Evaluating Multi-Phase and Granular Strategies for Evidence-Anchored Automated Scoring

Alessia Marigo; Laura Wright; Linda Malkin; Xin Xie

Evaluating Multi-Phase and Granular Strategies for Evidence-Anchored Automated Scoring

Alessia Marigo, Laura Wright, Linda Malkin, Xin Xie

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Abstract

This study evaluates few-shot large language models (LLMs) on middle-school geoscience responses (N=86 responses × 11 indicators), separating presence agreement from extract agreement. Joint prompting and annotation-like rubric guidance plus examples yield the clearest gains; lengthy rubric rewriting and clause-level parsing do not reliably improve extract alignment.

Anthology ID:: 2026.aimecon-wip.3
Volume:: Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress
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:: 17–23
Language:
URL:: https://aclanthology.org/2026.aimecon-wip.3/
DOI:
Bibkey:
Cite (ACL):: Alessia Marigo, Laura Wright, Linda Malkin, and Xin Xie. 2026. Evaluating Multi-Phase and Granular Strategies for Evidence-Anchored Automated Scoring. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress, pages 17–23, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).
Cite (Informal):: Evaluating Multi-Phase and Granular Strategies for Evidence-Anchored Automated Scoring (Marigo et al., AIME-Con 2026)
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PDF:: https://aclanthology.org/2026.aimecon-wip.3.pdf

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@inproceedings{marigo-etal-2026-evaluating,
    title = "Evaluating Multi-Phase and Granular Strategies for Evidence-Anchored Automated Scoring",
    author = "Marigo, Alessia  and
      Wright, Laura  and
      Malkin, Linda  and
      Xie, Xin",
    editor = "Wilson, Joshua  and
      Ormerod, Christopher  and
      Beiting-Parrish, Magdalen",
    booktitle = "Proceedings of the Artificial Intelligence in Measurement and Education Conference ({AIME}-Con): Works in Progress",
    month = oct,
    year = "2026",
    address = "Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States",
    publisher = "National Council on Measurement in Education (NCME)",
    url = "https://aclanthology.org/2026.aimecon-wip.3/",
    pages = "17--23",
    ISBN = "979-8-9983004-1-7",
    abstract = "This study evaluates few-shot large language models (LLMs) on middle-school geoscience responses (N=86 responses $\times$ 11 indicators), separating presence agreement from extract agreement. Joint prompting and annotation-like rubric guidance plus examples yield the clearest gains; lengthy rubric rewriting and clause-level parsing do not reliably improve extract alignment."
}

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%0 Conference Proceedings
%T Evaluating Multi-Phase and Granular Strategies for Evidence-Anchored Automated Scoring
%A Marigo, Alessia
%A Wright, Laura
%A Malkin, Linda
%A Xie, Xin
%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 marigo-etal-2026-evaluating
%X This study evaluates few-shot large language models (LLMs) on middle-school geoscience responses (N=86 responses \times 11 indicators), separating presence agreement from extract agreement. Joint prompting and annotation-like rubric guidance plus examples yield the clearest gains; lengthy rubric rewriting and clause-level parsing do not reliably improve extract alignment.
%U https://aclanthology.org/2026.aimecon-wip.3/
%P 17-23

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Markdown (Informal)

[Evaluating Multi-Phase and Granular Strategies for Evidence-Anchored Automated Scoring](https://aclanthology.org/2026.aimecon-wip.3/) (Marigo et al., AIME-Con 2026)

Evaluating Multi-Phase and Granular Strategies for Evidence-Anchored Automated Scoring (Marigo et al., AIME-Con 2026)

ACL

Alessia Marigo, Laura Wright, Linda Malkin, and Xin Xie. 2026. Evaluating Multi-Phase and Granular Strategies for Evidence-Anchored Automated Scoring. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress, pages 17–23, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).