Lesson Plan

• Describe the main components of an expert system and their functions.
• Explain how knowledge is represented using rules, frames, semantic networks, and fuzzy logic.
• Compare forward‑ and backward‑chaining inference strategies.
• Evaluate the advantages and limitations of expert systems in real‑world contexts.
• Apply the expert‑system interaction flow to a simple problem scenario.

• Projector and screen
• Whiteboard and markers
• Printed handout of the component summary table
• Slide deck covering components and inference mechanisms
• Worksheet with a case‑study scenario
• Laptop with a simple rule‑based expert‑system demo

Start with a quick poll: “Who has heard of AI systems that ‘think’ like experts?” Review prior learning on AI decision‑making and clarify that today’s focus is on expert systems. Explain that by the end of the lesson students will be able to identify and describe the components and structure of expert systems.

1. Do‑now (5'): Students write a brief definition of an expert system on a sticky note.
2. Mini‑lecture (10'): Present the five core components and layered architecture using slides and a diagram.
3. Interactive demo (10'): Show a simple rule‑based system; students input data and observe the inference engine’s output.
4. Group activity (15'): Using the worksheet, groups map the interaction flow, label each component, and discuss knowledge‑representation techniques.
5. Think‑pair‑share (5'): Compare forward and backward chaining with concrete examples.
6. Whole‑class summary (5'): Teacher consolidates key points, addresses misconceptions, and checks understanding with a quick Q&A.

Recap the five components, their purposes, and the two main inference strategies. Students complete an exit ticket by writing one advantage and one limitation of expert systems. For homework, assign a short research task to find a real‑world expert system and prepare a one‑minute presentation on its components.