — it follows fixed steps. A dog does all three, slowly. Land the definition only after they have felt the need for one.

Period 2 — Automation is not AI

This is the single most-missed distinction in the chapter, and the one most likely to appear in a project viva.

The distinction, in one line Automation follows instructions. AI improves from experience. A washing machine on a 40-minute cycle runs the same 40 minutes on its thousandth wash. A spam filter is wrong less often after a thousand emails.

Give five devices and have the class sort them into two columns. Ceiling fan regulator, automatic doors at a mall, a traffic signal on a fixed timer, YouTube recommendations, face unlock on a phone. The first three are automation. Expect an argument about traffic signals — some modern ones do adapt, which is a good place to end.

Period 3 — AI in their own day

Students list every AI they met between waking and reaching school. Typical harvest: face unlock, autocorrect, maps predicting traffic, YouTube or Instagram recommendations, voice assistants, UPI fraud checks.

Then the important question for each: "What did it learn from?" This plants the data idea that Chapter 2 builds on.

Period 4 — Human vs machine intelligence

Machines beat us on speed, memory, and never getting bored. Humans hold common sense, understanding context, and caring about the result. Mention Alan Turing and the Turing Test here — a machine passes if a person cannot tell whether they are talking to a human.

Worth asking: "If it fools you, does that mean it understands you?" Class 6 handles this better than most adults expect.

Periods 5–6 — How machines learn

Three types, taught by analogy before terminology:

Type	Analogy that lands	What the machine gets
Supervised	Learning with flashcards a teacher has already labelled	Data with the right answers attached
Unsupervised	Sorting a mixed box of buttons into groups without being told the groups	Data with no labels
Reinforcement	Learning to cycle — wobble, fall, adjust, repeat	Reward or no reward after each try

Use Period 6 for the unplugged activity in Section 04, which makes reinforcement learning physical rather than abstract.

Worksheet

Name: Class & Section: Date:

A · Choose the correct answer

1Which of these is not a sign of intelligence?

(a) Learning from mistakes(b) Solving a new problem(c) Repeating fixed steps(d) Thinking before acting

2An automatic door opens whenever someone stands in front of it. This is:

(a) Artificial Intelligence(b) Automation(c) Machine Learning(d) Reinforcement

3A photo app is shown 500 pictures already marked "cat" or "not cat". This is:

(a) Supervised learning(b) Unsupervised learning(c) Reinforcement learning(d) No learning

4A game character gets points for a good move and loses points for a bad one, improving each round. This is:

(a) Supervised learning(b) Unsupervised learning(c) Reinforcement learning(d) Automation

5The test that asks whether a person can tell a machine from a human is named after:

(a) Charles Babbage(b) Alan Turing(c) C. V. Raman(d) Ada Lovelace

B · Fill in the blanks

6Intelligence is the ability to learn, think and problems.

7Automation follows fixed , while AI improves with experience.

8Data that already has the correct answer attached is called data.

9In reinforcement learning, the machine learns from a after each attempt.

10Grouping similar items without being told the groups is learning.

C · Automation or AI?

Tick one column for each. Be ready to defend your answer.

Everyday example	Automation	AI
11. A ceiling fan regulator set to speed 3	☐	☐
12. Your phone unlocking when it sees your face	☐	☐
13. A microwave running for exactly 2 minutes	☐	☐
14. Maps suggesting a faster route because of traffic	☐	☐
15. A keyboard suggesting the next word as you type	☐	☐

D · Answer briefly

16In your own words, what makes something "intelligent"?

17Give one example of AI you used today, and say what it must have learned from.

18Name one thing humans do better than machines, and one thing machines do better than humans.

E · Think harder

19A shopkeeper says his new billing machine is "AI" because it adds prices instantly and never makes a mistake. Do you agree? Explain your reasoning.

