The Ethics of Technology and Artificial Intelligence

Active learning works for AI ethics because the topic demands critical reflection on real-world power structures. When students step into roles like legislators or affected citizens, they move from abstract concerns to concrete consequences, making ethical trade-offs visible and personal.

• 10th Grade
• Civics & Government
• 4 activities
• 25 min–55 min

Learning Objectives

1. Analyze the ethical dilemmas presented by algorithmic bias in AI systems used for hiring and criminal justice.
2. Evaluate the trade-offs between data privacy and the benefits of AI-driven services like personalized recommendations.
3. Design a set of ethical guidelines for the development and deployment of facial recognition technology.
4. Critique the potential societal impacts of widespread AI adoption on employment and human rights.
5. Synthesize arguments for and against government regulation of artificial intelligence.

Role Play

⏱ 55 min·Small Groups

Role Play: Congressional Hearing on AI Regulation

Students take roles as congressional committee members, AI company representatives, civil rights advocates, and affected community members in a mock hearing on AI regulation. Each role comes with a one-page brief outlining their position. The committee must produce a three-point regulatory proposal by the end of the session.

Prepare & details

Analyze the ethical dilemmas posed by advancements in artificial intelligence.

Facilitation Tip: During the Congressional Hearing, assign each student a role card with a specific perspective (tech CEO, civil rights advocate, etc.) and require them to cite at least one real-world example in their testimony.

Setup: Open space or rearranged desks for scenario staging

Materials: Character cards with backstory and goals, Scenario briefing sheet

• Apply
• Analyze
• Evaluate
• Social Awareness
• Self-Awareness

Gallery Walk

⏱ 35 min·Small Groups

Gallery Walk: AI Ethics Case Studies

Stations feature real-world examples , the COMPAS sentencing algorithm, Amazon's biased hiring tool, facial recognition misidentification errors, and AI-generated misinformation campaigns. Students rotate with a graphic organizer noting the harm caused, who was affected, and what accountability mechanism (if any) existed.

Prepare & details

Predict the potential societal impacts of widespread AI adoption.

Facilitation Tip: For the Gallery Walk, place case studies at stations with guiding questions like 'Who benefits and who is harmed?' to focus student observations before discussion.

Setup: Wall space or tables arranged around room perimeter

Materials: Large paper/poster boards, Markers, Sticky notes for feedback

• Understand
• Apply
• Analyze
• Create
• Relationship Skills
• Social Awareness

Think-Pair-Share

⏱ 25 min·Pairs

Think-Pair-Share: Where Should the Line Be?

Each student receives a specific AI application (medical diagnosis, hiring screening, predictive policing, content moderation). They individually decide whether regulation is needed and on what terms, then pair to compare reasoning before sharing with the class and discussing where agreement and disagreement cluster.

Prepare & details

Justify the need for ethical guidelines and regulations for emerging technologies.

Facilitation Tip: In the Think-Pair-Share, give students 2 minutes to write privately before pairing, then 3 minutes to discuss, to ensure quieter voices are heard.

Setup: Standard classroom seating; students turn to a neighbor

Materials: Discussion prompt (projected or printed), Optional: recording sheet for pairs

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

Formal Debate

⏱ 50 min·Whole Class

Formal Debate: AI Liability and Accountability

Half the class argues that AI companies should be legally liable for documented harms caused by their systems; the other half argues against. After preparation time, each side presents opening statements, rebuttals, and closing arguments. The class then votes and discusses what evidence or argument, if anything, shifted their thinking.

Prepare & details

Analyze the ethical dilemmas posed by advancements in artificial intelligence.

Facilitation Tip: In the Structured Debate, require teams to cite at least one regulation example (e.g., GDPR, EU AI Act) and one ethical principle (e.g., fairness, accountability) in their arguments.

Setup: Two teams facing each other, audience seating for the rest

Materials: Debate proposition card, Research brief for each side, Judging rubric for audience, Timer

• Analyze
• Evaluate
• Create
• Self-Management
• Decision-Making

Teaching This Topic

Teachers should anchor discussions in documented cases where AI systems failed or succeeded, because abstract principles like 'fairness' become tangible when tied to real outcomes. Avoid letting debates drift into hypotheticals; instead, ground each argument in evidence. Research shows students retain ethical reasoning better when they experience the tension between competing values directly, rather than receiving top-down lessons on what to think.

What to Expect

Successful learning looks like students grounding arguments in evidence from case studies and policy examples. They should articulate trade-offs between innovation and protection, and recognize their own civic stake in how AI systems are governed.

Watch Out for These Misconceptions

Common Misconception

During the Think-Pair-Share activity, watch for students asserting that AI is objective because it uses data rather than human opinions.

What to Teach Instead

Redirect the group to examine the case studies from the Gallery Walk, focusing on how biased data leads to biased outcomes. Ask them to identify specific examples where training data reflected historical discrimination.

Common Misconception

During the Structured Debate activity, watch for claims that regulating AI is just about slowing down innovation.

What to Teach Instead

Have students compare the EU AI Act’s risk-tiered approach with the U.S. approach using the debate materials. Ask them to categorize which applications would be prohibited, high-risk, or low-risk in each model, and discuss trade-offs.

Common Misconception

During the Congressional Hearing activity, watch for students assuming AI ethics is a concern only for computer scientists.

What to Teach Instead

After their testimonies, ask students to reflect on who is affected by the AI systems they discussed (e.g., loan applicants, patients, students). Have them revise their opening statements to include these stakeholders explicitly.

Assessment Ideas

Discussion Prompt

After the Congressional Hearing activity, pose this question to small groups: 'Imagine you are on a city council debating whether to implement AI-powered surveillance cameras. What are the top three ethical concerns you would raise, and what specific safeguards would you propose to address them?'

Quick Check

During the Gallery Walk activity, provide students with a short case study (e.g., an AI tutor, a loan application algorithm). Ask them to identify one potential ethical issue and one potential societal benefit, writing their answers in one to two sentences each.

Exit Ticket

After the Structured Debate activity, have students write on an index card one specific example of how AI is currently impacting their lives or communities. Then, ask them to write one question they have about the ethical implications of that specific AI use.

Extensions & Scaffolding

• Challenge students to draft a one-page policy memo proposing a new AI regulation, using evidence from the Gallery Walk case studies.
• Scaffolding: Provide sentence starters like 'One way to address bias is...' for students struggling to articulate solutions during the debate.
• Deeper exploration: Invite a local tech ethicist or civil rights lawyer to join the Congressional Hearing as a guest expert for Q&A.

Key Vocabulary

Algorithmic Bias	Systematic and repeatable errors in a computer system that create unfair outcomes, such as prioritizing one arbitrary group of users over others.
Data Privacy	The practice of protecting sensitive personal information from unauthorized access, use, disclosure, disruption, modification, or destruction.
Facial Recognition	A technology capable of identifying or verifying a person from a digital image or a video frame from a video source.
Machine Learning	A type of artificial intelligence that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.
Accountability	The obligation of an individual or organization to account for its activities and accept responsibility for them.