Computing · Year 9

Active learning ideas

Introduction to Artificial Intelligence

Active learning works well for artificial intelligence because it is an abstract concept that students encounter daily in tangible ways. By testing virtual assistants, analysing recommendation systems, and sorting technologies, students connect classroom ideas to real-world tools they already use. This hands-on approach reduces confusion and builds lasting understanding.

• National Curriculum Attainment Targets
• KS3: Computing - Impact of Technology
• KS3: Computing - Computational Thinking

• 25–40 min
• Pairs → Whole Class
• 4 activities

Activity 01

• Four Corners
• 30 min · Pairs

Pairs Demo: Virtual Assistant Challenges

Pairs access a device with Siri, Alexa, or Google Assistant and take turns asking 10 varied questions, from factual queries to riddles. They record successes, failures, and patterns in responses. Follow with a class share-out to compare findings.

Explain what constitutes 'intelligence' in the context of artificial intelligence.

Facilitation Tip

During the virtual assistant demo, circulate with a list of intentionally ambiguous phrases to test how assistants handle context, ensuring every pair encounters a moment of mismatch between human and machine understanding.

What to look for

Provide students with a slip of paper. Ask them to write down one AI application they encountered today and briefly explain what makes it 'intelligent' using a term from the lesson (e.g., 'uses NLP', 'learns from data').

• Understand
• Analyze
• Evaluate
• Self-Awareness
• Social Awareness

Activity 02

• Four Corners
• 40 min · Small Groups

Small Groups: Recommendation Tracker

Groups log into personal streaming accounts and note recent recommendations. They hypothesise algorithms by rating items differently, then observe changes over one session. Discuss how data drives suggestions and potential privacy issues.

Compare different applications of AI, such as virtual assistants and recommendation systems.

Facilitation Tip

For the recommendation tracker, provide each group with identical browsing history cards so they can compare suggestions and identify how the algorithm interprets past choices differently.

What to look for

Pose the question: 'If an AI can recommend a movie you'll love, but can't understand why you're sad, what does this tell us about the difference between AI intelligence and human intelligence?' Facilitate a brief class discussion, encouraging students to use vocabulary like 'narrow intelligence' or 'contextual understanding'.

• Understand
• Analyze
• Evaluate
• Self-Awareness
• Social Awareness

Activity 03

• Four Corners
• 25 min · Whole Class

Whole Class: AI Classification Sort

Display 20 images or descriptions of technologies on the board, such as fridges, chatbots, and chess programs. Class votes thumbs up or down for AI, then reveals criteria. Tally results and debate borderline cases.

Analyze the current limitations of AI in mimicking human cognitive abilities.

Facilitation Tip

When running the AI classification sort, ask each group to present one classification and defend it, requiring them to use evidence from the lesson’s vocabulary.

What to look for

Show students images of different technologies (e.g., a smart speaker, a calculator, a self-driving car, a basic alarm clock). Ask them to quickly classify each as 'AI-powered' or 'not AI-powered' and provide one reason for their choice.

• Understand
• Analyze
• Evaluate
• Self-Awareness
• Social Awareness

Activity 04

• Four Corners
• 35 min · Individual

Individual: AI Impact Journal

Students list five daily AI encounters, describe their function, and note one limitation each. They pair up to share and refine entries. Collect for a class mind map.

Explain what constitutes 'intelligence' in the context of artificial intelligence.

Facilitation Tip

Ask students to note where their journal entries include human-like reasoning versus algorithmic suggestion, so they can see the divide in their own writing.

What to look for

Provide students with a slip of paper. Ask them to write down one AI application they encountered today and briefly explain what makes it 'intelligent' using a term from the lesson (e.g., 'uses NLP', 'learns from data').

• Understand
• Analyze
• Evaluate
• Self-Awareness
• Social Awareness

A few notes on teaching this unit

Start with familiar tools students already trust, then expose gaps in their expectations. Avoid overloading with technical details; focus on what AI can and cannot do using everyday examples. Research shows that students grasp abstract concepts better when they first experience limitations, so plan activities that highlight errors or biases rather than perfection.

Successful learning looks like students confidently explaining AI as task-specific, noticing its strengths and limits, and applying core terms such as learning from data or natural language processing. They should critique examples rather than accept them at face value and express nuanced ideas about intelligence and technology.

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Watch Out for These Misconceptions

• During the Pairs Demo: Virtual Assistant Challenges, watch for students assuming the assistant understands their intent completely.

  Pause the demo after ambiguous queries and ask, 'Did the assistant interpret your phrase accurately, or did you have to rephrase? What does this show about its understanding?' Direct students to compare their results and note how often human-like comprehension was missing.

• During the Small Groups: Recommendation Tracker, watch for students assuming every app suggestion is AI-driven.

  Hand each group a mix of recommendation cards and non-AI suggestions (e.g., a basic weather app). Ask them to sort by algorithmic origin, then justify each choice with evidence from the app’s design or behaviour.

• During the Whole Class: AI Classification Sort, watch for students classifying all smart devices as AI without checking the underlying technology.

  Require each group to place a smart speaker, calculator, and self-driving car image on the board, then explain whether each uses learning algorithms or simple rules. Use the moment to clarify that ‘smart’ does not always mean ‘AI’.

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Methods used in this brief

• Four Corners