CBSE Class 10 · Artificial Intelligence, code 417 · Differentiate questions 2026-27

Difference between AI, ML and DL in Class 10, and every other pair the 417 paper can ask.

The difference between AI, ML and DL is the class 10 comparison asked most often, but it is not the only one. This page sets out every differentiate pair in the 2026-27 CBSE 417 syllabus as a short table, then gives a two-point exam answer you can write in under two minutes. In our live classes these are written to time and checked against the marking scheme on screen.

Live online · 34 pairs as tables · two-point answers · Venn diagrams drawn in class · answers checked point by point · first class free

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Courses where these comparisons are taught

The 417 course for the exam itself, the AI and ML course for students who want the ideas behind each pair, and Orange for seeing classification, clustering and evaluation happen without code.

Ans. The short version

AI, ML and DL differ in scope: artificial intelligence is any technique that lets a machine act intelligently, machine learning is the part of AI where the machine learns from data instead of fixed rules, and deep learning is the part of ML that uses many-layered neural networks and very large data, for example to recognise faces. So DL sits inside ML, which sits inside AI. In CBSE Class 10 AI (417), write two clear points per side with an example. Modern Age Coders practises every 417 differentiate pair live, Monday and Wednesday at 9:00 PM IST, or one to one.

Q1. The most asked comparison

What is the difference between AI, ML and DL in Class 10?

AI is the widest idea, ML is AI that learns from data, and DL is ML that learns through deep neural networks. Each is a subset of the one before it. The 2026 board paper asked this as a four-mark answer with a labelled Venn diagram.

PointAIMLDL
MeaningAny method that makes a machine behave intelligentlyMachines learn patterns from data and improve with experienceML using neural networks with many layers
ScopeWidest, the umbrellaSubset of AISubset of ML
Needs data?Not always: rules can be hand-writtenYes, examples to learn fromYes, very large amounts
FeaturesSet by people in rule systemsOften chosen by peoplePicked out by the network itself
ExampleA chess program following set rulesA spam filter trained on marked emailsFace unlock on a phone
The Venn diagram to drawthree nested circles
+-------------------------------+
| Artificial Intelligence       |
|   +-----------------------+   |
|   | Machine Learning      |   |
|   |   +---------------+   |   |
|   |   | Deep Learning |   |   |
|   |   +---------------+   |   |
|   +-----------------------+   |
+-------------------------------+

Label all three and write one example inside each ring. The diagram is where marks are most often lost: circles drawn side by side instead of one inside another.

Two-point exam answer. (1) AI is the broad field of making machines act intelligently; ML is a subset of AI in which machines learn from data; DL is a subset of ML that uses multi-layered neural networks. (2) AI can work from hand-written rules, ML needs examples, and DL needs very large data but finds features by itself, as in face recognition.

How a differentiate answer is marked

  1. Each point must say something about BOTH terms. "ML learns from data" alone is half a point.
  2. Two points for a two-mark item, three or four for a four-mark one, plus any diagram asked for.
  3. A table is the fastest layout. Add one example per side when the question says "with examples".

Q2. Unit 2, Advanced Concepts of Modeling

Rule based vs learning based, supervised vs unsupervised, classification vs regression

Unit 2 holds the largest cluster of compare questions in 417. Each pair below is three rows and a two-point answer.

Rule based vs learning based approach

PointRule basedLearning based
Source of logicRules written by a developerPatterns found in data
New situationsFails outside its rulesAdapts as it sees more data
ExampleA quiz bot with fixed answersA spam filter that keeps improving

Answer. (1) A rule based model follows rules a person writes; a learning based model works out its own rules from data. (2) A rule based model is static, while a learning based one adapts as new data arrives.

