18. Artificial Intelligence
Graphs and graph algorithms (Dijkstra, A*), machine learning categories (supervised, unsupervised, reinforcement), deep learning, back-propagation and regression.
Statometer40RegularNext Paper 380%Everything for this topic — study hub
A2 Level · 9618 · Paper 3
Statometer — what 32 real papers say about this topic and each of its 2 syllabus bullets
Regular · #7 of 8 in A2 Level · recomputed with every new session
Comes up most sessions for a few marks — know the definitions and one worked example.
- Next Paper 3
- 80%
- 8 in 10 chance it is set
- Marks a paper
- 5.6 / 75
- 8% of Paper 3 · fair share 17%
- Appeared in
- 26 / 32
- Paper 3 sittings 2021–2026
- Last set
- May/Jun 2026
- 9618/33 · Q9 · 8 marks · 3-series streak
What the papers say
- Set in 26 of 32 Paper 3 sittings — about 8 papers in 10.
- Worth about 5.6 marks a paper (8% of Paper 3, well under its fair share of 17%).
- Last set May/Jun 2026 · 9618/33 · Q9 for 8 marks — in the most recent series.
- Steady at around 5.3 marks a paper year on year.
- Lives on “Explain” and “Show” — 69% of its questions: full sentences with a reason, not one-word answers.
- 41% of its questions involve a diagram, table or figure — practise with pen and paper.
- 41% of its questions are set out as code, pseudocode or a table to complete.
- Its biggest question so far: 10 marks (May/Jun 2023 · 9618/32 · Q11).
- Inside the topic, §18.1 Graphs for AI carries the most marks (50%) and §18.1 Machine learning the least (50%).
- It is examined mostly as AO1 (Knowledge & understanding, 66%), the rest AO2 (34%) — definitions and descriptions in syllabus words score.
- The examiner has commented on 18 of its questions — read “What the examiner said” before you practise.
Command words
Share of questions using the word (a question can use several). What each wants →
Question shapes
- ≤ 6 mk20
- 7–9 mk6
- 10–12 mk3
- 13–15 mk0
- 16+ mk0
Average 6.2 marks a question · 41% with a figure or table · 41% with code · biggest 10 marks
Assessment objectives — how it is examined
Every part of every current-syllabus question filed under Cambridge's AO1 / AO2 / AO3 (from its command word and what it asks you to do), so you know whether this topic pays for definitions, for applying, or for judging and building.
- AO1 Knowledge & understanding
- AO2 Apply & analyse
- AO3 Design, program & evaluate
Paper 3 as a whole
| Paper 3 | Syllabus | Measured |
|---|---|---|
| AO1 Knowledge & understanding | 60% | 54% |
| AO2 Apply & analyse | 40% | 46% |
| AO3 Design, program & evaluate | 0% | 0% |
Syllabus = Cambridge's grid; measured = the bank's current-syllabus papers.
Inside the topic — every syllabus bullet, measured
Each part of each question is filed under the bullet it examines; the numbers are per Paper 3 sitting, exactly like the topic's. Open a bullet for its own Statometer.
18.1Graphs for AI#9 of 14 on Paper 3Regular · 4157% next paper2.5 marks17/32 sittings→ May/Jun 2026Regular · 4157%
2.5 · 50% of topic17/32May/Jun 2026latest series · →Set most sessions for a few marks; know the definition and one example.Syllabus: purpose and structure of a graph; searching a graph with Dijkstra's and A* algorithms
- Next Paper 3
- 57%
- 6 in 10
- Marks a paper
- 2.5
- 3% of the paper · 50% of the topic
- Asked in
- 17 / 32
- Paper 3 sittings · 17 questions
- Last asked
- May/Jun 2026
- 9618/33 · Q9 · 5 marks · 3-series streak
- Asked in 17 of 32 Paper 3 sittings — about 5 papers in 10.
- About 2.5 marks a paper (3% of Paper 3; 50% of the topic's marks across its 2 bullets).
- Last asked May/Jun 2026 · 9618/33 · Q9 (5 marks) — in the most recent series.
- Usually “Show” or “Explain”.
- Biggest chunk of marks so far: 8 in May/Jun 2026 · 9618/32 · Q9.
- It is examined mostly as AO2 (Apply & analyse, 70%), the rest AO1 (30%) — you must apply it to the given data or scenario — work it out, trace it, explain it in context.
