Premise-consequence chaining

State a premise, then generate three independent consequences and defend each: training forward inference instead of only recognising conclusions you've already seen.

When to use this: When you want to go deeper
Learning impact: Reliable gain
Learning evidence: Credible with caveats
Where you are with the topic: Applying in new contexts
How often to use it: Worth repeating often

When to use it

You recognise textbook implications but freeze when asked what else would follow.
A seminar or tutorial rewards forward inference, not only recall.
You want to test whether a premise actually commits you to uncomfortable results.

What to ask AI

Type something like: “Premise: [ ]. I will offer three consequences. For each, force me to label: entailed / probable / only if [hidden assumption].”

Ask it not to invent consequences for you until you have tried.

Defend each label.

How it helps

Much study trains recognising conclusions you have already seen.

Premise-consequence chaining trains forward inference: if this is true, what follows?

Labelling entailed vs probable vs “only if” exposes hidden assumptions.

How to run it

State a clear premise.
Generate three independent consequences yourself.
Label each: entailed, probable, or only-if (name the hidden assumption).
Defend the labels under challenge.
Add a fourth consequence you have not seen in notes.

Example exchange

You

Premise: demand for a good is perfectly inelastic. One consequence I drew: quantity doesn’t change when price rises.

Is that entailed by the definition, only probable, or true only under extra assumptions?

You

Entailed, by definition quantity doesn’t respond to price.

Good. Now invent a second consequence that isn’t just a restatement of the definition.

Copyable prompt

Premise: [ ]. I will offer three consequences. For each, force me to label:
entailed / probable / only if [hidden assumption]. Challenge weak labels. Then
ask me for a fourth consequence I haven't seen in notes.

The Tell

If you can recall textbook implications but cannot invent a fourth consequence, you are recognising, not chaining.

If the AI lists consequences for you first, scrap the list and start with yours.

Principle evidence

Strength of the underlying learning idea, not a claim about AI products.

The underlying learning idea is rated moderate. Generating independent consequences from a premise trains conditional reasoning and elaboration. Related to elaborative interrogation and inference practice.

AI delivery evidence

Whether an AI tutor delivers this method well is a separate question.

AI challenges to your labels can be wrong. Defend with course definitions. Delivery is speculative.