Supervised vs unsupervised vs reinforcement learning

PointSupervisedUnsupervisedReinforcement
DataLabelledUnlabelledNo fixed dataset; learns by acting
GoalPredict the labelFind hidden groups or linksMaximise reward over time
Sub-typesClassification, regressionClustering, associationTrial, error and reward
ExampleCat or dog photos with namesGrouping shoppers by habitA game agent learning to win

Answer. (1) Supervised learning trains on labelled data, unsupervised learning on unlabelled data. (2) Supervised predicts a known kind of output, such as a class; unsupervised discovers groups or links that nobody named, such as customer segments.

ANN vs CNN

PointANNCNN
Built forGeneral data, tables and numbersImages and other grid data
Key ideaLayers of connected nodesFilters slide over the image to pick out features

Answer. (1) An ANN is a general network of connected layers modelled on the brain; a CNN is a kind of deep network designed for images. (2) A CNN finds edges and shapes with sliding filters, which an ordinary ANN does not do.

Classification vs regression

PointClassificationRegression
OutputA category or labelA continuous number
Data typeDiscreteContinuous
ExampleIs this email spam or not?What will this flat sell for?

Answer. (1) Classification sorts data into fixed classes; regression predicts a number on a continuous scale. (2) Weather as "rainy or dry" is classification; tomorrow's rainfall in millimetres is regression.

Clustering vs association

PointClusteringAssociation
FindsGroups of similar itemsItems that occur together
ExampleGrouping songs by moodPeople who buy bread also buy butter

Answer. (1) Clustering puts similar data points into groups; association finds rules about which items appear together. (2) Clustering answers "who is like whom"; association answers "what goes with what".

Features vs labels, and training data vs testing data

PairFirstSecond
Feature vs labelAn input property, such as house sizeThe answer to predict, such as price
Training vs testing dataThe part the model learns from, usually the larger shareUnseen data held back to check the model

Answer (training vs testing). (1) Training data teaches the model; testing data is kept aside and used only to measure it. (2) Testing on the training data hides overfitting, because the model may have memorised it.

Full question sets for this unit are on CBSE Class 10 AI advanced modeling questions.

Q3. Units 1 and 3

Ethical frameworks, AI domains and evaluation metrics: the differentiate pairs

The 2026 board paper set sector-based against value-based frameworks as a two-mark answer. Unit 3 pairs are short but often carry a number.

Sector-based vs value-based framework

PointSector-basedValue-based
Built aroundOne field, such as health or financeCore moral principles
ExampleBioethics for medical AIRights-based, utility-based, virtue-based

Answer. (1) A sector-based framework is written for the ethical problems of one industry; a value-based framework applies general principles anywhere. (2) Bioethics is sector-based; utilitarian thinking is value-based.

Rights-based vs utility-based vs virtue-based

TypeAsksExample
Rights-basedDoes it respect each person's rights?No face data used without consent
Utility-basedDoes it do the most good overall?Traffic AI that cuts total travel time
Virtue-basedWhat would an honest, fair person do?Admitting a model's known weakness

Answer (rights vs utility). (1) Rights-based thinking protects every individual's rights, even if fewer people benefit; utility-based thinking picks the choice with the greatest overall good. (2) A rights view refuses data taken without consent; a utility view may accept a small harm for a large gain.

Data Science vs Computer Vision vs NLP

DomainData it usesExample
Data ScienceNumbers and tablesForecasting school canteen demand
Computer VisionImages and videoReading number plates
NLPText and speechTranslating a message

Answer. (1) The three domains differ by the type of data fed in: numbers, images or language. (2) So a sales forecast is Data Science, a face filter is Computer Vision and a chatbot is NLP.

Accuracy vs error

PointAccuracyError
MeasuresShare of predictions that are rightHow far predictions miss the true value
Better whenHigherLower

Answer. (1) Accuracy measures how often the model is right; error measures how far its predictions are from the true values. (2) Higher accuracy is better, lower error is better, so a model is chosen by maximising one or minimising the other.

