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.

The high capability of recent Large Language Models (LLMs) has led to concerns about possible misuse as cheating assistants in open-ended writing tasks in assessments. Although various detecting methods have been proposed, most of them have not been evaluated on or optimized for real-world samples from LLM-assisted cheating, where the generated text is often copy-typed imperfectly by the test-taker. In this paper, we present a framework for training LLM-generated text detectors that can effectively detect LLM-generated samples after being copy-typed. We enhance the existing transformer-based classifier training process with contrastive learning on constructed pairwise data and self-training on unlabeled data, and evaluate the improvements on a real-world dataset from the Duolingo English Test (DET), a high-stakes online English proficiency test. Our experiments demonstrate that the improved model outperforms the original transformer-based classifier and other baselines.