Mission library/9th Grade · Computing/Hexagonal Thinking

Mission brief·Hexagonal Thinking

Is AI Fair? Mapping the Ethics

Students will understand how computers learn from examples through supervised and unsupervised learning.

Grade9th GradeSubjectComputingDuration45 minClass size28 studentsMethodologyHexagonal Thinking

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Learning objectives · 3

Students can engage with: Differentiate between supervised and unsupervised learning paradigms.
Students can engage with: Explain the role of training data in supervised learning models.
Students can engage with: Predict appropriate applications for each type of machine learning.

Before Class

Materials Needed

☐Hexagonal Concept Cards (1 set per group)Included
☐Hexagonal Thinking GuideIncluded
☐Reflection WorksheetIncluded
☐Case Study BriefIncluded
☐Scissors and tape

Space Needed

Flat table or floor space for arranging hexagons

Topic Overview

Students will understand how computers learn from examples through supervised and unsupervised learning.

Key Questions

?Differentiate between supervised and unsupervised learning paradigms.
?Explain the role of training data in supervised learning models.
?Predict appropriate applications for each type of machine learning.

About the Methodology

Students receive hexagonal cards with key concepts, people, events, or ideas. They must arrange the hexagons so that touching sides represent connections, then explain why they placed each hexagon where they did. No single "correct" arrangement exists. The value is in the reasoning and discussion.

Learn about this methodology

Time Range

25-40 min

Group Size

12-32

Space Needed

Flat table or floor space for arranging hexagons

Bloom’s Level

Analyze, Evaluate, Create

Fun Factor

Peak Energy Moment

The 'Resumes' scenario feels like a mystery to solve. Students love pointing out the 'stupidity' of a smart machine.

The Surprise

The 'Aha!' moment usually happens when students realize 'Profit' and 'Fairness' are often pulled in opposite directions on their map.

What to Expect

Expect high engagement as they physically move cards and argue over which connection is 'more right.'

Mission Timeline

25m

Spark Briefing Action Debrief

Academic Standards

CSTA: 3A-AP-13

When your class is in the room

Ready to run Is AI Fair? Mapping the Ethics?

Launch puts you into the Co-Teacher view - live timer, step-by-step facilitation, in-context tips. You can step back to this overview anytime.

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Spark

5 min • Scenario

Read Aloud

A major tech company uses an AI to sort through 10,000 resumes. The AI was trained on the resumes of people currently at the company, most of whom are men. As a result, the AI begins automatically rejecting any resume that contains the word 'women\'s' (like 'women\'s chess club captain') because it hasn't seen that word in 'successful' profiles before. Is this AI broken, or is it doing exactly what it was told to do?

Teacher Notes

Use this to highlight that AI doesn't have a moral compass; it only follows patterns in the data we provide.

Briefing

7 min

Today we use Hexagonal Thinking to map how AI bias works. You will receive 18 hexagonal cards. Your goal is to arrange them so that every touching side represents a logical connection between two concepts. You must be able to explain why two cards are touching.

Group Formation

Divide the class into 7 groups of 4 students each.

Materials Needed

Hexagonal Concept Cards (1 set per group)Included
Hexagonal Thinking GuideIncluded
Reflection WorksheetIncluded
Case Study BriefIncluded
•Scissors and tape

Action

25 min • 100% Physical

Cut out the 18 hexagonal cards and read the 'Amazon Hiring Case Study' brief provided in the materials.

5 min

Have one student cut while others read the case study to save time.

Identify the 'Core' of the problem. Place the 'Discrimination' and 'Training Data' cards in the center of your desk.

4 min

Walk around and ensure groups aren't just making a straight line; encourage a cluster.

Connect the remaining 16 cards. For every connection made, the group must agree on a one-sentence justification.

8 min

Challenge groups to find 'triple points' where one hexagon touches two others simultaneously.

Gallery Walk: One 'expert' stays at the desk while other team members rotate to see how different groups connected concepts like 'Profit' and 'Accountability'.

5 min

Listen for students debating whether 'Profit' belongs near 'Bias' or 'Regulation'.

Complete the Reflection Worksheet, documenting your three strongest connections and proposing one 'fix' for the case study.

3 min

Collect these as your primary assessment of their conceptual understanding.

If things go sideways

▸If a group is stuck, ask: 'How does the lack of Representation in a tech company lead to Bias in the Training Data?'
▸If a group is moving too fast, ask them to find a connection between 'Autonomy' and 'Surveillance'.
▸Check for groups placing cards randomly; ask them to explain the link between any two touching edges.

Differentiation Tips

▸Advanced: Provide three blank hexagons and ask them to add their own complex terms like 'Feedback Loops' or 'Algorithmic Redlining'.
▸Struggling: Provide a 'Starter Map' showing the first three connections (e.g., Training Data -> Bias -> Discrimination).
▸ELL: Use the visual icons on the cards and allow them to draw a small picture on the back of each card to represent the term in their native language.

Debrief

8 min

Discussion Questions

1
Which connection was the hardest to agree on and why?
2
If we could only change one hexagon to make AI 'fairer,' which one would it be?
3
Who should be held responsible when an algorithm makes a biased decision: the coder, the company, or the data?

Exit Ticket

In your own words, how does 'Training Data' create 'Discrimination' in an AI system?

Connection to Next Lesson

Next mission, we will move from identifying bias to coding a simple decision-tree algorithm that attempts to filter for fairness.

Handouts

4 Materials Needed

Print Materials

01Hexagonal Concept Cards
02Hexagonal Thinking Guide
03Reflection Worksheet
04Case Study Brief

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