Computer Science · 9th Grade

Active learning ideas

The Role of Training Data Quality

Active learning works for this topic because students need to experience the consequences of data choices firsthand. Simply discussing automation’s impact won’t help them grasp how training data shapes outcomes. Simulation and investigation tasks let students test their assumptions and see how flawed data leads to real-world problems.

• Common Core State Standards
• CSTA: 3A-AP-13
• CSTA: 3A-IC-24

• 20–45 min
• Pairs → Whole Class
• 3 activities

Activity 01

• Simulation Game
• 45 min · Whole Class

Simulation Game: The Automation Wave

Assign students different 'jobs' (truck driver, surgeon, artist). Introduce 'AI breakthroughs' one by one. Students must decide if their job is automated, assisted, or unchanged, and then 're-skill' by finding a new role.

Analyze the role of training data quality in the success of an AI model.

Facilitation Tip

For the 'Simulation: The Automation Wave,' give teams a limited set of job roles and require them to justify their automation predictions using specific data-heavy criteria from the overview.

What to look for

Present students with two short descriptions of AI training datasets for a loan application model. One dataset is described as 'diverse and up-to-date,' the other as 'older and primarily from urban areas.' Ask students to write one sentence explaining which dataset is likely to produce a fairer model and why.

• Apply
• Analyze
• Evaluate
• Create
• Social Awareness
• Decision-Making

Activity 02

• Inquiry Circle
• 40 min · Small Groups

Inquiry Circle: Industry 4.0

Groups research how a specific industry (like farming or fashion) has changed due to technology over the last 50 years and predict what it will look like in 2050.

Critique the potential biases introduced by poor quality or unrepresentative training data.

Facilitation Tip

During 'Collaborative Investigation: Industry 4.0,' assign each group a different industry document to read first, then have them teach their findings to peers to ensure accountability.

What to look for

Facilitate a class discussion using the prompt: 'Imagine you are building an AI to recommend books. What potential biases could exist in your training data, and what specific steps would you take to ensure your data is representative of a wide range of readers?'

• Analyze
• Evaluate
• Create
• Self-Management
• Self-Awareness

Activity 03

• Think-Pair-Share
• 20 min · Pairs

Think-Pair-Share: The Un-automatable

Students brainstorm a list of skills they think a robot will *never* be able to do. They pair up to challenge each other's lists and narrow it down to the top three 'human-only' skills.

Design strategies for improving the quality and diversity of training datasets.

Facilitation Tip

In 'Think-Pair-Share: The Un-automatable,' provide a timer for the pair discussion phase to keep the energy focused and prevent off-topic conversations.

What to look for

Provide students with a scenario where an AI chatbot exhibits biased language. Ask them to identify one possible cause related to training data quality and suggest one method to improve the chatbot's responses.

• Understand
• Apply
• Analyze
• Self-Awareness
• Relationship Skills

A few notes on teaching this unit

Teachers should emphasize that data quality is not just a technical detail but a human-centered issue. Avoid presenting automation as an abstract future event; ground discussions in current real-world examples from students’ potential career fields. Research shows that students grasp complex systems better when they analyze concrete, relatable cases rather than theoretical scenarios.

Successful learning looks like students identifying which job tasks are automatable and explaining why training data quality matters. They should connect dataset characteristics to model fairness and articulate clear steps to improve data quality. Discussions should reflect nuanced understanding beyond initial misconceptions.

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Watch Out for These Misconceptions

• During the 'Simulation: The Automation Wave,' watch for students assuming automation will eliminate all jobs permanently. Redirect them by pointing to the simulation’s output showing how new roles emerge from automation.

  During the 'Collaborative Investigation: Industry 4.0,' have students compare job postings from 10 years ago and today in their assigned industry. Ask them to identify tasks that no longer exist and new ones that have appeared, reinforcing the idea that automation transforms rather than erases jobs.

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Methods used in this brief

• Simulation Game
• Inquiry Circle
• Think-Pair-Share