Experiment 01 · 6–8 minutes

A ball in two directions

Build one idealized motion model from equal-time observations, then use that same model to predict the observations.

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About this experiment

Research hypothesis: Constructing and then reversing one model may make its relationships easier to inspect.

Implemented: Two authored construction routes, deterministic feedback, comparison, and transfer.

Research vocabulary: Examples → model is the inductive/emergent route. Model → examples is ILP's deductive/emanative route.

Boundary: This is an authored educational model under stated idealizations. It is not a measurement tool, a live simulation, or evidence that the learning method is effective.

A concrete observation

Why does a horizontally launched ball trace a curve?

The dots mark one position every quarter second. Start with what you can see, build the model, then use that same model to predict the dots.

Idealized model: uniform gravity; air resistance ignored.

Equal-time traceOne position every 0.25 seconds
Horizontal projectile positionsFive positions at equal time intervals. Horizontal spacing is equal and downward spacing grows.release0.00 s0.25 s0.50 s0.75 s1.00 s

Look first: the horizontal and downward gaps do not change in the same way.

Sources and boundariesThe model states its boundary.Open details

Sources support the authored physics relationships. They do not validate this teaching method or turn the trace into measured data.

01
NASA

Newton’s Laws posters and activities

Supports the relationship between net force, inertia, and changes in motion used in the authored model.

Read source ↗
02
NASA

Flight Testing Newton’s Laws — Student Manual

Supports the idealized treatment of horizontal and vertical projectile-motion components.

Read source ↗

Limitations and open questions

  • The trace is an authored qualitative illustration, not measured flight data.
  • Uniform gravity and no air resistance are stated idealizations, not hidden assumptions.
  • The experiment tests interface coherence and comprehension prompts, not learning efficacy.
  • All feedback and pathway language is deterministic and authored; no AI adapts the route.