Last 12 sittings→ SteadyAssessment objectives
AO1 30%AO2 70%- AO1 Knowledge & understanding
- AO2 Apply & analyse
- AO3 Design, program & evaluate
- Show65%
- Explain59%
- Calculate53%
- State29%
18.1Machine learning#7 of 14 on Paper 3Regular · 4870% next paper2.6 marks21/32 sittings→ May/Jun 2026Regular · 4870%
2.6 · 50% of topic21/32May/Jun 2026latest series · →Set most sessions for a few marks; know the definition and one example.Syllabus: artificial neural networks; deep learning, machine learning and reinforcement learning (supervised and unsupervised categories); back-propagation of errors and regression
- Next Paper 3
- 70%
- 7 in 10
- Marks a paper
- 2.6
- 4% of the paper · 50% of the topic
- Asked in
- 21 / 32
- Paper 3 sittings · 21 questions
- Last asked
- May/Jun 2026
- 9618/33 · Q9 · 3 marks · 3-series streak
- Asked in 21 of 32 Paper 3 sittings — about 7 papers in 10.
- About 2.6 marks a paper (4% of Paper 3; 50% of the topic's marks across its 2 bullets).
- Last asked May/Jun 2026 · 9618/33 · Q9 (3 marks) — in the most recent series.
- Usually “Explain” or “Describe”: full sentences with a reason, not one-word answers.
- Biggest chunk of marks so far: 6 in Oct/Nov 2025 · 9618/32 · Q9.
- It is examined almost entirely as AO1 (Knowledge & understanding, 97%) — definitions and descriptions in syllabus words score.
Last 12 sittings→ SteadyAssessment objectives
AO1 97%- AO1 Knowledge & understanding
- AO2 Apply & analyse
- AO3 Design, program & evaluate
- Explain57%
- Describe43%
- State29%
- Show29%
12% of the topic's marks sit in question parts that belong to another topic (scenario questions cross sections) or that no bullet claims; they count for the topic, not for a bullet.
Marks a paper, year by year
By exam series
- May/Jun15/18 · 6.5 mk
- Oct/Nov11/14 · 4.4 mk
What you need to know2syllabus §18.1
- 18.1Graphs for AI — purpose and structure of a graph; searching a graph with Dijkstra's and A* algorithms
- 18.1Machine learning — artificial neural networks; deep learning, machine learning and reinforcement learning (supervised and unsupervised categories); back-propagation of errors and regression
Video lectures3ZAK's YouTube channel · play here
A22023374 views
O LevelA2Podcast2025537 views
O LevelA2Podcast2025226 views
Infographics2draw these the way the examiner expects · download as PNG
Graphs & Dijkstra's algorithm
Machine learning & neural networks
Key terms12use these exact words in the exam
Dotted terms are defined in the glossary.
Code help1referenced to the Cambridge pseudocode guide
CONSTANT N = 4CONSTANT INF = 9999DECLARE W : ARRAY[1:4, 1:4] OF INTEGERDECLARE Dist : ARRAY[1:4] OF INTEGERDECLARE Visited : ARRAY[1:4] OF BOOLEANDECLARE i, j, U, Best : INTEGERFOR i ← 1 TO NFOR j ← 1 TO NW[i, j] ← INFNEXT jDist[i] ← INFVisited[i] ← FALSENEXT iW[1,2] ← 4W[1,3] ← 1W[3,2] ← 2W[2,4] ← 5W[3,4] ← 8Dist[1] ← 0FOR i ← 1 TO NBest ← INFFOR j ← 1 TO NIF NOT Visited[j] AND Dist[j] < Best THENBest ← Dist[j]U ← jENDIFNEXT jVisited[U] ← TRUEFOR j ← 1 TO NIF W[U, j] < INF AND Dist[U] + W[U, j] < Dist[j] THENDist[j] ← Dist[U] + W[U, j]ENDIFNEXT jNEXT iFOR i ← 1 TO NOUTPUT "Node ", i, " distance ", Dist[i]NEXT i
Playground examples1runnable program for this topic
- Run
Graph as an adjacency matrix — breadth-first search
A 2D BOOLEAN array holds the edges; BFS uses a queue to visit every node reachable from node 1.
A2PseudocodeTheory in code 9618 §18.1
Declarative Lab2Prolog knowledge bases with sample queries
- Run
Routes through a network
Is there a path from one station to another? Directed edges plus a recursive route/2. Try a start with no outgoing edge.
A2Recursive rules 9618 §20.1 - Run
Exam: classifying animals
Rules chained on rules — the expert-system style question. Ask what a given animal is and which animals are mammals.
A2Exam-style scenarios 9618 §20.1, §18 AI
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