Precision vs recall

PointPrecisionRecall
FormulaTP / (TP + FP)TP / (TP + FN)
Question it answersOf the predicted Yes, how many were right?Of the real Yes, how many were found?
Matters most whenFalse alarms are costly (spam filter)Misses are costly (disease test)

Answer. (1) Precision divides true positives by all predicted positives; recall divides them by all actual positives. (2) With TP 45, FP 5 and FN 15, precision is 45/50 = 0.90 but recall is 45/60 = 0.75: the model is careful but misses cases.

False positive vs false negative

PointFalse positiveFalse negative
Prediction vs realityPredicted Yes, really NoPredicted No, really Yes
ExampleA real email sent to spamA sick patient told they are well

The numericals behind these are worked on confusion matrix class 10 AI numericals.

Q4. Units 5 and 6

Grayscale vs RGB, script bot vs smart bot and the other CV and NLP pairs

Computer vision pairs are mostly about how an image is stored; NLP pairs are about how text is cleaned and understood.

Grayscale vs RGB image

PointGrayscaleRGB
ChannelsOneThree: red, green, blue
Value per pixelOne number, 0 (black) to 255 (white)Three numbers, each 0 to 255
100 x 80 image stores8,000 values24,000 values

Answer. (1) A grayscale image has one channel showing only brightness; an RGB image has three channels that mix into colour. (2) An RGB image therefore stores three times as many values for the same size.

Computer vision vs image processing

PointImage processingComputer vision
AimChange or improve the imageUnderstand what the image shows
OutputAnother imageA label, a box or a decision

Answer. (1) Image processing changes an image, for example sharpening or resizing it; computer vision tries to understand what the image shows. (2) Computer vision is the wider field and often uses image processing as a first step, so CV is a superset of image processing.

Classification vs localisation vs detection vs segmentation

TaskOutput
Image classificationOne label for the whole image
Classification + localisationThe label and one box around the single object
Object detectionBoxes and labels for many objects
Instance segmentationThe exact pixel outline of each object

Pixel vs resolution

PointPixelResolution
What it isThe smallest dot of an imageHow many pixels the image has
Written asOne value, or three for colourWidth x height, such as 1920 x 1080

Answer. (1) A pixel is a single picture element holding a value; resolution is the count of pixels across and down. (2) Higher resolution means more pixels and finer detail; a pixel itself has no resolution.

Answer (classification vs detection). (1) Classification gives one label for the whole image; object detection finds and labels many objects. (2) Detection also gives each object's position as a box.

Script bot vs smart bot

PointScript botSmart bot
Works fromA fixed script of repliesAI and NLP, learning from data
HandlesOnly expected questionsVaried, open-ended questions
ExampleA pizza-order bot with buttonsA voice assistant

Answer. (1) A script bot follows a pre-written script and cannot go beyond it; a smart bot uses AI to understand and answer flexibly. (2) Script bots are simple to build and limited; smart bots need data but improve with use.

More NLP pairs

PairFirstSecond
Human vs computer languageCan be ambiguous; meaning depends on contextExact syntax; one meaning only
Syntax vs semanticsIs the grammar right?Does it make sense?
Stemming vs lemmatisationChops the ending, may leave a non-word ("studi")Returns a real word ("study"), slower
Bag of Words vs TFIDFRaw count of each wordCount weighted by how rare the word is across documents
Sentence segmentation vs tokenisationSplits text into sentencesSplits sentences into words and symbols

Worked counts for these are on Bag of Words and TFIDF for Class 10 AI and CBSE Class 10 AI NLP questions.

Q5. Part A and the practical units

Difference questions from Part A, and the pairs the viva asks

Part A short answers often ask for a difference too, and the practical viva leans on the Unit 4, 5 and 7 pairs that never appear in the theory paper.

