Evaluating Machine Learning, Transformer, and LLM Approaches for Large-Scale Automated Essay Scoring

Cyrus Sai-Cheong Chan; Charles Anifowose; Su Zhang; Winnie Wing-Yee Tse; Nizam Radwan; Hyunah Kim

Evaluating Machine Learning, Transformer, and LLM Approaches for Large-Scale Automated Essay Scoring

Cyrus Sai-Cheong Chan, Charles Anifowose, Su Zhang, Winnie Wing-Yee Tse, Nizam Radwan, Hyunah Kim

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Abstract

This study compared machine learning, transformer fine-tuning, prompt-based LLM, and novel ensemble approaches for automated essay scoring in a Canadian large-scale provincial assessment. Fine-tuned transformers achieved the highest reliability, followed by machine learning models. Meanwhile, prompt-based LLMs provided greater explainability, and ensemble architectures highlighted opportunities for balancing reliability and explainability.

Anthology ID:: 2026.aimecon-wip.40
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:: 315–321
Language:
URL:: https://aclanthology.org/2026.aimecon-wip.40/
DOI:
Bibkey:
Cite (ACL):: Cyrus Sai-Cheong Chan, Charles Anifowose, Su Zhang, Winnie Wing-Yee Tse, Nizam Radwan, and Hyunah Kim. 2026. Evaluating Machine Learning, Transformer, and LLM Approaches for Large-Scale Automated Essay Scoring. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress, pages 315–321, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).
Cite (Informal):: Evaluating Machine Learning, Transformer, and LLM Approaches for Large-Scale Automated Essay Scoring (Chan et al., AIME-Con 2026)
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PDF:: https://aclanthology.org/2026.aimecon-wip.40.pdf

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@inproceedings{chan-etal-2026-evaluating,
    title = "Evaluating Machine Learning, Transformer, and {LLM} Approaches for Large-Scale Automated Essay Scoring",
    author = "Chan, Cyrus Sai-Cheong  and
      Anifowose, Charles  and
      Zhang, Su  and
      Tse, Winnie Wing-Yee  and
      Radwan, Nizam  and
      Kim, Hyunah",
    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.40/",
    pages = "315--321",
    ISBN = "979-8-9983004-1-7",
    abstract = "This study compared machine learning, transformer fine-tuning, prompt-based LLM, and novel ensemble approaches for automated essay scoring in a Canadian large-scale provincial assessment. Fine-tuned transformers achieved the highest reliability, followed by machine learning models. Meanwhile, prompt-based LLMs provided greater explainability, and ensemble architectures highlighted opportunities for balancing reliability and explainability."
}

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%0 Conference Proceedings
%T Evaluating Machine Learning, Transformer, and LLM Approaches for Large-Scale Automated Essay Scoring
%A Chan, Cyrus Sai-Cheong
%A Anifowose, Charles
%A Zhang, Su
%A Tse, Winnie Wing-Yee
%A Radwan, Nizam
%A Kim, Hyunah
%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 chan-etal-2026-evaluating
%X This study compared machine learning, transformer fine-tuning, prompt-based LLM, and novel ensemble approaches for automated essay scoring in a Canadian large-scale provincial assessment. Fine-tuned transformers achieved the highest reliability, followed by machine learning models. Meanwhile, prompt-based LLMs provided greater explainability, and ensemble architectures highlighted opportunities for balancing reliability and explainability.
%U https://aclanthology.org/2026.aimecon-wip.40/
%P 315-321

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Markdown (Informal)

[Evaluating Machine Learning, Transformer, and LLM Approaches for Large-Scale Automated Essay Scoring](https://aclanthology.org/2026.aimecon-wip.40/) (Chan et al., AIME-Con 2026)

Evaluating Machine Learning, Transformer, and LLM Approaches for Large-Scale Automated Essay Scoring (Chan et al., AIME-Con 2026)

ACL

Cyrus Sai-Cheong Chan, Charles Anifowose, Su Zhang, Winnie Wing-Yee Tse, Nizam Radwan, and Hyunah Kim. 2026. Evaluating Machine Learning, Transformer, and LLM Approaches for Large-Scale Automated Essay Scoring. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress, pages 315–321, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).