Printable classroom package
Twelve bilingual, single-file AI applets for search, logic, probability, machine learning, vision, and reinforcement learning. Use online, embed in a learning-management system, or open locally. No login. No backend. No student-data collection.
Print this page or save it as PDF from the browser print dialog.
One applet, one misconception, one scenario, one exit response.
Lesson tour, scenario gallery, student response packet, then pair or written transfer.
Students complete a local packet and submit it through your LMS.
This sampler is optimized for contrast and first-use impact. It is not the full course order.
| Lesson | Applet | Focus | Student product |
|---|---|---|---|
| 1 | Pathfinding | Search as controlled exploration | Algorithm comparison explanation |
| 2 | Bayes Rule | Evidence, base rates, and posterior belief | Base-rate trap explanation |
| 3 | Overfitting | Model capacity and generalization | Underfit, reasonable fit, overfit comparison |
| 4 | Q-Learning | Reward, exploration, and value propagation | Policy-emergence explanation |
The full sequence follows search, logic, probability, machine learning, neural representation, vision, and reinforcement learning. Each card uses the same accent color as its applet.
Time: 20 min
Compare BFS, DFS, Dijkstra, and A* on the same grid. Inspect path cost, explored nodes, and frontier behavior.
Classroom use: Introduce controlled exploration, optimality, and heuristic focus.
Core question: Why does A* usually expand fewer nodes than BFS when the heuristic is useful?
Time: 25 min
Run greedy ascent and simulated annealing from the same landscape and compare their escape options.
Classroom use: Show why local improvement can stall and why escape strategies matter.
Core question: When can a worse local step produce a better final result?
Time: 25 min
Follow a logical agent as percepts become safe-cell conclusions, unresolved risks, and forced choices.
Classroom use: Bridge perception, inference, uncertainty, and safe action.
Core question: What is the difference between an unknown square and a square proven safe?
Time: 25 min
Rewrite propositions into conjunctive normal form and inspect how DPLL prunes assignments.
Classroom use: Formalize propositional reasoning and search over assignments.
Core question: Why do solvers prefer a standardized clause structure before search begins?
Time: 20 min
Change the base rate and test quality to see why high accuracy can still produce a surprising posterior.
Classroom use: Surface base-rate neglect before moving to graphical models.
Core question: Why can a highly accurate test still produce many false alarms when the event is rare?
Time: 30 min
Edit evidence, compare exact inference with sampling, and see explaining away in a causal graph.
Classroom use: Extend conditional probability into dependence and explaining away.
Core question: Why can evidence for one possible cause lower belief in another?
Time: 20 min
Move a query point, change k and vote weighting, and inspect every neighbor that determines the class.
Classroom use: Introduce supervised classification through visible neighborhood votes.
Core question: What does k control, and why can both small and large k fail?
Time: 25 min
Raise model complexity and compare training error with test error on fresh samples.
Classroom use: Separate training performance from generalization on new data.
Core question: Why can a model that fits the training data perfectly fail on new data?
Time: 30 min
Train a compact network and compare linear composition with a nonlinear hidden representation.
Classroom use: Build intuition for hidden representations and nonlinearity.
Core question: How do hidden units and nonlinearity change what a classifier can separate?
Time: 25 min
Change k and initialization, then watch assignment and center updates reshape the clusters.
Classroom use: Introduce iterative unsupervised clustering and initialization sensitivity.
Core question: What changes during each iteration, and when should the algorithm stop?
Time: 30 min
Slide kernels across an image, inspect each multiply-and-sum, and compare hand-built with learned filters.
Classroom use: Connect local filters to feature maps and learned visual representations.
Core question: Why can a small matrix reveal edges, blur, or sharpen an image?
Time: 30 min
Train by trial and error, inspect value backups, and compare exploration schedules on the same world.
Classroom use: Conclude with trial-and-error learning, delayed reward, and policy emergence.
Core question: How does useful behavior emerge from repeated trial and error?
| Evidence | What it shows | Where it comes from |
|---|---|---|
| Prediction | Initial intuition or misconception | student response packet or student template |
| Observation | Whether the learner noticed the relevant mechanism | Applet visual result and state summary |
| Explanation | Conceptual language and causal reasoning | Student written response |
| Transfer | Ability to apply the idea beyond the original applet | Transfer prompt |
AI Playgrounds has no backend and no student account system. student response packet draft text stays in the local browser. The public site may load privacy-light analytics on the canonical host; add ?noanalytics=1 to disable analytics for a session.
Each applet includes a skip link, visible focus states, reduced-motion CSS, ARIA live status, and a text-state summary. This supports accessibility, but it is not a substitute for a full human accessibility audit.
The project is MIT licensed. Teachers may link, fork, embed, print, adapt, and remix the materials for classroom use.
The suite is MIT licensed. Use it in class, fork one applet, or help other teachers find it.
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