AURA: Another Lesson plan from AURA Aware

1. Overview

Duration: 60 minutes Audience: Beginners with no prior AI background (e.g., business professionals, students) Format: Instructor‑led session with interactive discussion, mini‑exercises, and reflection

2. Learning Objectives

By the end of this lesson, learners will be able to:

1. Define Artificial Intelligence (AI) and distinguish it from related terms (machine learning, deep learning).
2. Outline key milestones in AI’s history—from symbolic “Good Old‑Fashioned AI” to modern neural networks.
3. Identify three real‑world examples of AI they encounter daily (e.g., recommendation engines, voice assistants, fraud detection).
4. Explain at least two common AI techniques (rule‑based systems vs. statistical learning) in plain language.
5. Reflect on one personal use case or question where AI might add value in their own context.

3. Materials & Prep

• Slide deck covering definitions, history timeline, and examples
• Short video clip (3–4 min) showing Netflix/YouTube recommendation in action
• Whiteboard or digital collaboration board (Miro, Jamboard)
• Handout: “AI Concepts Cheat Sheet”
• Quick quiz (5 questions) for knowledge check

4. Lesson Structure

Time

Activity

0–5 min

Welcome & Icebreaker

• Quick poll: “Name one AI system you interacted with today.”
• Capture responses on board. | | 5–15 min | Defining AI
• Present simple definition: “AI is the design of systems that perform tasks requiring human‑like intelligence.”
• Contrast with “machine learning” and “deep learning.”
• Learning Objective 1 (Define AI). | | 15–25 min| AI History Timeline
• Walk through five milestones: 1950s – Turing Test & symbolic AI 1980s – Expert Systems 2000s – Statistical ML & Big Data 2012 – Deep Learning breakthrough (ImageNet) Today – Generative AI & foundation models
• Learner task: Match milestone dates to descriptions in small groups.
• Learning Objective 2 (Outline milestones). | | 25–30 min| Video Clip
• Play a brief demo of a recommendation engine in action.
• Prompt: “What data do you think feeds these recommendations?” | | 30–45 min| Real‑World AI Examples
• Briefly introduce three domains:

• Recommendations (Netflix, Spotify)
• Conversational AI (Siri, Alexa)
• Automated Detection (fraud, spam filters)

• Activity (jigsaw): Each triad researches one domain for 5 minutes and then shares back.
• Learning Objective 3 (Identify examples). | | 45–50 min| Core AI Techniques
• Explain in plain terms: Rule‑Based Systems (if‑then logic) Statistical Learning (models trained on data) Neural Networks (layers of simulated neurons)
• Quick analogy: Comparing rule‑based to cooking from a recipe vs. learning by tasting.
• Learning Objective 4 (Explain techniques). | | 50–55 min| Personal Reflection
• Prompt: “How could AI help you in your role or daily life?”
• Learners jot one idea on a sticky note (physical or virtual) and post to the board.
• Learning Objective 5 (Reflect on use case). | | 55–60 min| Quiz & Wrap‑Up
• 5‑question multiple‑choice quiz via poll (Kahoot, Mentimeter).
• Recap key takeaways. |

5. Assessment & Follow‑Up

• Formative Assessment: Group matching, jigsaw presentations, quiz results.
• Homework: Read the provided “AI Concepts Cheat Sheet” and write a 200‑word summary of one AI technique.
• Next Lesson Preview: “How Does Machine Learning Work?” focusing on training, validation, and deployment.

6. Reflection & Adaptation

• Instructor Note: Adjust pacing based on learner engagement—if the history segment lags, shorten to focus on real-world examples.
• Feedback Loop: Collect “One thing I learned” and “One question I still have” before dismissal.