← IGCSE Computer Science Paper 1

AI characteristic: the ability to learn and adapt

An AI characteristic where a system changes its own behaviour as it gains new data or experience, so it improves over time. Some AI systems can do this, but it is not required.

Part of: IGCSE Computer Science 0478 — 6.3 Artificial Intelligence — Module 6

Understanding AI characteristic: the ability to learn and adapt in IGCSE Computer Science Paper 1

What it does: The system gains new data and adapts its own rules, which shows learning from new data and adapting rules. Some AI systems can learn and adapt, but it is not required. A system can still be AI without this characteristic.

Common mistakes: A system that learns and adapts must change its own rules using the error from each prediction. Here the rules are copied back unchanged, so nothing is learned. It should be rules ← adjust(rules, err). When the answer is correct, the system should reinforce what worked by increasing the weight. Subtracting 1 makes it worse with experience. It should be weight ← weight + 1.

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This module's key terms are also in a printable crossword (Modules 652–661) and word search (Modules 652–678).

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Each concept is reinforced from every angle — learned, then applied, then tested.

Learning

Duo

Which statement about the ability to learn and adapt is correct?

An AI system changes its own behaviour as it gains new data or experienceAn AI system only changes when a programmer rewrites its code

Matches

All AI systems must learn and adapt, or they are not AI

↓

Learning and adapting is not required. Some AI systems, such as expert systems, do not change themselves, but can still be AI.

Applying

Meanings

Term

learn and adapt

Meaning

An AI characteristic where a system changes its own behaviour as it gains new data or experience, so it improves over time. Some AI systems can do this, but it is not required.

Practice

new_data ← INPUT()

Testing

Gaps

newData

Check Answer

Spots

3 err ← actual - prediction

4 rules ← rules

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Quick Questions

1. Which statement about the ability to learn and adapt is correct?

A. An AI system changes its own behaviour as it gains new data or experience ✓

B. An AI system only changes when a programmer rewrites its code

Learning and adapting means the system itself changes its behaviour based on new data or experience, without a programmer rewriting it.

2. A spam filter gets better at spotting junk email as users mark more messages as spam. What is it showing?

A. Following a fixed rule that never changes

B. Learning from new data ✓

The filter uses the new data from users to improve its decisions. This is learning and adapting.

3. Do all AI systems have to learn and adapt?

A. No, some AI systems can learn but it is not required ✓

B. Yes, a system cannot be AI unless it learns

Learning is possible but not required. A simple expert system with fixed rules can still be AI.

4. Which is an example of adapting rules or data?

A. A system updates its own rules after seeing the results of past decisions ✓

B. A system gives the same answer every time whatever happens

Adapting means the system changes its own rules or data. Giving the same answer every time shows no adaptation.

5. Which type of AI is most closely linked to the ability to learn and adapt?

A. A fixed program with no changes

B. Machine learning ✓

Machine learning systems improve their own performance as they are given more data, so they link directly to this characteristic.

Match the Pairs

learn and adapt → An AI system changes its own behaviour as it gains new data or experience, so it improves over time

machine learning → The type of AI that can learn and adapt by changing its own rules or data as it receives new data

A spam filter learns from new emails that users mark as spam and updates its own rules, so it gets better at spotting spam → new data -> update rules -> better results

An expert system uses a fixed rule base and only changes when a person edits it, so it does not have to learn and adapt → learning = optional

All AI systems must learn and adapt, or they are not AI → Learning and adapting is not required. Some AI systems, such as expert systems, do not change themselves, but can still be AI.

A programmer edits the rules by hand, so the system has learned and adapted → Learning and adapting means the system changes its own behaviour using new data or experience. A person editing the rules is not the system adapting itself.

Key Term

learn and adapt

An AI characteristic where a system changes its own behaviour as it gains new data or experience, so it improves over time. Some AI systems can do this, but it is not required.

Fill in the Blanks

1. INPUT newData data ← data + newData

Hint: Type the keyword that reads new data into the system so it can learn from it.

2. IF prediction ≠ actual THEN rules ← update(rules, newData) ENDIF

Hint: Type the keyword that starts the code run when the condition is true, so the rules adapt after a wrong prediction.

3. // fixed expert system learns ← FALSE isAI ← TRUE

Hint: Type the Boolean value showing this AI system does not learn, because learning is possible but not required.

4. FOR count ← 1 TO 100 model ← update(model, newData) NEXT count

Hint: Type the keyword that sets the upper limit of the loop, as a machine learning model keeps updating with new data.

5. storedData ← storedData + newData

Hint: Type the variable that holds the newly received data being added to the stored data so the system adapts its data.

Spot the Error

Question 1

A WHILE newData EXISTS

B prediction ← guess(rules)

C err ← actual - prediction

D rules ← rules

E ENDWHILE

Line D — A system that learns and adapts must change its own rules using the error from each prediction. Here the rules are copied back unchanged, so nothing is learned. It should be rules ← adjust(rules, err).

Question 2

A INPUT answer

B IF answer = correct THEN

C weight ← weight - 1

D ENDIF

Line C — When the answer is correct, the system should reinforce what worked by increasing the weight. Subtracting 1 makes it worse with experience. It should be weight ← weight + 1.

Question 3

A INPUT word

B IF word IN vocabulary THEN

C ADD word TO vocabulary

D ENDIF

Line B — The system should only learn a word it does not already know. With IN, it would add words that are already in the vocabulary and never learn new ones. It should be IF word NOT IN vocabulary THEN.

Question 4

A newData ← TRUE

B IF newData AND canLearn THEN

C OUTPUT 'No change'

D ENDIF

Line C — When new data arrives and the system can learn, it should update its model. Outputting 'No change' means it does not adapt. It should be model ← retrain(model).

Question 5

A OUTPUT newData

B model ← learn(model, newData)

C OUTPUT predict(model)

Line A — A learning system must take in new data as input so it can learn from it. OUTPUT sends data out instead. It should be INPUT newData.

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