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2210 · 0478Paper 1 · Computer Systems§6.1, §6.2, §6.3

6. Automated and Emerging Technologies

Automated systems with sensors, microprocessors and actuators; robotics; and artificial intelligence including expert systems and machine learning.

In the 2029–31 syllabus this splits into Topic 6 (Automated systems and robotics, named sectors only) and Topic 7 (AI with machine learning and neural networks, plus quantum computers). What changes in 2029 → · Study the new topics: 6. Automated systems and robotics, 7. Emerging technologies

Statometer71CoreNext Paper 189%
Marks a paper11.1 · 15%Rank#8 of 10 · #6 on P1Trend · last 12→Oct/Nov 24 · 13: 10 · Feb/Mar 25 · 12: 14 · May/Jun 25 · 11: 7 · May/Jun 25 · 12: 23 · May/Jun 25 · 13: 15 · Oct/Nov 25 · 11: 11 · Oct/Nov 25 · 12: 16 · Oct/Nov 25 · 13: 12 · Feb/Mar 26 · 12: 11 · May/Jun 26 · 11: 11 · May/Jun 26 · 12: 10 · May/Jun 26 · 13: 7 marks
9 in 10 chance in the next paper

Everything for this topic — study hub

O Level / IGCSE · 2210 · 0478 · Paper 1

This topic is rows 18–20 of the study route.Open it on your study route

Statometer — what 25 real papers say about this topic and each of its 3 syllabus bullets

Core · #8 of 10 in O Level / IGCSE · recomputed with every new session

71CORE
Core#8 of 10 in O Level / IGCSE#6 on Paper 1→ Steady

A regular, well-paid topic — you cannot afford a gap here.

Next Paper 1
89%
9 in 10 chance it is set
Marks a paper
11.1 / 75
15% of Paper 1 · fair share 17%
Appeared in
23 / 25
Paper 1 sittings 2023–2026
Last set
May/Jun 2026
0478/13 · Q6 · 7 marks · 11-series streak
Marks in each of the last 12 Paper 1 sittingsOct/Nov 24 → May/Jun 26
Oct/Nov 24 · 13: 10 marksFeb/Mar 25 · 12: 14 marksMay/Jun 25 · 11: 7 marksMay/Jun 25 · 12: 23 marksMay/Jun 25 · 13: 15 marksOct/Nov 25 · 11: 11 marksOct/Nov 25 · 12: 16 marksOct/Nov 25 · 13: 12 marksFeb/Mar 26 · 12: 11 marksMay/Jun 26 · 11: 11 marksMay/Jun 26 · 12: 10 marksMay/Jun 26 · 13: 7 marks

What the papers say

  • Set in 23 of 25 Paper 1 sittings on the current syllabus — treat it as certain.
  • Worth about 11.1 marks a paper (15% of Paper 1).
  • Last set May/Jun 2026 · 0478/13 · Q6 for 7 marks — in the most recent series.
  • Set in each of the last 11 series without a miss.
  • Steady at around 11.9 marks a paper year on year.
  • Lives on “Describe” and “Give” — 71% of its questions: full sentences with a reason, not one-word answers.
  • Most of its marks (88%) come in extended questions of 6+ marks — plan the answer before writing.
  • Its biggest question so far: 23 marks (May/Jun 2025 · 2210/12 · Q5).
  • Inside the topic, §6.1 Automated systems carries the most marks (63%) and §6.2 Robotics the least (13%).
  • It is examined almost entirely as AO1 (Knowledge & understanding, 78%) — definitions and descriptions in syllabus words score.
  • The examiner has commented on 52 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

  • 1–2 mk1
  • 3–4 mk11
  • 5–6 mk27
  • 7–9 mk44
  • 10+ mk20

Average 7.9 marks a question · 14% with a figure or table · 22% with code · biggest 23 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 to a context
  • AO3 Evaluate & judge

Paper 1 as a whole

Paper 1SyllabusMeasured
AO1 Knowledge & understanding60%64%
AO2 Apply to a context20%24%
AO3 Evaluate & judge20%12%

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 1 sitting, exactly like the topic's. Open a bullet for its own Statometer.

  • 6.1Automated systems#17 of 20 on Paper 1Regular · 4352% next paper2.4 marks13/25 sittings↘ May/Jun 2026

    Set most sessions for a few marks; know the definition and one example.Syllabus: sensors, microprocessors and actuators working together; advantages and disadvantages in industry, transport, agriculture, weather, gaming, lighting and science

