AI and Automation: Economic and Social ImpactsActivities & Teaching Strategies
Active learning works for this topic because students need to wrestle with real-world trade-offs and ethical dilemmas that AI and automation present. Group discussions and simulations help them move beyond abstract concepts to concrete, evidence-based reasoning about jobs, policies, and values that shape society.
Learning Objectives
- 1Analyze the potential displacement of routine jobs in manufacturing and transportation sectors due to automation.
- 2Evaluate the ethical implications of bias in AI-driven hiring algorithms.
- 3Compare the economic impacts of AI adoption in different Canadian industries, such as finance and healthcare.
- 4Synthesize arguments for and against government regulation of autonomous decision-making systems.
- 5Predict emerging job roles created by advancements in AI and data science.
Want a complete lesson plan with these objectives? Generate a Mission →
Debate Carousel: AI Job Impacts
Divide class into teams to research pro and con arguments on AI displacing jobs. Teams rotate to four stations, debating against opponents and noting key points. Conclude with a whole-class vote and reflection on evidence.
Prepare & details
Predict the potential impact of AI and automation on future job markets.
Facilitation Tip: During the Debate Carousel, assign roles (e.g., economist, worker, policy advisor) to ensure each group member contributes data-driven arguments from their perspective.
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
Jigsaw: Ethical Dilemmas
Assign groups real cases like biased facial recognition or self-driving car decisions. Each group becomes experts, then jigsaws to teach others. Groups create posters summarizing dilemmas and proposed solutions.
Prepare & details
Analyze the ethical dilemmas surrounding autonomous decision-making systems.
Facilitation Tip: For the Case Study Jigsaw, provide a one-page summary with key facts and ethical questions so students can focus on analysis rather than research.
Setup: Flexible seating for regrouping
Materials: Expert group reading packets, Note-taking template, Summary graphic organizer
Policy Simulation: Regulating AI
Students role-play as government officials, industry reps, and citizens in a mock hearing on AI regulation. Present positions, negotiate bills, and vote on policies. Debrief on trade-offs.
Prepare & details
Evaluate the role of policy and regulation in guiding the development of AI.
Facilitation Tip: In the Policy Simulation, give each group a stakeholder agenda sheet to guide their claims and trade-offs, preventing vague or idealistic recommendations.
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
Future Jobs Brainstorm: Pairs Prediction
Pairs list 10 jobs at risk from automation and 10 new ones it creates, using online tools for data. Share predictions in a class gallery walk and discuss retraining needs.
Prepare & details
Predict the potential impact of AI and automation on future job markets.
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
Teaching This Topic
Teachers should model how to weigh evidence by sharing examples of flawed AI systems or unintended economic consequences. Avoid presenting AI as purely beneficent or destructive; instead, frame it as a tool whose outcomes depend on human choices. Research shows students retain more when they see policy as a living, iterative process rather than a fixed solution.
What to Expect
Successful learning looks like students grounding their arguments in credible sources, anticipating multiple perspectives, and revising their views when presented with counter-evidence. They should articulate specific connections between AI capabilities, economic shifts, and policy solutions with clear reasoning.
These activities are a starting point. A full mission is the experience.
- Complete facilitation script with teacher dialogue
- Printable student materials, ready for class
- Differentiation strategies for every learner
Watch Out for These Misconceptions
Common MisconceptionDuring the Debate Carousel, watch for students claiming AI will eliminate all human jobs without citing labor market data.
What to Teach Instead
Redirect them to the Statistics Canada job reports provided in the debate packet, asking them to specify which sectors show growth in human-led roles like AI maintenance or data ethics.
Common MisconceptionDuring the Case Study Jigsaw, watch for students assuming automation only affects low-skill workers.
What to Teach Instead
Use the case study rotation to guide them to examples from accounting or legal research, then ask them to identify the cognitive tasks being automated and the new oversight roles created.
Common MisconceptionDuring the Policy Simulation, watch for students believing AI ethics are handled by developers alone.
What to Teach Instead
After the simulation, have them review their group’s final policy draft and highlight where public input, legal safeguards, or worker protections were included or omitted.
Assessment Ideas
After the Policy Simulation, pose the question: 'Imagine you are a policymaker in Ontario. What are the top three actions you would recommend to mitigate the negative social impacts of AI and automation on the workforce, and why?' Students should provide specific examples to support their recommendations, referencing their simulation findings.
During the Case Study Jigsaw, present students with a scenario describing an AI system used for hiring. Ask them to identify one potential ethical dilemma and one potential economic impact, writing their answers on a sticky note before moving to the next case study.
After the Future Jobs Brainstorm, have students write on an index card: 1. One job they predict will be significantly impacted by AI in the next 10 years. 2. One new job they predict will emerge due to AI. 3. A brief explanation for each prediction, using terms from their paired discussion.
Extensions & Scaffolding
- Challenge students who finish early to design a policy proposal for a specific AI ethical dilemma, citing at least two counterarguments and responses.
- For students who struggle, provide sentence starters for debates and jigsaw notes with partially filled ethical dilemma tables.
- Deeper exploration: Invite a guest speaker from a local tech policy organization to discuss real-world trade-offs and current legislative efforts.
Key Vocabulary
| Automation | The use of technology, such as AI and robotics, to perform tasks previously done by humans. |
| Algorithmic Bias | Systematic and repeatable errors in a computer system that create unfair outcomes, such as in AI decision-making processes. |
| Job Displacement | The loss of employment for workers whose jobs are replaced by technology or automation. |
| Reskilling | The process of learning new skills to adapt to changing job market demands, often in response to technological advancements. |
| Autonomous Systems | Technology that can operate and make decisions independently without direct human intervention. |
Suggested Methodologies
More in Impacts of Computing on Society
Access to Technology and Equity
Analyze the barriers to technology access and how they impact socio-economic opportunities.
2 methodologies
Inclusive Design and Accessibility
Explore principles of inclusive design to ensure technology is accessible to individuals with diverse needs.
2 methodologies
Bias in AI and Algorithms
Examine how biases in data collection and algorithmic design can lead to unfair or discriminatory outcomes.
2 methodologies
Privacy and Surveillance in the Digital Age
Explore the tension between individual privacy rights and the collection of personal data by governments and corporations.
2 methodologies
Intellectual Property and Digital Rights
Understand concepts of copyright, patents, and open-source licensing in the context of software and digital content.
2 methodologies
Ready to teach AI and Automation: Economic and Social Impacts?
Generate a full mission with everything you need
Generate a Mission