S2A3: Thompson Sampling and Stochastic Exposure Control for High-Stakes CATs

James Sharpnack, Alexander Tsigler, J.R. Lockwood, Steven Nydick, Alina A. von Davier


Abstract
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.
Anthology ID:
2026.aimecon-main.8
Volume:
Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers
Month:
October
Year:
2026
Address:
Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States
Editors:
Joshua Wilson, Christopher Ormerod, Magdalen Beiting-Parrish
Venue:
AIME-Con
SIG:
Publisher:
National Council on Measurement in Education (NCME)
Note:
Pages:
76–82
Language:
URL:
https://aclanthology.org/2026.aimecon-main.8/
DOI:
Bibkey:
Cite (ACL):
James Sharpnack, Alexander Tsigler, J.R. Lockwood, Steven Nydick, and Alina A. von Davier. 2026. S2A3: Thompson Sampling and Stochastic Exposure Control for High-Stakes CATs. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 76–82, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).
Cite (Informal):
S2A3: Thompson Sampling and Stochastic Exposure Control for High-Stakes CATs (Sharpnack et al., AIME-Con 2026)
Copy Citation:
PDF:
https://aclanthology.org/2026.aimecon-main.8.pdf