A ten-minute comparison
Open a featured experiment, ask for a prediction, run the comparison, and collect one explanation.
Open an exampleFoundational AI, made manipulable
Twelve bilingual interactives for search, logic, probability, machine learning, vision, and reinforcement learning. Each runs as one portable HTML file, with no account or backend.
BFS and A* find the same shortest path. A* usually explores less because the heuristic focuses the search.
Start with a featured experiment or filter by the idea you need to teach.
The applets are designed for a short demonstration, a full inquiry cycle, or independent practice. Student work stays on the learner's device until they submit it through your normal classroom system.
Open a featured experiment, ask for a prediction, run the comparison, and collect one explanation.
Open an exampleUse the scenario gallery, visual explanation, and response packet as one Predict, Observe, Explain, Transfer sequence.
Open the Teacher PackMove from search and logic to probability, model evaluation, and reinforcement learning.
Open the curriculum mapThe contribution is not one new algorithm visualization. It is a coherent classroom packaging pattern across the foundational AI canon.
AI Playgrounds is an open teaching project. Use it in class, reuse it under MIT, or support discovery with a GitHub star or a shared link.
Use the tagged release as the artifact of record, cite the project, inspect the implementation, or adapt one applet for your own course.
The design paper, citation metadata, architecture notes, and exact release information connect the public artifact to its scholarly account.
The source is intentionally plain HTML, CSS, and JavaScript. Fork the project, take one applet, translate it, or change the scenarios without adopting a build system.