Answer key & teaching notes

Q	Answer	What to watch for
1	(c)	Repeating fixed steps is the definition of automation, not intelligence.
2	(b)	Common error: students pick AI because it seems "smart". Ask whether the door gets better at its job.
3	(a)	The labels are the giveaway.
4	(c)	Reward and penalty signal reinforcement.
5	(b)	Alan Turing.
6	solve	Accept "solve" or "handle".
7	instructions / rules	Both acceptable.
8	labelled
9	reward	Accept "reward or penalty", "feedback".
10	unsupervised
11	Automation	Fixed setting, no learning.
12	AI	Recognises a face it was trained on.
13	Automation
14	AI	Learns from live and historical traffic data.
15	AI	Improves as it learns your typing.

Q16–18 — mark on reasoning, not wording 16. Full marks for any answer naming learning, thinking, or solving new problems. Do not require the textbook phrasing.
17. Must name a real example and a plausible data source. "Autocorrect — it learned from lots of text" is complete.
18. Humans: common sense, understanding context, caring about outcomes. Machines: speed, memory, no tiredness.

The discriminating question 19. Expected: disagree. The billing machine follows fixed arithmetic rules and never improves — it is automation, however fast and accurate. Accept a well-argued "agree" only if the student describes the machine learning something, e.g. predicting what a regular customer usually buys. Reasoning is the assessment here, not the verdict.

Unplugged activity · "Train the Robot"

Period 6 · 35 minutes · No devices, no internet, no lab required.

What you need

Chalk or floor tape, a blindfold or a simple paper crown pulled low, and about 3×3 metres of floor. Works in a normal classroom with desks pushed back.

How it runs

Mark a 4×4 grid on the floor. Place a "treasure" (a duster) on one square. Mark a start square.
One student is the Robot, blindfolded. The rest are the Environment.
The Robot may only say one of: forward, left, right. It cannot ask where the treasure is.
After each move the class says only "warmer" (reward) or "colder" (no reward). No other words. This rule is what makes it work — enforce it strictly.
Count the moves needed. Then reset and run the same Robot again with the treasure in the same place.

The moment the concept lands The second run is dramatically faster. Ask: "Nobody told the Robot the answer. So why did it improve?" The class arrives at learning from feedback on its own — which is reinforcement learning, exactly as the curriculum describes it.

Extension for a fast class

Move the treasure and run again. Performance collapses. This opens the real question: the Robot learned one map, not "how to find things" — an honest, age-appropriate first look at the limits of AI.

Practical notes Use the paper crown rather than a blindfold if any student is uncomfortable — it works just as well. With more than 40 students, run two grids in parallel and have groups compare move counts, which adds a data-collection angle for Chapter 2.

Project brief & rubric

Because there is no written examination, the project is the assessment — and the artefact you retain as evidence.

Brief given to students "AI in My Home" — Over one week, find five things in or around your home that you believe use AI, and two that look clever but are only automation. For each, record: what it does, why you placed it in that group, and what data you think it learned from. Present on one chart or two pages.

Criterion	4 — Exceeds	3 — Meets	2 — Approaching	1 — Beginning
Identifying AI	5 correct, including a non-obvious one	5 correct examples	3–4 correct	Fewer than 3
AI vs automation	Both correct, with a clear reason for each	Both correctly sorted	One correct	Confuses the two
Reasoning about data	Plausible data source for most examples	Names data for some	Vague or repeated	Not attempted
Presentation	Clear, original, well organised	Organised and readable	Untidy but complete	Incomplete

Suggested: 16 marks total, recorded as a grade or descriptor rather than a percentage, consistent with continuous and qualitative assessment at this stage.

Evidence record

Keep one of these per class, per chapter. It takes about two minutes to fill and gives a simple answer to the question what did we actually do?

Field	Record
School
Class & section
Chapter taught	AI Ch. 1 — Introduction to AI and Everyday Examples
Periods used
Dates
Teacher
Activity conducted	Train the Robot (unplugged, reinforcement learning)
Assessment used	Project — "AI in My Home", rubric-scored
Students assessed
Samples retained	☐ 3 student projects ☐ Photograph of the work (no children or names visible), if school policy permits ☐ Completed worksheets
Teacher's note

Why this page matters more than it looks Retaining three marked projects, a photograph of the work and a signed record per chapter turns "we covered the AI curriculum" into something demonstrable. It is the difference between a claim and a file.