Introduction to Artificial Intelligence

Students need more than abstract discussions to grasp how AI ethics shape real-world outcomes. Active learning lets them simulate ethical dilemmas, test biases in real datasets, and reflect on AI’s presence in their daily lives, making abstract concepts tangible and memorable.

• JC 1
• Computing
• 3 activities
• 30 min–45 min

Learning Objectives

1. Explain the core principles of machine learning and deep learning.
2. Analyze common AI applications in sectors like healthcare, finance, and transportation.
3. Classify different types of AI, such as supervised, unsupervised, and reinforcement learning.
4. Evaluate the historical progression of AI development from early concepts to modern systems.

Formal Debate

⏱ 45 min·Whole Class

Formal Debate: The Trolley Problem for AI

Students are assigned to represent different stakeholders (car manufacturers, passengers, pedestrians) in a debate about how a self-driving car should be programmed to act in an unavoidable accident scenario.

Prepare & details

Explain the fundamental concepts behind Artificial Intelligence.

Facilitation Tip: During the Trolley Problem for AI debate, assign roles clearly so students must defend positions they may not personally hold, deepening their reasoning.

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

Inquiry Circle

⏱ 40 min·Small Groups

Inquiry Circle: Bias in the Machine

Groups are given a sample dataset used to train a fictional 'loan approval' AI. They must identify potential sources of bias (e.g., historical data that favors certain demographics) and propose ways to make the model fairer.

Prepare & details

Analyze examples of AI in everyday technology.

Facilitation Tip: For Bias in the Machine, provide a pre-labeled dataset with obvious flaws so students can trace how bias enters and spreads through AI systems.

Setup: Groups at tables with access to source materials

Materials: Source material collection, Inquiry cycle worksheet, Question generation protocol, Findings presentation template

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

Gallery Walk

⏱ 30 min·Small Groups

Gallery Walk: AI in My Life

Students create posters showing an AI application they use (e.g., TikTok's algorithm, ChatGPT). They must identify one ethical risk and one benefit for each application, then move around the room to comment on their peers' findings.

Prepare & details

Predict future applications of AI based on current trends.

Facilitation Tip: In the Gallery Walk, ask students to bring examples from their own lives to create authenticity and personal investment in the 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

Teaching This Topic

Teachers should balance technical exposure with ethical reflection, avoiding the trap of presenting AI as purely neutral or purely dangerous. Research shows that students grasp complex topics like bias best when they first experience the mechanics of AI before interrogating its impacts. Use concrete examples to ground discussions, and encourage students to critique systems rather than accept their outputs as inevitable.

What to Expect

Successful learning looks like students questioning assumptions about AI’s neutrality, identifying bias in real-world examples, and articulating the trade-offs between automation’s benefits and its human costs. They should connect technical processes to ethical implications.

Watch Out for These Misconceptions

Common Misconception

During the debate or subsequent discussions, watch for statements that AI is objective because it relies on math and data.

What to Teach Instead

Use the Bias in the Machine activity where students train a simple classifier on a biased dataset. Have them observe how the AI replicates the bias, then ask them to explain why math alone cannot guarantee objectivity.

Common Misconception

During the Gallery Walk or post-activity reflections, listen for assumptions that AI will replace all human jobs.

What to Teach Instead

Refer back to the Trolley Problem for AI materials, where students debate trade-offs. Ask them to identify tasks that require human-only skills, such as empathy or ethical judgment, and connect these to the idea that AI augments rather than replaces human roles.

Assessment Ideas

Quick Check

After the Bias in the Machine activity, present students with three scenarios: (1) a spam filter identifying unwanted emails, (2) a navigation app suggesting the fastest route, and (3) a chatbot answering customer queries. Ask students to identify which scenario best represents machine learning and explain why, focusing on how training data influences outcomes.

Discussion Prompt

During the Gallery Walk, facilitate a class discussion using the prompt: 'Beyond recommendation engines and virtual assistants, what is one less obvious application of AI you have encountered or can imagine? How does it function at a basic level?' Encourage students to share examples from the Gallery Walk and explain the AI's role in their own words.

Exit Ticket

After the Trolley Problem for AI debate, have students write down one historical milestone in AI development and one potential future application of AI. Ask them to briefly explain the significance of the milestone and the impact of the future application, using evidence from the debate to support their reasoning.

Extensions & Scaffolding

• Challenge students to design a policy recommendation for mitigating bias in hiring algorithms after the Bias in the Machine activity.
• Scaffolding: Provide sentence starters for students struggling to articulate ethical concerns during the debate, such as 'This scenario prioritizes _____ over _____ because...'.
• Deeper exploration: Have students research an AI application in healthcare and prepare a short presentation on how bias could affect patient outcomes.

Key Vocabulary

Artificial Intelligence (AI)	The simulation of human intelligence processes by machines, especially computer systems. This includes learning, problem-solving, and decision-making.
Machine Learning (ML)	A subset of AI that enables systems to learn from data and improve performance on a task without being explicitly programmed. It involves algorithms that identify patterns.
Deep Learning (DL)	A subset of ML that uses artificial neural networks with multiple layers (deep architectures) to learn complex patterns from large amounts of data.
Neural Network	A computing system inspired by the biological neural networks that constitute animal brains. It consists of interconnected nodes or neurons that process information.
Algorithm	A set of rules or instructions followed in calculations or other problem-solving operations, especially by a computer. In AI, algorithms enable learning and decision-making.