What is Adaptive testing?
Also called Computerised adaptive testing, CAT
Adaptive testing is a test design in which each question is chosen from a calibrated bank on the basis of the answers already given: a correct answer leads to a harder question, an incorrect one to an easier question. It reaches a given level of precision in far fewer items than a fixed test.
What it is for
A fixed test spends most of its items on the wrong people. A very strong candidate answers the easy half correctly and learns nothing about themselves; a weak candidate guesses at the hard half. Adaptive testing removes that waste by aiming each item at the current estimate of the person's ability, where the answer carries the most information.
The practical consequences are shorter tests at equal precision, less scope for copying between candidates because no two people see the same sequence, and a score that is comparable across people who answered entirely different questions.
What it requires before it works
Adaptive delivery is not a setting you switch on. It requires an item bank whose items have been calibrated on a common scale — usually under item response theory — with enough responses per item for the parameters to be stable, plus content-balancing and exposure-control rules so that the algorithm does not keep serving the same well-behaved items to everybody.
A test described as adaptive without a calibrated bank behind it is usually branching: a rule that sends you to section B if you scored badly in section A. Branching is a legitimate design, but it is not adaptive testing and the scores it produces are not equatable across paths.
Not the same as branching
Branching routes a candidate between fixed blocks on a rule. Adaptive testing selects the next single item from a calibrated bank to maximise information about the person. Ask which one a vendor means, and ask how many responses per item the calibration used.
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