    Next Paper 1
    52%
    5 in 10
    Marks a paper
    2.4
    3% of the paper · 63% of the topic
    Asked in
    13 / 25
    Paper 1 sittings · 71 questions
    Last asked
    May/Jun 2026
    2210/11 · Q7 · 2.5 marks · 11-series streak
    • Asked in 13 of 25 Paper 1 sittings — about 5 papers in 10.
    • About 2.4 marks a paper (3% of Paper 1; 63% of the topic's marks across its 3 bullets).
    • Last asked May/Jun 2026 · 2210/11 · Q7 (2.5 marks) — in the most recent series.
    • Asked in each of the last 11 series.
    • Easing: 2.8 → 2.3 marks a paper.
    • Usually “Describe” or “Explain”: full sentences with a reason, not one-word answers.
    • Biggest chunk of marks so far: 12 in May/Jun 2025 · 0478/13 · Q6.
    • It is examined almost entirely as AO1 (Knowledge & understanding, 83%) — definitions and descriptions in syllabus words score.
    Last 12 sittings↘ Easing
    Oct/Nov 24 · 13: 6 marksFeb/Mar 25 · 12: 6 marksMay/Jun 25 · 11: 0 marksMay/Jun 25 · 12: 1 marksMay/Jun 25 · 13: 12 marksOct/Nov 25 · 11: 0 marksOct/Nov 25 · 12: 2 marksOct/Nov 25 · 13: 0 marksFeb/Mar 26 · 12: 3.5 marksMay/Jun 26 · 11: 2.5 marksMay/Jun 26 · 12: 0 marksMay/Jun 26 · 13: 0 marks

    Assessment objectives

    • AO1 Knowledge & understanding
    • AO2 Apply to a context
    • AO3 Evaluate & judge
    • Describe55%
    • Explain34%
    • Give30%
    • Identify27%
  • 6.2Robotics#16 of 20 on Paper 1Regular · 4547% next paper2.9 marks11/25 sittings→ May/Jun 2026

    Set most sessions for a few marks; know the definition and one example.Syllabus: what robotics is; the characteristics of a robot; the roles robots perform and the advantages and disadvantages of using them

    Next Paper 1
    47%
    2 in 4
    Marks a paper
    2.9
    4% of the paper · 13% of the topic
    Asked in
    11 / 25
    Paper 1 sittings · 16 questions
    Last asked
    May/Jun 2026
    2210/11 · Q7 · 6.5 marks · 4-series streak
    • Asked in 11 of 25 Paper 1 sittings — roughly one paper in 2.
    • About 2.9 marks a paper (4% of Paper 1; 13% of the topic's marks across its 3 bullets).
    • Last asked May/Jun 2026 · 2210/11 · Q7 (6.5 marks) — in the most recent series.
    • Asked in each of the last 4 series.
    • Usually “Give” or “Explain”: short, precise answers in syllabus words.
    • Biggest chunk of marks so far: 12.5 in May/Jun 2024 · 2210/11 · Q8.
    • It is examined mostly as AO1 (Knowledge & understanding, 58%), the rest AO3 (36%) — definitions and descriptions in syllabus words score.
    Last 12 sittings→ Steady
    Oct/Nov 24 · 13: 0 marksFeb/Mar 25 · 12: 0 marksMay/Jun 25 · 11: 6.5 marksMay/Jun 25 · 12: 7 marksMay/Jun 25 · 13: 0 marksOct/Nov 25 · 11: 8 marksOct/Nov 25 · 12: 3 marksOct/Nov 25 · 13: 0 marksFeb/Mar 26 · 12: 5.5 marksMay/Jun 26 · 11: 6.5 marksMay/Jun 26 · 12: 0 marksMay/Jun 26 · 13: 0 marks

    Assessment objectives

    • AO1 Knowledge & understanding
    • AO2 Apply to a context
    • AO3 Evaluate & judge
    • Give81%
    • Explain63%
    • Describe50%
    • State25%
  • 6.3Artificial intelligence#9 of 20 on Paper 1Core · 6275% next paper3.6 marks19/25 sittings→ May/Jun 2026

    A regular earner inside the topic — most papers touch it.Syllabus: what AI is; its main characteristics; expert systems (knowledge base, rule base, inference engine, interface) and machine learning

    Next Paper 1
    75%
    7 in 10
    Marks a paper
    3.6
    5% of the paper · 24% of the topic
    Asked in
    19 / 25
    Paper 1 sittings · 33 questions
    Last asked
    May/Jun 2026
    0478/13 · Q6 · 7 marks
    • Asked in 19 of 25 Paper 1 sittings — about 8 papers in 10.
    • About 3.6 marks a paper (5% of Paper 1; 24% of the topic's marks across its 3 bullets).
    • Last asked May/Jun 2026 · 0478/13 · Q6 (7 marks) — in the most recent series.
    • Usually “Describe” or “Give”: full sentences with a reason, not one-word answers.
    • Biggest chunk of marks so far: 9 in Oct/Nov 2023 · 2210/13 · Q10.
    • It is examined almost entirely as AO1 (Knowledge & understanding, 93%) — definitions and descriptions in syllabus words score.
    Last 12 sittings→ Steady
    Oct/Nov 24 · 13: 4 marksFeb/Mar 25 · 12: 6 marksMay/Jun 25 · 11: 0.5 marksMay/Jun 25 · 12: 3 marksMay/Jun 25 · 13: 0 marksOct/Nov 25 · 11: 3 marksOct/Nov 25 · 12: 4 marksOct/Nov 25 · 13: 6 marksFeb/Mar 26 · 12: 0 marksMay/Jun 26 · 11: 2 marksMay/Jun 26 · 12: 4 marksMay/Jun 26 · 13: 7 marks