Part A pairTwo-point answer
Verbal vs non-verbal communicationVerbal uses spoken or written words; non-verbal uses gestures, posture and expression.
Internal vs external motivationInternal comes from interest or satisfaction; external comes from rewards, marks or praise.
Virus vs wormA virus needs a host file to be opened; a worm spreads across a network by itself.
Wage employment vs self-employmentA wage earner works for someone else for fixed pay; a self-employed person runs their own work and bears the risk.
Renewable vs non-renewable resourcesRenewable ones, like sunlight and wind, are replaced naturally; non-renewable ones, like coal, run out.
Practical and viva pairTwo-point answer
Mean vs median (Unit 7)Mean adds and divides; median is the middle value after sorting. For 2, 3, 3, 12 the mean is 5, the median 3, so median resists an outlier.
Python list vs NumPy array (Unit 7)A list can mix types; a NumPy array holds one type and does maths on all items at once.
Code vs no-code AI (Unit 4)Code AI is written in a language such as Python; no-code AI is built by dragging blocks, as in Orange.
Convolution vs pooling (Unit 5 practical)Convolution slides a kernel to pick out features; pooling shrinks the result and keeps the strongest values.
Max vs average pooling (Unit 5 practical)Max keeps the largest value in each window; average keeps the mean of the window.

The two-mark Part A answers are explained on Part A employability skills, and the viva itself on CBSE Class 10 AI viva questions.

Q6. How are these practised in class?

How the live batch turns comparison tables into full marks

Reading a table is easy. Writing two clean points from memory in ninety seconds is the skill the paper tests, so that is what we practise.

Written to the clock

Monday's class ends with three pairs from the unit just taught. Students write them in a shared doc or on paper held to the camera, two minutes each.

Checked point by point

On Wednesday the teacher marks a few answers on screen against scheme-style points. A point that names only one term is struck out, and the class sees why.

Venns and tables drawn live

Diagram questions, like AI, ML and DL, are drawn on the shared board until every student can do them unprompted in the mocks.

These sit inside the timed papers on the sample paper and mock tests page; every other question shape is on CBSE Class 10 AI important questions.

Batch or personalised: you choose

Practise compare questions in the batch, or one to one

The batch writes and checks the pairs unit by unit through the year, alongside the rest of the syllabus. One to one is for a student who knows the ideas but loses marks in the writing: answers are rebuilt line by line until each point covers both terms. Monthly billing, no admission fee, stop at the end of any month.

Option 1 · Fixed batch timings

Group batch, Monday and Wednesday 9 PM

₹1,499

per month, two live classes a week

  • Monday and Wednesday, 9:00 PM IST, live on video
  • Fifteen to twenty Class 10 AI students, one teacher
  • Pairs written to time after every unit
  • Answers marked on screen against scheme points
  • Compare questions inside every full mock
Book a free demo in this batch

Option 2 · Choose your own timing

Personalised one to one

₹4,999

per month, one private class a week, at a time set with you

  • Days and time picked by your family
  • Every written answer read and corrected
  • Weak units drilled until both sides are covered
  • Venn and table layouts practised one to one
  • Same CBSE 417 syllabus and teachers
Ask for a personalised plan

If your child says "I knew it but lost marks", the gap is usually in writing, and a few one to one sessions close it. If whole units are still new, the batch covers them in order with the compare questions built in. Fees are shown in your own currency.

Studying outside India?

Difference between questions for CBSE students abroad

The 417 paper written in a CBSE school in Muscat or Doha is the same one written in Delhi, so the same compare questions come up. Here is the batch hour this winter.

Where you liveMon and Wed 9:00 PM ISTBest format
India (IST)
every Indian city
9:00 PMBatch fits
UAE and Oman
Dubai, Abu Dhabi, Sharjah, Muscat
7:30 PMBatch fits
Saudi Arabia, Qatar, Kuwait, Bahrain
Riyadh, Jeddah, Dammam, Doha, Kuwait City, Manama
6:30 PMBatch fits
Nepal
Kathmandu
9:15 PMBatch fits
Ethiopia and East Africa
Addis Ababa and East Africa Time
6:30 PMBatch fits
Singapore and Malaysia
Singapore, Kuala Lumpur
11:30 PMChoose 1-on-1

Where 9:00 PM IST falls too late, one to one is booked at a local hour. More for families outside India is on CBSE Class 10 AI online classes in the Gulf and abroad.

