AI Playgrounds

AI mechanisms, made manipulable

Move the idea.
See what changes.

Fifteen interactive AI labs, from search and probability to language models and agent tool use. Every lab has a standalone offline HTML download; no account or backend is required.

Live proof: same maze, different search

BFS and A* find the same shortest path. A* usually explores less because the heuristic focuses the search.

BFS0
A*0
Run the comparison to reveal the search traces.

Start in five minutes

Choose one question, make a prediction, then inspect what the algorithm does.

AI Playgrounds is a multilingual, offline-ready suite for learning AI through controlled experiments rather than passive animation. No account, installation, or student data submission is required.

Learner: inspect a mechanism

Change one meaningful condition, predict the result, and explain which state change caused it.

Open a featured experiment →

Educator: assign a bounded task

Use a Quick Assign for 10–15 minutes or a longer Activity Pack without creating a separate worksheet system.

Open the Teacher Pack →

Researcher or developer: audit the model

Inspect the model assumptions, limitations, architecture, and published verification summaries before reuse. Development sources and tests are maintained separately.

Open research and citation →

What the release evidence establishes

Deterministic algorithm behaviour, bounded browser rendering, static release integrity, and reproducible deployment for the tested cases.

What it does not establish

Learning gains, classroom adoption, accessibility conformance, or superiority to another teaching method. Those require separate empirical evidence.

Privacy and uptake measurement

Aggregate use signals without learner profiles.

On the public GitHub Pages site only, the project counts page views, coarse campaign sources, and a first substantive interaction per applet session. It does not send experiment values, student work, names, email addresses, exact location, or persistent cross-site identifiers. Offline and local copies send nothing. Global Privacy Control, Do Not Track, and the opt-out below are respected.

Read the measurement specification

Explore the fifteen applets

Start with a featured experiment or filter by the idea you need to teach.

Use one in class

The applets are designed for a short demonstration, a Quick Assign, a full inquiry cycle, or independent practice. Student work stays on the learner's device until they submit it through your normal classroom system.

A 10–15 minute Quick Assign

Open a stable Quick Assign link, ask for a prediction, run the bounded comparison, and collect one explanation and transfer response.

Open an example

A complete lesson

Use the scenario gallery, visual explanation, and response packet as one Predict, Observe, Explain, Transfer sequence.

Open the Teacher Pack

A course sequence

Move from search and logic through probability, machine learning, neural networks, vision, reinforcement learning, adversarial search, and selected modern extensions.

Open the curriculum map

What makes the suite different

The contribution is not one new algorithm visualization. It is a coherent classroom packaging pattern across classical and selected modern AI mechanisms.

One concept at a timeEach applet opens on a concrete behavior rather than a menu or setup screen.
Four-language learner appletsAll fifteen learner applets support English, Simplified Chinese, Vietnamese, and Spanish; educator/support surfaces publish their own explicit language boundary.
Portable by designDownload any lab as one standalone HTML file for offline use. Website pages may use neighbouring assets.
Misconception and fidelity notesTeacher guidance names the error the applet targets and what the model leaves out.
Classroom evidence flowScenario comparisons, Quick Assigns, and local response packets keep student reasoning visible without sending responses to this project.
Inspectable qualityRelease verification covers algorithms, localization, browser behavior, responsive layouts, HCI, and publication boundaries. Development tests are kept separately.

Use it, adapt it, or help others find it

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.

Research, software, and reuse

Use the tagged release as the artifact of record, cite the project, inspect the implementation, or adapt one applet for your own course.

For researchers and reviewers

The research companion record, citation metadata, architecture notes, and exact release information connect the public artifact to its scholarly and reproducibility context.

Offline classroom files

Download one standalone HTML, copy or rename it, and give it to students. No installation, server, account, or neighbouring files required. The download page is in English; each lab includes all four learner languages.

Download HTML

Download offline HTML