Lesson Plan

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Lesson Plan
Grade: Date: 17/01/2026
Subject: Computer Science
Lesson Topic: Show understanding of how graphs can be used to aid Artificial Intelligence (AI)
Learning Objective/s:
  • Describe how graph representations model AI problem states and actions.
  • Compare adjacency matrix and adjacency list for different AI scenarios.
  • Explain the operation and suitability of DFS, BFS, Dijkstra, and A* search.
  • Evaluate heuristic properties (admissible, consistent) and their impact on A*.
  • Apply an appropriate graph‑search algorithm to a simple AI problem.
Materials Needed:
  • Projector or interactive whiteboard
  • Slides covering graph representations and search algorithms
  • Sample code snippets (Python/Java) for DFS, BFS, Dijkstra, A*
  • Handout with graph diagrams and algorithm comparison table
  • Worksheets with path‑finding exercises
  • Computers with IDE installed (e.g., VS Code)
 Introduction:
Begin with a short video of a game character navigating a maze, prompting students to consider how the character decides its route. Recall previous work on data structures and ask learners to identify how states and moves can be visualised as a graph. Explain that by the end of the lesson they will be able to choose and justify a graph‑based AI search technique for a given problem.
Lesson Structure:
  1. Do‑now (5') – Quick quiz on vertices, edges, and adjacency representations.
  2. Mini‑lecture (10') – Overview of adjacency matrix vs. list with examples.
  3. Demonstration (10') – Live coding of BFS and Dijkstra on a sample weighted graph.
  4. Guided practice (15') – Pairs convert a game map into both representations and run BFS to find the shortest un‑weighted path.
  5. Heuristics discussion (10') – Explain admissible and consistent heuristics; illustrate A* on the same graph.
  6. Application activity (15') – Groups design a simple AI scenario (e.g., robot navigation) and select the most suitable search algorithm, justifying their choice.
  7. Check for understanding (5') – Exit ticket: one advantage and one limitation of A* versus Dijkstra.
Conclusion:
Summarise how graph models enable AI agents to explore solution spaces and why algorithm choice depends on graph density and heuristic quality. Students submit an exit ticket summarising one key insight and receive a brief homework: implement A* on a provided grid map. Reinforce that mastering graph‑based search is foundational for advanced AI topics.