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    <titleInfo>
        <title>Automatic Prompt Engineering for Generative AI–Based Essay Scoring</title>
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        <namePart type="given">Yue</namePart>
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            <title>Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress</title>
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        <name type="personal">
            <namePart type="given">Joshua</namePart>
            <namePart type="family">Wilson</namePart>
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            <namePart type="given">Christopher</namePart>
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        <name type="personal">
            <namePart type="given">Magdalen</namePart>
            <namePart type="family">Beiting-Parrish</namePart>
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    <abstract>This study evaluated automatic prompt engineering (APE) using one assignment in the PERSUADE 2.0 dataset. The APE approach achieved higher QWK (.812) than the research-informed, zero-shot baseline prompting approach (.646). Descriptive comparisons examined gender and English language learner subgroups. Findings support the benefits of APE for automated essay scoring (AES).</abstract>
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        <url>https://aclanthology.org/2026.aimecon-wip.34/</url>
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%0 Conference Proceedings
%T Automatic Prompt Engineering for Generative AI–Based Essay Scoring
%A Huang, Yue
%Y Wilson, Joshua
%Y Ormerod, Christopher
%Y Beiting-Parrish, Magdalen
%S Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress
%D 2026
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-9983004-1-7
%F huang-2026-automatic
%X This study evaluated automatic prompt engineering (APE) using one assignment in the PERSUADE 2.0 dataset. The APE approach achieved higher QWK (.812) than the research-informed, zero-shot baseline prompting approach (.646). Descriptive comparisons examined gender and English language learner subgroups. Findings support the benefits of APE for automated essay scoring (AES).
%U https://aclanthology.org/2026.aimecon-wip.34/
%P 267-274

Markdown (Informal)

[Automatic Prompt Engineering for Generative AI–Based Essay Scoring](https://aclanthology.org/2026.aimecon-wip.34/) (Huang, AIME-Con 2026)

• Automatic Prompt Engineering for Generative AI–Based Essay Scoring (Huang, AIME-Con 2026)

ACL

• Yue Huang. 2026. Automatic Prompt Engineering for Generative AI–Based Essay Scoring. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress, pages 267–274, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).