A* Tree Visualization: Static vs Dynamic Generation

Current Implementation: Pre-Generated

The searchSteps array contains 12 hardcoded step objects, each with manually defined treeNodes and treeEdges. The animation simply indexes through these static snapshots.

Pros of Static Approach

• Full control over pedagogical narrative
• Can highlight specific teaching points
• Easier to craft explanations for each step

Cons of Static Approach

• Not a real A* implementation
• Cannot adapt to constraint changes
• Manual errors (like incorrect pruning) can slip in
• Students see the result of A*, not A* itself

Proposed: Dynamic A* Engine

A rebuilt version with a live algorithm that:

1. Actually runs A* - Real open/closed list management, f-score calculations
2. Generates tree on-the-fly - Nodes appear as algorithm discovers them
3. Configurable constraints - Toggle rules to see different search behaviors
4. Guaranteed correctness - Algorithm logic prevents manual errors

Educational Value

Recommendation

For teaching A* algorithm mechanics, dynamic generation provides more authentic learning. Students can:

• Modify heuristics and observe impact
• Add/remove constraints
• Step through actual algorithm decisions
• Trust that what they see is algorithmically correct