6-8 Fair or Unfair? AI and Responsibility
Should AI grade essays? Can it be biased? This interactive 6–8 lesson challenges students to think critically about AI responsibility. Through debates, role-plays, and creative projects, learners explore fairness, ethics, and the power of responsible decision-making in our digital world.
6th - 8th Grade
AI and Responsibility
Notes & Preparation
- Project Length: 1 class period
- Instructor Note:
- This lesson centers on helping students understand their responsibility when using and designing AI.
- Guide them to recognize bias, debate ethical scenarios, and express responsible practices through discussion, journaling, and poster creation
Standards
State Standards: AL, AZ, CA, CO, IN, MA, NY, OR, SD, WA, WY
ISTE Standards:
#2 Digital Citizen, #3 Knowledge Constructor, #6 Creative Communicator
NET Standards:
#1, #2, #6
Learning Objectives
#1-4, #8, #9, #11, #13, #21, #25, #26, #27, #32, #37, #38, #41, #42
CS Standards:
Bias and responsible computing
Engage
Opening: Begin with a debate prompt such as, “Should AI grade student essays?” to spark interest and surface prior knowledge.
Opening Debate Prompt:
Have students physically move to one side of the room to “agree” or “disagree” with the statement. Ask a few students from each side to explain their reasoning. This sparks debate and surfaces their prior knowledge about AI’s capabilities and limitations.
Computer Introduction: Review what a computer does (follows instructions), what intelligence means (learning, problem-solving), and how AI combines the two to make decisions.
Project Description: Explain the lesson’s goal: Students will explore scenarios where AI is used responsibly and irresponsibly, examine bias through an activity, and create a poster to advocate for fair and responsible AI use.
Explore
- Scenario Role-Plays: In small groups, act out responsible vs. irresponsible AI decisions (homework, hiring, policing, recommendations).
- Code.org AI for Oceans Bias Activity: Compare results of training AI with fair vs. unfair data.
- Introduction to Responsible AI
- https://studio.code.org/courses/oceans/units/1/lessons/1/levels/1
Explain
- Bias: When data is incomplete or unfair, AI decisions are biased.
- Ethics: Considering what’s fair or unfair.
- Responsibility: Humans decide how AI is used.
- Key Point: AI reflects human choices; it’s our responsibility to ensure fairness.
Learning Outcomes
Students will:
- Students will recognize bias in AI training.
- Students will debate ethical dilemmas in AI use.
- Students will commit to responsible AI practices.
- Students will create persuasive posters advocating for fairness and responsibility in AI
Project
Have the student:
Journal Reflection (5 min)
- Have students spend a few minutes writing a reflection in a class journal or on a shared document, answering the question: “Why does fairness matter when we are teaching AI?”
- Class Discussion (10 min): As a class, develop a School AI Responsibility Pledge. Have students contribute ideas for a simple, clear pledge that everyone can agree to.
- Project Creation (25 min): In pairs or small groups, students will create an artifact to promote responsible AI. Give them the choice of one of the following options:
AI Responsibility Poster:
- Instructions: Using a digital tool like Canva or physical art supplies, have each group design a poster.
- Title/Slogan: A catchy slogan about AI and responsibility (e.g., “AI is Fair, if we are Aware”).
- Short Message: A brief, persuasive message about fairness and responsibility in AI.
- Visuals: Use images and graphics to make the message clear and compelling.
AI PSA (Public Service Announcement) Video:
- Instructions: Students will create a short video (30-60 seconds) that educates their peers on AI responsibility.
- Content: The video should include a clear slogan, a scenario, and a call to action.
AI Ethics Infographic:
- Instructions: Students will create a visual representation of a real-world example of AI bias.
- Content: The infographic should explain the problem and a proposed solution using data and clear visuals.
AI Responsibility Board Game:
- Instructions: Students will design a board game that teaches fair data and responsible AI.
- Content: They must create game rules, design the board, and write “chance” cards that present ethical dilemmas
Evaluate
After class, note:
- Formative: Observe students’ participation in the debates and role-plays, and their contributions to the class pledge.
- Summative:
- AI Responsibility Journal: Review student reflections to assess their understanding of fairness in AI.
- Class AI Responsibility Pledge: Evaluate the collaborative nature of the final pledge.
- Canva Posters: Assess student understanding of responsible AI by the clarity of their message and the persuasive nature of their work.
- Success Criteria: Students can define bias in AI, articulate responsible AI use, and create persuasive artifacts to promote fairness.
Elaborate
- Research: Have advanced students research real-world examples of biased AI (e.g., facial recognition, social media algorithms).
- Cross-Curricular: Connect the lesson to ELA (persuasive writing) and Social Studies (technology, justice, and civic responsibility).
- Career Exploration: Explore careers in AI ethics, law, and data science