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The rest of the Class 10 series

More pages for Class 10 board students

Every page in the CBSE Class 10 AI (417) series, grouped by what you need: classes, each unit, question practice, notes and revision, and the exam itself.

ICSE Class 10 Computer Applications (Java): The Tue and Wed ICSE Java batch ICSE Computer Applications syllabus ICSE Java programs practice ICSE CA important questions

Questions about difference questions

What students ask about difference between questions in Class 10 AI

What is the difference between AI, ML and DL in Class 10?

AI is the broad field of making machines behave intelligently, including by fixed rules. ML is a subset of AI where machines learn patterns from data. DL is a subset of ML that uses neural networks with many layers and large data, and finds features by itself, as in face recognition. Draw three nested circles to show it.

How do I write a difference between answer in CBSE Class 10 AI?

Write each point so it says something about both terms, give two points for a two-mark question and three or four for a four-mark one, and add an example per side when asked. A small table is the quickest, clearest layout and examiners can see each point at a glance.

What is the difference between classification and regression?

Classification predicts a category, such as spam or not spam, from discrete classes. Regression predicts a continuous number, such as a house price or tomorrow's temperature. Both are types of supervised learning, so both need labelled training data. If the answer is a word, it is classification; if it is a measurement, regression.

What is the difference between supervised and unsupervised learning?

Supervised learning trains on labelled data and predicts a known kind of output, through classification or regression. Unsupervised learning uses unlabelled data and finds its own groups or links, through clustering or association. Reinforcement learning is the third type: it learns by trial, error and reward.

What is the difference between a script bot and a smart bot?

A script bot follows a fixed set of pre-written replies and cannot handle questions outside its script, like a menu-driven order bot. A smart bot uses AI and NLP to understand varied questions and improves with data, like a voice assistant. Script bots are easy to build; smart bots are more flexible.

What is the difference between rule based and learning based AI?

In a rule based approach a developer writes the rules and the system follows them, so it cannot adapt beyond them. In a learning based approach the machine works out its own rules from data and keeps improving as new data arrives. A fixed quiz bot is rule based; a spam filter is learning based.

What is the difference between grayscale and RGB images?

A grayscale image has one channel, so each pixel holds one value from 0 (black) to 255 (white). An RGB image has three channels, red, green and blue, so each pixel holds three values. An RGB image of the same size stores three times as many numbers.

Which differentiate questions came in the 2026 Class 10 AI paper?

The 2026 board paper asked for the difference between sector-based and value-based ethical frameworks as a two-mark answer, and for AI, ML and DL with a labelled Venn diagram as a four-mark answer. We do not predict which pairs will appear in 2027, so this page covers every pair in the syllabus.

Are convolution and pooling asked in the Class 10 AI theory paper?

In the 2026-27 curriculum, the convolution operator, kernels and CNN layers sit in the practical part of Unit 5. They are more likely in the practical exam and viva. ANN against CNN is in Unit 2 theory, so that comparison can appear in the written paper.

Do your classes cover difference questions?

Yes, as part of ordinary teaching. After each unit students write its pairs to time, and the teacher marks them on screen against scheme-style points. This is included in the monthly fee shown on this page, in the batch or one to one, with no admission fee and a free first class.

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Write three difference answers with a teacher, free

In the free first class your child writes three short compare answers, starting with AI, ML and DL, and the teacher marks them live, point by point. You will see exactly where marks slip, and the mentor will say plainly whether the batch or one to one suits your child.

New to the subject? Start with the 417 syllabus explained.

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