    Assessment objectives

    • AO1 Knowledge & understanding
    • AO2 Apply to a context
    • AO3 Evaluate & judge
    • Describe55%
    • Give46%
    • Explain46%
    • Identify21%

15% 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

1011121314151617181920212223242526

Grey years are the previous syllabus (2010–2022, 76 questions) — history, not counted in the rates.

By exam series

  • Feb/Mar4/4 · 16.3 mk
  • May/Jun11/12 · 9.8 mk
  • Oct/Nov8/9 · 10.3 mk

What you need to know3syllabus §6.1, §6.2, §6.3

  1. 6.1Automated systems — sensors, microprocessors and actuators working together; advantages and disadvantages in industry, transport, agriculture, weather, gaming, lighting and science
  2. 6.2Robotics — what robotics is; the characteristics of a robot; the roles robots perform and the advantages and disadvantages of using them
  3. 6.3Artificial intelligence — what AI is; its main characteristics; expert systems (knowledge base, rule base, inference engine, interface) and machine learning

Video lectures3ZAK's YouTube channel · play here

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Infographics2draw these the way the examiner expects · download as PNG

Automated system: sensor → microprocessor → actuatorA feedback loop: the actuator changes the environment, the sensor measures it again, and the cyclerepeats.Sensormeasures the environmentADCanalogue → digitalMicroprocessorcompares with stored valueActuatormotor, valve, heaterfeedback: the environment changes, the sensor reads the new valueSensors you can nametemperature · light · pressure · moisture · humidity · pH · infra-red · motion · acoustic · gasflow · level · proximity · magnetic field · accelerometerWorked example — greenhouseIF Temp > 25 THEN open vent (actuator ON)IF Temp < 18 THEN heater ONELSE no action — loop back and read the sensor againWhy automate?+ faster, consistent, 24/7, safer in hazardous places+ no human error; can be more efficient with resources– expensive to set up and maintain; single point of failure– depends on sensor accuracy; can be hackedRobot = mechanical structure + electrical parts (sensors, microprocessor) + programmable. Most just repeat instructions.cswithzak.com

Automated systems: sensor → actuator

O Level
Artificial intelligence: expert systems & machine learningAI collects data and rules, uses them to reason, and can learn from mistakes. Two flavours are on thesyllabus.Expert systemUser interfacequestions in, advice outInference engineapplies rules to factsKnowledge basefacts from expertsRule baseIF … THEN … rulesExplanation system: says WHY it reached a conclusion.Examples: medical diagnosis, fault finding, chess, oil prospecting, tax.+ consistent, always available, keeps rare expertise– needs experts to build it and keep it up to dateMachine learningDataexamplesTrainingfind patternsModelPrediction on new datathe program adapts without being reprogrammedLearns from experience: spam filters, recommendation, speechrecognition, self-driving cars, fraud detection.Accuracy improves as more data is processed.Characteristics of AI (syllabus wording)collection of data and rulesability to reasonability to learn and adaptExpert system = knowledge base + rule base + inference engine + user interface. Learn what each part does.Machine learning is a subset of AI: the system improves its performance from data rather than fixed rules only.cswithzak.com

Expert systems & machine learning

O Level

Browse all infographics →

Key terms9use these exact words in the exam

sensoractuatormicroprocessorroboticsartificial intelligenceexpert systemmachine learningknowledge baseinference engine

Dotted terms are defined in the glossary.

Code help2referenced to the Cambridge pseudocode guide

Greenhouse controller (sensor → microprocessor → actuator)

pseudocode Run in Playground
DECLARE Temp : REAL
DECLARE Reading : INTEGER
FOR Reading ← 1 TO 3
INPUT Temp // from the temperature sensor
IF Temp > 25.0 THEN
OUTPUT "Open vent (actuator ON)"
ELSE
IF Temp < 18.0 THEN
OUTPUT "Heater ON"
ELSE
OUTPUT "No action"
ENDIF
ENDIF
NEXT Reading

💡 In the Playground, type inputs like 27.5, 15, 21 in the INPUT box.

Expert-system style rules

pseudocode Run in Playground
DECLARE Fever, Cough, Rash : BOOLEAN
Fever ← TRUE
Cough ← TRUE
Rash ← FALSE
// rule base
IF Fever AND Cough THEN
OUTPUT "Possible flu (rule 1)"
ENDIF
IF Fever AND Rash THEN
OUTPUT "Possible measles (rule 2)"
ENDIF

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