Warm up
Visit the Thinking Gym for a quick logic warm up.
AI Summary and games
CPD tracking
Fixed hours for this level: 4. Timed assessment time is included once on pass.
View in My CPDYou made it through Foundations, Intermediate and Advanced. This page is a calm recap with a few games and labs. Use it to test what stuck, spot gaps, and build the kind of confidence that comes from knowing what you do not know yet.
Quick recap
Across the track you saw how messy data becomes something a model can use, how models learn patterns and also learn the wrong lessons, and how real products wrap AI in systems, monitoring, and rules.
Foundations gave you the vocabulary: data, labels, features, tokens, training, and evaluation. Intermediate asked the harder questions: what happens when the world changes, when users behave strategically, when your data is biased, or when the cost of a mistake is uneven.
The key shift is this: traditional software is mostly deterministic, which means you can reason about exact behaviour from the code. AI systems are probabilistic, which means you manage distributions, uncertainty, and failure modes. You do not ship a single correct answer. You ship a system that is usually helpful, sometimes wrong, and always in need of feedback and boundaries.
If you remember one practical idea, make it this. Always ask what the model is optimising, what it is blind to, and what you will do when it fails quietly.
The AI journey in one picture
Data, models, and systems wrapped in safety.
Data and tokens
Collect, clean, and chunk.
Models
Train, evaluate, avoid overfit.
Systems
Serve, monitor, add tools.
Safety
Policies, guardrails, evidence.
Games and labs hub
These drills are short. Set a timer, play a couple, and notice which ideas feel strong and which feel fuzzy. Keep it light. The value is in the small moments where your intuition improves.
As you play, connect each result to a real system: search ranking, recommendations, content moderation, summarisation, customer support automation, or a decision assist tool.
Concept games
Before you start: this is not about memorising definitions. It is about being able to explain an idea clearly enough that you can spot misuse in a meeting.
After you finish: pick one concept you struggled with and write a one sentence explanation for future you. Then add the real world tie in. Where would this show up in search, recommendations, moderation, or automation.
Before you start: treat this like a sanity check for your mental model. In real work, the odd one out is often the hidden assumption that breaks your system.
After you finish: ask what decision this improves. For example, would you change the data, change the metric, add a human review step, or decide not to automate this at all.
Scenario labs
Before you start: this is the practical skill. Reading an AI story, spotting what is risky, and choosing the smallest change that improves outcomes.
After you finish: name the system you were implicitly building. Was it search, recommendations, moderation, or automation. Then write down one quiet failure mode you would monitor for, like drift, bias, or users gaming the inputs.
Before you start: focus on building a small system you could actually run, not a magic brain. You are practising scoping, data choices, and where guardrails belong.
After you finish: imagine you are shipping it into a real product. Who owns it. What is the rollback plan. What happens when users are unhappy. Those questions are the bridge into more advanced AI work.
Build your own challenge
Before you start: teaching is a cheat code. If you can write a good question, you probably understand the idea. If you cannot, you just found your next learning target.
After you finish: add one question that connects to a real system (search, recommendations, moderation, automation) and one question that tests judgement (when to add humans, when to stop the rollout, when not to use AI).
More practice games
Explore all practice games including cybersecurity, digitalisation, and cross-topic drills.
View All Practice Games →
Final reflection and next steps
AI track master quiz
Scenario: A model looks excellent in training but fails in production. What is a first evaluation mistake to check
Scenario: A model is confidently wrong on edge cases. Name one signal you would look for
Scenario: You are building an agent that can call tools. Why add guardrails
Scenario: A classification system is used for fraud. Which metric often matters most and why
Scenario: You ship a generative feature. What is one practical safety habit
Scenario: A regulator asks what the model is for, what it cannot do, and who owns it. What document helps
Use these reflection prompts to close the loop:
- What part of AI feels most interesting to you?
- What part still feels confusing?
- What is one small thing you could try at work or at home?
Responsible AI reflection
Keep it real. Where could AI fail quietly in your context, such as wrong but confident answers, missing edge cases, or harmful ranking.
How would bias and data quality show up in practice, such as who gets misclassified, who gets ignored, and whose language is treated as suspicious.
And the most important question: when should you not use AI at all, because the cost of mistakes, the lack of data, or the need for explainability makes automation the wrong move.
Next moves
Revisit any section that felt shaky, then head back to the AI overview or into your CPD dashboard to track the hours you want to invest next.