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Beginner · module B8

Computational Thinking and the AI Project Cycle

By the end: Break a problem into steps, express a solution as a flowchart or decision tree, and plan an AI project from problem to deployment

Covers
How to think through a problem step by step, and the six stages used to plan any AI project
Contents
4 chapters, 20 topics
Time
About 3 hours
Level
Beginner
Your progress
0 / 20

This module builds on How Artificial Intelligence Works: Data and Learning. We suggest looking at it first, but the choice is yours.

Chapter 1 Computational Thinking

  1. 1.1

    Decomposition

    Breaking a large problem into smaller parts

    Unplugged: plan a school event step by step

    6 minBeing written
  2. 1.2

    Pattern Recognition

    Finding what repeats or is similar

    Interactive: complete the pattern

    6 minBeing written
  3. 1.3

    Abstraction

    Keeping what matters and ignoring the rest

    Interactive: which details does a map need?

    6 minBeing written
  4. 1.4

    Algorithms

    Precise steps that solve a problem, such as a recipe

    Unplugged: write instructions a partner must follow exactly

    8 minBeing written
  5. 1.5

    Debugging

    Finding and fixing mistakes in steps

    Unplugged game: robot says

    6 minBeing written

Chapter 2 Algorithms and Logic

  1. 2.1

    Flowcharts

    Drawing a process with standard symbols

    Interactive: complete a flowchart

    8 minBeing written
  2. 2.2

    Decisions and Loops in Flowcharts

    Yes-or-no branches and repeated steps

    Unplugged: human flowchart

    8 minBeing written
  3. 2.3

    Decision Trees

    Making decisions with a series of if-then questions

    Interactive: build an animal-guessing tree

    10 minBeing written
  4. 2.4

    Optimisation: Finding the Best Solution

    Shortest paths and best choices

    Unplugged: string map for the shortest route

    8 minBeing written

Chapter 3 The AI Project Cycle

  1. 3.1

    Overview of the AI Project Cycle

    Problem scoping, data acquisition, data exploration, modelling, evaluation and deployment

    Interactive: order the six stages

    8 minBeing written
  2. 3.2

    Problem Scoping and the 4Ws Canvas

    Who, what, where and why of a problem, and the problem statement

    Template: fill the 4Ws canvas

    10 minBeing written
  3. 3.3

    Stakeholders and Design Thinking

    Understanding the people affected, with empathy maps

    Activity: draw an empathy map

    8 minBeing written
  4. 3.4

    Data Acquisition and System Maps

    Choosing data features and sources, and drawing how parts connect

    Activity: draw a system map

    10 minBeing written
  5. 3.5

    Data Exploration

    Using charts to understand data before modelling

    Spreadsheet lab: explore a small dataset

    10 minBeing written
  6. 3.6

    Modelling: Rule-Based and Learning-Based

    Choosing between written rules and a learned model

    Card sort

    8 minBeing written
  7. 3.7

    Evaluation: Measuring Mistakes

    True and false positives and negatives, and why not all mistakes are equal

    Interactive: evaluate a spam filter

    10 minBeing written
  8. 3.8

    Deployment

    Putting a model to use, and checking it keeps working

    Case study: preventing avoidable blindness

    6 minBeing written

Chapter 4 No-Code AI

  1. 4.1

    Building Models with No-Code Tools

    Training image, sound and pose models without programming

    Interactive lab: build a sound classifier

    15 minBeing written
  2. 4.2

    Testing an AI Tool: Can We Trust It?

    Checking a chatbot and a prediction tool for accuracy and fairness

    Lab: run a test plan and record results

    12 minBeing written
  3. 4.3

    Module Review and Assessment

    Recap of the module with a scored quiz

    Module quiz with explanations

    10 minBeing written