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        <title>Prior-Informed 3PL Calibration: Reducing Sample Size via Predictive Modeling</title>
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    <abstract>This simulation study evaluates whether predictive item-parameter priors can reduce respondent requirements for 3PL IRT calibration. Across twenty seven prior-quality configurations and seven sample sizes (400–1600), six configurations were able to match a 2000- respondent baseline at 400 respondents, yielding an 80% sample-size reduction when informative priors were properly incorporated.</abstract>
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%0 Conference Proceedings
%T Prior-Informed 3PL Calibration: Reducing Sample Size via Predictive Modeling
%A Liu, Ruitao
%A Tan, Aixin
%Y Wilson, Joshua
%Y Ormerod, Christopher
%Y Beiting-Parrish, Magdalen
%S Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers
%D 2026
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-9983004-0-0
%F liu-tan-2026-prior
%X This simulation study evaluates whether predictive item-parameter priors can reduce respondent requirements for 3PL IRT calibration. Across twenty seven prior-quality configurations and seven sample sizes (400–1600), six configurations were able to match a 2000- respondent baseline at 400 respondents, yielding an 80% sample-size reduction when informative priors were properly incorporated.
%U https://aclanthology.org/2026.aimecon-main.5/
%P 36-42

Markdown (Informal)

[Prior-Informed 3PL Calibration: Reducing Sample Size via Predictive Modeling](https://aclanthology.org/2026.aimecon-main.5/) (Liu & Tan, AIME-Con 2026)

• Prior-Informed 3PL Calibration: Reducing Sample Size via Predictive Modeling (Liu & Tan, AIME-Con 2026)

ACL

• Ruitao Liu and Aixin Tan. 2026. Prior-Informed 3PL Calibration: Reducing Sample Size via Predictive Modeling. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 36–42, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).