Discipline: Physical Sciences and Mathematics

Students build a streaming vandalism detector for live Wikipedia edits in which a fast rule-based tier decides which few of ~1,500 edits per minute are worth escalating to a slow, rate-limited real LLM (Gemini) — confronting the cost, latency, and failure modes of putting AI inside a production data pipeline.

Students defend Wikipedia from vandals with data structures instead of infrastructure: a sliding-window edit-velocity tracker (hash map of queues), a hand-built binary min-heap, and a budget-bounded top-K selection decide which few suspicious edits earn a question to an expensive, rate-limited Oracle — a stand-in for a real LLM.

Students use Copilot and NotebookLM to create a dataset and develop statistical analyses including regression.

Students consider and dicuss key literacies when using generative AI tools.

Instructor asks ChatGPT questions related to a key concept to find one where ChatGPT has conceptual errors. Ask students to find the errors and explain them.

Role playing can deepen student’s engagement with course content and help them develop skills while learning course material. Students will use AI to develop their characters and help them craft compelling arguments in the role-play scenarios