We introduce S2A3, a unified Bayesian framework for high-stakes computerized adaptive testing that eliminates separate item piloting. Thompson sampling routes uncertain items to informative test-takers while soft scoring attenuates their influence on ability estimates. Stochastic Sympson-Hetter exposure control ensures bank security. Validation on the Duolingo English Test confirms rapid calibration.

We analyzed data from 25,969 test takers of a high-stakes, computer-adaptive English proficiency test to examine relationships between repeated use of AI-generated practice tests and performance, affect, and score-sharing behavior. Taking 1–3 practice tests was associated with higher scores and confidence, while higher usage showed different engagement and outcome