A collection of classroom activities that integrate Generative AI into Data Structures & Algorithms courses. These activities use tree data structures as the subject matter to teach students critical thinking, effective prompting, web-accessibility, and responsible AI use.
Focuses on learning the structure and semantics of a Binary and Binary Search Tree as well as critical evaluation of AI-generated explanations. Students receive a deliberately flawed statement about binary search trees from their instructor and must identify, correct, and justify the error using definitions, counterexamples, and credible sources - without AI assistance.
Teaches prompt engineering through binary tree construction. Students iteratively refine prompts - adding constraints like input values, height, completeness, and traversal orders - to observe how specificity reduces ambiguity in AI-generated outputs.
Connects BST traversals to digital accessibility. Students build binary search trees, use an LLM to generate alt-text descriptions based on different traversals, then exchange descriptions with a partner to reconstruct each other's trees - highlighting the importance of clear, complete textual representations.
- Clarissa Cheung
- Sampada Sharma
- Amanpreet Kapoor
This project is licensed under CC0 1.0 Universal.
Amanpreet Kapoor — kapooramanpreet@ufl.edu