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        <title>Effects of Demographic Representations and Model Training Methods on Automated Scoring Engines</title>
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    <abstract>This study evaluated whether automated scoring engines maintain stable performance when training data composition and training methods vary. We manipulated demographic representation (gender, English language learner, race, student with disabilities) and compared feature-based versus transformer-based models. Results showed performances were stable across subgroup-representation densities and transformer models exhibited greater stability.</abstract>
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%0 Conference Proceedings
%T Effects of Demographic Representations and Model Training Methods on Automated Scoring Engines
%A Xu, Yangmeng
%A Bellows, Martha
%A Wolfe, Edward W.
%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 xu-etal-2026-effects
%X This study evaluated whether automated scoring engines maintain stable performance when training data composition and training methods vary. We manipulated demographic representation (gender, English language learner, race, student with disabilities) and compared feature-based versus transformer-based models. Results showed performances were stable across subgroup-representation densities and transformer models exhibited greater stability.
%U https://aclanthology.org/2026.aimecon-main.16/
%P 154-160

Markdown (Informal)

[Effects of Demographic Representations and Model Training Methods on Automated Scoring Engines](https://aclanthology.org/2026.aimecon-main.16/) (Xu et al., AIME-Con 2026)

• Effects of Demographic Representations and Model Training Methods on Automated Scoring Engines (Xu et al., AIME-Con 2026)

ACL

• Yangmeng Xu, Martha Bellows, and Edward W Wolfe. 2026. Effects of Demographic Representations and Model Training Methods on Automated Scoring Engines. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 154–160, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).